MKT 352 Assignment #10 (Individual)
“Reading - Chapter 13”
(Due: November 20th (Fri) 11:59pm)
* Your Name: Enter Your Name Here
* Directions
(1) Read the textbook chapter 13 & its lecture notes, and provide your answers to the following questions.
You should type in your answers to this document, save it as your name (e.g.,
sanghaklee.docx), and submit it to the canvas dropbox.
* Questions
(Q1) [chapter13] Below are the descriptions about cross tabulation in the textbook. Fill in the blanks.
(1-a) “Cross tabulation is an important tool for studying the relationships between two (or more)
__________ variables. It is most used multivariate data analysis technique in applied marketing research.”
Enter Your Answer Here
(1-b) “The (i) __________ chi-square (
χ2
) test of (ii) __________ assesses the degree to which the
variables in a cross-tabulation analysis are independent of one another.”
Enter Your Answer Here
(Q2) [chapter13] A W.P. Carey marketing research team wanted to examine the relationship between
gender and internet usage, and conducted a cross-tab analysis.
Internet Usage
Gender Light User Heavy User Total
Male 30 20 50
Female 20 30 50
Total 50 50 100
(2-a) Compute the chi-square statistic that measures the distance between “the observed data” and “the
expected data under the assumption that gender and internet usage are independent”. (Provide your
answer as well as the process you went through.)
Enter Your Answer Here
(2-b) With alpha(significance level)=0.10, what is the rejection region of the chi-square distribution?
1
Enter Your Answer Here
(2-c) With alpha(significance level)=0.10, what is the conclusion about the relationship between gender
and internet usage?
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(Q3) [chapter13] Answer the following questions regarding independent samples t-test for means.
(3-a) Explain what independent samples t-test for means is.
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(3-b) Provide an example of its application.
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(Q4) [chapter13] A bank manager wanted to investigate the factors that had an impact on the customer
satisfaction. For this, the marketing team developed a linear regression model as follows:
[
Customer Satisfaction
]
=α+β1×
[
Teller Friedliness
]
+β2×
[
Service Time
]
+ε
After collecting the data, they conducted a regression analysis using SPSS. Below are the results (=SPSS
outputs):
Model Summary
Model R R Square
Std. Error of the
Estimate
1 .911(a) .830 .18554
(a) Predictors: (Constant), Service_Time, Teller_Friendliness
ANOVA(b)
Model
Sum of
Squares Df Mean Square F Sig.
2
1 Regression 124.461 2 62.230 1807.738 .000(a)
Residual 25.577 743 .034
Total 150.038 745
(a) Predictors: (Constant), Service_Time, Teller_Friendliness
(b) Dependent Variable: Customer_Satisfaction
Coefficients(a)
Model
Unstandardized
Coefficients t Sig.
B Std. Error
1 (Constant) .887 .186 4.767 .000
Teller_Friendliness .694 .025 27.839 .000
Service_Time .209 .010 21.971 .000
(a) Dependent Variable: Customer_Satisfaction
(4-a) What percentage of the total variation in Customer_Satisfaction is explained by this model?
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(4-b) If the Teller_Friendliness rating is increased by 1 unit, what will be the corresponding change in
Customer_Satisfaction (with holding Service_Time constant)?
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(4-c) Based on the estimated regression function, what is the predicted value of Customer_Satisfaction if
both Teller_Friendliness rating and Service_Time ratings are set to be 6?
Enter Your Answer Here
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