Question 1

SCENARIO 14-16
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 30 different vehicle models were collected: $Y$ (Accel Time): Acceleration time in sec.
$X _ { I }$ (Engine Size): c.c.
$X _ { 2 }$ (Sedan): 1 if the vehicle model is a sedan and 0 otherwise

The regression results using acceleration time as the dependent variable and the remaining variables as the independent variables are presented below.

$\begin{array}{lr}
\hline{\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.6096 \
\text { R Square } & 0.3716 \
\text { Adjusted R Square } & 0.3251 \
\text { Standard Error } & 1.4629 \
\text { Observations } & 30 \
\hline
\end{array}$

ANOVA

$\begin{array}{lrrrrrr}
\hline & \text { Coefficients } & \text { Standard Error } & \text { t Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & 7.1052 & 0.6574 & 10.8086 & 0.0000 & 5.7564 & 8.4540 \
\text { Engine Size } & -0.0005 & 0.0001 & -3.6477 & 0.0011 & -0.0008 & -0.0002 \
\text { Sedan } & 0.7264 & 0.5564 & 1.3056 & 0.2027 & -0.4152 & 1.8681 \
\hline
\end{array}$

-Referring to Scenario 14-16, there is enough evidence to conclude that engine
size makes a significant contribution to the regression model in the presence of the other
independent variable at a 5% level of significance.

Accepted Answer

A) True 
 B)False

Question 2

SCENARIO 14-18
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 and whether the TOEFL criterion is at least 90
(Toefl90 = 1 if yes, 0 otherwise.) The dependent variable, Y, is school type (Type = 1 if private and
0 otherwise).
The PHStat output is given below:
Binary Logistic Regression $$\begin{array}{l}
\begin{array} { l r r r r } 
\hline { \text { Predictor } } & \text { Coefficients } & \text { SE Coef } &  { Z } & p \text {-Value } \
\hline \text { Intercept } & - 3.9594 & 1.6741 & - 2.3650 & 0.0180 \
\text { SAT } & 0.0028 & 0.0011 & 2.5459 & 0.0109 \
\text { Toefl90:1 } & 0.1928 & 0.5827 & 0.3309 & 0.7407
\end{array}\\
\begin{array} { l l } 
\text { Deviance } & 101.9826
\end{array}
\end{array}$$
-Referring to Scenario 14-18, what is the estimated probability that a school with a mean SAT
score of 1250 and a TOEFL criterion that is at least 90?

Accepted Answer

The answer of SCENARIO 14-18
A logistic regression model was estimated...

Question 3

14-22 Introduction to Multiple Regression One of the most common questions of prospective house buyers pertains to the cost of heating in dollars $( Y )$. To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit $\left( X _ { 1 } \right)$ and the amount of insulation in inches $\left( X _ { 2 } \right)$. Given below is EXCEL output of the regression model.
$\begin{array}{lr}
\hline {\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.5270 \
\text { R Square } & 0.2778 \
\text { Adjusted R Square } & 0.1928 \
\text { Standard Error } & 40.9107 \
\text { Observations } & 20 \
\hline
\end{array}$

ANOVA

$\begin{array}{lrrrrrr} 
& \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & 448.2925 & 90.7853 & 4.9379 & 0.0001 & 256.7522 & 639.8328 \
\text { Temperature } & -2.7621 & 1.2371 & -2.2327 & 0.0393 & -5.3721 & -0.1520 \
\text { Insulation } & -15.9408 & 10.0638 & -1.5840 & 0.1316 & -37.1736
\end{array}$

Also $\operatorname { SSR } \left( X _ { 1 } \mid X _ { 2 } \right) = 8343.3572$ and $\operatorname { SSR } \left( X _ { 2 } \mid X _ { 1 } \right) = 4199.2672$
-Referring to Scenario 14-6, the partial F test for $H _ { 0 }$ : Variable $X _ { 1 }$ does not significantly improve the model after variable $X _ { 2 }$ has been included $H _ { l }$ : Variable $X _ { l }$ significantly improves the model after variable $X _ { 2 }$ has been included has $\underline{\quad\quad}$and $\underline{\quad\quad}$degrees of freedom.

Accepted Answer

The answer of 14-22 Introduction to Multiple Regression One of...

Question 4

SCENARIO 14-11
A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client
(measured in pounds). Two variables thought to affect weight loss are client's length of time on the
weight-loss program and time of session. These variables are described below: $ Y= $ Weight loss (in pounds)
$ X_{1}= $ Length of time in weight-loss program (in months)
$ X_{2}=1 $ if morning session, 0 if not
Data for 25 clients on a weight-loss program at the clinic were collected and used to fit the interaction model: $ Y=\beta_{0}+\beta_{1} X_{1}+\beta_{2} X_{2}+\beta_{3} X_{1} X_{2}+\varepsilon $

$\text { Output from Microsoft Excel follows: }$

$\begin{array}{lr}
{\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.7308 \
\text { R Square } & 0.5341 \
\text { Adjusted R Square } & 0.4675 \
\text { Standard Error } & 43.3275 \
\text { Observations } & 25 \
\hline
\end{array}$

$\text { ANOVA }$

$\begin{array}{lrrrrrrr}
\hline & \text { Coefficients } & \text { Standard Error } & {t \text { Stot }} & \rho_{\text {-value }} & \text { Lower 99\% } & \text { Upper 99\% } \
\hline \text { Intercept } & -20.7298 & 22.3710 & -0.9266 & 0.3646 & -84.0702 & 42.6106 \
\text { Length } & 7.2472 & 1.4992 & 4.8340 & 0.0001 & 3.0024 & 11.4919 \
\text { Morn } & 90.1981 & 40.2336 & 2.2419 & 0.0359 & -23.7176 & 204.1138 \
\text { Length × Morn } & -5.1024 & 3.3511 & -1.5226 & 0.1428 & -14.5905 & 4.3857
\end{array}$

-In a multiple regression model, the adjusted $r ^ { 2 }$

Accepted Answer

A)  cannot be negative. 
B)  can sometimes be negative. 
C)  can sometimes be greater than +1. 
D)  has to fall between 0 and +1.

Question 5

SCENARIO 14-15
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), mean teacher salary in thousands of dollars (Salaries), and instructional spending per
pupil in thousands of dollars (Spending) of 47 schools in the state. Following is the multiple regression output with $Y = \%$ Passing as the dependent variable, $X _ { 1 } =$
Salaries and $X _ { 2 } =$ Spending:

$\begin{array}{lr}
\hline {\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.4276 \
\text { R Square } & 0.1828 \
\text { Adjusted R Square } & 0.1457 \
\text { Standard Error } & 5.7351 \
\text { Observations } & 47 \
\hline
\end{array}$

ANOVA

$\begin{array}{lrrrrrr}
\hline & \text { Coefficients } & \text { Standard Error } & \text { t Stat } & \rho \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & -72.9916 & 45.9106 & -1.5899 & 0.1190 & -165.5184 & 19.5352 \
\text { Salary } & 2.7939 & 0.8974 & 3.1133 & 0.0032 & 0.9853 & 4.6025 \
\text { Spending } & 0.3742 & 0.9782 & 0.3825 & 0.7039 & -1.5972 & 2.3455 \
\hline
\end{array}$

-Referring to Scenario 14-15, which of the following is the correct null hypothesis to test whether mean teacher salary has any effect on percentage of students passing the proficiency test,
Taking into account the effect of instructional spending per pupil? a) $H _ { 0 } : \beta _ { 0 } = 0$
b) $H _ { 0 } : \beta _ { 1 } = 0$
c) $H _ { 0 } : \beta _ { 2 } = 0$
d) $H _ { 0 } : \beta _ { 3 } = 0$

Accepted Answer

The answer of SCENARIO 14-15
The superintendent of a school district...

Question 6

A regression had the following results: SST = 102.55, SSE = 82.04. It can be
said that 20.0% of the variation in the dependent variable is explained by the independent
variables in the regression.

Accepted Answer

A) True 
 B)False

Question 7

SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $ \$ 1,000 $ ) and 2 variables: age $ \left(X_{1}=\mathrm{Age}\right) $ and experience in the field $ \left(X_{2}=\right. $ Exper). He took a sample of 20 employees and obtained the following Microsoft Excel output:

$\begin{array}{lr}
\hline {\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.8535 \
\text { R Square } & 0.7284 \
\text { Adjusted R Square } & 0.6964 \
\text { Standard Error } & 10.5630 \
\text { Observations } & 20 \
\hline
\end{array}$

$\text { ANOVA }$

$\begin{array}{lrrrrrr} 
& \text { Coefficients } & \text { Standard Error } & {\text { t Stat }} & \text { P-value } & \text { Lower 95\% } &{\text { O5\% }} \
\hline \text { Intercept } & 1.5740 & 9.2723 & 0.1698 & 0.8672 & -17.9888 & 21.1368 \
\text { Age } & 1.3045 & 0.1956 & 6.6678 & 0.0000 & 0.8917 & 1.7173 \
\text { Exper } & -0.1478 & 0.1944 & -0.7604 & 0.4574 & -0.5580 & 0.2624 \
\hline
\end{array}$
 Also the sum of squares due to the regression for the model that includes only Age is 5022.0654 while the
sum of squares due to the regression for the model that includes only Exper is 125.9848.
-Referring to Scenario 14-8, the value of the partial F test statistic is ____ for $H _ { 0 }$ : Variable $X _ { 2 }$ does not significantly improve the model after variable $X _ { I }$ has been included 
$H _ { 1 }$ : Variable $X _ { 2 }$ significantly improves the model after variable $X _ { 1 }$ has been included

Accepted Answer

The answer of SCENARIO 14-8 A financial analyst wanted to...

Question 8

SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $ \$ 1,000 $ ) and 2 variables: age $ \left(X_{1}=\mathrm{Age}\right) $ and experience in the field $ \left(X_{2}=\right. $ Exper). He took a sample of 20 employees and obtained the following Microsoft Excel output:

$\begin{array}{lr}
\hline {\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.8535 \
\text { R Square } & 0.7284 \
\text { Adjusted R Square } & 0.6964 \
\text { Standard Error } & 10.5630 \
\text { Observations } & 20 \
\hline
\end{array}$

$\text { ANOVA }$

$\begin{array}{lrrrrrr} 
& \text { Coefficients } & \text { Standard Error } & {\text { t Stat }} & \text { P-value } & \text { Lower 95\% } &{\text { O5\% }} \
\hline \text { Intercept } & 1.5740 & 9.2723 & 0.1698 & 0.8672 & -17.9888 & 21.1368 \
\text { Age } & 1.3045 & 0.1956 & 6.6678 & 0.0000 & 0.8917 & 1.7173 \
\text { Exper } & -0.1478 & 0.1944 & -0.7604 & 0.4574 & -0.5580 & 0.2624 \
\hline
\end{array}$
 Also the sum of squares due to the regression for the model that includes only Age is 5022.0654 while the
sum of squares due to the regression for the model that includes only Exper is 125.9848.
-Referring to Scenario 14-8, the analyst wants to use a t test to test for the
significance of the coefficient of $X _ { 2 }$ . At a level of significance of 0.01, the department head would
decide that $\beta _ { 2 } \neq 0$ .

Accepted Answer

A) True 
 B)False

Question 9

SCENARIO 14-17
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) and a dummy variable for management position (Manager: 1 = yes, 0 = no).
The results of the regression analysis are given below: $\begin{array}{l}
\hline \ { \text { Regression Statistics } } \
\hline \text { Multiple R } & 0.6391 \
\text { R Square } & 0.4085 \
\text { Adjusted R Square } & 0.3765 \
\text { Standard Error } & 18.8929 \
\text { Observations } & 40 \
\hline
\end{array}$

$\text { ANOVA }$

$\begin{array} { l r r r r } 
\hline & \text { Coefficients } & \text { Standard Error } & { t \text { Stat } } &  { \text { P-value } } \
\hline \text { Intercept } & - 0.2143 & 11.5796 & - 0.0185 & 0.9853 \
\text { Age } & 1.4448 & 0.3160 & 4.5717 & 0.0000 \
\text { Manager } & - 22.5761 & 11.3488 & - 1.9893 & 0.0541 \
\hline

\end{array}$
-Referring to Scenario 14-17, the null hypothesis $$H _ { 0 } : \beta _ { 1 } = \beta _ { 2 } = 0$$ implies that the number of
weeks a worker is unemployed due to a layoff is not affected by any of the explanatory variables.

Accepted Answer

A) True 
 B)False

Question 10

In trying to construct a model to estimate grades on a statistics test, a professor
wanted to include, among other factors, whether the person had taken the course previously. To
do this, the professor included a dummy variable in her regression model that was equal to 1 if
the person had previously taken the course, and 0 otherwise. The interpretation of the coefficient
associated with this dummy variable would be the mean amount the repeat students tended to be
above or below non-repeaters, with all other factors the same.

Accepted Answer

A) True 
 B)False

Question 11

SCENARIO 14-4
A real estate builder wishes to determine how house size (House) is influenced by family income
(Income) and family size (Size). House size is measured in hundreds of square feet and income is
measured in thousands of dollars. The builder randomly selected 50 families and ran the multiple
regression. Partial Microsoft Excel output is provided below: 
-Referring to Scenario 14-4, one individual in the sample had an annual income of $40,000 and a
family size of 1. This individual owned a home with an area of 1,000 square feet (House =
10.00). What is the residual (in hundreds of square feet) for this data point?

Accepted Answer

The answer of SCENARIO 14-4
A real estate builder wishes to...

Question 12

SCENARIO 14-20-A
You are the CEO of a dairy company. You are planning to expand milk production by purchasing
additional cows, lands and hiring more workers. From the existing 50 farms owned by the company,
you have collected data on total milk production (in liters), the number of milking cows, land size (in
acres) and the number of laborers. The data are shown below and also available in the Excel file
Scenario14-20-DataA.XLSX.
S 
You believe that the number of milking cows $\left( X _ { 1 } \right)$, land size $\left( X _ { 2 } \right)$ and the number of laborers $\left( X _ { 3 } \right)$ are the best predictors for total milk production on any given farm.
-Referring to Scenario 14-20-A, the lower and upper limits of the 95% confidence interval for
the mean total milk production of a farm with 30 milking cows, 20 acres of land and 3 laborers
are _____ liters and _____ liters, respectively.

Accepted Answer

The answer of SCENARIO 14-20-A
You are the CEO of a...

Question 13

SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory $( Y )$ depends on his performance rating $\left( X _ { 1 } \right)$ and the number of economics courses the employee successfully completed in college $\left( X _ { 2 } \right)$. The professor randomly selects 6 workers and collects the following information:

-Referring to Scenario 14-2, for these data, what is the estimated coefficient for the number of economics courses taken, $b _ { 2 } $ ?

Accepted Answer

A)  0.616 
B)  1.054 
C)  6.932 
D)  9.103

Question 14

SCENARIO 14-20-B
You are the CEO of a dairy company. You are planning to expand milk production by purchasing
additional cows, lands and hiring more workers. From the existing 50 farms owned by the company,
you have collected data on total milk production (in liters), the number of milking cows, land size (in
acres) and the number of laborers. The data are shown below and also available in the Excel file
Scenario14-20-DataB.XLSX.
MILK 84686 101876 103248 70508 76072 86615 87508 105195 120351 68658 
You believe that the number of milking cows $\left( X _ { 1 } \right)$, land size $\left( X _ { 2 } \right)$ and the number of laborers $\left( X _ { 3 } \right)$ are the best predictors for total milk production on any given farm.
-Referring to Scenario 14-20-B, the estimated mean total milk production of a farm with 30
milking cows, 20 acres of land and 3 laborers is _____ liters.

Accepted Answer

The answer of SCENARIO 14-20-B
You are the CEO of a...

Question 15

SCENARIO 14-18
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 and whether the TOEFL criterion is at least 90
(Toefl90 = 1 if yes, 0 otherwise.) The dependent variable, Y, is school type (Type = 1 if private and
0 otherwise).
The PHStat output is given below:
Binary Logistic Regression $$\begin{array}{l}
\begin{array} { l r r r r } 
\hline { \text { Predictor } } & \text { Coefficients } & \text { SE Coef } &  { Z } & p \text {-Value } \
\hline \text { Intercept } & - 3.9594 & 1.6741 & - 2.3650 & 0.0180 \
\text { SAT } & 0.0028 & 0.0011 & 2.5459 & 0.0109 \
\text { Toefl90:1 } & 0.1928 & 0.5827 & 0.3309 & 0.7407
\end{array}\\
\begin{array} { l l } 
\text { Deviance } & 101.9826
\end{array}
\end{array}$$
-Referring to Scenario 14-18, there is not enough evidence to conclude that
Toefl90 makes a significant contribution to the model in the presence of SAT at a 0.05 level of
significance.

Accepted Answer

A) True 
 B)False

Question 16

SCENARIO 14-16
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 30 different vehicle models were collected: $Y$ (Accel Time): Acceleration time in sec.
$X _ { I }$ (Engine Size): c.c.
$X _ { 2 }$ (Sedan): 1 if the vehicle model is a sedan and 0 otherwise

The regression results using acceleration time as the dependent variable and the remaining variables as the independent variables are presented below.

$\begin{array}{lr}
\hline{\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.6096 \
\text { R Square } & 0.3716 \
\text { Adjusted R Square } & 0.3251 \
\text { Standard Error } & 1.4629 \
\text { Observations } & 30 \
\hline
\end{array}$

ANOVA

$\begin{array}{lrrrrrr}
\hline & \text { Coefficients } & \text { Standard Error } & \text { t Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & 7.1052 & 0.6574 & 10.8086 & 0.0000 & 5.7564 & 8.4540 \
\text { Engine Size } & -0.0005 & 0.0001 & -3.6477 & 0.0011 & -0.0008 & -0.0002 \
\text { Sedan } & 0.7264 & 0.5564 & 1.3056 & 0.2027 & -0.4152 & 1.8681 \
\hline
\end{array}$

-Referring to Scenario 14-16, ________ of the variation in Accel Time can be explained by the
two independent variables after taking into consideration the number of independent variables
and the number of observations.

Accepted Answer

The answer of SCENARIO 14-16
What are the factors that determine...

Question 17

SCENARIO 14-15
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), mean teacher salary in thousands of dollars (Salaries), and instructional spending per
pupil in thousands of dollars (Spending) of 47 schools in the state. Following is the multiple regression output with $Y = \%$ Passing as the dependent variable, $X _ { 1 } =$
Salaries and $X _ { 2 } =$ Spending:

$\begin{array}{lr}
\hline {\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.4276 \
\text { R Square } & 0.1828 \
\text { Adjusted R Square } & 0.1457 \
\text { Standard Error } & 5.7351 \
\text { Observations } & 47 \
\hline
\end{array}$

ANOVA

$\begin{array}{lrrrrrr}
\hline & \text { Coefficients } & \text { Standard Error } & \text { t Stat } & \rho \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & -72.9916 & 45.9106 & -1.5899 & 0.1190 & -165.5184 & 19.5352 \
\text { Salary } & 2.7939 & 0.8974 & 3.1133 & 0.0032 & 0.9853 & 4.6025 \
\text { Spending } & 0.3742 & 0.9782 & 0.3825 & 0.7039 & -1.5972 & 2.3455 \
\hline
\end{array}$

-Referring to Scenario 14-15, what is the value of the test statistic to determine whether there is
a significant relationship between percentage of students passing the proficiency test and the
entire set of explanatory variables?

Accepted Answer

The answer of SCENARIO 14-15
The superintendent of a school district...

Question 18

SCENARIO 14-16
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 30 different vehicle models were collected: $Y$ (Accel Time): Acceleration time in sec.
$X _ { I }$ (Engine Size): c.c.
$X _ { 2 }$ (Sedan): 1 if the vehicle model is a sedan and 0 otherwise

The regression results using acceleration time as the dependent variable and the remaining variables as the independent variables are presented below.

$\begin{array}{lr}
\hline{\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.6096 \
\text { R Square } & 0.3716 \
\text { Adjusted R Square } & 0.3251 \
\text { Standard Error } & 1.4629 \
\text { Observations } & 30 \
\hline
\end{array}$

ANOVA

$\begin{array}{lrrrrrr}
\hline & \text { Coefficients } & \text { Standard Error } & \text { t Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & 7.1052 & 0.6574 & 10.8086 & 0.0000 & 5.7564 & 8.4540 \
\text { Engine Size } & -0.0005 & 0.0001 & -3.6477 & 0.0011 & -0.0008 & -0.0002 \
\text { Sedan } & 0.7264 & 0.5564 & 1.3056 & 0.2027 & -0.4152 & 1.8681 \
\hline
\end{array}$

-Referring to Scenario 14-16, what is the p-value of the test statistic to determine whether
engine size makes a significant contribution to the regression model in the presence of the other
independent variable at a 5% level of significance?

Accepted Answer

The answer of SCENARIO 14-16
What are the factors that determine...

Question 19

14-30 Introduction to Multiple Regression 
-Referring to Scenario 14-7, the predicted GPA for a student carrying 15 course units and who
has a total SAT of 1,100 is ________.

Accepted Answer

The answer of 14-30 Introduction to Multiple Regression 
-Referring to...

Question 20

SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $ \$ 1,000 $ ) and 2 variables: age $ \left(X_{1}=\mathrm{Age}\right) $ and experience in the field $ \left(X_{2}=\right. $ Exper). He took a sample of 20 employees and obtained the following Microsoft Excel output:

$\begin{array}{lr}
\hline {\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.8535 \
\text { R Square } & 0.7284 \
\text { Adjusted R Square } & 0.6964 \
\text { Standard Error } & 10.5630 \
\text { Observations } & 20 \
\hline
\end{array}$

$\text { ANOVA }$

$\begin{array}{lrrrrrr} 
& \text { Coefficients } & \text { Standard Error } & {\text { t Stat }} & \text { P-value } & \text { Lower 95\% } &{\text { O5\% }} \
\hline \text { Intercept } & 1.5740 & 9.2723 & 0.1698 & 0.8672 & -17.9888 & 21.1368 \
\text { Age } & 1.3045 & 0.1956 & 6.6678 & 0.0000 & 0.8917 & 1.7173 \
\text { Exper } & -0.1478 & 0.1944 & -0.7604 & 0.4574 & -0.5580 & 0.2624 \
\hline
\end{array}$
 Also the sum of squares due to the regression for the model that includes only Age is 5022.0654 while the
sum of squares due to the regression for the model that includes only Exper is 125.9848.
-Referring to Scenario 14-8, the predicted salary (in $1,000) for a 35-year-old person with 10
years of experience is ________.

Accepted Answer

The answer of SCENARIO 14-8 A financial analyst wanted to...

Exam 14: Introduction to Multiple

A regression had the following results: SST = 102.55, SSE = 82.04. It can be said that 20.0% of the variation in the dependent variable is explained by the independent variables in the regression.

14-30 Introduction to Multiple Regression -Referring to Scenario 14-7, the predicted GPA for a student carrying 15 course units and who has a total SAT of 1,100 is ________.