Business Statistics-Research on Mobile Phone in Vietnam-Free Essay

1. ABSTRACT:
Mobile phone becomes the necessary important thing to everybody in the modern life. Therefore, we want to know what the aspects that everybody concern when buying a mobile phone are. Are the price affected by the other factors? The result brings us many surprises; the gender is not the element has the impact on the price. In this report, we will point out the other significant aspects affect that factor.
2. INTRODUCTION:
Vietnam is the developing country in the world, which is a good place investment for foreign companies or businesses. Therefore, it promotes the competition between many companies.
. One example is the price of mobile phone is decreased but the functions are graded up and have more functions that are modern.
This survey shows that it is a common for a person who prefer buying a mobile phone based on the distributor or outside with lower price. Beside that, the owner took into account many aspects in order to have a satisfied mobile phone and a suitable price.
What could be the basic elements and how much does it influence to decide the changes mobile phone? In addition, one could be interested in to interrelate and interact among those aspects each other.
3. SURVEY ON THE LITERATURE
At the last 5-9 years, Vietnamese people consider as mobile phone is luxury thing with very expensive price. However at the end of 2004, the number of new subscriber increased dramatically 57.82% with 2,462,792 new subscribers. The total subscriber in 2004 is 4,259,411 subscribers (http://vietnamnet.vn/). According to EIU (Economists magazine of UK), they predict there will be 27.4 phones user / 100 Vietnamese people in 2011. Now, the mobile phones network is better and it can be cover all of 62 provinces and cities in Vietnam with many services: GPRS, developing the 3G and push-to-talk services. Furthermore, in 2004 the tax of mobile phones is changed from 15% to 10%, and 10% to 5% (http://www.mof.gov.vn). For all the above advantages, mobile phone becomes one of the most effective tools in communication field. Every body has a phone for many purposes: for assisting in working, for contact with every body, for entertainment with music, games for some helpful function such as: camera, Wireless, GPS.
4. METHODOLOGY AND ANALYSIS
a. Data collection instrument
We have designed the survey form, which is shown in the appendix. Our survey has 12 questions, which are divided, into three sections. In the first section which includes four questions, we ask people about their personal information such as gender, age, occupation and whether they use mobile phone or not. The second section includes six questions, which ask people some information about their mobile phone. For instance, brand, length of using time, price are considered. In the last section, we want to know what factors people prefer when buying mobile phone such as function, price, camera, etc.
b. Sampling Plan

Nowadays, with the significant development of telecommunication, mobile has become a necessary tools for every one in social life. There are various kinds of phone with different price, functions, design… for different hobbies. Therefore, our survey, which is show the association between the gender or age of owner and the price of the mobile phone, is very useful for referencing.
First of all, our group decides to choose students and staff in RMIT University as our survey’s sample. The reason is it is convenience for us to do the survey, moreover, we can sure that almost student as well as staff here use mobile phone and they are eager to do our survey.
Secondly, we discuss about the method of selecting a sample and the answer is Stratified Random sampling. We divided the sample into 4 groups which is has different characteristic: Staff, Bachelor, HED and English student .The purpose of this is to get the overall opinion not only focus on one style. . Then, we must choose random sampling in male or female, because we can’t choose exactly 50% for each of them. The advantages of Stratified Sampling method are that it is more representative of the population, simple, cheap, quite exactly and logical.
Finally, we begin to hand out our survey, the place that we choose is everywhere in the campus which can help us to accomplish the task, such as labs, ILC, canteen, classroom etc.
And weekday in the morning afternoon from 1 to 5 pm is the suitable time for us to complete because that is the time that most RMIT staff and students present at the school or finish studying so they are able to do the survey quicker and we reduce the sampling error such as coverage error.
c. Data collection Process
There are two types of probabilities of sampling that we use to collect the data:
• Simple Random Sample: it means that the chance of being selected of each individual is equal to one another.
• Stratified Sample: we divided the population into 4 groups which has the same characteristic. Then, we took a sample of each to do analysis.
There are five members in our group. Four members issued the survey and one member had to collect and organize the data. We split into four different areas with four targets: English, HED, Degree students and Staff. For English students, we waited outside the English lab or some English classrooms and asked any students who came out or in these areas. For HED and Degree students, it was much easier because they were everywhere. For Staff, we chose teachers most because they are common in the number of staff. We tried to do the survey on various kinds of people such as age, sex because they are one of the most important parts in our survey and we could reduce sampling error.
 Response rate
We issued 100 surveys and got 100 answers, which meant the response rate is 100%.
 Positive and negative side:
• Positive sides:
Each group has their own characteristics that represent the entire population effectively.
• Negative sides:
The number of RMIT students and staffs is about 3000 but we only took 100 to do the survey, so the sampling error will surely occur which has a significant effect on our results.
Coverage Error: unintended bias collection
d. Analysis Technique:
In this section, we use three kinds of analysis: Univariate, Bivariate and Multivariate.
Univariate analysis: this type is divided into two:
• Descriptive Statistics: to show each question’s results about Central of Tendency, Std Deviation and Frequency Distribution…
• Independent- sample t- test
• Bivariate Analysis: one way ANOVA (Analysis of Variation), this method is used on questions which have more than two options. We also use Chi- square to analyze.
• Multivariate Analysis: Regression Linear is used to show the impacts of factors to price of mobile phone.
5. Analysis
a. Univariate

Question 1:


In this question, we have 51 males and 49 female answer this question. 51 males out of 100 people is about 51% compare with 49% for females.





How gender causes impact on the mobile phone price?




We do the T-Test in order to find out what is the impact that gender. We do the T-Test in order to find out what is the impact that gender make on the price of mobile phone. Base on the statistic chart, we got the Mean- the amounts of money people have to pay in average for having a cell phone. As the result, we can conclude that Male spend more than Female for having a mobile phone with the Mean are 4639411.76 compare with 4147959.18. SD for male is 3942194.522 which means the money male spend for mobile phone is 3942194.522 VND cluster around the mean of 4639411.76 VND. SD for female is 2529533.043 VND, implying the answer scatter around the mean of 4147959.18 VND within the area of 2529533.043 VND.

Question 2:



We asked 100 persons and got 100% response. For people with the ages ’10-20’, we got 38 with 38% valid and the same number for the ages’20-30’. The ages between 30 and 45, 18 response have 18% valid. Moreover, we have just 6% for people over 45 years old.

Descriptive Statistics


The minimum value is 1 which is represent for ’10-20’ and the maximum one is 4, that represent for the age ‘above 45’. The mean is 1.92 that means the average ages fall in people from 10 and 30. The SD shows that the age of people cluster around the mean of 1.92 within the area .895



Question 3: What is your occupation?




In this question, we distribute our member to classify who is the English, HED student, Degree or staff. We got the answers are divided into 25% valid for each occupation.



Question 5:


People who have used mobile phone less than 1 year are 10 persons which have 10% valid. We got 45% valid for 45 people use mobile phone ‘1-3 years’ and the same valid for people have used More than 3 years.





Minimum valid is 1 which represent for people using mobile phone Less than 1 year and the maximum valid is 3 for those using more than 3 years. The mean is 2.35 that mean the average of price of mobile phone fall into two categories ‘1-3 year’ and ‘more than 3 years’. SD is .657 shows that the duration of using mobile phone is distributed around the mean 2.35 within the area is .657

Question 6:

In this question, we listed 6 most popular brand names of mobile phone for people easy to recognize and ‘other’ for another brand. We got back 100% response, the statistic numbers show that Nokia is the best brand which has 45% valid out of 100. Next position is Samsung that has 22% compare with 100%. People choose Motorola as 3rd with 15% valid. Sony Ericsson and other brands have the same position as 4th place with 8 out of 100. Next, LG and Siemens are two brands which people least using with 1% valid.

Question 7:


This question is scale question which requires people tick in numbers with have value from 1 to 10 to show their thinking about the role of mobile phone for their life. The minimum value is 1 which means Not Important, 5 for Normal and 10 for Very Important. The highest value is 8 with has 25% valid. 23 people out of 100 think that mobile phone is extreme important with them. Next, 23% valid is belong to 7. The following positions are belonging to 9 with 11% valid; 5 with 9% compare with 8% valid for number 8. There is just 1 person think that mobile phone is not important at all and 1 rank mobile phone role number 4, both of them have 1% valid.


The mean of this question is 7.81 imply that people consider that mobile phone’s role is important enough with them, SD is 1.739 means the role of mobile phone cluster around the mean 7.81 within the area of 1.739.


Question 8:


69 people think that buying a mobile phone from distribution is safe, quality…with 69% valid compare with 31% valid of buying an outside phone with lower price.



Where you buy your mobile and how it affects to the price of mobile phone?





By doing T-Test, we can come up to some conclusions. If people want to have a good cell phone from excusive distribution, they have to pay more (4479130.43 VND), 4219354.84 VND for those buy mobile phone from outside with no warranty.
SD for distribution is 3267089.791, implying that when you buy the cell phone from Distribution, the approximate money you pay is 3267089.791 VND cluster around the mean (4479130.43 VND). SD for buying outside is 3478234.165 that mean buying the cell phone from Outside, the approximate money you pay is 3267089.791 VND distributes around the mean (4219354.84 VND)


Question 9:



The minimum value for this question is 1 which means Never change their phone, 5 for Sometime and 10 for very often.
We got answer for this question is 1 and maximum value is 8. 31 people out of 100 think that they sometimes change they mobile for some reasons, it has 31% valid. 21% valid for number 3, next place is number 4 with has 14% valid. The following position is 2 with 12% valid. There are 9 people choose number 1 that means they never change their mobile for any reason which has 9% valid. Next are 6 with 6% valid, 8 with just 4% valid and last one is 7 with 3% valid.
Mean is 3.96 which imply that the duration of people fall in two the range between 3 and 4, so people sometime change their phone. SD is 1.729 means the duration of mobile phone cluster around the mean 3.96 within the area of 1.729


Question 11.1:



We have 58 person interested in the function (camera) with 58% valid and 42 people don’t need this function with 42% valid.




Question 11.2:

People seem consider that Internet connection is not necessary, so 82 out of 100 say ‘no’ for this one with 82% valid. There is just 8 people need this function for their mobile phone associated with 8% valid


Question 11.3:



This function got a good result from people; they like to use their phone as a Mp3 music player. 76 persons would like to have this function with 76% valid. 24 out of 100 don’t need this one (24% valid)


Question 11.4:

3 out 4 people were asked say ‘no’ with games and a quarter say ‘yes’, the statistic valid are 75% and 25%






Question 11: Which function that affects to the mobile phone price?
a. Camera


We asked people about do they want to have camera in your mobile phone. For the people say “yes”, they have to pay approximate 4821724.14 VND and 3814285.71 VND for those say “no”, so the people want to have camera in their mobile phone have to pay more ( about 1000000 VND) than those don’t need this function.
b. Internet Connection


This T-Test shows us that if people want to have the internet connection in the mobile phone, they have to pay 6600555.56 VND and just 3915243.90 VND for having no internet connection. The different amount is up to nearly 2600000VND- a big amount. That prove the internet connection is very expensive function.

c. Mp3 music player


Depend on the statistic chart; the numbers of people having the Mp3 music player spend 4170526.32 VND while those don’t use this function have to spend 5120833.33 VND. In this case, it doesn’t mean this function (Mp3 music player) makes the price of cell phone decrease. There are some other reasons: People may use some mobile phones which have no Mp3 music player but they also have the high value because those phone maybe fashionable and luxurious.
d. Games


Base on the result, we can conclude that Games make people pay more. The mean of the mobile phone have no this function is 4174666.67_ which is also the amount of money people spend their phone in VND and 5070400.00 VND for the cell phone which have Games. The different between two mean is around 900000VND, so Games isn’t an expensive function.

Question 12.1:

Depend on the research, we got 19 people think brand is most important with 19% valid. 20 out of 100 (20% valid) ranked it as 2nd place; 7 people consider it number 3 with 7% valid compare with 17% for number 4. People think that brand is not a matter got the highest valid (22%) and the rest one chooses the brand as the lowest aspect when they buy a mobile phone (15%)

According to the analysis chart; mean is 3.48 that imply people thinking about the Brand fall into 3 and 4 valid, they consider that brand is just normal. SD is 1.778 that means Brand cluster around the mean of 3.48 within the area of 1.778.



Question 12.2:


We asked people about how the fashionable aspect important with them, 24 out of 100 thinks that their mobile should be fashionable and has the valid (9%), and 2nd place has 9 persons (9%) compare with 10% valid for 3rd one. The following position 4 and 5 got the same percentages (14%). Number of people don’t think fashionable is important has the highest valid (29%)



Mean is 3.72 that imply people think about fashionable aspect in average. SD is 1.970 means the fashionable aspect scatter around the means within the area 1.970


Question 12.3:


For this question, we got 3% valid for ranking number 1,15 out of 100 people ranked it number 2 (15%).The highest valid (26%) is ranking number 3.We got 20%, 21%, 15% in order for ranking 4 to 6.



Base on the analysis chart, we got mean is 3.86 which show us that the majority of response fall ranking number 3 and 4. The distribution is fluctuated around the mean of 3.86 with the area of 1.393 because SD is 1.393


Question 12.4:


23 out of 100 persons choose function is the most important with them (23%) For ranking number 2, we have the 16% valid and 17% for 3rd place.20 person ranked Function as number 4.the following ranking number 4 and 5 got 14 & 15% valid.



The minimum value is 1 and maximum is 6. Mean is 3.16, implying what people think about the Function fall between 3 and 4. SD is 1.656 that means the Function scatter around the Mean of 3.16 within the area of 1.656



Question 12.5:


In this question, 17 persons consider that Price is most important, 25 out of 100 people ranked the price as 2nd (25%), 23% valid for raking number 3, the 4 and 6 places have the same valid (10%) and 15% valid for the 5th place.



In this chart, mean is 3.11, which is average to this answer. SD is show that the Price cluster around the mean within the value 1.595



Question 12.6:


Base on our research, 14% people ranked Convenience is most important, 15 out of 100 consider it as number 2, number 3 has 17% valid, 19% for 4th place, and 14% for 5th one. There are 21 people think that Convenience is least important with them and it also has the highest valid (21%)



The analysis chart, mean is 3.67 shows that the convenience fall between the 3 and 4 place, implying the average for this answer. SD is 1.712 means the Convenience aspect distribution around the mean of 3.67 within the area of 1.712


b. Bivariate
Q.1: The relationship between the price of the mobile phone and the gender

Group Statistics


H0: M female = M male;
H1: M female ≠ M male



t critical = 1.98 ,t obtained = 0.739 so t |obtained | < t |critical | P critical = 0.05; P obtained = 0.462 so P| obtained| > P| critical|
Base on the statistic chart above, we got t obtained is less than t critical or p obtained is greater than p critical, so applying the Hypothesis testing, we do not reject the Null hypothesis (Ho)
Conclusion: the gender does not affect the price of the mobile phone

Q.2: The relationship between the price of the mobile phone and the age



Ho: M 10-20 = M 20 – 30 = M 30- 45 =M >45;
H1: M 10-20 ≠ M 20 – 30 ≠ M 30- 45 ≠ M >45

ANOVA
How much money does your current mobile phone cost?


F obtained = 1.525; F critical = 2.68 so F |obtained| < F |critical | P critical = 0.05; P obtained = 0.213 so P |obtained| > P |critical|
Based on the above table and comparison, we see that the f critical is lager than the f obtained as well as P obtained and P critical. As a result, we do not reject the null hypothesis. Therefore, there is insufficient evidence that the price is different between the ages.
38 people from 10 to 20 years old as well as 38 people from 20 to 30 years old will buy the cell phone with the minimum price is 1 000 000 VND and the maximum price is 16 000 000 VND. The price of 10- 20 years old scatter around the mean 470421 with the standard deviation is 3808184. And for the price of 20-30 years old, the standard deviation is 3356661 which cluster around the mean of 486579.
18 people from 30-45 years old will spent for their mobile phone from 1 000 000 VND (minimum) to 16 000 000 VND (maximum).the standard deviation 2099859 clusters around the mean of 3333333.
And the final group is above 45 years old, which has 6 people. the price will between 1 000 000 VND to 5 000 000 VND. The mean is 2700000 that is scattered by the standard deviation of 3318467.

Conclusion: the different of ages does not affect the price of the mobile phone

Q.3: Is the price of mobile phone affected by the occupations?



Ho: M English = M HED = M Bachelor = M Staff;
H1: M English ≠ M HED ≠ M Bachelor ≠ M Staff

ANOVA
How much money does your current mobile phone cost?


f obtained = 2.938; f critical = 2.68 so f |obtained| > f |critical |
P critical = 0.05; P obtained = 0.035 so P |obtained| < P |critical Based on the above judgment, we see that the f obtained is lager than the f critical as well as P critical and P obtained. As a result, the null hypothesis is rejected. Therefore, there is sufficient evidence that price of the mobile phone is different between the occupation. When doing the survey, we decided that each group will contain 25 people in order to reduce the error to a minimum rate. The first group is English student, the minimum and maximum price they are willing to pay for their current mobile phone is significant different, from 1 800 000 VND to 16 000 000 VND. Moreover, the standard deviation is 3622025 which shows how the data fluctuate around the mean(X bar = 5636000) The second group is HED student, this group also has a large different between the maximum price (15 500 000 VND) and the minimum price (1 000 000 VND) for their current cell phone. The standard deviation 3163571 clusters around the mean 4438000. The next group is the Degree student, the price falls between 1 000 000 VND to 13 560 000 VND, and the mean is 4572400 which is scattered around by the standard deviation 3894948. The final group is staff. The price that they will pay is from 1 000 000 VND (minimum) to 10 000 000 VND (maximum).Moreover, the standard deviation is 1818680 which is show how the value distributed around the mean-2948000. In Conclusion: the price of the mobile phone is affected by the classification of occupations. The reason is most of the student concern about their mobile phone more than the staff that just needs the phone to communication (call, receive a call or send, receive massage).the student represent for the teenager generation ;therefore, they spent more money for the functions, for the fashionable. Q.5: Does the time using the mobile phone have any impacts on the price of it? Ho: M < 1 year = M 1-3 years = M > 3 years
H1: M < 1 year ≠ M 1-3 years ≠ M > 3 years

ANOVA

How much money does your current mobile phone cost?



f obtained = 4.657; f critical = 3.07 so f |obtained | > f |critical |
P critical = 0.05; P obtained = 0.012 so P |obtained| < P |critical | Based on the above finding, we decide to reject the null hypothesis because the f obtained is lager than the f critical as well as P critical and P obtained. As a result, there is sufficient evidence that the time using the cell phone affects the price. Only 10 out of 100 people started to mobile phone within this year. And the price falls from 1 000 000 VND to 8 000 000 VND. The standard deviation-2787277 informs the value scatter around the mean 4065000. Half of the rest people use from 1-3 years. The maximum price for their phone is 12 650 000 VND and the minimum is 1 000 000 VND. The mean-3412222 is clustered around by the standard deviation 2108886. And more than 3 years is the time that the last 45 people use their phone. And the price is falls between 1 500 000 VND to 16 000 000 VND. the standard deviation is 4070677 shows how the data fluctuate around the mean-5459111. In sum, the price of the mobile phone is impacted by the amount of time we use it. This relationship is caused by the knowledge and thought. The more time we use the mobile phone, the more knowledge we understand about it, therefore, the more money we are willing to pay. The one who use mobile more than 3 years are likely to spend more than the others. The reason is they can understand the important role of phone in their lives and they have more knowledge to choose about the function which suitable for their need and it will cost more money. Q.6: The relationship between the price of the mobile phone and the brand which their mobile phone belongs to Ho: M Nokia = M LG = M Samsung = M Siemens = M Motorola = M Sony Ericsson = M others H1: M Nokia ≠ M LG ≠ M Samsung ≠ M Siemens ≠ M Motorola ≠ M Sony Ericsson ≠ M others ANOVA How much money does your current mobile phone cost? f obtained= 1.842 ; f critical = 2.17 so f | obtained | < f | critical | P critical = 0.05; P obtained = 0.099 so we have: P | obtained| > P | critical |
Based on the above table and comparison, we see that the f critical is lager than the f obtained as well as P obtained and P critical. As a result, we do not reject the null hypothesis. Therefore, there is insufficient evidence that the price is different between the brands.
The majority 45 people choose Nokia as their first choice, therefore, the price they willing to pay is very various, from the minimum 1 000 000 VND to 16 000 000 VND. Moreover, the standard deviation is 4274484.339 which represent the data scatter around the mean-5084666.67 within the 4274484.339 value.
The next choice for their mobile phone is Samsung (22 people), however, because of the quickly decreased price, people just buy this brand with the medium rate from 1 000 000 VND to 5 000 000 VND. The mean is 2 956 818.18 which is clustered around by the standard deviation 1239844.024.
The third brand is Motorola which has chosen by 15 people, the price falls between 1000000 VND to 7500000 VND, the standard deviation 1734935.157 fluctuate around the mean 3300000.00.
Other brand which has 8 people chose is Sonny Ericsson and the others like Panasonic, O2…
The Sonny Ericsson price is from the minimum 2 500 000 VND to the maximum 8 000 000 VND has the data scatter around the mean 4662500.00 within 2263333.509 value (the standard deviation).
While the other phone like Panasonic and O2 has the prices fall between 2 000 000 VND to 10 000 000 VND, the mean is 6337500.00 which show the average price they will pay is 6 337 500 VND and the standard deviation is 2932058.029 which show the area that most of the price cluster around the mean.
Moreover, the LG and Siemens brand just has 1 choice; therefore, the figure can say anything.
In Conclusion: there is no relationship between the price of the mobile phone and the brand which it is belong to.

Q7: Is the price of mobile phone affected by the role of it in life?

Ho: µ1 = µ2 = µ3 = µ4 = µ5 = µ6 = µ7 = µ8 = µ9 = µ10
H1: µ1 ≠ µ2 ≠ µ3 ≠ µ4 ≠ µ5 ≠ µ6 ≠ µ7 ≠ µ8 ≠ µ9 ≠ µ10


ANOVA(b)



P obtained = 0.015; P critical = 0.05 so P obtained < P critical F obtained = 6.123; F critical (α, df1, df2) = 3.92 so F obtained > F critical
After doing hypothesis testing; we can se that p obtained is less than p critical and F obtained is greater than F critical, so we reject the Null hypothesis.
And here is the scatter Plot showing the relationship between role and price:



The X value (Horizontal) represents the role of mobile phone and Y value (Vertical) represents the price of mobile phone. As it is shown, we can see clearly that when the role increases, price will go up, too, which tells us that this diagram is positive. Furthermore, the role of mobile phone has a certain effect on the price of mobile phone. Moreover, according to the coefficient analysis table, we give more explanations about relationship between role and price:
Y= 784504.8+ 462752.3 X1
X increases by 1 unit and Y increases by 784504.8 VND
When X=0, Y= 784504.8 VND. It means if role has no effect on price, price will be 784504.8 VND individually.
Conclusion: the role of the mobile phone impacts significantly to price of it. The ways how people think about how important their cell phone is affect to the amount of money they spend on that field.







Q8: Is there any relationship between the price of the mobile phone and the place where you buy it (from distribution or outside)?



Ho: µ distribution = µ outside;
H1: µ distribution ≠ µ outside



P obtained = 0.719; P critical = 0.05 so P obtained > P critical
T critical = 1.96; T obtained = 0.36 so T obtained < T critical The Hypothesis testing show us that the p obtained is greater than p critical or the T obtained is less than T critical, so we do not reject the Null hypothesis (Ho) Conclusion: the price of the mobile phone is not affected by the classification of place buying it. Q9: Does the frequency of changing cell phone impact the price? Ho: µ1 = µ2 = µ3 = µ4 = µ5 = µ6 = µ7 = µ8 = µ9 = µ10 H1: µ1 ≠ µ2 ≠ µ3 ≠ µ4 ≠ µ5 ≠ µ6 ≠ µ7 ≠ µ8 ≠ µ9 ≠ µ10 ANOVA(b) P obtained = 0.006; P critical = 0.05 so P obtained < P critical F obtained = 7.852; F critical (α, df1, df2) = 3.92 so F obtained > F critical
The factor that also affects the price is the length of using phone which means that how often people change their mobile phone. When people change mobile phone, they tend to buy a more expensive one than the previous
We use Hypothesis testing to test whether the frequency of changing mobile phone cause impact on he price of cell phone or not. Because the p obtained is less than p critical and F obtained is more than F critical, so we reject the Null Hypothesis (Ho) that means the frequency of changing the cell phone affect the price the cell phone.
Moreover, we also do the regression analysis to prove our conclusion. This diagram below will show clearly about the change in price when considering frequency of changing phone:




Coefficients(a)


As we can see, this diagram also shows positive side, which tells us that the two valuables have certain effects on each other. In addition, we use the regression
Y= 2328188 + 462752.3 X2
It is shown that when X (change frequency) goes up by 1 unit, price (Y) will also increase by 462752.3 VND and if X= 0, Y = 2328188 VND
Generally, we can point out a common thing about relationship among Price and role and Length of use. If Y is the cost of mobile phone, X will be the role or Length. When X increases, Y will probably increase, too.
Conclusion: the frequency of changing cell phone affects the price a lot. How often they change the mobile phone is the aspect which causes impact on the price of mobile phone. The more times they change their phone, the more money they have to spend.

Q11: The relationship between the price of the mobile phone and the functions of it (Camera, Internet connection, MP3 music player, Games)?

Q11.1: Price VS camera function:



Ho: µ camera = µ Price;
H1: µ camera ≠ µ price



P obtained = 0.135; P critical = 0.05 so P obtained > P critical
T obtained = 1.508; T critical = 1.96 so T obtained < T critical The hypothesis testing prove that the function camera doesn’t cause any impact on the price of mobile phone because p obtained is greater than p critical and the t obtained is less than t critical. Conclusion: the price of the mobile phone is not affected by the categorization of function: Camera. Q11.2: Price VS Internet connection: Ho: µ Internet Connection = µ Price; H1: µ Internet Connection ≠ µ price P obtained = 0.002; P critical = 0.05 so P obtained < P critical T obtained = 3.256; T critical = 1.96 so T obtained > T critical
After doing the hypothesis testing; we can give the conclusion that the function_ Internet connection causes a significant influence on the mobile phone price.
Conclusion: the price of the mobile phone is influenced by the categorization of function: Internet connection. Recent research prove that which phone has this function is always more expensive than others, so if people want to use this function, they have to pay more.
Q11.3: Price VS MP3 music player:



Ho: µ Mp3 music player = µ Price;
H1: µ Mp3 music player ≠ µ price



P obtained = 0.223; P critical = 0.05 so P obtained > P critical
T obtained = -1.226; T critical = 1.96 so T obtained < T critical The hypothesis testing show that the p obtained is greater than p critical and T obtained is less than T critical, so the Mp3 music player doesn’t make any impact on the price of the mobile phone. Conclusion: the price of the mobile phone is not impacted by the categorization of function: MP3 player. Q11.4: price VS Games: Ho: µ Games = µ Price; H1: µ Games ≠ µ price P obtained = 0.244; P critical = 0.05 so P obtained > P critical
T obtained = 1.171; T critical = 1.96 so T obtained < T critical After doing Hypothesis testing, we reject the Null hypothesis (Ho) because P obtained is greater than P critical or T obtained is less than T critical, so it means the Function doesn’t affect to the price. Conclusion: the price of the mobile phone is not affected by the categorization of function: Camera. Q12: Factors people take into consideration when buying mobile phones: This section analyses factors that people take into consideration when they buy mobile phones. Respondents were asked rank the following features from 1-6: brand name, fashionable phone, size of the phone, function, price and convenience. Please note that the scale of 1 represents taking high consideration, whereas 6 means taking low consideration. When you choose a mobile phone, what do you consider? Brand When you choose a mobile phone, what do you consider? Fashionable When you choose a mobile phone, what do you consider? Size When you choose a mobile phone, what do you consider? Function When you choose a mobile phone, what do you consider? Price When you choose a mobile phone, what do you consider? Convenience On the scale of 1 to 6, we consider 1-3 as considerable and 4-6 as less considerable. The ranking, derived from the above six tables, is reorganized as follow. It can be seen from the above table that, when making a decision to buy a new mobile phone, people consider price of the most important factor, followed by functions. Based on the ranking, we analyse whether people who pay for certain features pay for mobile phones at different price. An independent sample T-test was used as follows. (We just show which factor cause impact on the mobile phone price) Cost of mobile phone VS price factor Group Statistics **t (Obtained) = -4.834, p (obtained) = 0.000. HO: µ of money spent by people who take price into consideration = µ of people who don’t H1: The above two are not equal Since T (Obtained) of 4.834 is greater than T (critical) of 1.96 at 5% level of significant, the null hypothesis is rejected. This means that people who consider price as an important factors in making decision to buy mobile phone are likely to pay less than those who don’t think that price is an important factor. In other words, price conscious consumers are likely to buy cheaper mobile phones. Cost of mobile phone VS Brand name Group Statistics **t (Obtained) = 1.893 and p (obtained) = 0.61. Ho: µ of money spent by people who take brand name into consideration = M of those who don’t H1: The above two are not equal Since T (obtained) of 1.893 is greater T(critical) at 1.64 at .10 level of significant, the null hypothesis is rejected. People who take into consideration brand name are likely to pay more n mobile phone than those who don’t. This implies that people who pay attention a certain brand name when they buy mobile phones are likely to spend more money. Cost of Mobile Phone VS Fashion Group Statistics **t (Obtained) = 2.808 and p (obtained) = 0.006. Ho: µ of money spent by people who buy mobile phones based on fashion = µ of those who don’t H1: The above two are not equal. Because t (obtained) of 2.808 is greater than t (critical) at 1.96, we reject the hull hypothesis. People who buy fashionable mobile phones are likely to spend more money than those who don’t care about fashion. To sum up: there are three significant factors that influence how much money people spend on buying mobile phones. Those are price of the phone, brand name and fashion. People who are price conscious are likely to pay less than those who are not. People who look for a certain brand name are likely to pay higher than those who don’t care about brand. Those who look for fashionable phones are likely to pay higher than people don’t pay attention to fashion. 6. Chi- Square Test Chi-Square Tests a 6 cells (50,0%) have expected count less than 5. The minimum expected count is ,60. Ho: the age impacts on the time using mobile phone H1: the age doesn’t impact on the time using mobile phone Since X2 obtained = 21.397 is greater than X2 critical = 12.592 as well as P critical and P obtained (0.05 > 0.002), we reject the null hypothesis. Therefore, there is sufficient evidence to conclude that the age is one of the factors that impact on the time using mobile phone. We can notice on the above tables that the older you are, the longer you use mobile phone. We can easy accept this issue because the more age we have, the more important we understand about the telecommunication, therefore, we need tools such as computer, mobile phone to support our lives.







a .15 cells (71,4%) have expected count less than 5. The minimum expected count is, 10.

Ho: the duration using mobile phone affects the brand of the phone
H1: the duration using mobile phone doesn’t affect the brand of the phone

Based on the table, the P obtained =0.031 smaller than P critical =0.05 as well as X2 critical and X2 obtained (21.026 < 22.605), therefore the null hypotheses is rejected. There is sufficient evidence that the brand of the phone is affected by the duration using mobile phone. People who start to use mobile phone always choose a safe way for their phone like choose the brand has a well known reputation such as Nokia and Samsung, Sony Ericsson. Moreover, Nokia is the first choice for the one who use mobile phone from 1 to 3 years because of its stable price. However, for more than 3 years using mobile phone, they are the one that have a good knowledge and confident to choose the phone which is suitable for their habit, therefore, the brand is more various, such as ATC and O2 . To sum up, the brand that the cell phone belongs to is influenced a lot by the time using, 7. Multivariate analysis: In this section, we will point out 4 factors that can affect significantly the price of the mobile phone: Coefficients (a) a. Dependent Variable: How much money does your current mobile phone cost? To analyze this, we use Sample Regression Function (SRF): Y= b0+ b1 Xi Y is the dependent variable which represents the price of mobile phone Xi is independent variable which represents role, duration and age. Y= b0 + b1 role+ b2 Duration+ b3 age+ b4 gender When X=0 which means those factors have no effect on price, Y is equal to 2609594.938 VND. In addition, we compare the effects of those four factors to price to see whether they have effects or not. Firstly in the role of the mobile phone: P obtained = 0.082 is less than P critical =0.05, as a result the null hypothesis is rejected and there is sufficient evidence that the role is the factor impact the price of the phone. Y= 2609594.983+ 328079.098X1 When X1 increases by 1, Y will increase by 328079.098 VND. This is clear that the more important the role is, the more people will pay for their mobile phone. So role has a significant effect on price. If X=0, Y will be 2609594.983, meaning when role has no effect on price among three factors, price will be equal to 2609594.983 VND Secondly is the duration changing mobile: X2, P obtained = 0.01 is equal to P critical =0.01, as a result we reject the null hypothesis. Y= 2609594.938+ 496584.371X2 If X2 increases by 1, Y will increase by 496584.938 VND. In conclusion, when people change mobile phone more frequently, price will also be affected. Changing mobile phone means that people want their phone to be more modern or having more functions, so price will go up according to some factors. Moreover, if we assume that frequency of changing phone has no concern with price, it will become 260.9594.983 VND Finally is the age: X3, P obtained = 0.025 is less than P critical =0.05, as a result the null hypothesis is rejected. Y= 2609594.983+ 808115.649X3 If X goes up by 1 unit (10 years old), Y will rise by 808115.649 VND, If X=0, this also leads to the value of Y as 260.9594.983 VND From this result, we can see that when age increases by 10 years, price will probably rise by 808115.649 VND, a large amount of money. In conclusion, all the above three factor has a strong influence on the price however the age has the most significant effect on price among three factors. To explain this, we look at the fact that from the lowest interval of age (10 to 20), people from this interval are often students, they don’t have much money to buy an expensive phone. But when they are older, they get a job; they have more money so they will surely pay more for their belongings especially mobile phone. 8. Self- Critism In our group project, the hypothesis testing ,regression, chi-square are the most effective and helpful analyzing tools that help us to show the relationship between prices of the mobile in Vietnam with the other factors that affect this feature. It’s a first time we do this project with survey so that we cannot avoid getting errors. Especially from sample errors, its difference between the very small population (RMIT university) and a huge sample (Vietnam) become a definitely factors effect the fault. For example, the average gender and price of their phones of in RMIT University include: student with HED, English, Degree and staff with teacher, receptionist (Sample mean) is different from the average gender and price of their phone in Vietnam (Population mean). Firstly, all of the people I survey have valuable mobile phones. The average price of their mobile phones will be higher than the Vietnamese people. Some of them have 2 phones for different purposes, so they choose the better phones to give us information. Secondly, the student and receptionist may be not doing the survey honest because they busy with their jobs. It’s funny that I ask a design student the cost of his phones and I receive the answer that: it cost him a lot of time and effort in designing this specific phones. With him, it’s invaluable. Or a student answered that his mobile was worth billion. Moreover, it’s hard to have exactly answered in the ranking question. It can’t be perfect. For example that: many people using phones for taking pictures and listening music. They can’t decide which one is more important. Or, they can’t decide the role of phones in their life: may be its 5 (average) or 6, 4 (a little bit important or not). Thirdly, because the prices of phones are decrease regularly, it’s hard for them to have exactly price. Furthermore, some people gave us the price of their phones when they buy it new from the seller for a long time ago; it’s not exactly the price in recent time. The other people give us the price when they buy second hand from the others people. It’s also a problem with the origin of their phones, some people can’t know exactly about it because it’s a gift from the other people. It become complicated in having an exactly answer. 9. Conclusion By using different statistic technique, the report can satisfied This report used a survey technique to show the association between the gender or age, and the price or functions of the mobile phones people in Vietnam use. Using different statistic technique, the results have shown that the price of the mobile phone is affected by:  Classification of occupations  The amount of time we use it  The role as well as the frequency of changing your cell phone  The Internet Connection function,  Element about price and Fashion Moreover is the association between many aspects around the price such as age, gender with the duration as well as the brand of mobile phone. This report can become a decision-making process to help us in deciding what the mobile phone they should choose depending on the price to purchase, the brand and the functions. Furthermore, this report can be useful information for Mobile Phones Company and supplier to have information about the Vietnamese people and market to have good strategy for investment. 10. APPENDIX: Survey The mobile phone in Vietnam Method: Selecting a sample and stratified random samples. Team 01, Group Sta4 We are a group of students doing a research on the mobile phone and show the association between gender or age, and the price or functions of the mobile phones people in Vietnam use. Would you please help us to complete our project in Statistic subject because your feedback is very important for us. You can stick more than one answers, fill in the blank, and rank from 1- most consider to 6 – less consider. We thank you for participating in our survey. 1) What is your gender? 1.  Male 2.  Female 2) How old are you? 1.  10-20 2.  30-45 3.  20-30 4.  Above 45 3) What is your occupation? …………………………………………………………. 4) Do you use mobile phone? 1.  Yes 2.  No (If Yes – continue to answer) 5) How long have you been using your mobile phone? 1.  Less than 1 year 2.  1-3 years 3.  More than 3 years 6) Which brand does your mobile phone belong to? 1.  Nokia 2.  LG 3.  Samsung 4.  Siemens 5.  Motorola 6.  Sony Ericsson 7.  Others: (Please write specific your answers) …………………………………………………… 7) What do you think about role of mobile phone? Not important Normal Very important ├─────────────────┼─────────────────┤ 1 2 3 4 5 6 7 8 9 10 8) Do you prefer buying a mobile phone from the distributors or outside with lower price? 1.  Distribution 2.  Outside 9) How often do you change your mobile phone? Never Sometimes Very often ├─────────────────┼─────────────────┤ 1 2 3 4 5 6 7 8 9 10 10) How much money does your current mobile phone cost? ( fill in the blank ) ………………………………………………………… 11) Which functions are you interested in when buying a mobile phone? (You can stick more than one) 1.  Camera 2.  Internet connection 3.  Mp3 music player 4.  Games 12) When you choose a mobile phone, what do you consider? (Rank from 1 – most consider to 6 - less consider) 1.  Brand 2.  Fashionable 3.  Size 4.  Function 5.  Price 6.  Convenience Thanks for your corporation.  Reference list • Dinh Hang 2004, Vietnamnet, http://vietnamnet.vn/cntt/vienthong/2004/12/358232/ , viewed at 15/09/2007. • Thong Tan Xa Vietnam 2007, EIU Information, http://www.baokhanhhoa.com.vn/Kinhte-Dulich/2007/07/227373/, viewed at 15/09/2007. • Hoang Anh 2004, Magazine of Vietnam Economic, http://www.mof.gov.vn/Default.aspx?tabid=82&ItemID=16629 , viewed at 15/9/07 Original source: Grab Your Example Essays Now

Statistics Project-Helmets and Law-Free Essay

Executive Summary

Recently, the government has passed a new law wearing helmets inside the city. This law was a controversial issued for many years but when this was about to acted it failed because the citizens are against the policy. However, due to the dramatically increase in number death causing by traffic accidents, government strictly launch the law this time. Base on that issue, we decided to choose wearing helmets and how other factors can effect on wearing helmets as our project. However, because we do not have enough conditions involving money, time and human, RMIT was chosen as our population. Moreover, as our purpose was to figure out whether the relationship between law and number of people wearing helmets so we only did observation two weeks before and after the law passed to see whether the law will affect on the number of people wearing helmets. In regarding to that, this report will present our data and how we do the analysis to define the possible relationship between wearing helmets and other factors such as law and different times of the day through a chain of Z Test, Confidence Interval for proportions and hypothesis testing. Once this project is finished, it should be able to show what problems may occurs and whether the law is applied effectively. It also can provide ideas for researches on the differences in the number of people died in accident before and after the law were passed so the future direction for traffic accidents will probably be defined.
Introduction
According to Vietnam Net, a local popular online newspaper, in 2006, the number of people died by traffic accidents in Ho Chi Minh has jumped to a warning level with around 1.332 traffic accidents resulted in 1.014 people died mainly by brain injuries. Since 32 Law declared that wearing helmets in highways from 15 September and in all the streets from 15th December is compulsory, it seems to be the best solution for the problem. However, people especially students still prefer not to wear them when driving out. In regarding to this issue, this report will look at RMIT Southern Vietnam International University as a sample in an observation research to analyze how students react to the law and how the law and other factors such as time affect on the number of students wearing helmets. In order to do that, this report will first describe the methodology in collecting information and the related problems by measuring central tendency, variation as well as diagrams such as histogram, Pareto will be used to illustrate the issues. Then, these problems will be analyzed through performing Z test, confidence interval for proportions as well as hypothesis testing to see whether there are any other factors affecting on the effectiveness level of the 32 law as well as defining if there are any possible relationship between wearing helmets and other factors such as times and the law . Finally, proposed solutions to the issue will be given.
Data Collection
This project was done by Heroes Teams including Tam, Chuong, Duy, Khoa and Dung. We want to know whether the number of students wearing helmets after 15th December will be different. At first, we was going to conduct a survey to get what students think about the law so that we can based on their opinions to estimate whether they will obey the law or not. Then guessing how different it would be in comparing to before the law. However, it seems to be that survey research may give us irrelevant and biased data. Therefore, we have planned to conduct an observation research to have an accurate data about the number students wearing helmets before and after 15th December by observing two week before and after the law. From 15 September students just have to wear the helmet on the highways so we decided to count the number of students wearing helmet at the intersection between Nguyen Van Linh Boulevard and RMIT University. Each member in group will stand on the intersection two hours: one hour in the morning and another in the afternoon, to count following the schedule that shown below. At the end of the day, the last person will also be in charge of summary the data collecting in this day. Overall, we have done our task successfully. All of us have tried our best to manage our timetable in order to complete the team’ tasks although we need to stand under the sun for around one hour. In addition, although RMIT students in total are about 3000, only a thousand or more study each day. Consequently, a sample of 2000 students will be observer using the simple random sample with replacement in four week. In average, 100 students, a half in the morning and another half in the afternoon per day up to 4 weeks. The reasons why we decide to use this sampling strategy are because we cannot control who will be observed and students used to have different timetable for each day so using another strategies such as systematic, stratified or cluster or even simple random sample without replacement seem to be impossible. We also have discussed and determined two very important factors that we need to observer carefully such as the number of students wearing helmets as well as whether the way they used to wear the helmets is correct. We want to have accurate figures about these factors to prevent getting irrelevant data. The data collected two weeks before and after the law was provided below:

Week Students Law Date Days Morning Wear Helmets Afternoon Wear Helmets
1 Khoa Before 12/3/2007 Monday 7:00 - 8:00 13 12:00 - 1:00 20
1 Tam Before 12/4/2007 Tuesday 8:00 - 9:00 14 1:00 - 2:00 25
1 Chuong Before 12/5/2007 Wednesday 9:00 - 10:00 20 2:00 - 3:00 21
1 Dung Before 12/6/2007 Thursday 10:00 - 11:00 19 3:00 - 4:00 23
1 Duy Before 12/7/2007 Friday 11:00 - 12:00 23 4:00 - 5:00 27
2 Khoa Before 12/10/2007 Monday 7:00 - 8:00 17 12:00 - 1:00 26
2 Tam Before 12/11/2007 Tuesday 8:00 - 9:00 26 1:00 - 2:00 29
2 Chuong Before 12/12/2007 Wednesday 9:00 - 10:00 31 2:00 - 3:00 30
2 Dung Before 12/13/2007 Thursday 10:00 - 11:00 32 3:00 - 4:00 15
2 Duy Before 12/14/2007 Friday 11:00 - 12:00 35 4:00 - 5:00 32

Week Students Law Date Days Morning Wear Helmets Afternoon Wear Helmets
3 Khoa After 12/17/2007 Monday 7:00 - 8:00 41 12:00 - 1:00 49
3 Tam After 12/18/2007 Tuesday 8:00 - 9:00 45 1:00 - 2:00 48
3 Chuong After 12/19/2007 Wednesday 9:00 - 10:00 47 2:00 - 3:00 48
3 Dung After 12/20/2007 Thursday 10:00 - 11:00 46 3:00 - 4:00 49
3 Duy After 12/21/2007 Friday 11:00 - 12:00 49 4:00 - 5:00 48
4 Khoa After 12/24/2007 Monday 7:00 - 8:00 50 12:00 - 1:00 50
4 Tam After 12/25/2007 Tuesday 8:00 - 9:00 48 1:00 - 2:00 49
4 Chuong After 12/26/2007 Wednesday 9:00 - 10:00 50 2:00 - 3:00 50
4 Dung After 12/27/2007 Thursday 10:00 - 11:00 49 3:00 - 4:00 50
4 Duy After 12/28/2007 Friday 11:00 - 12:00 50 4:00 - 5:00 50

Data summary

Firstly, after two weeks before law we find out that there are many students do not wear helmet while they was driving on Nguyen Van Linh highway. In the first week, there were only 207 over 500 occupied 41.4 % students wearing helmets. One week after, the figures increase slightly to 273. This could have been due to the fact that some students have already prepared for the law in 15th December.

After the law, in the third week, there are 470 over 500 students wearing helmet at the intersection between Nguyen Van Linh Boulevard and RMIT University. The last week, this number has increased by 25 to 495 students. This could be because students have become more familiar with the law. However, there was still a small proportion not wearing helmets such as the students living near Nguyen Van Linh highways.
Clearly, the number of students wearing helmets has increased significantly after 15th December from just 207 students jump up to approximately 495 students. Beside the law, there also some other factors probably affecting on the number of students wearing helmets such as weather, times. For example, in the morning the number of students wears helmet is less than the number of students wears helmet in the afternoon.

The reason might be that they want to protect their skin in the afternoon. Furthermore, the number of times polices guards on Nguyen Van Linh Street in the afternoon also less than in the morning. As it can be seen in the diagrams, the number of students wearing helmets in the morning is just 705 while this figures for the afternoon is 739. In order to have a more clear view about whether the law and times determined the change in the number of students wearing helmets, the following part will discuss in more detail.
Wearing Helmets and Time

Wear Helmets In The Morning Wear Helmets In The Afternoon
N Valid 20 20
Missing 0 0
Mean 35,25 36,95
Std. Error of Mean 3,085 2,901
Median 38,00 40,00
Mode 50 50
Std. Deviation 13,795 12,976
Variance 190,303 168,366
Skewness -,365 -,241
Range 37 35
Minimum 13 15
Maximum 50 50
Sum 705 739

The number of students wearing helmets in both the morning and afternoon has a negative skewness distribution. The reason for that may due to the fact that in the first two week the figures is quite low and then it increase significantly to approximately 500 students. The central tendency including mean, mode and median are quite different between the morning and afternoon. Although, the average number of students wearing helmets in the morning is less than in the afternoon, its standard errors of the mean are higher than in the afternoon.
In addition, the number of wearing helmets students has reached the maximum value of 50 students, the number we observe for each one hour. However, because of the different between data two weeks before and after the law, the standard deviation for both in the morning and afternoon means that the real data is far from their means.

This negative skewness graph above describes each figure we have collected in the morning in frequency. It can be seen that that the number of students wearing helmets has increased in quantity from 13 students to 50 students at the end of week 4.
Also, the total hours we have used to collect the data in the morning was 20 hours. Although the data collected has a mean of 35.25 implying that the average number of students wearing helmets per day is about 35.25. However, its data spread far from the mean with the standard deviation up to 13.795.

In the afternoon, the average number of students wearing helmets is 36.95 with a high standard deviation of 12.976 proving that the data is different between two periods of time. From the minimum value of 15, the figure also reaches a peak of 50 in the last two weeks. It can be understand more clearly through the box and whisker below:









Through the above box and whisker plots, we can see how the data is distributed in the number of students wearing helmets in the morning and the afternoon. Clearly, the distance from the minimum value to the mean is less than from the maximum value. Also, the figure in the afternoon is obviously higher than in the morning.
Effects of the Law
In order to understand more about how the law effects on the number of students wearing helmets, a data analysis has been shown below:

In the first two week, only a few students wearing helmets when they are driving into the intersection of Nguyen Van Linh and RMIT University. Although the maximum value is quite high, the average students wearing helmets per day are just 23 students. Also, the data spread quite far from the mean with a standard deviation of 7.746.
This may be because while some students have already prepared for the law, others still prefer not wearing helmets so the change in number of students was unstable.

The highest and lowest numbers of students wearing helmets in the first two weeks are 35 and 13 respectively. However, after 15th December, this figure has gone up significantly to a new minimum level of 40 and maximum of 50. The standard deviation also decreases sharply to about 2.877 reducing the distances between data and the mean.

Before the law, the average students wearing helmets in Nguyen Van Linh Street was about 25 students, which is higher than in comparing to the figure in the morning. However, the numbers are spread quite far from the mean with the standard deviation up to 5.16 while the standard deviation in the morning is just 2.6. This may be because students used to driving out more at the same period of time. Although the number wearing helmets in the afternoon is quite higher than in the morning, this figure maximum was only 32 while it is 35 in the morning.


Although the standard deviation is quite high before the law, it is only 0.876 after 15th December. In the last two week, the number of students wearing helmets in the afternoon increase significantly with the mean of 49.1 and reach a peak of 50 first time at the end of week 3. The minimum of 32 in the first two weeks has also jumped up to 49.
Obviously, the law has played an essential role in the increasing of the figures. In order to have a more clear view about how the law has effect on the number of students wearing helmets both in the morning and the afternoon, we can also look at the Pareto diagram below:

Overall, in the last two week, the figures has increase nearly double in both the morning and afternoon from just more than 110 to over 240 students in the afternoon wearing helmets and a large increase from 89 to nearly 150 students in the morning.

Finally, another problems need to be mentioned is the way of students wearing helmet. Before the law, no one cares about the way they wear the helmet correctly or not. However, after the law, the police will catch the students who wears helmet incorrectly. For example, the police will charge 50,000 VND for those who do not wear the helmet belt. In some case, some students wears the helmet belt but it still incorrect, compare with the helmet rule. So, it explains why the percentage of students wearing helmet incorrectly is high with over 88% (Vietnam Net). In addition, wearing helmets incorrectly is very dangerous for the drivers because if accident happened, instead of their brain, their neck may be damaged seriously and it can lead to death.
Inferential Statistics



In this part, we decided to use some statistic components to analyze any possible relationship between different variables and the number of people wearing helmets included:

 Using Confidence Interval Estimate for the Proportion to define the quantity of helmets people wearing before and after the law.

 Using Z test for proportion in term number of successes to check whether the proportion of people not wearing helmets is 0.5

 Using Z test for two difference proportion to identify possible relationship between law, times and wearing helmets.

Confidence Interval Estimate for the Proportion

A 95% confidence interval estimate of the population proportion of RMIT students can be used to evaluate the number of students wearing helmets in 1000 RMIT students before and after law separately. There were 478 and 966 students wearing helmets before and after law respectively.

Before law:

Sample Size 1000
Number of Successes 478
Confidence Level 95%

Intermediate Calculations
Sample Proportion 0.478
Z Value -1.95996398
Standard Error of the Proportion 0.015796075
Interval Half Width 0.030959739

Confidence Interval
Interval Lower Limit 0.447040261
Interval Upper Limit 0.508959739
We have 0.447≤p≤0.508. Therefore, there is 95% confidence that between 44.7% and 50.8% of 1000 RMIT students wearing helmets before law.

After law:


Sample Size 1000
Number of Successes 966
Confidence Level 95%

Intermediate Calculations
Sample Proportion 0.966
Z Value -1.95996398
Standard Error of the Proportion 0.005730969
Interval Half Width 0.011232492

Confidence Interval
Interval Lower Limit 0.954767508
Interval Upper Limit 0.977232492


We have 0.954≤p≤0.977. Therefore, there is 95% confidence that between 95.4% and 97.7% of 1000 RMIT students wearing helmets before law. This is much higher comparing to the figure before.
Base on those statistics of our sample we can 95% confidently estimate before a new law about wearing helmets was passed there were only 44.704% - 50.508% of RMIT students wear helmets. However, when the government really acted this law, the percentage of RMIT students wearing helmets increased significantly to 95.476% - 97.723%.

Z Test for the Proportion In Terms of the Number of Successes


Our assumption from observing the data is that the number of students wearing helmets before law is equal to the number of students not wearing one. In order to know whether it is correct or not, we decide to use the Z test for the proportion in terms of the number of students obey the helmets law when driving on Nguyen Van Linh Boulevard. In 1000 students we have observed in the first two week, there are 478 students wearing helmets and 522 do not.

In terms of proportions, the null and alternative hypotheses are stated as follows:
Ho: p=0.5 that is the proportion of students wearing helmets was 0.5
H1:p1#0.5 that is the proportion of students do not wearing helmets before law was 0.5

Because we want to know whether our assumption is right, a two-tail test is used with a level of significant α of 0.05. The decision rule is
Reject Ho if Z < -1.96 or if Z > +1.96
Otherwise do not reject Ho

Null Hypothesis p= 0.5
Level of Significance 0.05
Number of Successes 478
Sample Size 1000

Intermediate Calculations
Sample Proportion 0.478
Standard Error 0.015811388
Z Test Statistic -1.39140217

Two-Tail Test
Lower Critical Value -1.959963985
Upper Critical value 1.959963985
p-Value 0.164103507
Do not reject the null hypothesis



Because Z= -1.39 > -1.96, we do not reject Ho. Thus, there is evidence that the proportion of students wearing helmets is 0.5. That mean before the law, the number of people wearing helmets is just about equal to the number of people do not wear one. However, after the law was passed, there is a significant change in this figure with 966, almost double the number of people wearing helmets before law.
Z Test for Two Proportions

In evaluating differences between two proportions on the basic of two samples, that is 1000 RMIT students before and after law as well as 1000 students in the morning and afternoon, we decided to use Z test for different between two proportions. This Z test mainly will be used to determine whether the number of students wearing helmets after the law will be higher than before and if the figures in the afternoon is higher than in the morning. Therefore, we used lower-tailed test.
Law and Wearing Helmets

The null and alternative hypotheses are
Ho: p1≥p2 that is the number of students wearing helmets before law is greater than the figures after the law.
H1: p1 +1.96
Otherwise do not reject Ho.
Hypothesized Difference 0
Level of Significance 0.05
Before law
Number of Successes 478
Sample Size 1000
After law
Number of Successes 966
Sample Size 1000

Intermediate Calculations
Group 1 Proportion 0.478
Group 2 Proportion 0.966
Difference in Two Proportions -0.488
Average Proportion 0.722
Z Test Statistic -24.35644092

Lower-Tail Test
Lower Critical Value -1.644853627
p-Value 2.4772E-131
Reject the null hypothesis
From the result, using the 0.05 level of significance, we see that Z test statistic is too small compare with lower critical value (-24.356 << -1.644) therefore we reject the null hypothesis Ho. Hence, it can be concluded that the law has effect and lead to an increase in the number of students wearing helmets. This explained why the figures has increased significantly from a low level of 478 in the first two week to approximately 966 students wearing helmets at the end of week 4. Times and Wearing Helmets The null and alternative hypotheses are Ho: p1=p2 that is the number of students wearing helmets in the morning is equal to the figure in the afternoon H1: p1#p2 that means conversely. Since the test is to be carried out at the 0.05 level of significance, the critical values are -1.96 and +1.96. The decision rule is: Reject Ho if Z < -1.96 Or if Z > +1.96
Otherwise do not reject Ho.
Hypothesized Difference 0
Level of Significance 0.05
Morning
Number of Successes 705
Sample Size 1000
Afternoon
Number of Successes 739
Sample Size 1000

Intermediate Calculations
Group 1 Proportion 0.705
Group 2 Proportion 0.739
Difference in Two Proportions -0.034
Average Proportion 0.722
Z Test Statistic -1.696965146

Two-Tail Test
Lower Critical Value -1.959963985
Upper Critical Value 1.959963985
p-Value 0.08970325
Do not reject the null hypothesis

After doing the test, using the 0.05 level of significance we see that Z test Statistic (-1.696) is larger than the lower critical value (+-1.959) and smaller than the upper critical value which means the null hypothesis is not rejected. In brief, there is evidence that time does not effect on the number of students wearing helmets.
After performing different analysis, we realized that although the number of people wearing helmets before 15th December is just equal to others, this number changed significantly after the law was passed. To illustrate, through confidence interval for the proportion, it can be seen that there is 95% confidence that between 44.7% and 50.8% of 1000 RMIT students wearing helmets before law. However, this figure has jumped up to 95.476% - 97.723% after the law.
Reflection

Achievement


When doing our report, we have made some decisions effecting positively on the process of collecting data:

 The sample size is just enough for us to observer with an average of 50 students that one members need to count each hours so we have finished our tasks successfully. In addition, the sample is large so its sampling error is very low.

 The sample proportion for each week is equal and measured scientifically, that is 500 students were observed.
General Problems



Overall, there are some mistakes that we have met during doing this project. The most important mistake is choosing observation as the mean of Data collection. The reason for why we call it was an error because base on our observation there were too little information and problems that we can analysis in the Inferential Statistics part. Therefore, this part only takes a small place in our report and contains only some problems even though this is the highest score session.

Beside, the most serious problem was that we have chosen wrong sample for our original purpose. In particular, we have seen a relationship between the number of accidents and the law. However, because our sample and population is RMIT students while the statistics of accidents and deaths are for the Ho Chi Minh City. As a result, we cannot make a conclusion about this relationship. In addition, we have thought narrowly when think that the data for accidents and deaths are easy to find. In contract, these kinds of data update really slowly and are mainly put in some articles instead of a report. Therefore, we cannot find the data for the number of accidents after 15th December to put into the inferential part.


Sampling error



Although we have taken a sample of about 2000 students two weeks before and after 15th December, sampling error is unavoidable because we do not take all the population because it is costly, time-consuming and ineffectively. Nevertheless, our sample has very small sampling error because we have decided to choose up to two over three of all RMIT students.

Sampling bias


Since we only observe from 7am to 5pm and do not observe in Saturday so there might be some missing data in the other time especially in the evening. Because, we do not have enough condition to observe at these time so it is also unavoidable. In addition, some time students come to school as a large group so it is quite difficult for one person to count all. Therefore, there is a chance one person may be counted more than two times while the others were not be counted. However, we have developed a scientific schedule for all members and learn from our past experiences in order to reduce these errors to the minimum level.
Lurking Variables


 Location: some students live near RMIT University so they sometimes do not wear helmets when driving to school.
 Outside students: sometime students from outside or from high schools pupils coming to visit RMIT University and sometime we cannot define who is the students that we need to count. Therefore, the number of students wearing helmets may be over counted.
 Hypothesis-Testing error: using a sample statistic to make a decision about a population parameter may lead to type I and type II errors.






Recommendations

As we have mentioned above that our observation did not support for our original purpose and therefore, a number of other problems also occurred during project. We will provide some basic ideas that might help to improve it below:

 Firstly, to accomplish our objectives, we should choose another place as well as a larger sample to do observation like a specific street so that we can get data about not only whether law and times effecting on the number of people wearing helmets but also the number of accidents and deaths could be found through asking or giving to students the survey included some questions like: “Have you made any accidents” and “If yes, when did you make that?”, for example.

 Secondly for those remaining error, we suggest that enlarging our observation schedule including Saturday and observe in the evening probably help us cover all kind of RMIT students so they may have a same chance to be observed. Furthermore, we should also ask them whether they are RMIT students or not before counting them.

 Finally, we should attempt to control the lurking variable in order to get more accurate data about the situation.
Conclusion




After four weeks observing at the intersection between Nguyen Van Linh highways and RMIT University, we have organized and analyzed the data having from observation and concluded that there are differences in the number of people wearing helmet effecting by other factors such as times of the day and law. After using hypothesis testing, Z test for proportion and confidence interval to check and determine on how law and times influence on the number of people wearing helmets, we found strong evidence that the number of people wearing helmets after the law is also increase significantly comparing to before. However, it was also pointed out that the different times of the day do not effect on the number of people wearing helmets. Nevertheless, it cannot be concluded without a controlled experiment because the results might also be due to other factors such as the sampling error, selection bias. Besides, it was a pity for us because we cannot perform the test for the number of people death and injuries or whether students wearing helmets correctly or not because there were not any up-to-date data from 15th December.

References


 Levine, D, Stephan, D, Krehbiel, T & Berenson, M., 2005, Statistics for Managers: Using Microsoft Excel, 4th edn, Pearson Education Inc., Upper Saddle River, New Jersey.

 Vietnam Net, “TPHCM: Mỗi ngày hơn 50 người chết và bị thương vì TNGT”, viewed 23th December 2007,
< http://vietnamnet.vn/baylenvietnam/giaothong/2007/04/687470/ >

 Vietnam Net, “Đội mũ bảo hiểm sai cách, nhiều người nhập viện cấp cứu”, viewed 30th December 2007,
< http://vietnamnet.vn/xahoi/2007/12/761406/ >

Business Statistics-parents or educators are paying more attention to nowadays, is the independence of teenagers-Free Essay

I. ABSTRACT:
A growing concern in Vietnam, that people especially parents or educators are paying more attention to nowadays, is the independence of teenagers. While many people think that young generation tends to depend on family, others believe that teenagers are more active in earning money and prove their abilities. To have a deep insight into this problem, we conducted a survey which focuses on students planning or running their own business during the university period. Based on the specific statistics from the survey and detailed analysis, we can have the relevant accurate conclusion about this issue.

II. INTRODUCTION
Surprisingly, in 2002, amongst 40.000 entrepreneurs being investigated in Vietnam, there were only 7.28% of them who were under 30 years old (Vietnamnet). This means that students running their own business hold a very small proportion. However, recent economic booming in Vietnam has changed teenagers’ thinking about doing business. They have deeper understanding about the benefit that running business brings to them. This change fosters us to make this survey to draw out the correct consideration about this issue. Moreover, this survey also can assist us in accumulating experience and gaining valuable knowledge; therefore, we can have a thorough grasp of the procedure of statistical thinking which has a strong relationship with another subject such as marketing research. Our report will be divided into three parts: descriptive statistics which summarizes the main points in the survey and also the relationships between factors such as gender, semester, department and educational environment. The next part is inferential statistics in which we draw conclusions based on the sample statistics. In final part, we will present some recommendations in order to enlighten students’ mind about doing business.

III. LITERATURE:
According to the real statistics from the study of Vietnamnet, the proportion of Vietnamese students running business is much less than other nations. The first reason is that most students would like to work for Government companies after graduating in order to have stable income, receive good welfare and retirement pension. Secondly, being scared of failure and preferring avoid risks prevent students from catching opportunities to do business. However, economists state that teenagers should alter their mind and be more active, confident to face with challenging. By this way, they can contribute to the development of Vietnam economy. Therefore, we decide to carry out this topic relied on the credible responses from students of the three universities including Foreign Trade University (FTU), University of Economic HCM City (UEH) and RMIT University.
(Nhat Vy 2005)

IV. DATA COLLECTION INSTRUMENT:
We decided to choose primary sources, specifically conducting survey because there are no available sources about our topic. Our survey is divided into three sections. The first one is the background of the students such as gender, semester, department; the next is their plans, main financial support as well as purposes. The final section is their attitude and thinking toward the success and failure when running business.

1. SAMPLING PLAN:
It is obvious that all of the students from universities related to economic majors in Ho Chi Minh City are our population. However, the numbers of students are too large to examine. In addition, due to the limitation of time and money, we decided to draw a sample. We selected 120 students randomly in three different universities and 40 students from each university, one is the international university- RMIT University, the others are the national universities- UEHHo Chi Minh City (HCMC) and FTU. The differences of background, education environment and financial condition of these students are the reason why we have chosen these three universities.
Sampling Method
We made up our mind to use probability sample “which is one in which the subjects of the sample are chosen on the basis of known probabilities” (Levine, M., David et al, 2005, p.10). Simple random and stratified sample seem to be the best choice.
Simple Random: is defined as “the method that every individual or item from the frame has an equal chance of being selected and selection may be with replacement and without replacement” (Levine, M., David et al, 2005, p 11). We decided to use simple random without replacement because it is simple to use. Furthermore, when every individual has the same chance of selection, it will ensure the fairness of the result.
Stratified Sample: “is one in which population divided into two or more groups according to some common characteristics, and then simple random sample selected from each group” (topic: introduction to statistic, 2007). “Stratified sample ensures representation of individuals across the entire population” (topic: introduction to statistic, 2007) is the reason why we applied this method.
We used stratified sample two times. Firstly, we divided our sample into three groups of university – 40 students from each. Secondly, in those 40 students, we separated 20 students from semester 1 to 4 and 20 from semester 5 to 8. Moreover, amongst 40 those students, we also made survey with 20 females and 20 males. The reason why we split students into two groups is that we want to figure out whether the semester periods and gender influence on the students’ decision on planning or running business during the university time or not.

2. PLAN FOR THE SURVEY
There are five members in our team. Firstly, we discussed about the questions going to ask in this survey. We finally showed our consensus on 10 questions after many times discussing and come up with the process of making the survey paper. We also unified the method of sampling (stratified sampling method) and our sample size was randomly 120 students in three different universities. Moreover, it also was created in both Vietnamese and English so that everyone who was surveyed could understand clearly and provide accurate answers.
After that, we started to run our surveying process firstly in RMIT campus. Then, we came to the other university campuses and continued with their students.
Finally, based on the results supported by those students, we can summarize and analyze data.

3. ADVANTAGES AND DISADVANTAGES
3.1 Advantages:
- Our sample included students from three different universities relating economic majors in HCM City. Therefore, our conclusion may be more objective.
- Sample size of 120 students which is large enough for us to conclude that the sampling distribution can be safely approached by Normal Distribution. Moreover, it also allows us to calculate and verify the results by applying Z-table and t-table.
- Stratified sampling method which divided our sample by semester and gender helps us to present the individuals’ performance across the rest population.
- Avoid measurement error: After many discussing hours, our questionnaire is fairly complete and clear which reflects aspects requiring to open a business such as financial support, kind of business, capital.
3.2 Disadvantages:
- There are some unclear responses due to the fact that we did not sit next to each individual respondent. Hence, we cannot ensure they answer honestly or not.
- Coverage error: Our sample just includes three common universities in HCM City. By this way, it cannot reveal the exact conclusion about other universities fairly.
- Sampling error: There is a difference between the result of our sample and the actual value of population because our sample size is smaller than the population size. Moreover, our sample just focuses on students studying economic majors; hence, in some way, it cannot reflect exactly the proportion of young Vietnamese people planning to do business. Thus, sampling error is inevitable.
V. DATA SUMMARY
1. IMPORTANT QUESTIONS
1.1 Gender:


1.2 Semesters:


1.3 Are you planning or running your small business during university time?

The bar chart shows the proportion of students of the three universities who plan running business while studying or not. The result presents that there are 50% (20 students) RMIT students who planning or running business and the rest (50%) say “no”. While there are 33 students of the UEH (82%) say “yes”, only 7students (18%) say “no” when being asked. However, the percentage of students who plan to run business or not of FTU seems to be similar with 21 students say “yes” and 19 say “no”.

1.4 If you plan or run business, what is your main financial support?

As can be seen from the (), most three usniversities’ students have their main financial support from their family. RMIT and UEH have the same proportion with 56% of the total while FTU has a higher rate at 63%. It can be understood that family always plays the most significant role in the decision of starting to run a business of teenagers recently. Borrowing moneys from bank also occupies an important rate of students with 20% and 14% in total of RMIT and FTU. However, UEH stduents do not consider the investments from bank for their financial support. They can find investments from other ways and that rates at 23% totally. To conclude, students can find their own way to get the main financial support for their business. Generally, most of them believe their family is the safest and the best financial investment in order to plan or run a business.

1.5 What kind of business are planning or running?

According to the diagram, opening the fashion shop occupies the highest proportion with 26% totally of students from RMIT University. On the other hand, both UEH and FTU students like to open their business in other types that make up 37% and 34% in total. It can be seen that students want to build up their own business which has to be new, unique and also easy to manage the operations.

1.6 How much money have you invested or will invest in your business?










Here are 3 summary tables of the statistical figures from the 3 universities. As can be seen, RMIT has the highest Mean, Median and Mode of the 3 universities (Mean is 3,216.425$, Median is 2,110$ and Mode is 3000$). However, we can not modify the shape of its population’s distribution because the Median is smaller than Mode and Mean. On the other hand, both UEH and FTU have the similar shape of distribution that have the Mean is larger than Median and then Mode (UEH has the Mean at 1,566$, 1,050$ of Median and Mode is 500$ while FTU has the Mean at 1,740.25$, 1,300$ of Median and Mode is 300$). It can be understand that their data are positive, or right-skewed that has a long tail on the right of the distribution and a distortion to the right that is caused by extremely large values. About the standard deviation, RMIT still gets the highest with 3,147.11$ for each investment for each student’s plan on business. It may be due to the fact that the main financial support of RMIT students is their family thereby satisfying all their needs for their plan on business. Next is FTU with the standard deviation of 1,897.74$ and finally is UEH with 1,349.85$. In general, RMIT students have the highest investments for their business compared with the other 2 universities.

1.8 What are your purposes to start planning / running your business?

The pareto diagram above shows some purpose patterns that students of FTU give when being asked for the reason of running business during the university period. As can be seen, the most important reason which makes students feel interested in doing business is earning money (50%). Then, there is 20% ideas claim that they want to do business when studying to accumulate experiences for their future careers. Following, while some students assert that they like the feeling of challenging in the business environment (10%), there is very small percentage of students has other purpose for opening a business, just 1%.

The pareto diagram above gives the proportion of the four main purposes that students of the UEH City present when running their own business. There is 36.49% ideas state that earning money is the most vital reason when doing business and 27.03% of them think accumulating experiences is the main purpose because there are many real experiences they cannot gain from school. Just by running the real business, they can have valuable experiences. Next, other reason, which is 20.27%, is considerably more common than the reason of preferring challenging, which makes up 16.22%.

The proportion of the four main reasons of doing business which are given by the students of RMIT University Vietnam is illustrated by the pareto diagram above. Earning money has the highest percentage amongst the four (52.63%). Subsequently, accumulating experiences is the second reason that students choose after earning money which constitutes 36.84%. There is 7.02% students state that they decide to do their own business because they like the sensation of challenging and 3.51% presents other reasons.
As can be seen, most students from the sample think that earning money (46.37%) and accumulating experience (27.96%) purposes are the most important reasons for doing business. While students of FTU and RMIT University state that sense of challenging encourage them to run business, UEH’ s students have other reasons for their decisions.

2. COMBINED QUESTIONS:
2.1
 Gender: Male / Female
 Are you planning or running your small business during university time?
The three column charts show the proportion of males and females in three universities including the FTU, UEH and RMIT university, divided into whether they plan and do business or not.

The FTU shows a contrary result with the greater percentage of males who do not have plan or do business (9 students) than the ones do (11 students). On the other hand, there are twelve (12) females say “yes” and eight (8) of them say “no” when being asked.



While the gender effects extremely in business plan in RMIT University and FTU, UEH meets a different situation. The result of survey shows that the numbers of male and female who have plan and do business seem to be equal with sixteen (16) amongst 20 males and seventeen (17) amongst 20 females. On the contrary, there are only 4 males and 3 females answer “no” for this question.


In RMIT university, there are thirteen (13) among 20 males who are being asked, answered that they have plan or actually have their own business while there are only seven (7) females say “yes” for this question. Surprisingly, the oppose result of female describes that the number students who tend to do business (7 students) is much less than the students who do not (13 students).
2.2
 Semester: 1-4 5-8
 Are you planning or running your small business during university time?
The bar chart shows the numbers of students from sem 1-4 and sem 5-8 in the response to having plan to do business or not.

As can be seen, students from sem 1-4 who plan to do business in this univeristy,which account for 12 are more than students from sem 5-8, which are 9. In addition, students from sem 1-4 who don’t have plan is 8 out of 20 which are less than students from sem 5-8, which make up 11. Surprisingly, the numbers of students who want to run businees from sem 1-4 are more than the numbers of students from sem 5-8. In Foregin Trade Univeristy, it could be that the numbers of sem students take do not impact on their thinking and desire to do business during university time.

This chart illustrates that there are a large number of students from semester 1-4 and semester 5-8 have plan to do business. However, those numbers from semester 5-8 are more than students from semester 1-4. There are 18 out of 20 students from semester 5-8 said that they want to run business during university time while students from semester 1-4 are 15. It can be said that in UEH, the numbers of semester taken may influence on the thinking of students. It seems that when students study higher and become mature, they will think about running their own business.

The bar chart presents that students from semester 1-4 who tend to do business in RMIT University are less than students from semester 5-8. 8 out of 20 students from semester 1-4 answer “yes” compare with 12 out of students from semester 5-8. Students from semester 1-4 who do not have plan, which is 12 are more than students from semester 5-8 which constitutes 8. It could be that the circumstance of RMIT University is the same with UEHHCMC. When students study higher level, they have accumulated experience and become mature. As a result, they may want to challenge themselves. They tend to have their own plan and own way to develop. Therefore, it can explain why students from semester 5-8 who plan to do business are more than students from semester 1-4.

2.4
 Gender : male /female
 What are your purposes to start planning / running your business? (more than 1 answer is accepted)


In the FTU case, the number of male (6 students) who like to face with changing is more than female (4 students). Beside that, most of FTU’s students tend to do business to earn money with 15 males compared to 16 females. Moreover, there are 11 female and 9 male who do business to gain experince. There is only 1 male has other purpose while this number for female situation is zero.


The side by side bar chart above shows the relationship between the gender and the purpose of doing business in OEH. It appears that male decide to run business because they like feelings of challenging (7 students) more than female (5 students). Surprisingly, there are more female students stating that earning money (16 students) and other reasons (4 students) encourage them to do business than males, which constitue 11 for earning money purpose and 1 for other purpose. 10 male and 10 female agree with each other that experience is a factor that lead them to run business.

The connection between the gender and the reason why RMIT’s students prefer to run business is illustrated in the side by side bar chart above. As can be seen, there is the equality in the amount of male and female students stating that preferring challenging sensation (2 students of each) and other reasons (1 student of each) make them want to do business. Besides that, when doing business, money is the motivation. Hence, this number for the earning money reason between male and female seems to be equal (16 for male and 14 for female). Moreover, the number of female (11 students) who run business for gaining experience purpose is greater than male (10 students).

VI. INFERENTIAL STATISTIC
In this report, we choose to use hypothesis testing and regression analysis. In the first section, we apply hypothesis testing to check that whether the gender and semester affect the decision of doing business of Vietnamese young people or not. Moreover, “t test for differences in two means” also assists us to compare the differences in the amount of money that students invest in their own business amongst three universities. Next section is the regression analysis in which we use “measures of variation” and “hypothesis testing and confidence interval for population slope” to check whether there is the relationship between the age and the capital or not.



1. HYPOTHESIS STATISTIC
1.1 WHETHER THE SEMESTER FACTOR IS INDEPENDENT TO THE DECISION OF PLANNING BUSINESS OR NOT
1.1.1 FTU
Results
Critical Value 3.841459
Chi-Square Test Statistic 0.902256
p-Value 0.342178
Do not reject the null hypothesis

Because p-value =34.21% is more than level of significance (α=0.05),not reject Ho. There is not enough evidence to claim the semester affects the decision to plan business at FTU students.
1.1.2 UEH
Results
Critical Value 3.841459
Chi-Square Test Statistic 1.558442
p-Value 0.211894
Do not reject the null hypothesis

Since P-value = 21.19% is more than the level of significance (α=0.05), not reject Ho. There is not enough evidence to claim the semester affects business plan at UEH students.
1.1.3 RMIT
Results
Critical Value 3.841459
Chi-Square Test Statistic 1.6
p-Value 0.205903
Do not reject the null hypothesis

P-value of 20.59% indicated the probability of having a sample mean (n=40) which is ether semester independent to planning business or not given that the null hypothesis is true since 20.59% is more than 5% ( level of significance) ,do not reject Ho. There is not enough evidence to conclude the semester do not independent to planning business at RMIT students.

1.2 WHETHER GENDER FACTOR IS INDEPENDENT TO THE DECISION OF PLANNING BUSINESS OR NOT
1.2.1 FTU
Results
Critical Value 3.841459
Chi-Square Test Statistic 0.902256
p-Value 0.342178
Do not reject the null hypothesis

Since P-value =34.21% is above the level of significance (α=0.05), not reject Ho. There is not enough evidence to conclude the gender affect business plan at FTU students.
1.2.2 UEH
Results
hjCritical Value 3.841459
Chi-Square Test Statistic 0.17316
p-Value 0.677318
Do not reject the null hypothesis

Because p-value=67.73% is higher than level of significance(α). There is not enough evidence to conclude the gender do not independent to planning business at UEH students.
1.2.3 RMIT
Results
Critical Value 3.841459
Chi-Square Test Statistic 3.6
p-Value 0.05778
Do not reject the null hypothesis

Since P-value = 5.78% is above the level of significance of 5%(α), not reject Ho. There is not enough evidence to conclude the gender do not independent to planning business at RMIT students.
In general, gender and semester do not affect the decision making of planning or running business of the students in three universities.

2. COMPARISON
In this part, we would like to use “t test for differences in two means” to compare the differences between the money each student from each university invest in their business with other university.
We assume that 1= UEH
2 = FTU
3 = RMIT
2.1 UEH & FTU
H0: 1 - 2 > 0 [UEH’s students invest more money than FTU’s students]
H1: 1 - 2 < 0 [FTU’s students invest more money than UEH’s students] COMPARISION CAPITAL BETWEEN UEH &FTU Data Hypothesized Difference 0 Level of Significance 0.05 Population 1 Sample Sample Size 40 Sample Mean 1566 Sample Standard Deviation 1349 Population 2 Sample Sample Size 40 Sample Mean 1742 Sample Standard Deviation 1895 Intermediate Calculations Population 1 Sample Degrees of Freedom 39 Population 2 Sample Degrees of Freedom 39 Total Degrees of Freedom 78 Pooled Variance 2705413 Difference in Sample Means -176 t Test Statistic -0.47853 Lower-Tail Test Lower Critical Value -1.66462 p-Value 0.316805 Do not reject the null hypothesis According to the table above, since p- value = 0.31605 > = 0.05, we do not reject the null hypothesis. This means that there is not sufficient evidence to claim that FTU’s students invest more money than UEH’s students do.
2.2 FTU & RMIT
H0: 2 - ¬3 > 0 [ FTU’s students invest more money than RMIT’s students]
H1: 2 - 3 < 0 [ RMIT’s students invest more money than FTU’s students] CAPITAL COMPARISION BETWEEN FTU & RMIT Data Hypothesized Difference 0 Level of Significance 0.05 Population 1 Sample Sample Size 40 Sample Mean 1742 Sample Standard Deviation 1895 Population 2 Sample Sample Size 40 Sample Mean 3216 Sample Standard Deviation 3147 Intermediate Calculations Population 1 Sample Degrees of Freedom 39 Population 2 Sample Degrees of Freedom 39 Total Degrees of Freedom 78 Pooled Variance 6747317 Difference in Sample Means -1474 t Test Statistic -2.53774 Lower-Tail Test Lower Critical Value -1.66462 p-Value 0.006575 Reject the null hypothesis The table above presents that p value= 0.006575. Since p value < = 0.05, we reject the null hypothesis. There is sufficient evidence to conclude that RMIT’s students invest more money than FTU’s students do. 2.3 RMIT & UEH H0: 3 - 1> 0 [RMIT’s students invest more money than UEH’s students]
H1¬: 3 - 1< 0 [UEH’s students invest more money than RMIT’s students] CAPITAL COMPARISION BETWEEN RMIT & UEH Data Hypothesized Difference 0 Level of Significance 0.05 Population 1 Sample Sample Size 40 Sample Mean 3216 Sample Standard Deviation 3147 Population 2 Sample Sample Size 40 Sample Mean 1566 Sample Standard Deviation 1349 Intermediate Calculations Population 1 Sample Degrees of Freedom 39 Population 2 Sample Degrees of Freedom 39 Total Degrees of Freedom 78 Pooled Variance 5861705 Difference in Sample Means 1650 t Test Statistic 3.047803 Lower-Tail Test Lower Critical Value -1.66462 p-Value 0.998427 Do not reject the null hypothesis According to the table above, since p value= 0.998427 > = 0.05, we do not reject the null hypothesis. There is not enough evidence to conclude that UEH’s students invest more money than RMIT’s students do.

3. REGRESSION ANALYSIS
3.1 FTU
FTU

Regression Statistics
Multiple R 0.060600843
R Square 0.003672462
Adjusted R Square -0.022546684
Standard Error 1.919014829
Observations 40

ANOVA
df SS MS F Significance F
Regression 1 0.515816761 0.515816761 0.140067955 0.710293818
Residual 38 139.9394807 3.682617914
Total 39 140.4552975

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 2.923504929 3.176141101 0.92045814 0.36314014 -3.506256525 9.353266383
-0.056818964 0.151818228 -0.374256537 0.710293818 -0.364158896 0.250520968
• Slope: b1= -0.056818964 which shows that for each one-year-older students are, the amount of money they invest in their business would decrease $95.68 on average.
• Y- intercept: b0 = 2.923504929
• I = b0 + b1 Xi = 2.9235 – 0.05681. Xi: Negative relationship
• R Square: r2 = 0.003672462: There is 0.367 % of variation in the capital that students invest in their business can be explained by the variation in the age of students. The remaining of 99.633% of the variation in the capital can be explained by other factors which we ignore in this study.
• Multiple R: r = 0.060600843: is closed to zero so there is a weak relationship between capital and age of the students.
H0: 1= 0 (no significant relationship between age and capital)
H1: 1 0 (there is a relationship between age and capital)
• Standard error: SYX = 1.919014829
• = 0.151818228
• t Stat = - 0.374256537
Assume that = 0.05
Critical Value= t /2, n-2 = t0.025, 38= 2.0244
Since the test statistic (t=- 0.374256537) falls into the non-rejection region (t=-0.374256537> CV= -2.0244) then we do not reject the null hypothesis. There is not enough evidence to conclude that the age of the student affect the money they invest in their own business.
3.2 UEH
UEH

Regression Statistics
Multiple R 0.40134593
R Square 0.161078556
Adjusted R Square 0.139001676
Standard Error 1.252529755
Observations 40

ANOVA
df SS MS F Significance F
Regression 1 11.4465901 11.4465901 7.296255401 0.010265102
Residual 38 59.6155699 1.568830787
Total 39 71.06216

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 9.645036179 2.997502057 3.217691263 0.002642853 3.576910561 15.7131618
Slope -0.407004342 0.150677716 -2.701158159 0.010265102 -0.712035428 -0.101973256

• Slope: b1= -0.407004342 which shows that for each one-year-older students are, the amount of money they invest in their business would decrease $407 on average.
• Y- intercept: b0 = 9.645036179
• I = b0 + b1 Xi = 9.645036179 – 0.407004342. Xi: Negative relationship
• R Square: r2 = 0.161078556: There is 16 % of variation in the capital that students invest in their business can be explained by the variation in the age of students. The remaining of 84% of the variation in the capital can be explained by other factors which we ignore in this study.
• Multiple R: r = 0.40134593: is closed to zero so there is a weak relationship between capital and age of the students.
H0: 1= 0 (no significant relationship between age and capital)
H1: 1 0 (there is a relationship between age and capital)
• Standard error: SYX = 1.252529755
• = 0.150677716
• t Stat = - 2.701158159
Assume that = 0.05
Critical Value= t /2, n-2 = t0.025, 38= 2.0244
Since the test statistic (t=- 2.701158159) falls into the rejection region (t=- 2.701158159< CV= -2.0244) then we reject the null hypothesis. There is enough evidence to conclude that the age of the student affect the money they invest in their own business. 3.3 RMIT RMIT Regression Statistics Multiple R 0.112057547 R Square 0.012556894 Adjusted R Square -0.013428451 Standard Error 3.168178574 Observations 40 ANOVA df SS MS F Significance F Regression 1 4.850349637 4.850349637 0.483229836 0.491192181 Residual 38 381.4195081 10.03735548 Total 39 386.2698578 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -1.075504022 6.194416523 -0.17362475 0.863082002 -13.61544457 11.46443653 age 0.21116502 0.303770158 0.695147348 0.491192181 -0.40378551 0.826115549 • Slope: b1= 0.21116502 which shows that for each one-year-older students are, the amount of money they invest in their business would increase $211 on average. • Y- intercept: b0 = - 1.075504022 • I = b0 + b1 Xi = - 1.075504022 + 0.21116502. Xi: Positive relationship • R Square: r2 = 0.012556894: There is 1.256 % of variation in the capital that students invest in their business can be explained by the variation in the age of students. The remaining of 98.744% of the variation in the capital can be explained by other factors which we ignore in this study. • Multiple R: r = 0.112057547: is closed to zero so there is a weak relationship between capital and age of the students. H0: 1= 0 (no significant relationship between age and capital) H1: 1 0 (there is a relationship between age and capital) • Standard error: SYX = 3.168178574 • = 0.303770158 • t Stat = 0.695147348 Assume that = 0.05 Critical Value= t /2, n-2 = t0.025, 38= 2.0244 Since the test statistic (t= 0.695147348) falls into the non- rejection region (t= 0.695147348< CV= 2.0244) then we do not reject the null hypothesis. There is enough evidence to conclude that the age of the student affect the money they invest in their own business. VII. RECOMMENDATION In this study, we have applied hypothesis testing and regression analysis to examine the relationship between the decision of planning or running business of students and other factors affecting that decision. The useful functions of excel and PHStat helps us to calculate statistical numbers fast and exactly. Therefore, we can minimize errors and draw the more accurate conclusion about the attitude of Vietnamese young people in doing business during university period. Through the study, we can conclude that the gender and semester factors do not affect the idea of doing business. Additionally, by using hypothesis testing and confidence interval for population slope, we clearly see that age factor does not have any influence on the students’ investment. It seems that the decision to do business during the university period is not based on those factors we examine in this study. However, errors about sample mean, sample standard deviation still exist because the sample size is too small comparing to population size. In addition, the sample we choose to conduct survey focus on students study in business education; therefore, it may not represent the whole population, which means that it cannot reflect clearly the trend of doing business of Vietnamese youth in the whole countries. Consequently, increasing sample size and choosing right sampling method is necessary to minimize these errors and make the research more accurate. Moreover, we should enlarge the survey area by conducting survey in different education sector. Last but not least, preparing a detailed survey minimizing the potential error is also essential to make the research more credible. People can based on the research to have a deep insight into the trend of doing business of Vietnamese youth in the globalization era. VIII. CONCLUSION Following the purpose of our research, getting a deep insight understanding about the trend of running business during the university period of students, we apply descriptive and inferential Statistics. Firstly, in data summary and Descriptive Statistics part, we use lot of charts to analyze the underlying reasons affecting decision on running business. For example: We see that over 50% interviewees have main financial support coming from family. Secondly, we used different statistical methods such as hypothesis testing and regression analysis to examine whether the gender, semesters and age affect the decision of doing business of Vietnamese young people or not. In the result of our sample , the fact that both gender and semester have no relationship with the decision of planning or running business of students at RMITVN, UEH and FTU. On the other hand, age only affects this decision at UEH . In general, we can say that we have achieved our planed purpose in this research because finally the analysis has shown that virtually those factors we mention in the survey do not affect the idea of doing business. Furthermore, there is some errors such as dishonest answer and the lack of paying attention which can reduce the quality, especially credibility although the statistical method and calculating process are right. IX. REFERENCE LIST Levine, M., David, Stephen David, Krehbiel, C., Timothy and Berenson, L., Mark. 2005, Statistic for Managers using Microsoft Excel, Fourth Edition, Prentice- Hall, New Jersey, p.10. Levine, M., David, Stephen David, Krehbiel, C., Timothy and Berenson, L., Mark. 2005, Statistic for Managers using Microsoft Excel, Fourth Edition, Prentice- Hall, New Jersey, p.11. Nhat Vy. 2005, ‘Why young Vietnamese young people still dare to do business?’, VietnamNet, viewed 24 November 2007, .

‘Topic: introduction to statistic’ 2007, RMIT University, viewed 22 November 2007,
.




IX. APPENDIX
SURVEY
Topic: The proportion of students plan or run small business during the university time.
Purpose: We are students of Commerce Faculty from RMIT Vietnam University. With the purpose of performing a statistical number of students planning or running business during the university times, we send this survey to you and hope that we will receive your honest respondents.
1. Gender:  Male  Female
2. Semester: a/ 1-4  4-8
b/ age:______
3. Department:
 IT  Design  Commerce  HED
4. Are you planning or running your small business during university time?
 Yes  No
5. If you plan or run business, what is your main financial support?
 Family  Bank Friends  Others
6. What kind of business are planning or running?
 Fashion shop  Investing in Stock Market  Souvenir
Coffee shop  Online Selling  Others
7. How much money have you invested or will invest in your business?
Less $ 500 $ 500 – 1000  $ 1000 – 2000  more than $ 2000
¬¬¬¬please write the exact number: ¬¬¬¬¬__________________USD
8. What are your purposes to start planning / running your business? (More than one option would be accepted)
 Challenging  Earn money  Accumulate experience  Others
9. Starting to run a business when studying, do you think you can succeed?
 Yes  No
10. If you fail, do you want to try again? If yes, why?
 Yes  No
Your reason_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

--- Thanks for your cooperation ---

FTU:
i = 833
i = 69.61
. Yi = 1440.55
2 = 17507
2 = 261.5941
A or SSXY = = = -9.07825
B or SSX = = = 159.775
C or SST = = = 140.4553
b1 = = = = -0.05682
b1= -0.05682 which shows that for each one-year-older students are, the amount of money they invest in their business would decrease $95.92 on average.
b0 = - b1 = 2.9235
I = b0 + b1 Xi = 2.9235 – 0.05682. Xi: Positive relationship
= 0.00367
There is 0.367 % of variation in the capital that students invest in their business can be explained by the variation in the age of students. The remaining of 99.633% of the variation in the capital can be explained by other factors which we ignore in this study.
r = = = 0.0606: is closed to zero so there is a weak relationship between capital and age of the students.
H0: 1= 0 (no significant relationship between age and capital)
H1: 1 0 (there is a relationship between age and capital)
= 1.9190
= 0.1518
t = = -0.3743
Assume that = 0.05
Critical Value= t /2, n-2 = t0.025, 38= 2.0244
Since the test statistic (t=- 0.3743) falls into the non-rejection region (t=-0.3743> CV= -2.0244) then we do not reject the null hypothesis. There is not enough evidence to conclude that the age of the student affect the money they invest in their own business.

ECONOMIC:
i = 794
i = 62.64
. Yi = 1215.28
2 = 15830
2 = 169.1564
A or SSXY = = = -28.124
B or SSX = = = 69.1
C or SST = = = 71.06216
b1 = = = = -0.407
b1= -0.407 which shows that for each one-year-older students are, the amount of money they invest in their business would decrease $407 on average.
b0 = - b1 = 9.64495
I = b0 + b1 Xi = 9.64495 – 0.407 Xi: Negative relationship
= 0.16
There is 16% of variation in the capital that students invest in their business can be explained by the variation in the age of students. The remaining of 84% of the variation in the capital can be explained by other factors which we ignore in this study.
r = = = -0.40134: is closed to zero so there is a weak relationship between capital and age of the students.
H0: 1= 0 (no significant relationship between age and capital)
H1: 1 0 (there is a relationship between age and capital)
= 1.25253
= 0.150678
t = = - 2.70112
Assume that = 0.05
Critical Value= t /2, n-2 = t0.025, 38= 2.0244
Since the test statistic (t= -2.70112) falls into the rejection region (t=-2.70112< CV= -2.0244) then we reject the null hypothesis. There is enough evidence to conclude that the age of the student affect the money they invest in their own business. RMIT i = 813 i = 128.657 . Yi = 2637.923 2 = 16633 2 = 800.08545 A or SSXY = = = 22.969475 B or SSX = = = 108.775 C or SST = = = 386.26986 b1 = = = = 0.2111 b1= 0.2111 which shows that for each one-year-older students are, the amount of money they invest in their business would increase $211 on average. b0 = - b1 = -1.07215 I = b0 + b1 Xi = -1.07215 + 0.2111 Xi: Positive relationship = 0.01256: Positive relationship There is 1.256 % of variation in the capital that students invest in their business can be explained by the variation in the age of students. The remaining of 98.744% of the variation in the capital can be explained by other factors which we ignore in this study. r = = = 0.112: is closed to zero so there is a weak relationship between capital and age of the students. H0: 1= 0 (no significant relationship between age and capital) H1: 1 0 (there is a relationship between age and capital) = 3.1682 = 0.30377 t = = 0.69493 Assume that = 0.05 Critical Value= t /2, n-2 = t0.025, 38= 2.0244 Since the test statistic (t= 0.69493) falls into the non-rejection region (t=0.69493> CV= 2.0244) then we do not reject the null hypothesis. There is not enough evidence to conclude that the age of the student affect the money they invest in their own business.



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