Question 1

SCENARIO 17-1
A real estate builder wishes to determine how house size (House) is influenced by family income
(Income), family size (Size), and education of the head of household (School). House size is
measured in hundreds of square feet, income is measured in thousands of dollars, and education is in
years. The builder randomly selected 50 families and ran the multiple regression. Microsoft Excel
output is provided below: SUMMARY OUTPUT
Regression Statistics
$\begin{array} { l l } \text { Multiple R } & 0.865 \ \text { R Square } & 0.748 \ \text { Adjusted R Square } & 0.726 \ \text { Standard Error } & 5.195 \ \text { Observations } & 50 \end{array}$
ANOVA
$\begin{array} { l c c c c c } & d f & \text { SS } & \text { MS } & F & \text { Signif } F \ \text { Regression } & & 3605.7736 & 1201.9245 & & 0.0000 \ \text { Residual } & & 1214.2264 & 26.3962 & & \ \text { Total } & 49 & 4820.0000 & & & \end{array}$

$\begin{array} { l c l c c } & \text { Coeff } & \text { StdError } & t \text { Stat } & P \text {-value } \ \text { Intercept } & - 1.6335 & 5.8078 & - 0.281 & 0.7798 \ \text { Income } & 0.4485 & 0.1137 & 3.9545 & 0.0003 \ \text { Size } & 4.2615 & 0.8062 & 5.286 & 0.0001 \ \text { School } & - 0.6517 & 0.4319 & - 1.509 & 0.1383 \end{array}$
-Referring to Scenario 17-1, which of the following values for the level of significance is the smallest for which the regression model as a whole is significant?

Accepted Answer

A)   0.0005
B)   0.001
C)   0.01
D)   0.05  A

Question 2

Double-clicking a cell in a PivotTable causes Excel to drill down and display the
underlying data in a new worksheet.

Accepted Answer

A) True 
 B)False  True

Question 3

SCENARIO 17-8
The superintendent of a school district wanted to predict the percentage of students passing a sixth-
grade proficiency test. She obtained the data on percentage of students passing the proficiency test
(% Passing), daily mean of the percentage of students attending class (% Attendance), mean teacher
salary in dollars (Salaries), and instructional spending per pupil in dollars (Spending) of 47 schools in
the state. Following is the multiple regression output with $Y = \%$ Passing as the dependent variable, $X _ { 1 } = \%$ Attendance, $X _ { 2 } =$ Salaries and $X _ { 3 } =$ Spending:

$\begin{array}{lr}
\hline{\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.7930 \
\text { R Square } & 0.6288 \
\text { Adjusted R } & 0.6029 \
\text { Square } & \
\text { Standard } & 10.4570 \
\text { Error } & \
\text { Observations } & 47 \
\hline
\end{array}$

ANOVA

$\begin{array}{lrrrrrr}
\hline & \text { Coefficients } & \begin{array}{c}
\text { Standard } \
\text { Error }
\end{array} & \text { t Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & -753.4225 & 101.1149 & -7.4511 & 0.0000 & -957.3401 & -549.5050 \
\text { \% Attendance } & 8.5014 & 1.0771 & 7.8929 & 0.0000 & 6.3292 & 10.6735 \
\text { Salary } & 0.000000685 & 0.0006 & 0.0011 & 0.9991 & -0.0013 & 0.0013 \
\text { Spending } & 0.0060 & 0.0046 & 1.2879 & 0.2047 & -0.0034 & 0.0153 \
\hline
\end{array}$
-Referring to Scenario 17-8, what is the value of the test statistic when testing whether
instructional spending per pupil has any effect on percentage of students passing the proficiency
test, taking into account the effect of all the other independent variables?

Accepted Answer

1.2879

Question 4

SCENARIO 17-8
The superintendent of a school district wanted to predict the percentage of students passing a sixth-
grade proficiency test. She obtained the data on percentage of students passing the proficiency test
(% Passing), daily mean of the percentage of students attending class (% Attendance), mean teacher
salary in dollars (Salaries), and instructional spending per pupil in dollars (Spending) of 47 schools in
the state. Following is the multiple regression output with $Y = \%$ Passing as the dependent variable, $X _ { 1 } = \%$ Attendance, $X _ { 2 } =$ Salaries and $X _ { 3 } =$ Spending:

$\begin{array}{lr}
\hline{\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.7930 \
\text { R Square } & 0.6288 \
\text { Adjusted R } & 0.6029 \
\text { Square } & \
\text { Standard } & 10.4570 \
\text { Error } & \
\text { Observations } & 47 \
\hline
\end{array}$

ANOVA

$\begin{array}{lrrrrrr}
\hline & \text { Coefficients } & \begin{array}{c}
\text { Standard } \
\text { Error }
\end{array} & \text { t Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & -753.4225 & 101.1149 & -7.4511 & 0.0000 & -957.3401 & -549.5050 \
\text { \% Attendance } & 8.5014 & 1.0771 & 7.8929 & 0.0000 & 6.3292 & 10.6735 \
\text { Salary } & 0.000000685 & 0.0006 & 0.0011 & 0.9991 & -0.0013 & 0.0013 \
\text { Spending } & 0.0060 & 0.0046 & 1.2879 & 0.2047 & -0.0034 & 0.0153 \
\hline
\end{array}$
-Referring to Scenario 17-8, the null hypothesis $$H _ { 0 } : \beta _ { 1 } = \beta _ { 2 } = \beta _ { 3 } = 0$$ implies that percentage
of students passing the proficiency test is not affected by any of the explanatory variables.

Accepted Answer

A) True 
 B)False

Question 5

SCENARIO 17-1
A real estate builder wishes to determine how house size (House) is influenced by family income
(Income), family size (Size), and education of the head of household (School). House size is
measured in hundreds of square feet, income is measured in thousands of dollars, and education is in
years. The builder randomly selected 50 families and ran the multiple regression. Microsoft Excel
output is provided below: SUMMARY OUTPUT
Regression Statistics
$\begin{array} { l l } \text { Multiple R } & 0.865 \ \text { R Square } & 0.748 \ \text { Adjusted R Square } & 0.726 \ \text { Standard Error } & 5.195 \ \text { Observations } & 50 \end{array}$
ANOVA
$\begin{array} { l c c c c c } & d f & \text { SS } & \text { MS } & F & \text { Signif } F \ \text { Regression } & & 3605.7736 & 1201.9245 & & 0.0000 \ \text { Residual } & & 1214.2264 & 26.3962 & & \ \text { Total } & 49 & 4820.0000 & & & \end{array}$

$\begin{array} { l c l c c } & \text { Coeff } & \text { StdError } & t \text { Stat } & P \text {-value } \ \text { Intercept } & - 1.6335 & 5.8078 & - 0.281 & 0.7798 \ \text { Income } & 0.4485 & 0.1137 & 3.9545 & 0.0003 \ \text { Size } & 4.2615 & 0.8062 & 5.286 & 0.0001 \ \text { School } & - 0.6517 & 0.4319 & - 1.509 & 0.1383 \end{array}$
-Referring to Scenario 17-1, which of the following values for the level of significance is the smallest for which at least two explanatory variables are significant individually?

Accepted Answer

A)   0.01
B)   0.025
C)   0.05
D)   0.15

Question 6

SCENARIO 17-12
The marketing manager for a nationally franchised lawn service company would like to study the
characteristics that differentiate home owners who do and do not have a lawn service. A random
sample of 30 home owners located in a suburban area near a large city was selected; 15 did not have
a lawn service (code 0) and 15 had a lawn service (code 1). Additional information available
concerning these 30 home owners includes family income (Income, in thousands of dollars), lawn
size (Lawn Size, in thousands of square feet), attitude toward outdoor recreational activities (Atitude 0
= unfavorable, 1 = favorable), number of teenagers in the household (Teenager), and age of the head
of the household (Age).
The Minitab output is given below: 
-Referring to Scenario 17-12, what should be the decision ('reject' or 'do not reject') on the null
hypothesis when testing whether LawnSize makes a significant contribution to the model in the
presence of the other independent variables at a 0.05 level of significance?

Accepted Answer

The answer of SCENARIO 17-12
The marketing manager for a nationally...

Question 7

SCENARIO 17-1
A real estate builder wishes to determine how house size (House) is influenced by family income
(Income), family size (Size), and education of the head of household (School). House size is
measured in hundreds of square feet, income is measured in thousands of dollars, and education is in
years. The builder randomly selected 50 families and ran the multiple regression. Microsoft Excel
output is provided below: SUMMARY OUTPUT
Regression Statistics
$\begin{array} { l l } \text { Multiple R } & 0.865 \ \text { R Square } & 0.748 \ \text { Adjusted R Square } & 0.726 \ \text { Standard Error } & 5.195 \ \text { Observations } & 50 \end{array}$
ANOVA
$\begin{array} { l c c c c c } & d f & \text { SS } & \text { MS } & F & \text { Signif } F \ \text { Regression } & & 3605.7736 & 1201.9245 & & 0.0000 \ \text { Residual } & & 1214.2264 & 26.3962 & & \ \text { Total } & 49 & 4820.0000 & & & \end{array}$

$\begin{array} { l c l c c } & \text { Coeff } & \text { StdError } & t \text { Stat } & P \text {-value } \ \text { Intercept } & - 1.6335 & 5.8078 & - 0.281 & 0.7798 \ \text { Income } & 0.4485 & 0.1137 & 3.9545 & 0.0003 \ \text { Size } & 4.2615 & 0.8062 & 5.286 & 0.0001 \ \text { School } & - 0.6517 & 0.4319 & - 1.509 & 0.1383 \end{array}$
-Referring to Scenario 17-1, what minimum annual income would an individual with a family size of 9 and 10 years of education need to attain a predicted 5,000 square foot home (House = 50)?

Accepted Answer

A)   $44.14 thousand
B)   $56.75 thousand
C)   $178.33 thousand
D)   $211.85 thousand

Question 8

The director of admissions at a state college is interested in seeing if admissions status (admitted, waiting list, denied admission) at his college is related to the type of community (urban, rural,
Suburban) in which an applicant resides. Which of the following tests will be the most
Appropriate?

Accepted Answer

A)    x2x ^ { 2 }x2  test for independence.
B)   Two-way ANOVA F test for the type of community effect.
C)   Two-way ANOVA F test for interaction effect.
D)   Kruskal-Wallis rank Test.

Question 9

SCENARIO 17-5
You worked as an intern at We Always Win Car Insurance Company last summer. You notice that
individual car insurance premiums depend very much on the age of the individual, the number of
traffic tickets received by the individual, and the population density of the city in which the individual
lives. You performed a regression analysis in EXCEL and obtained the following information: \(\begin{array}{l}
\begin{array}{lr}
\hline {\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.63 \
\text { R Square } & 0.40 \
\text { Adjusted R Square } & 0.23 \
\text { Standard Error } & 50.00 \
\text { Observations } & 15.00 \
\hline
\end{array}\\
\text { ANOVA }\
\begin{array}{lrcccr}
\hline & d f &{\text { SS }} & \text { MS } & \text { F }&{\text { Significance } F} \
\hline \text { Regression } & 3 & & 5994.24 & 2.40 & 0.12 \
\text { Residual } & 11 & 27496.82 & & & \
\text { Total } & & 45479.54 & & & \
\hline
\end{array}\\
\begin{array}{lrrrrrr}
\hline & \text { Coefficients } & \begin{array}{c}
\text { Standard } \
\text { Error }
\end{array} & \text { t Stat } & \text { P-value } & \text { Lower 99.0\% } & \text { Upper 99.0\% } \
\hline \text { Intercept } & 123.80 & 48.71 & 2.54 & 0.03 & -27.47 & 275.07 \
\text { AGE } & -0.82 & 0.87 & -0.95 & 0.36 & -3.51 & 1.87 \
\text { TICKETS } & 21.25 & 10.66 & 1.99 & 0.07 & -11.86 & 54.37 \
\text { DENSITY } & -3.14 & 6.46 & -0.49 & 0.64 & -23.19 & 16.91 \
\hline
\end{array}
\end{array}\)
 AGE -0.82 0.87 -0.95 0.36 -3.51 1.87
TICKETS 21.25 10.66 1.99 0.07 -11.86 54.37
DENSITY -3.14 6.46 -0.49 0.64 -23.19 16.91
-Referring to Scenario 17-5, the total degrees of freedom that are missing in the ANOVA table
should be ______.

Accepted Answer

The answer of SCENARIO 17-5
You worked as an intern at...

Question 10

SCENARIO 17-8
The superintendent of a school district wanted to predict the percentage of students passing a sixth-
grade proficiency test. She obtained the data on percentage of students passing the proficiency test
(% Passing), daily mean of the percentage of students attending class (% Attendance), mean teacher
salary in dollars (Salaries), and instructional spending per pupil in dollars (Spending) of 47 schools in
the state. Following is the multiple regression output with $Y = \%$ Passing as the dependent variable, $X _ { 1 } = \%$ Attendance, $X _ { 2 } =$ Salaries and $X _ { 3 } =$ Spending:

$\begin{array}{lr}
\hline{\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.7930 \
\text { R Square } & 0.6288 \
\text { Adjusted R } & 0.6029 \
\text { Square } & \
\text { Standard } & 10.4570 \
\text { Error } & \
\text { Observations } & 47 \
\hline
\end{array}$

ANOVA

$\begin{array}{lrrrrrr}
\hline & \text { Coefficients } & \begin{array}{c}
\text { Standard } \
\text { Error }
\end{array} & \text { t Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & -753.4225 & 101.1149 & -7.4511 & 0.0000 & -957.3401 & -549.5050 \
\text { \% Attendance } & 8.5014 & 1.0771 & 7.8929 & 0.0000 & 6.3292 & 10.6735 \
\text { Salary } & 0.000000685 & 0.0006 & 0.0011 & 0.9991 & -0.0013 & 0.0013 \
\text { Spending } & 0.0060 & 0.0046 & 1.2879 & 0.2047 & -0.0034 & 0.0153 \
\hline
\end{array}$
-Referring to Scenario 17-8, you can conclude that mean teacher salary
individually has no impact on the mean percentage of students passing the proficiency test, taking
into account the effect of all the other independent variables, at a 1% level of significance based
solely on the 95% confidence interval estimate for $\beta _ { 2 }$ .

Accepted Answer

A) True 
 B)False

Question 11

SCENARIO 17-10 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy) and the independent variables are the age of the worker (Age), the number of years of education received (Edu), the number of years at the previous job (Job Yr), a dummy variable for marital status (Married: $1 =$ married, $0 =$ otherwise), a dummy variable for head of household (Head: $1 =$ yes, $0 =$ no) and a dummy variable for management position (Manager: $1 =$ yes, $0 =$ no). We shall call this Model 1. The coefficient of partial determination ( $R _ { \mathrm { y } } ^ { 2 }$ (All raiables excopt $j$ ) ) of each of the 6 predictors are, respectively, $0.2807$, $0.0386,0.0317,0.0141,0.0958$, and $0.1201$.

$\begin{array}{lr}
\hline{\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.7035 \
\text { R Square } & 0.4949 \
\text { Adjusted R } & 0.4030 \
\text { Square } & \
\text { Standard } & 18.4861 \
\text { Error } & \
\text { Observations } & 40 \
\hline
\end{array}$
 $\text { ANOVA }$

$$\begin{array}{l}
\begin{array} { l r r r r r r } 
\hline & \text { Coefficients } & \text { Standard Error } & { t \text { Stat } } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & 32.6595 & 23.18302 & 1.4088 & 0.1683 & - 14.5067 & 79.8257 \
\text { Age } & 1.2915 & 0.3599 & 3.5883 & 0.0011 & 0.5592 & 2.0238 \
\text { Edu } & - 1.3537 & 1.1766 & - 1.1504 & 0.2582 & - 3.7476 & 1.0402 \
\text { Job Yr } & 0.6171 & 0.5940 & 1.0389 & 0.3064 & - 0.5914 & 1.8257 \
\text { Married } & - 5.2189 & 7.6068 & - 0.6861 & 0.4974 & - 20.6950 & 10.2571 \
\text { Head } & - 14.2978 & 7.6479 & - 1.8695 & 0.0704 & - 29.8575 & 1.2618 \
\text { Manager } & - 24.8203 & 11.6932 & - 2.1226 & 0.0414 & - 48.6102 & - 1.0303 \
\hline
\end{array}
\end{array}$$
-Referring to Scenario 17-10 Model 1, the null hypothesis should be rejected at a
10% level of significance when testing whether age has any effect on the number of weeks a
worker is unemployed due to a layoff.

Accepted Answer

A) True 
 B)False

Question 12

The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test. She obtained the data on percentage of students passing the
Proficiency test (% Passing), daily mean of the percentage of students attending class (%
Attendance), mean teacher salary in dollars (Salaries), and instructional spending per pupil in
Dollars (Spending) of 47 schools in the state. She believed that holding everything else constant,
Instructional spending per pupil had a positive but decreasing impact on percentage. Which of
The following would be the most appropriate analysis to perform?

Accepted Answer

A)   Autoregressive modeling.
B)   Exponential smoothing.
C)   Least-squares forecasting with monthly or quarterly data.
D)   Linear regression with log transformation.

Question 13

SCENARIO 17-10 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy) and the independent variables are the age of the worker (Age), the number of years of education received (Edu), the number of years at the previous job (Job Yr), a dummy variable for marital status (Married: $1 =$ married, $0 =$ otherwise), a dummy variable for head of household (Head: $1 =$ yes, $0 =$ no) and a dummy variable for management position (Manager: $1 =$ yes, $0 =$ no). We shall call this Model 1. The coefficient of partial determination ( $R _ { \mathrm { y } } ^ { 2 }$ (All raiables excopt $j$ ) ) of each of the 6 predictors are, respectively, $0.2807$, $0.0386,0.0317,0.0141,0.0958$, and $0.1201$.

$\begin{array}{lr}
\hline{\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.7035 \
\text { R Square } & 0.4949 \
\text { Adjusted R } & 0.4030 \
\text { Square } & \
\text { Standard } & 18.4861 \
\text { Error } & \
\text { Observations } & 40 \
\hline
\end{array}$
 $\text { ANOVA }$

$$\begin{array}{l}
\begin{array} { l r r r r r r } 
\hline & \text { Coefficients } & \text { Standard Error } & { t \text { Stat } } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & 32.6595 & 23.18302 & 1.4088 & 0.1683 & - 14.5067 & 79.8257 \
\text { Age } & 1.2915 & 0.3599 & 3.5883 & 0.0011 & 0.5592 & 2.0238 \
\text { Edu } & - 1.3537 & 1.1766 & - 1.1504 & 0.2582 & - 3.7476 & 1.0402 \
\text { Job Yr } & 0.6171 & 0.5940 & 1.0389 & 0.3064 & - 0.5914 & 1.8257 \
\text { Married } & - 5.2189 & 7.6068 & - 0.6861 & 0.4974 & - 20.6950 & 10.2571 \
\text { Head } & - 14.2978 & 7.6479 & - 1.8695 & 0.0704 & - 29.8575 & 1.2618 \
\text { Manager } & - 24.8203 & 11.6932 & - 2.1226 & 0.0414 & - 48.6102 & - 1.0303 \
\hline
\end{array}
\end{array}$$
-Referring to Scenario 17-10 Model 1, which of the following is the correct alternative hypothesis to test whether age has any effect on the number of weeks a worker is unemployed
Due to a layoff while holding constant the effect of all the other independent variables? a) $H _ { 1 } : \beta _ { 0 } \neq 0$
b) $H _ { 1 } : \beta _ { 1 } \neq 0$
c) $H _ { 1 } : \beta _ { 2 } \neq 0$
d) $H _ { 1 } : \beta _ { 3 } \neq 0$

Accepted Answer

The answer of SCENARIO 17-10 Given below are results from...

Question 14

An entrepreneur is considering the purchase of a coin-operated laundry. The current owner claims that over the past 5 years, the mean daily revenue was $675 with a standard deviation of $75. A
Sample of 30 days reveals a daily mean revenue of $625 and a standard deviation of $70. Which
Of the following tests will be the most appropriate?

Accepted Answer

A)   t test for the mean.
B)   Z test for the proportion.
C)   Pooled-variance t test.
D)   Separate-variance t test.

Question 15

Which of the following is NOT one of the categories of predictive analytics methods?

Accepted Answer

A)   Clustering
B)   Recommendation
C)   Association
D)   Prediction

Question 16

SCENARIO 17-12
The marketing manager for a nationally franchised lawn service company would like to study the
characteristics that differentiate home owners who do and do not have a lawn service. A random
sample of 30 home owners located in a suburban area near a large city was selected; 15 did not have
a lawn service (code 0) and 15 had a lawn service (code 1). Additional information available
concerning these 30 home owners includes family income (Income, in thousands of dollars), lawn
size (Lawn Size, in thousands of square feet), attitude toward outdoor recreational activities (Atitude 0
= unfavorable, 1 = favorable), number of teenagers in the household (Teenager), and age of the head
of the household (Age).
The Minitab output is given below: 
-Referring to Scenario 17-12, there is not enough evidence to conclude that
Teenager makes a significant contribution to the model in the presence of the other independent
variables at a 0.05 level of significance.

Accepted Answer

A) True 
 B)False

Question 17

SCENARIO 17-10 Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy) and the independent variables are the age of the worker (Age), the number of years of education received (Edu), the number of years at the previous job (Job Yr), a dummy variable for marital status (Married: $1 =$ married, $0 =$ otherwise), a dummy variable for head of household (Head: $1 =$ yes, $0 =$ no) and a dummy variable for management position (Manager: $1 =$ yes, $0 =$ no). We shall call this Model 1. The coefficient of partial determination ( $R _ { \mathrm { y } } ^ { 2 }$ (All raiables excopt $j$ ) ) of each of the 6 predictors are, respectively, $0.2807$, $0.0386,0.0317,0.0141,0.0958$, and $0.1201$.

$\begin{array}{lr}
\hline{\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.7035 \
\text { R Square } & 0.4949 \
\text { Adjusted R } & 0.4030 \
\text { Square } & \
\text { Standard } & 18.4861 \
\text { Error } & \
\text { Observations } & 40 \
\hline
\end{array}$
 $\text { ANOVA }$

$$\begin{array}{l}
\begin{array} { l r r r r r r } 
\hline & \text { Coefficients } & \text { Standard Error } & { t \text { Stat } } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & 32.6595 & 23.18302 & 1.4088 & 0.1683 & - 14.5067 & 79.8257 \
\text { Age } & 1.2915 & 0.3599 & 3.5883 & 0.0011 & 0.5592 & 2.0238 \
\text { Edu } & - 1.3537 & 1.1766 & - 1.1504 & 0.2582 & - 3.7476 & 1.0402 \
\text { Job Yr } & 0.6171 & 0.5940 & 1.0389 & 0.3064 & - 0.5914 & 1.8257 \
\text { Married } & - 5.2189 & 7.6068 & - 0.6861 & 0.4974 & - 20.6950 & 10.2571 \
\text { Head } & - 14.2978 & 7.6479 & - 1.8695 & 0.0704 & - 29.8575 & 1.2618 \
\text { Manager } & - 24.8203 & 11.6932 & - 2.1226 & 0.0414 & - 48.6102 & - 1.0303 \
\hline
\end{array}
\end{array}$$
-Referring to Scenario 17-10 Model 1, the alternative hypothesis H1 : At least one of ?j ? 0
for j = 1, 2, 3, 4, 5, 6 implies that the number of weeks a worker is unemployed due to a layoff is
affected by at least one of the explanatory variables.

Accepted Answer

A) True 
 B)False

Question 18

SCENARIO 17-1
A real estate builder wishes to determine how house size (House) is influenced by family income
(Income), family size (Size), and education of the head of household (School). House size is
measured in hundreds of square feet, income is measured in thousands of dollars, and education is in
years. The builder randomly selected 50 families and ran the multiple regression. Microsoft Excel
output is provided below: SUMMARY OUTPUT
Regression Statistics
$\begin{array} { l l } \text { Multiple R } & 0.865 \ \text { R Square } & 0.748 \ \text { Adjusted R Square } & 0.726 \ \text { Standard Error } & 5.195 \ \text { Observations } & 50 \end{array}$
ANOVA
$\begin{array} { l c c c c c } & d f & \text { SS } & \text { MS } & F & \text { Signif } F \ \text { Regression } & & 3605.7736 & 1201.9245 & & 0.0000 \ \text { Residual } & & 1214.2264 & 26.3962 & & \ \text { Total } & 49 & 4820.0000 & & & \end{array}$

$\begin{array} { l c l c c } & \text { Coeff } & \text { StdError } & t \text { Stat } & P \text {-value } \ \text { Intercept } & - 1.6335 & 5.8078 & - 0.281 & 0.7798 \ \text { Income } & 0.4485 & 0.1137 & 3.9545 & 0.0003 \ \text { Size } & 4.2615 & 0.8062 & 5.286 & 0.0001 \ \text { School } & - 0.6517 & 0.4319 & - 1.509 & 0.1383 \end{array}$
-Referring to Scenario 17-1, what is the predicted house size (in hundreds of square feet) for an individual earning an annual income of $40,000, having a family size of 4, and going to school a
Total of 13 years?

Accepted Answer

A)   11.43
B)   15.15
C)   24.88
D)   53.87

Question 19

Some business analytics involve starting with many variables, followed by filtering
the data by exploring specific combinations of categorical values or numerical range. In Excel, this
approach is mimicked by using a slicer.

Accepted Answer

A) True 
 B)False

Question 20

Suppose that past history shows that 6% of college students prefer Brand A Cola. A sample of 10,000 students is to be selected. Which of the following distributions would you use to compute
The probability that at least half of them will prefer Brand A cola?

Accepted Answer

A)   Hypergeometric distribution.
B)   Poisson distribution.
C)   Binomial distribution.
D)   Uniform distribution.

Exam 17: Getting Ready to Analyze Data

Double-clicking a cell in a PivotTable causes Excel to drill down and display the underlying data in a new worksheet.

The director of admissions at a state college is interested in seeing if admissions status (admitted, waiting list, denied admission) at his college is related to the type of community (urban, rural, Suburban) in which an applicant resides. Which of the following tests will be the most Appropriate?

Which of the following is NOT one of the categories of predictive analytics methods?

Some business analytics involve starting with many variables, followed by filtering the data by exploring specific combinations of categorical values or numerical range. In Excel, this approach is mimicked by using a slicer.

Suppose that past history shows that 6% of college students prefer Brand A Cola. A sample of 10,000 students is to be selected. Which of the following distributions would you use to compute The probability that at least half of them will prefer Brand A cola?