Exercise 2: Sample Design and Evaluation
The information can be summarized as follows:
N1= N2
Standard Deviation= 15
Difference in Performance= 5
Power= .8
After entering the given information, the window looks as follows, which shows us that N1= N2= 142

In the window above, change the power to .9, then N1= N2 = 190

In the window above, change the sigma1=15, sigma2=12, and don’t select Egual Sigmas checkbox, thus I get N1= N2= 156
In the window above, change the N1=200 (control group), N2=120 (testing group), and select Independent in Allocation, thus I get .9046 to be the power.

=((61-64.5)-(0))/√((16*16)/200+(13*13)/120) = (-3.5)/1.6396 = -2.1347 Critical Value: Zα/2= Z0.05/2= @qnorm(1-0.05/2)= 1.96
When comparing the test statistic to the critical value: Z=2.1347>1.96, we reject the null hypothesis. We can calculate the P-value using the EViews command:
Show @tdist (t, d.f)
In this EViews command, t stands for the appropriate test statistic and d.f are the degrees of freedom. The appropriate test statistic was calculated above, namely Z=2.1347. For the degrees of freedom, we can insert NA+NB-2. Show @tdist (2.1347, 318)= 0.03355

Since the P-value= 0.033550, and β1= 0.86361050000
ls price c assessval
Dependent Variable: PRICE
Method: Least Squares
Date: 01/21/13 Time: 16:07
Sample: 1 650 IF PRICE>50000
Included observations: 562

...How many standard deviations is my hypothesis (sample mean) is away from the actual (null hypothesis population mean)
T – statistic
Rejecting the null may be a mistake = p –value
ONE SAMPLE
3 formulas
T.Dist.rt (t, sample size - 1 “df”) -> alternative that mu is bigger than a
1 – T.Dist.rt (t, sample size – 1) -> mu is less than a
T.Dist.2t(t,samplesize - 1) -> not equal to
p < significant level reject the null
NEVER accept null
TWO SAMPLE
directly get the p-value
chance that under the null hypthoesis, you have a difference in the sample mean that is as extreme or more as what you have now. If that probability is small, it is something in the nature not due to chance.
* Paired: T.Test (sample 1, sample 2, # of tails , 1)
* not equal to: number of tails = 2
* greater than or less than: number of tails = 1
* Type 1 = paired data (ex: every UNC mba student’s salary before they entered the program and salaries after graduate )
* salaries have a significant increase after mba?
*
* Independent: T.Test (sample 1, sample 2, # tails, 2)
* Type 2 = independent (ex: UNC mbas vs. DUKE mbas)
*
*
*
* Regression Coefficient:
*
* Null hypothesis: THIS regression coefficient = 0
* alternative hypothesis: THIS particular regression coefficient of interest is not 0
*
* (driver’s p-value and coefficient in ANOVA)
*
* THIS driver’s p-value is less than...

...Statistics and DecisionMaking
A study on the impact of online shopping websites on student’s buying behavior in India.
2013
Lakshmi Muraleedharan Nair
TP023857
MSc Global Marketing Management
UCMF1210GMM
Date: 20/03/2013
Statistics and DecisionMaking
A study on the impact of online shopping websites on student’s buying behavior in India.
2013
Lakshmi Muraleedharan Nair
TP023857
MSc Global Marketing Management
UCMF1210GMM
Date: 20/03/2013
CONTENTS
INTRODUCTION 1
TITLE 2
AIMS 2
OBJECTIVES 3
QUESTIONAIRE 3
INTERVIEW QUESTIONS 8
METHODOLOGY 9
INTRODUCTION 11
FINDINGS AND RESULTS 12
FINDINGS ON OBJECTIVE 1 12
FINDINGS FOR OBJECTIVE 2 20
FINDINGS FOR OBJECTIVE 3 22
FURTHER FINDINGS BASED ON THE QUESTIONNAIRES AND INTERVIEWS 31
CONCLUSION 37
RECOMMENDATIONS 38
REFERENCES 39
APPENDIX 40
INTRODUCTION
The world of online retailing or ecommerce has been growing rapidly. E-commerce is changing constantly from its early stages itself (Emerging Commerce Trends for 2012, 2012).
Thus online shopping or online retailing has grown to a large extend in. There has been a rapid increase in the number of online shopping websites for clothes, apparels, accessories, electronics and many other products. India has seen the entry of many online retailers in the past years. Now online shopping has been very common, especially among the...

...
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QNT 561 FinalExam
1) Which of the following measures of central location is affected most by extreme values? A. MeanB. MedianC. Mode D. Geometric mean
2) A correlation matrix…A.Shows all simple coefficients of correlation between variablesB. shows only correlations that are zeroC. shoes the correlations that are positiveD. shows only the correlations that are statistically significant
3) In a set of observations, which measure of central tendency reports the value that occurs most often? A. Mean B. MedianC. ModeD. Geometric mean
4) Which level of measurement is required for the median? A. Nominal B. OrdinalC. IntervalD. Ratio
5) The mean and the variance are equal in…A. the normal distributionB. the binomial distributionC. the Poisson distributionD. the hypergeometric distribution
6) The difference between the sample mean and the population mean is called the…A. margin of errorB. population standard deviationC. standard error of the meanD. sampling error
7) A dummy variable or indicator variable… A. may assume only a value of 0 or 1B. is another term for the dependent variableC. is a quantitative variableD. is a variable at a ratio or interval level of measurement
8) A Type I error is…A. the correct decisionB. a value determined from the test statisticC. rejecting the null hypothesis when it is trueD. accepting the null hypothesis...

...remaining 55 points are alotted to the essay question.
The last question of this exam is on page 12.
Regarding the multiple choice questions: Use the scrap form. Please mark the box associated with the answer you deem to be correct. Do not forget to write your name on the scrap form and mark the right student number boxes. Regarding the open questions: Explain every single step in your reasoning. Answers without explanation never yield any points.
Good luck!
Multiple Choice Questions 1. Sixty percent of the pupils of some very large secondary school own a mobile phone. A group of 8 randomly chosen pupils of this school is asked whether they own a mobile phone. What is the probability that exactly 5 of them own a mobile phone? a. That probability is approximately 0.005. b. That probability is approximately 0.124. c. That probability is approximately 0.279. d. That probability is approximately 0.625. 2. The operation manager of a company that manufactures shirts wants to determine whether there are diﬀerences in the quality of workmanship among the three daily shifts. She randomly selects 600 recently made shirts and carefully inspects them. Each shirt is classiﬁed as either perfect or as ﬂawed and the shift that produced the shirt is also recorded. In the test on the relation between quality and shift that the manager can now perform, what is the number of degrees of freedom of the test statistic? a. The number of degrees of freedom...

...examine if there is a correlation between the shoe size and the height. Using Excel, we obtain the following table (see sheet CORRELATION)
| Shoe size | Height |
Shoe size | 1 | |
Height | 0.86434 | 1 |
As we can see that the correlation coefficient R=0.86434 is positive and close to 1, it suggests that there is a strong positive relationship between the shoe size and the height.
Next, let’s formulate the following data (see SHOW SIZE DATA)
Using Excel to get the following descriptive statistics for both variables: shoe sizes for females (FEMALE) and shoe sizes for males (MALES) we get the below charts:
(FEMALE)
Descriptive Statistics |
| |
Mean | 7.111111111 |
Standard Error | 0.266775577 |
Median | 7 |
Mode | 7.5 |
Standard Deviation | 1.131832917 |
Sample Variance | 1.281045752 |
Kurtosis | 1.83070498 |
Skewness | 0.854222139 |
Range | 5 |
Minimum | 5 |
Maximum | 10 |
Sum | 128 |
Count | 18 |
(MALE)
Descriptive Statistics |
| |
Mean | 11.29411765 |
Standard Error | 0.437360952 |
Median | 11 |
Mode | 11 |
Standard Deviation | 1.8032854 |
Sample Variance | 3.251838235 |
Kurtosis | 0.687533151 |
Skewness | -0.454073545 |
Range | 7 |
Minimum | 7 |
Maximum | 14 |
Sum | 192 |
Count | 17 |
From the above tables, we can see that the average shoe size:
For females was 7.11 with a standard deviation of 1.13
For males was 11.29 with a...

...STATISTICS AND DECISION-MAKING IN HRM
(word count 1155)
The word statistics has a Latin origin where the word status means state. Statistics is defined as the science that helps us understand how to collect, organize and interpret numbers or other information (data) about some topic (Bennett, et al., 2003). It is a discipline of data collection and summarizing to aid understanding anddecision-making. It is also concerned with evaluation of the present status and predicting the future (Stockberger, 1996). Statistics studies the nature of a relationship between two or more variables of interest by using the numerical statistical data. Moreover only statistics makes it possible to analyze these relationships (Kachigan, 1986).
These features make statistics an essential component of every business system. Managers of modern world are more inclined to apply the standards of management based on concrete statistical data, widely applying statistics in support of the business policies and procedures. The collection and processing of data increases awareness of managers thereby improving business outcomes.
With further development during the twentieth century statistics became one of the fundamentals of management science. Management science is the discipline that employs scientific techniques of...

...Assignment #1
“MakingDecisions Based on Demand and Forecasting”
Natalia Donskaya
Instructor: Dr. Atia Yasmeen
ECO 550
October 28, 2012
Domino’s Pizza is considering entering the marketplace in your community. Conduct research about the demographics of your community, for example the population size and average income per household, and other independent variables, such as price of pizza and price of soda, for this assignment. By conducting a demand analysis and forecast for pizza, you will be able to make a decision whether Domino’s should establish a presence in your community.
1. Report the demographic and independent variables that are relevant to complete a demand analysis providing a rationale for the selection of the variables.
Companies can set specific service goals for their customers in the areas of demand forecasting and order fulfillment, such as the percent of orders they need to fill in a very short time; and they can set inventory policies at each node of the supply chain. By programming these policies directly into supply chain software, companies enable the software to monitor performance against these goals, and to send automatic alerts whenever a stated level of performance is in jeopardy. This allows companies to continuously evaluate both consumer demand and supplier performance with added automation, while improving the accuracy of consumer demand forecasts. Companies can also granularly look...