Question 1

SCENARIO 17-11
A logistic regression model was estimated in order to predict the probability that a randomly chosen
university or college would be a private university using information on mean total Scholastic
Aptitude Test score (SAT) at the university or college, the room and board expense measured in
thousands of dollars (Room/Brd), and whether the TOEFL criterion is at least 550 (Toefl550 = 1 if
yes, 0 otherwise.) The dependent variable, Y, is school type (Type = 1 if private and 0 otherwise).
The Minitab output is given below: 
-Referring to Scenario 17-11, which of the following is the correct expression for the estimated model? a) $Y = - 27.118 + 0.015$ SAT $- 0.390$ Toefl $550 + 2.078$ Room / Brd
b) $\hat { Y } = - 27.118 + 0.015 \mathrm { SAT } - 0.390$ Toefl550 $+ 2.078 \mathrm { Room } / \mathrm { Brd }$
c) $\ln ($ odds ratio $) = - 27.118 + 0.015$ SAT $- 0.390$ Toefl $550 + 2.078 \mathrm { Room } / \mathrm { Brd }$
d) $\ln ($ estimated odds ratio $) = - 27.118 + 0.015$ SAT $- 0.390$ Toefl $550 + 2.078 \mathrm { Room } / \mathrm { Brd }$

Accepted Answer

The answer of SCENARIO 17-11
A logistic regression model was estimated...

Question 2

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, when the builder used a simple linear regression model with house size (House) as the dependent variable and education (School) as the independent variable, he
Obtained an r2 value of 23.0%. What additional percentage of the total variation in house size has
Been explained by including family size and income in the multiple regression?

Accepted Answer

A)  2.8% 
B)  51.8% 
C)  72.6% 
D)  74.8%

Question 3

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 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

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

Question 4

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 multiple regression model is significant at a 10%
level of significance.

Accepted Answer

A) True 
 B)False

Question 5

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 and using both Model 1 and Model 2, there is
sufficient evidence to conclude that the independent variables that are not significant individually
are also not significant as a group in explaining the variation in the dependent variable at a 5%
level of significance?

Accepted Answer

A) True 
 B)False

Question 6

The Tampa International Airport (TIA) has been criticized for the waiting times associated with departing flights. While the critics acknowledge that many flights have little or no waiting times,
Their complaints deal more specifically with the longer waits attributed to some flights. The critics
Are interested in showing, mathematically, exactly what the problems are. Which type of
Distribution would best model the waiting times of the departing flights at TIA?

Accepted Answer

A)  Uniform distribution 
B)  Binomial distribution 
C)  Normal distribution 
D)  Exponential distribution

Question 7

Suppose the light bulbs in a factory burn out at a rate of 50 bulbs per month. Which of the following distributions would you use to determine the probability that the next two light bulbs
Will burn out 2 days apart?

Accepted Answer

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

Question 8

SCENARIO 17-9
What are the factors that determine the acceleration time (in sec.) from 0 to 60 miles per hour of a
car? Data on the following variables for 171 different vehicle models were collected:
Accel Time: Acceleration time in sec.
Cargo Vol: Cargo volume in cu. ft.
HP: Horsepower
MPG: Miles per gallon
SUV: 1 if the vehicle model is an SUV with Coupe as the base when SUV and Sedan are both 0
Sedan: 1 if the vehicle model is a sedan with Coupe as the base when SUV and Sedan are both 0
The regression results using acceleration time as the dependent variable and the remaining variables
as the independent variables are presented below.  
-Referring to Scenario 17-9, the 0 to 60 miles per hour acceleration time of a
coupe is predicted to be 0.7679 seconds lower than that of an SUV.

Accepted Answer

A) True 
 B)False

Question 9

A political pollster randomly selects a sample of 100 voters each day for 8 successive days and asks how many will vote for the incumbent. The pollster wishes to see if the percentage favoring
The incumbent candidate is too erratic. Which of the following would be the most appropriate
Analysis to perform?

Accepted Answer

A)  Multiple linear regression. 
B)  Exponential smoothing. 
C)  Construct a p-chart. 
D)  Perform a Levene's test.

Question 10

SCENARIO 17-9
What are the factors that determine the acceleration time (in sec.) from 0 to 60 miles per hour of a
car? Data on the following variables for 171 different vehicle models were collected:
Accel Time: Acceleration time in sec.
Cargo Vol: Cargo volume in cu. ft.
HP: Horsepower
MPG: Miles per gallon
SUV: 1 if the vehicle model is an SUV with Coupe as the base when SUV and Sedan are both 0
Sedan: 1 if the vehicle model is a sedan with Coupe as the base when SUV and Sedan are both 0
The regression results using acceleration time as the dependent variable and the remaining variables
as the independent variables are presented below.  
-Referring to Scenario 17-9, there is enough evidence to conclude that Cargo Vol
makes a significant contribution to the regression model in the presence of the other independent
variables at a 5% level of significance.

Accepted Answer

A) True 
 B)False

Question 11

Data mining uses various techniques to extract useful information from huge
depositories of data.

Accepted Answer

A) True 
 B)False

Question 12

A buyer for a manufacturing plant suspects that his primary supplier of raw materials is overcharging. In order to determine if his suspicion is correct, he contacts a second supplier and
Asks for the prices on various identical materials. He wants to compare these prices with those of
His primary supplier. He collected data on 6 different materials from both suppliers. He believes
That the differences are normally distributed. Which of the following tests will be the most
Appropriate?

Accepted Answer

A)  Pooled-variance t test. 
B)  Paired t test. 
C)  Wilcoxon rank sum test. 
D)  Kruskal-Wallis rank Test.

Question 13

SCENARIO 17-3 A financial analyst wanted to examine the relationship between salary (in $\$ 1,000$ ) and 4 variables: age $\left( X _ { 1 } = \mathrm { Age } \right)$, experience in the field $\left( X _ { 2 } = \right.$ Exper $)$, number of degrees $\left( X _ { 3 } = \right.$ Degrees $)$, and number of previous jobs in the field ( $X _ { 4 } =$ Prevjobs). He took a sample of 20 employees and obtained the following Microsoft Excel output:

SUMMARY OUTPUT

$\begin{array}{ll}
{\text { Regression Statistics }} \
\text { Multiple R } & 0.992 \
\text { R Square } & 0.984 \
\text { Adjusted R Square } & 0.979 \
\text { Standard Error } & 2.26743 \
\text { Observations } & 20
\end{array}$

$\begin{array}{lrrrrr} 
& d f &{\text { SS }} & \text { MS } & F & \text { Signif } F \
\text { Regression } & 4 & 4609.83164 & 1152.45791 & 224.160 & 0.0001 \
\text { Residual } & 15 & 77.11836 & 5.14122 & & \
\text { Total } & 19 & 4686.95000 & & &
\end{array}$

$\begin{array}{lrrrr} 
& {\text { Coeff }} & \text { StdError } & {\text { Stat }} & \text { P-value } \
\text { Intercept } & -9.611198 & 2.77988638 & -3.457 & 0.0035 \
\text { Age } & 1.327695 & 0.11491930 & 11.553 & 0.0001 \
\text { Exper } & -0.106705 & 0.14265559 & -0.748 & 0.4660 \
\text { Degrees } & 7.311332 & 0.80324187 & 9.102 & 0.0001 \
\text { Prevjobs } & -0.504168 & 0.44771573 & -1.126 & 0.2778
\end{array}$
-Referring to Scenario 17-3, the F test for the significance of the entire regression
performed at a level of significance of 0.01 leads to a rejection of the null hypothesis.

Accepted Answer

A) True 
 B)False

Question 14

SCENARIO 17-9
What are the factors that determine the acceleration time (in sec.) from 0 to 60 miles per hour of a
car? Data on the following variables for 171 different vehicle models were collected:
Accel Time: Acceleration time in sec.
Cargo Vol: Cargo volume in cu. ft.
HP: Horsepower
MPG: Miles per gallon
SUV: 1 if the vehicle model is an SUV with Coupe as the base when SUV and Sedan are both 0
Sedan: 1 if the vehicle model is a sedan with Coupe as the base when SUV and Sedan are both 0
The regression results using acceleration time as the dependent variable and the remaining variables
as the independent variables are presented below.  
-Referring to Scenario 17-9, which of the following assumptions is most likely violated based on the residual plot of the residuals versus predicted Y?

Accepted Answer

A)  Independence of errors. 
B)  Normality of errors. 
C)  Equal variance. 
D)  None.

Question 15

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, ________ of the variation in the number of weeks a
worker is unemployed due to a layoff can be explained by whether the worker is in a management
position while controlling for the other independent variables.

Accepted Answer

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

Question 16

SCENARIO 17-14
The treemap below shows the amounts (size) measured in billions of US dollars and percentage
changes from prior year (color) of business-to-consumer ecommerce sales last year for North
America, Asia Pacific, Western Europe, Central & Eastern Europe, Latin America, and Middle East
& Africa. 
-Referring to Scenario 17-14, the Western Europe region has the largest amount
of business-to-consumer ecommerce sales last year.

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, predict the number of weeks being unemployed due to a
layoff for a worker who is a thirty-year old, has 10 years of education, has 15 years of experience
at the previous job, is married, is the head of household and is a manager.

Accepted Answer

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

Question 18

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 null hypothesis to test whether being married or not makes a difference in the mean number of weeks a worker is
Unemployed due to a layoff while holding constant the effect of all the other independent
Variables? a) $H _ { 0 } : \beta _ { 1 } = 0$
b) $H _ { 0 } : \beta _ { 2 } = 0$
c) $H _ { 0 } : \beta _ { 3 } = 0$
d) $H _ { 0 } : \beta _ { 4 } = 0$

Accepted Answer

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

Question 19

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 a correct statement?

Accepted Answer

A)  On average, a worker who is a year older is estimated to stay jobless longer by approximately 32.66 weeks while holding constant the effects of all the remaining
Independent variables. 
B)  On average, a worker who is a year older is estimated to stay jobless longer by approximately 1.29 weeks while holding constant the effects of all the remaining
Independent variables. 
C)  On average, a worker who is a year older is estimated to stay jobless shorter by approximately 1.35 weeks while holding constant the effects of all the remaining
Independent variables. 
D)  On average, a worker who is a year older is estimated to stay jobless longer by approximately 0.62 weeks while holding constant the effects of all the remaining
Independent variables.

Question 20

An insurance company evaluates many variables about a person before deciding on
an appropriate rate for automobile insurance. A representative from a local insurance agency
selected a random sample of 100 insured drivers and recorded, X, the amount of claims each
made in the last 3 years. A Pareto chart can be used to present this information.

Accepted Answer

A) True 
 B)False

Exam 17: Getting Ready to Analyze Data

Suppose the light bulbs in a factory burn out at a rate of 50 bulbs per month. Which of the following distributions would you use to determine the probability that the next two light bulbs Will burn out 2 days apart?

Data mining uses various techniques to extract useful information from huge depositories of data.