Question 1

Instruction 13.25
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.
 Model 1
Regression Statistics
$\begin{array} { l l } \text { Multiple R } & 0.7035 \ \text { R Square } & 0.4949 \ \text { Adj. R Square } & 0.4030 \ \text { Std. Error } & 18.4861 \ \text { Observations } & 40 \end{array}$

ANOVA
$\begin{array} { l l l l l l l } & d f & \text { SS } & \text { MS } & F & \text { Signiff } & \ \text { Regression } & 6 & 11048.6415 & 1841.4402 & 5.3885 & 0.00057 & \ \text { Residual } & 33 & 11277.2586 & 341.7351 & & & \ \text { Total } & 39 & 223325.9 & & & & \ & & & & & & \ & \text { Coeff } & \text { StdError } & \text { tStat } & p \text { value } & \text { Lower 95\% } & \text { Upper95\% } \ \text { Intercept } & 32.6595 & 23.18302 & 1.4088 & 0.1683 & - 14.5067 & 79.8257 \ \text { Age } & 1.2915 & 0.3599 & 3.5883 & 0.0011 & 0.5592 & 2.0238 \ \text { Edu } & - 1.3537 & 1.1766 & - 1.1504 & 0.2582 & - 3.7476 & 1.0402 \ \text { Job Yr } & 0.6171 & 0.5940 & 1.0389 & 0.3064 & - 0.5914 & 1.8257 \ \text { Married } & - 5.2189 & 7.6068 & - 0.6861 & 0.4974 & - 20.6950 & 10.2571 \ \text { Head } & - 14.2978 & 7.6479 & - 1.8695 & 0.0704 & - 29.8575 & 1.2618 \ \text { Manager } & - 24.8203 & 11.6932 & - 2.1226 & 0.0414 & - 48.6102 & - 1.0303 \end{array}$ Model 2 is the regression analysis where the dependent variable is Unemploy and the independent variables are Age and Manager. The results of the regression analysis are given below:
 Mode 2
Regression Statistics
$\begin{array} { l l } \text { Multiple R } & 0.6391 \ \text { R Square } & 0.4085 \ \text { Adj. R Square } & 0.3765 \ \text { Std. Error } & 18.8929 \ \text { Observations } & 40 \end{array}$

ANOVA
$\begin{array} { l l l l l l } & d f & \text { SS } & \text { MS } & F & \text { Signiff } \ \text { Regression } & 2 & 9119.0897 & 4559.5448 & 12.7740 & 0.0000 \ \text { Residual } & 37 & 13206.8103 & 356.9408 & & \ \text { Total } & 39 & 22325.9 & & & \ & & & & & \ & \text { Coeff } & \text { StdError } & t \text { Stat } & p \text { value } & \ \text { Intercept } & - 0.2143 & 11.5796 & - 0.0185 & 0.9853 & \ \text { Age } & 1.4448 & 0.3160 & 4.5717 & 0.0000 & \ \text { Manager } & - 22.5761 & 11.3488 & - 1.9893 & 0.0541 & \end{array}$
-Referring to Instruction 13.25 Model 1,the alternative hypothesis $H _ { 1 }$ : 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 related to at least one of the explanatory variables.

Accepted Answer

A) True 
 B)False

Question 2

Instruction 13.25
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.
 Model 1
Regression Statistics
$\begin{array} { l l } \text { Multiple R } & 0.7035 \ \text { R Square } & 0.4949 \ \text { Adj. R Square } & 0.4030 \ \text { Std. Error } & 18.4861 \ \text { Observations } & 40 \end{array}$

ANOVA
$\begin{array} { l l l l l l l } & d f & \text { SS } & \text { MS } & F & \text { Signiff } & \ \text { Regression } & 6 & 11048.6415 & 1841.4402 & 5.3885 & 0.00057 & \ \text { Residual } & 33 & 11277.2586 & 341.7351 & & & \ \text { Total } & 39 & 223325.9 & & & & \ & & & & & & \ & \text { Coeff } & \text { StdError } & \text { tStat } & p \text { value } & \text { Lower 95\% } & \text { Upper95\% } \ \text { Intercept } & 32.6595 & 23.18302 & 1.4088 & 0.1683 & - 14.5067 & 79.8257 \ \text { Age } & 1.2915 & 0.3599 & 3.5883 & 0.0011 & 0.5592 & 2.0238 \ \text { Edu } & - 1.3537 & 1.1766 & - 1.1504 & 0.2582 & - 3.7476 & 1.0402 \ \text { Job Yr } & 0.6171 & 0.5940 & 1.0389 & 0.3064 & - 0.5914 & 1.8257 \ \text { Married } & - 5.2189 & 7.6068 & - 0.6861 & 0.4974 & - 20.6950 & 10.2571 \ \text { Head } & - 14.2978 & 7.6479 & - 1.8695 & 0.0704 & - 29.8575 & 1.2618 \ \text { Manager } & - 24.8203 & 11.6932 & - 2.1226 & 0.0414 & - 48.6102 & - 1.0303 \end{array}$ Model 2 is the regression analysis where the dependent variable is Unemploy and the independent variables are Age and Manager. The results of the regression analysis are given below:
 Mode 2
Regression Statistics
$\begin{array} { l l } \text { Multiple R } & 0.6391 \ \text { R Square } & 0.4085 \ \text { Adj. R Square } & 0.3765 \ \text { Std. Error } & 18.8929 \ \text { Observations } & 40 \end{array}$

ANOVA
$\begin{array} { l l l l l l } & d f & \text { SS } & \text { MS } & F & \text { Signiff } \ \text { Regression } & 2 & 9119.0897 & 4559.5448 & 12.7740 & 0.0000 \ \text { Residual } & 37 & 13206.8103 & 356.9408 & & \ \text { Total } & 39 & 22325.9 & & & \ & & & & & \ & \text { Coeff } & \text { StdError } & t \text { Stat } & p \text { value } & \ \text { Intercept } & - 0.2143 & 11.5796 & - 0.0185 & 0.9853 & \ \text { Age } & 1.4448 & 0.3160 & 4.5717 & 0.0000 & \ \text { Manager } & - 22.5761 & 11.3488 & - 1.9893 & 0.0541 & \end{array}$
-Referring to Instruction 13.25 Model 1,the null hypothesis H0: ?1 = ?2 = ?3 = ?4 = ?5 = ?6 = 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 3

From the coefficient of multiple determination,you cannot detect the strength of the relationship between Y and any individual independent variable.

Accepted Answer

A) True 
 B)False

Question 4

Instruction 13.18
One of the most common questions of prospective house buyers pertains to the average cost of heating in dollars (Y).
To provide its customers with information on that matter, a large real estate firm used the following four variables to predict heating costs: the daily minimum outside temperature in degrees of Celsius (XS1U1B11S1U1B0), the amount of insulation in cm (XS1U1B12S1U1B0), the number of windows in the house (XS1U1B13S1U1B0) and the age of the furnace in years (XS1U1B14S1U1B0). Given below are the Microsoft Excel outputs of two regression models.
  
-Referring to Instruction 13.18 and allowing for a 1% probability of committing a Type I error,what is the decision and conclusion for the test H0: β1 = β2 = β3 = β4 = 0 vs.H1: At least one βj ≠ 0,j = 1,2,...,4 using Model 1?

Accepted Answer

A)  Reject H0 and conclude that the four independent variables taken as a group do not have significant linear effects on heating costs. 
B)  Do not reject H0 and conclude that the four independent variables have significant individual linear effects on heating costs. 
C)  Reject H0 and conclude that the four independent variables taken as a group have significant linear effects on average heating costs. 
D)  Do not reject H0 and conclude that the four independent variables taken as a group do not have significant linear effects on heating costs.

Question 5

Instruction 13.13
A financial analyst wanted to examine the relationship between salary (in $1,000) and four variables: age (XS1U1B11S1U1B0 = Age), experience in the field (XS1U1B12S1U1B0 = Exper), number of degrees (XS1U1B13S1U1B0 = Degrees) and number of previous jobs in the field (XS1U1B14S1U1B0 = Prevjobs). He took a sample of 20 employees and obtained the following Microsoft Excel output:
 $\text { OUTPUT }$
$\begin{array}{ll}
\text { SUMMARY}\
\text { Regression }&\text { Statistics}\
\text { Multiple R } & 0.992 \
\text { R Square } & 0.984 \
\text { Adj. R Square } & 0.979 \
\text { Std. Error } & 2.26743 \
\text { Observations } & 20
\end{array}$

$\begin{array}{l}
\text { ANOVA }\
\begin{array}{llllll} 
& \text { df } & \text { SS } & \text { MS } & F & \text { Signif F } \
\text { Regression } & 4 & 4609.83164 & 1152.45791 & 224.160 & 0.0001 \
\text { Residual } & 15 & 77.11836 & 5.14122 & & \
\text { Total } & 19 & 4686.95000 & & &
\end{array}
\end{array}$

$\begin{array}{lllll} 
& \text { Coeff } & \text { Std Error } & t \text { Stat } & p \text { value } \
\text { Intercept } & -9.611198 & 2.77988638 & -3.457 & 0.0035 \
\text { Age } & 1.327695 & 0.11491930 & 11.553 & 0.0001 \
\text { Exper } & -0.106705 & 0.14265559 & -0.748 & 0.4660 \
\text { Degrees } & 7.311332 & 0.80324187 & 9.102 & 0.0001 \
\text { Prevjobs } & -0.504168 & 0.44771573 & -1.126 & 0.2778
\end{array}$

Note: Adj. R Square = Adjusted R Square; Std. Error = Standard Error
-Referring to Instruction 13.13,the critical value of an F test on the entire regression for a level of significance of 0.01 is ___________.

Accepted Answer

The answer of Instruction 13.13
A financial analyst wanted to examine...

Question 6

Instruction 13.33
An econometrician is interested in evaluating the relation of demand for building materials to mortgage rates in Sydney and Melbourne. He believes that the appropriate model is
$\begin{array}{l}
Y = 10 + 5 X _ { 1 } + 8 X _ { 2 }\\
\text { where } \begin{aligned}
X _ { 1 } & = \text { mortgage rate in } \% \
X _ { 2 } & = 1 \text { if Sydney, } 0 \text { if Melbourne } \
Y & = \text { demand in } \$ 100 \text { per capita }
\end{aligned}
\end{array}$
-Referring to Instruction 13.33,the predicted demand in Melbourne when the mortgage rate is 8% is ___________ per capita.

Accepted Answer

The answer of Instruction 13.33
An econometrician is interested in evaluating...

Question 7

The denominator degrees of freedom for the partial F statistic are

Accepted Answer

A)  n -k -1. 
B)  k -1. 
C)  n -1. 
D)  n -k -2.

Question 8

You need to proceed with extreme caution when using a multiple regression model that has one or more large VIF values.

Accepted Answer

A) True 
 B)False

Question 9

Instruction 13.37
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.
 Model 1
Regression Statistics
$\begin{array} { l l } \text { Multiple R } & 0.7035 \ \text { R Square } & 0.4949 \ \text { Adj. R Square } & 0.4030 \ \text { Std. Error } & 18.4861 \ \text { Observations } & 40 \end{array}$

ANOVA
$\begin{array} { l l l l l l l } & d f & \text { SS } & \text { MS } & F & \text { Signif F } & \ \text { Regression } & 6 & 11048.6415 & 1841.4402 & 5.3885 & 0.00057 & \ \text { Residual } & 33 & 11277.2586 & 341.7351 & & & \ \text { Total } & 39 & 223325.9 & & & & \ & & & & & & \ & \text { Coeff } & \text { StdError } & \text { tStat } & p \text { value } & \text { Lower 95\% } & \text { Upper95\% } \ \text { Intercept } & 32.6595 & 23.18302 & 1.4088 & 0.1683 & - 14.5067 & 79.8257 \ \text { Age } & 1.2915 & 0.3599 & 3.5883 & 0.0011 & 0.5592 & 2.0238 \ \text { Edu } & - 1.3537 & 1.1766 & - 1.1504 & 0.2582 & - 3.7476 & 1.0402 \ \text { Job Yr } & 0.6171 & 0.5940 & 1.0389 & 0.3064 & - 0.5914 & 1.8257 \ \text { Married } & - 5.2189 & 7.6068 & - 0.6861 & 0.4974 & - 20.6950 & 10.2571 \ \text { Head } & - 14.2978 & 7.6479 & - 1.8695 & 0.0704 & - 29.8575 & 1.2618 \ \text { Manager } & - 24.8203 & 11.6932 & - 2.1226 & 0.0414 & - 48.6102 & - 1.0303 \end{array}$ Model 2 is the regression analysis where the dependent variable is Unemploy and the independent variables are Age and Manager. The results of the regression analysis are given below:
 Mode 2
Regression Statistics
$\begin{array} { l l } \text { Multiple R } & 0.6391 \ \text { R Square } & 0.4085 \ \text { Adj. R Square } & 0.3765 \ \text { Std. Error } & 18.8929 \ \text { Observations } & 40 \end{array}$

ANOVA
$\begin{array} { l l l l l l } & d f & \text { SS } & \text { MS } & F & \text { Signif F } \ \text { Regression } & 2 & 9119.0897 & 4559.5448 & 12.7740 & 0.0000 \ \text { Residual } & 37 & 13206.8103 & 356.9408 & & \ \text { Total } & 39 & 22325.9 & & & \ & & & & & \ & \text { Coeff } & \text { StdError } & t \text { Stat } & p \text { value } & \ \text { Intercept } & - 0.2143 & 11.5796 & - 0.0185 & 0.9853 & \ \text { Age } & 1.4448 & 0.3160 & 4.5717 & 0.0000 & \ \text { Manager } & - 22.5761 & 11.3488 & - 1.9893 & 0.0541 & \end{array}$
-Referring to Instruction 13.37 Model 1,which of the following is the correct alternative 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?

Accepted Answer

A)  H1: ?2 ? 0 
B)  H1: ?1 ? 0 
C)  H1: ?3 ? 0 
D)  H1: ?4 ? 0

Question 10

Instruction 13.15
An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross domestic product ($ billions) and aggregate price (consumer price index). The Microsoft Excel output of this regression is partially reproduced below.
 OUTPUT
SUMMARY
Regression Statistics
$\begin{array} { l l } \text { MultipleR } & 0.991 \ \text { R Square } & 0.982 \ \text { Adj. R Square } & 0.976 \ \text { Std. Error } & 0.299 \ \text { Observations } & 10 \end{array}$

ANOVA
$\begin{array} { l l l l l l } & d f & \text { SS } & \text { MS } & F & \text { Signiff } \ \text { Regression } & 2 & 33.4163 & 16.7082 & 186.325 & 0.0001 \ \text { Residual } & 7 & 0.6277 & 0.0897 & & \ \text { Total } & 9 & 34.0440 & & & \ & & & & & \ & \text { Coeff } & \text { StdError } & t \text { Stat } & p \text { value } & \ \text { Intercept } & - 1.6335 & 0.5674 & - 0.152 & 0.8837 & \ \text { GDP } & 0.7654 & 0.0574 & 13.340 & 0.0001 & \ \text { Price } & - 0.0006 & 0.0028 & - 0.219 & 0.8330 & \end{array}$ Note: Adj. R Square = Adjusted R Square; Std. Error = Standard Error
-Referring to Instruction 13.15,when the economist used a simple linear regression model with consumption as the dependent variable and GDP as the independent variable,he obtained an r2 value of 0.971.What additional percentage of the total variation of consumption has been explained by including aggregate prices in the multiple regression?

Accepted Answer

A)  98.2 
B)  1.1 
C)  11.1 
D)  2.8

Question 11

Instruction 13.29
An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross domestic product ($ billions) and aggregate price (consumer price index). The Microsoft Excel output of this regression is partially reproduced below.
 OUTPUT
SUMMARY
Regression Statistics
$\begin{array} { l l } \text { MultipleR } & 0.991 \ \text { R Square } & 0.982 \ \text { Adj. R Square } & 0.976 \ \text { Std. Error } & 0.299 \ \text { Observations } & 10 \end{array}$

ANOVA
$\begin{array} { l l l l l l } & d f & \text { SS } & \text { MS } & F & \text { Signiff } \ \text { Regression } & 2 & 33.4163 & 16.7082 & 186.325 & 0.0001 \ \text { Residual } & 7 & 0.6277 & 0.0897 & & \ \text { Total } & 9 & 34.0440 & & & \ & & & & & \ & \text { Coeff } & \text { StdError } & t \text { Stat } & p \text { value } & \ \text { Intercept } & - 1.6335 & 0.5674 & - 0.152 & 0.8837 & \ \text { GDP } & 0.7654 & 0.0574 & 13.340 & 0.0001 & \ \text { Price } & - 0.0006 & 0.0028 & - 0.219 & 0.8330 & \end{array}$ Note: Adj. R Square = Adjusted R Square; Std. Error = Standard Error
-Referring to Instruction 13.29,to test for the significance of the coefficient on aggregate price index,the p-value is

Accepted Answer

A)  0.0001. 
B)  0.9999. 
C)  0.8837. 
D)  0.8330.

Question 12

Instruction 13.22
The education department's regional executive officer wanted to predict the percentage of students passing a Grade 6 proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing), daily average of the percentage of students attending class (% Attendance), average 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, XS1U1B11S1U1B0 = % Attendance, XS1U1B12 S1U1B0= Salaries and XS1U1B13 S1U1B0= Spending:
 
-Referring to Instruction 13.22,there is sufficient evidence that at least one of the explanatory variables is related to the percentage of students passing the proficiency test.

Accepted Answer

A) True 
 B)False

Question 13

Instruction 13.14
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.
 Model 1
Regression Statistics
$\begin{array} { l l } \text { Multiple R } & 0.7035 \ \text { R Square } & 0.4949 \ \text { Adj. R Square } & 0.4030 \ \text { Std. Error } & 18.4861 \ \text { Observations } & 40 \end{array}$

ANOVA
$\begin{array} { l l l l l l l } & d f & \text { SS } & \text { MS } & F & \text { Signiff } & \ \text { Regression } & 6 & 11048.6415 & 1841.4402 & 5.3885 & 0.00057 & \ \text { Residual } & 33 & 11277.2586 & 341.7351 & & & \ \text { Total } & 39 & 223325.9 & & & & \ & & & & & & \ & \text { Coeff } & \text { StdError } & \text { tStat } & p \text { value } & \text { Lower 95\% } & \text { Upper95\% } \ \text { Intercept } & 32.6595 & 23.18302 & 1.4088 & 0.1683 & - 14.5067 & 79.8257 \ \text { Age } & 1.2915 & 0.3599 & 3.5883 & 0.0011 & 0.5592 & 2.0238 \ \text { Edu } & - 1.3537 & 1.1766 & - 1.1504 & 0.2582 & - 3.7476 & 1.0402 \ \text { Job Yr } & 0.6171 & 0.5940 & 1.0389 & 0.3064 & - 0.5914 & 1.8257 \ \text { Married } & - 5.2189 & 7.6068 & - 0.6861 & 0.4974 & - 20.6950 & 10.2571 \ \text { Head } & - 14.2978 & 7.6479 & - 1.8695 & 0.0704 & - 29.8575 & 1.2618 \ \text { Manager } & - 24.8203 & 11.6932 & - 2.1226 & 0.0414 & - 48.6102 & - 1.0303 \end{array}$ Model 2 is the regression analysis where the dependent variable is Unemploy and the independent variables are Age and Manager. The results of the regression analysis are given below:
 Mode 2
Regression Statistics
$\begin{array} { l l } \text { Multiple R } & 0.6391 \ \text { R Square } & 0.4085 \ \text { Adj. R Square } & 0.3765 \ \text { Std. Error } & 18.8929 \ \text { Observations } & 40 \end{array}$
ANOVA
$\begin{array} { l l l l l l } & d f & \text { SS } & \text { MS } & F & \text { Signiff } \ \text { Regression } & 2 & 9119.0897 & 4559.5448 & 12.7740 & 0.0000 \ \text { Residual } & 37 & 13206.8103 & 356.9408 & & \ \text { Total } & 39 & 22325.9 & & & \ & & & & & \ & \text { Coeff } & \text { StdError } & t \text { Stat } & p \text { value } & \ \text { Intercept } & - 0.2143 & 11.5796 & - 0.0185 & 0.9853 & \ \text { Age } & 1.4448 & 0.3160 & 4.5717 & 0.0000 & \ \text { Manager } & - 22.5761 & 11.3488 & - 1.9893 & 0.0541 & \end{array}$
-Referring to Instruction 13.14 Model 1,estimate the mean number of weeks being unemployed due to a layoff for a worker who is a 30-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 Instruction 13.14
Given below are results from the...

Question 14

Instruction 13.10
The education department's regional executive officer wanted to predict the percentage of students passing a Grade 6 proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing), daily average of the percentage of students attending class (% Attendance), average 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, XS1U1B11S1U1B0 = % Attendance, XS1U1B12 S1U1B0= Salaries and XS1U1B13 S1U1B0= Spending:
 $\begin{array}{|l|r|}
\hline\text { Regression } & \text { Statistics } \
\hline \text { Multiple R } & 0.7930 \
\hline \text { R Square } & 0.6288 \
\hline \text { Adj. R Square } & 0.602 \
\hline \text { Standard } & \
\text { Error } & 10.4570 \
\hline \text { Observations } & 47\
\hline
\end{array}$

$\begin{array}{|l|r|r|r|r|r|}
\hline \text { ANOVA } & & & & \
\hline & \text { d } & \text { SS } & \text { MS } & \text { F } & \text { Signif F } \
\hline \text { Regression } & 3 & 7965.08 & 2655.03 & 24.2802 & 0.0000 \
\hline \text { Residual } & 43 & 4702.02 & 109.35 & & \
\hline \text { Total } & 46 & 12667.11 & & & \
\hline
\end{array}$

$\begin{array}{|l|r|r|r|r|r|r|} 
\hline & \text { Coeff } & \text { StolFrro } & \text { tSta } & \text { p-va/ue } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & -753.4225 & 101.1149 & -7.4511 & 0.0000 & -957.3401 & -549.5050 \
\hline \text { \% Attendance } & 8.5014 & 1.0771 & 7.8929 & 0.0000 & 6.3292 & 10.6735 \
\hline \text { Salary } & 0.000000685 & 0.0006 & 0.0011 & 0.9991 & -0.0013 & 0.0013 \
\hline \text { Spending } & 0.0060 & 0.0046 & 1.2879 & 0.2047 & -0.0034 & 0.0153 \
\hline
\end{array}$
-Referring to Instruction 13.10,estimate the mean percentage of students passing the proficiency test for all the schools that have a daily mean of 95% of students attending class,a mean teacher salary of 40,000 dollars and an instructional spending per pupil of 2,000 dollars.

Accepted Answer

The answer of Instruction 13.10
The education department's regional executive officer...

Question 15

Instruction 13.24 A financial analyst wanted to examine the relationship between salary (in $1,000) and four variables: age (XS1U1B11S1U1B0 = Age), experience in the field (XS1U1B12S1U1B0 = Exper), number of degrees (XS1U1B13S1U1B0 = Degrees) and number of previous jobs in the field (XS1U1B14S1U1B0 = Prevjobs). He took a sample of 20 employees and obtained the following Microsoft Excel output: Note: Adj. R Square = Adjusted R Square; Std. Error = Standard Error -Referring to Instruction 13.24,the analyst decided to construct a 99% confidence interval for $\beta$₃.The confidence interval is from _____ to _____.

Accepted Answer

The answer of Instruction 13.24
A financial analyst wanted to examine...

Question 16

Instruction 13.22
The education department's regional executive officer wanted to predict the percentage of students passing a Grade 6 proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing), daily average of the percentage of students attending class (% Attendance), average 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, XS1U1B11S1U1B0 = % Attendance, XS1U1B12 S1U1B0= Salaries and XS1U1B13 S1U1B0= Spending:
 
-Referring to Instruction 13.22,the alternative hypothesis H1: At least one of βj ≠ 0 for j = 1,2,3 implies that percentage of students passing the proficiency test is affected by all of the explanatory variables.

Accepted Answer

A) True 
 B)False

Question 17

Instruction 13.13
A financial analyst wanted to examine the relationship between salary (in $1,000) and four variables: age (XS1U1B11S1U1B0 = Age), experience in the field (XS1U1B12S1U1B0 = Exper), number of degrees (XS1U1B13S1U1B0 = Degrees) and number of previous jobs in the field (XS1U1B14S1U1B0 = Prevjobs). He took a sample of 20 employees and obtained the following Microsoft Excel output:
 $\text { OUTPUT }$
$\begin{array}{ll}
\text { SUMMARY}\
\text { Regression }&\text { Statistics}\
\text { Multiple R } & 0.992 \
\text { R Square } & 0.984 \
\text { Adj. R Square } & 0.979 \
\text { Std. Error } & 2.26743 \
\text { Observations } & 20
\end{array}$

$\begin{array}{l}
\text { ANOVA }\
\begin{array}{llllll} 
& \text { df } & \text { SS } & \text { MS } & F & \text { Signif F } \
\text { Regression } & 4 & 4609.83164 & 1152.45791 & 224.160 & 0.0001 \
\text { Residual } & 15 & 77.11836 & 5.14122 & & \
\text { Total } & 19 & 4686.95000 & & &
\end{array}
\end{array}$

$\begin{array}{lllll} 
& \text { Coeff } & \text { Std Error } & t \text { Stat } & p \text { value } \
\text { Intercept } & -9.611198 & 2.77988638 & -3.457 & 0.0035 \
\text { Age } & 1.327695 & 0.11491930 & 11.553 & 0.0001 \
\text { Exper } & -0.106705 & 0.14265559 & -0.748 & 0.4660 \
\text { Degrees } & 7.311332 & 0.80324187 & 9.102 & 0.0001 \
\text { Prevjobs } & -0.504168 & 0.44771573 & -1.126 & 0.2778
\end{array}$

Note: Adj. R Square = Adjusted R Square; Std. Error = Standard Error
-Referring to Instruction 13.13,the p-value of the F test for the significance of the entire regression is ___________.

Accepted Answer

The answer of Instruction 13.13
A financial analyst wanted to examine...

Question 18

Instruction 13.16
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 metres, 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:
 OUTPUT
SUMMARY
Regression Statistics
$\begin{array} { l l } \text { Multiple R } & 0.865 \ \text { R Square } & 0.748 \ \text { Adj. R Square } & 0.726 \ \text { Std. Error } & 5.195 \ \text { Observations } & 50 \end{array}$

ANOVA
$\begin{array} { l l l l l l } & d f & \text { SS } & \text { MS } & F & \text { Signiff } \ \text { Regression } & & 3605.7736 & 901.4434 & & 0.0001 \ \text { Residual } & & 1214.2264 & 26.9828 & & \ \text { Total } & 49 & 4820.0000 & & & \ & & & & & \ & \text { Coeff } & \text { StdError } & t \text { Stat } & p \text { value } & \ \text { Intercept } & - 1.6335 & 5.8078 & - 0.281 & 0.7798 & \ \text { Income } & 0.4485 & 0.1137 & 3.9545 & 0.0003 & \ \text { Size } & 4.2615 & 0.8062 & 5.286 & 0.0001 & \ \text { School } & - 0.6517 & 0.4319 & - 1.509 & 0.1383 & \end{array}$ Note: Adj. R Square = Adjusted R Square; Std. Error = Standard Error
-Referring to Instruction 13.16,which of the following values for the level of significance is the smallest for which all explanatory variables are significant individually?

Accepted Answer

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

Question 19

In a multiple regression model,the value of the coefficient of multiple determination

Accepted Answer

A)  can fall between any pair of real numbers. 
B)  has to fall between -1 and +1. 
C)  has to fall between -1 and 0. 
D)  has to fall between 0 and +1.

Question 20

Instruction 13.22
The education department's regional executive officer wanted to predict the percentage of students passing a Grade 6 proficiency test. She obtained the data on percentage of students passing the proficiency test (% Passing), daily average of the percentage of students attending class (% Attendance), average 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, XS1U1B11S1U1B0 = % Attendance, XS1U1B12 S1U1B0= Salaries and XS1U1B13 S1U1B0= Spending:
 
-Referring to Instruction 13.22,which of the following is the correct alternative hypothesis 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

A)  H1: At least one of βj ≠ 0 for j = 1, 2, 3 
B)  H1: β1 = β2 = β3 ≠ 0 
C)  H1: At least one of βj ≠ 0 for j = 0, 1, 2, 3 
D)  H1: β0 = β1 = β2 = β3 ≠ 0

Exam 13: Introduction to Multiple Regression

From the coefficient of multiple determination,you cannot detect the strength of the relationship between Y and any individual independent variable.

The denominator degrees of freedom for the partial F statistic are

You need to proceed with extreme caution when using a multiple regression model that has one or more large VIF values.

In a multiple regression model,the value of the coefficient of multiple determination