Question 1

In a regression analysis, the regression equation is given by y = 12 - 6x. If SSE = 510 and SST = 1000, then the coefficient of correlation is

Accepted Answer

A)  -0.7 
B)  +0.7 
C)  0.49 
D)  -0.49 
A)  -0.7 
B)  +0.7 
C)  0.49 
D)  -0.49

Question 2

Exhibit 12-2
You are given the following information about y and x.
 $\begin{array}{cc}\text { Dependent Variable Y) }&\text { Independent Variable } \mathbf{X} \text { ) }\
5 & 1 \
4 & 2 \
3 & 3 \
2 & 4 \
1 & 5
\end{array}$
-Refer to Exhibit 12-2. The coefficient of determination equals

Accepted Answer

A)  0 
B)  -1 
C)  +1 
D)  -0.5 
A)  0 
B)  -1 
C)  +1 
D)  -0.5

Question 3

Exhibit 12-2
You are given the following information about y and x.
 $\begin{array}{cc}\text { Dependent Variable Y) }&\text { Independent Variable } \mathbf{X} \text { ) }\
5 & 1 \
4 & 2 \
3 & 3 \
2 & 4 \
1 & 5
\end{array}$
-Refer to Exhibit 12-2. The least squares estimate of b? slope) equals

Accepted Answer

A)  1 
B)  -1 
C)  6 
D)  5 
A)  1 
B)  -1 
C)  6 
D)  5

Question 4

If the coefficient of correlation is -0.4, then the slope of the regression line

Accepted Answer

A)  must also be -0.4 
B)  can be either negative or positive 
C)  must be negative 
D)  must be 0.16 
A)  must also be -0.4 
B)  can be either negative or positive 
C)  must be negative 
D)  must be 0.16

Question 5

SSE can never be

Accepted Answer

A)  larger than SST 
B)  smaller than SST 
C)  equal to 1 
D)  equal to zero 
A)  larger than SST 
B)  smaller than SST 
C)  equal to 1 
D)  equal to zero

Question 6

Exhibit 12-4
Regression analysis was applied between sales data Y in $1,000s) and advertising data x in $100s) and the following information was obtained.
= 12 + 1.8 x
 $\hat { Y }$ n = 17
SSR = 225
SSE = 75
Sb₁ = 0.2683
-Refer to Exhibit 12-4. The critical t value for testing the significance of the slope at 95% confidence is

Accepted Answer

A)  1.753 
B)  2.131 
C)  1.746 
D)  2.120 
A)  1.753 
B)  2.131 
C)  1.746 
D)  2.120

Question 7

Exhibit 12-10
The following information regarding a dependent variable Y and an independent variable X is provided.
 $$\begin{array} { l } 
\mathrm { n } = 4 \
\Sigma \mathrm { X } = 16 \
\Sigma \mathrm { Y } = 28 \
\Sigma \mathrm { Y } - { \bar { Y )} } \mathrm { X } - \bar { X } ) = - 8 \
\Sigma \mathrm { X } - \bar { X } ) ^ { 2 } = 8 \
\mathrm { SST } = 42 \
\mathrm { SSE } = 34
\end{array}$$
-Refer to Exhibit 12-10. The coefficient of correlation is

Accepted Answer

A)  0.1905 
B)  -0.1905 
C)  0.4364 
D)  -0.4364 
A)  0.1905 
B)  -0.1905 
C)  0.4364 
D)  -0.4364

Question 8

Exhibit 12-2
You are given the following information about y and x.
 $\begin{array}{cc}\text { Dependent Variable Y) }&\text { Independent Variable } \mathbf{X} \text { ) }\
5 & 1 \
4 & 2 \
3 & 3 \
2 & 4 \
1 & 5
\end{array}$
-Refer to Exhibit 12-2. The sample correlation coefficient equals

Accepted Answer

A)  0 
B)  +1 
C)  -1 
D)  -0.5 
A)  0 
B)  +1 
C)  -1 
D)  -0.5

Question 9

If the coefficient of correlation is 0.90, then the coefficient of determination

Accepted Answer

A)  is also 0.9 
B)  is either 0.81 or -0.81 
C)  can be either negative or positive 
D)  must be 0.81 
A)  is also 0.9 
B)  is either 0.81 or -0.81 
C)  can be either negative or positive 
D)  must be 0.81

Question 10

In regression and correlation analysis, if SSE and SST are known, then with this information the

Accepted Answer

A)  coefficient of determination can be computed 
B)  slope of the line can be computed 
C)  Y intercept can be computed 
D)  x intercept can be computed 
A)  coefficient of determination can be computed 
B)  slope of the line can be computed 
C)  Y intercept can be computed 
D)  x intercept can be computed

Question 11

Exhibit 12-6
For the following data the value of SSE = 0.4130.
 $$\begin{array} { c c } 
\text { Dependent Varinble } Y )& \text { Independent Varinble X)} \
15 & 4 \
17 & 6 \
23 & 2 \
17 & 4
\end{array}$$
-Refer to Exhibit 12-6. The y intercept is

Accepted Answer

A)  -1.5 
B)  24 
C)  0.50 
D)  -0.707 
A)  -1.5 
B)  24 
C)  0.50 
D)  -0.707

Question 12

If there is a very strong correlation between two variables then the coefficient of determination must be

Accepted Answer

A)  much larger than 1, if the correlation is positive 
B)  much smaller than -1, if the correlation is negative 
C)  any value larger than 1 
D)  None of these alternatives is correct. 
A)  much larger than 1, if the correlation is positive 
B)  much smaller than -1, if the correlation is negative 
C)  any value larger than 1 
D)  None of these alternatives is correct.

Question 13

Exhibit 12-10
The following information regarding a dependent variable Y and an independent variable X is provided.
 $$\begin{array} { l } 
\mathrm { n } = 4 \
\Sigma \mathrm { X } = 16 \
\Sigma \mathrm { Y } = 28 \
\Sigma \mathrm { Y } - { \bar { Y )} } \mathrm { X } - \bar { X } ) = - 8 \
\Sigma \mathrm { X } - \bar { X } ) ^ { 2 } = 8 \
\mathrm { SST } = 42 \
\mathrm { SSE } = 34
\end{array}$$
-Assume you have noted the following prices for paperback books and the number of pages that each book contains.
 $$\begin{array} { c c c } 
\text { Book } &\text { Pages} \mathbf{x} \text { ) } &\text { Price } \mathbf{y)} \
\text { A } & 500 &\$ 7.00 \
\mathrm {~B} & 700 & 7.50 \
\mathrm { C } & 750 & 9.00 \
\mathrm { D } & 590 & 6.50 \
\mathrm { E } & 540 & 7.50 \
\mathrm {~F} & 650 & 7.00 \
\mathrm { G } & 480 & 4.50
\end{array}$$ 
a. Develop a least-squares estimated regression line.
b. Compute the coefficient of determination and explain its meaning.
c. Compute the correlation coefficient between the price and the number of pages. Test to see if x and y are related. Use ? = 0.10.

Accepted Answer

a. = 1.0416 + 0.0099x
 @#LAT-DLM& 
b. r 2 = .5629;

Question 14

Exhibit 12-8
The following information regarding a dependent variable Y and an independent variable X is provided
 $$\begin{array} { l } 
\mathrm { n } = 4 \
\Sigma \mathrm { X } = 90 \
\Sigma \mathrm { Y } = 340 \
\Sigma \mathrm { Y } -  { \bar { Y) } \mathrm { X } } - \bar { X } ) = - 156 \
\Sigma \mathrm { X } - \bar { X } ) ^ { 2 } = 234 \
\Sigma \mathrm { Y } - \bar { Y } ) ^ { 2 } = 1974 \
\mathrm { SSR } = 104
\end{array}$$
-Refer to Exhibit 12-8. The slope of the regression equation is

Accepted Answer

A)  -0.667 
B)  0.667 
C)  100 
D)  -100 
A)  -0.667 
B)  0.667 
C)  100 
D)  -100

Question 15

In a regression and correlation analysis if R² = 1, then

Accepted Answer

A)  SSE must also be equal to one 
B)  SSE must be equal to zero 
C)  SSE can be any positive value 
D)  SSE must be negative 
A)  SSE must also be equal to one 
B)  SSE must be equal to zero 
C)  SSE can be any positive value 
D)  SSE must be negative

Question 16

If all the points of a scatter diagram lie on the least squares regression line, then the coefficient of determination for these variables based on these data is

Accepted Answer

A)  0 
B)  1 
C)  either 1 or -1, depending upon whether the relationship is positive or negative 
D)  could be any value between -1 and 1 
A)  0 
B)  1 
C)  either 1 or -1, depending upon whether the relationship is positive or negative 
D)  could be any value between -1 and 1

Question 17

Exhibit 12-3
You are given the following information about y and x.
 $\begin{array}{cc}
\text { Dependent Variable Y) }&\text { Independent Variable } \mathbf{X} \text { ) }\
12 & 4 \
3 & 6 \
7 & 2 \
6 & 4
\end{array}$
-Refer to Exhibit 12-3. The least squares estimate of b? equals

Accepted Answer

A)  1 
B)  -1 
C)  -11 
D)  11 
A)  1 
B)  -1 
C)  -11 
D)  11

Question 18

The standard error is the

Accepted Answer

A)  t-statistic squared 
B)  square root of SSE 
C)  square root of SST 
D)  square root of MSE 
A)  t-statistic squared 
B)  square root of SSE 
C)  square root of SST 
D)  square root of MSE

Question 19

Exhibit 12-8
The following information regarding a dependent variable Y and an independent variable X is provided
 $$\begin{array} { l } 
\mathrm { n } = 4 \
\Sigma \mathrm { X } = 90 \
\Sigma \mathrm { Y } = 340 \
\Sigma \mathrm { Y } -  { \bar { Y) } \mathrm { X } } - \bar { X } ) = - 156 \
\Sigma \mathrm { X } - \bar { X } ) ^ { 2 } = 234 \
\Sigma \mathrm { Y } - \bar { Y } ) ^ { 2 } = 1974 \
\mathrm { SSR } = 104
\end{array}$$
-Refer to Exhibit 12-8. The Y intercept is

Accepted Answer

A)  -0.667 
B)  0.667 
C)  100 
D)  -100 
A)  -0.667 
B)  0.667 
C)  100 
D)  -100

Question 20

Larger values of R² imply that the observations are more closely grouped about the

Accepted Answer

A)  average value of the independent variables 
B)  average value of the dependent variable 
C)  least squares line 
D)  origin 
A)  average value of the independent variables 
B)  average value of the dependent variable 
C)  least squares line 
D)  origin

In a regression analysis, the regression equation is given by y = 12 - 6x. If SSE = 510 and SST = 1000, then the coefficient of correlation is

Exhibit 12-2 You are given the following information about y and x. Dependent Variable Y) Independent Variable ) 5 1 4 2 3 3 2 4 1 5 -Refer to Exhibit 12-2. The coefficient of determination equals

Exhibit 12-2 You are given the following information about y and x. Dependent Variable Y) Independent Variable ) 5 1 4 2 3 3 2 4 1 5 -Refer to Exhibit 12-2. The least squares estimate of b? slope) equals

If the coefficient of correlation is -0.4, then the slope of the regression line

SSE can never be

Exhibit 12-10 The following information regarding a dependent variable Y and an independent variable X is provided. =4 \Sigma=16 \Sigma=28 \Sigma- -)=-8 \Sigma-=8 =42 =34 -Refer to Exhibit 12-10. The coefficient of correlation is

Exhibit 12-2 You are given the following information about y and x. Dependent Variable Y) Independent Variable ) 5 1 4 2 3 3 2 4 1 5 -Refer to Exhibit 12-2. The sample correlation coefficient equals

If the coefficient of correlation is 0.90, then the coefficient of determination

In regression and correlation analysis, if SSE and SST are known, then with this information the

Exhibit 12-6 For the following data the value of SSE = 0.4130. Dependent Varinble Y) Independent Varinble X) 15 4 17 6 23 2 17 4 -Refer to Exhibit 12-6. The y intercept is

If there is a very strong correlation between two variables then the coefficient of determination must be

Exhibit 12-8 The following information regarding a dependent variable Y and an independent variable X is provided =4 \Sigma=90 \Sigma=340 \Sigma- -)=-156 \Sigma-=234 \Sigma-=1974 =104 -Refer to Exhibit 12-8. The slope of the regression equation is

In a regression and correlation analysis if R² = 1, then

If all the points of a scatter diagram lie on the least squares regression line, then the coefficient of determination for these variables based on these data is

Exhibit 12-3 You are given the following information about y and x. Dependent Variable Y) Independent Variable ) 12 4 3 6 7 2 6 4 -Refer to Exhibit 12-3. The least squares estimate of b? equals

The standard error is the

Exhibit 12-8 The following information regarding a dependent variable Y and an independent variable X is provided =4 \Sigma=90 \Sigma=340 \Sigma- -)=-156 \Sigma-=234 \Sigma-=1974 =104 -Refer to Exhibit 12-8. The Y intercept is

Larger values of R² imply that the observations are more closely grouped about the

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Exam 12: Simple Linear Regression

In a regression analysis, the regression equation is given by y = 12 - 6x. If SSE = 510 and SST = 1000, then the coefficient of correlation is

Exhibit 12-2 You are given the following information about y and x. Dependent Variable Y) Independent Variable ) 5 1 4 2 3 3 2 4 1 5 -Refer to Exhibit 12-2. The coefficient of determination equals

Exhibit 12-2 You are given the following information about y and x. Dependent Variable Y) Independent Variable ) 5 1 4 2 3 3 2 4 1 5 -Refer to Exhibit 12-2. The least squares estimate of b? slope) equals

If the coefficient of correlation is -0.4, then the slope of the regression line

SSE can never be

Exhibit 12-10 The following information regarding a dependent variable Y and an independent variable X is provided. =4 \Sigma=16 \Sigma=28 \Sigma- -)=-8 \Sigma-=8 =42 =34 -Refer to Exhibit 12-10. The coefficient of correlation is

Exhibit 12-2 You are given the following information about y and x. Dependent Variable Y) Independent Variable ) 5 1 4 2 3 3 2 4 1 5 -Refer to Exhibit 12-2. The sample correlation coefficient equals

If the coefficient of correlation is 0.90, then the coefficient of determination

In regression and correlation analysis, if SSE and SST are known, then with this information the

Exhibit 12-6 For the following data the value of SSE = 0.4130. Dependent Varinble Y) Independent Varinble X) 15 4 17 6 23 2 17 4 -Refer to Exhibit 12-6. The y intercept is

If there is a very strong correlation between two variables then the coefficient of determination must be

Exhibit 12-8 The following information regarding a dependent variable Y and an independent variable X is provided =4 \Sigma=90 \Sigma=340 \Sigma- -)=-156 \Sigma-=234 \Sigma-=1974 =104 -Refer to Exhibit 12-8. The slope of the regression equation is

In a regression and correlation analysis if R² = 1, then

If all the points of a scatter diagram lie on the least squares regression line, then the coefficient of determination for these variables based on these data is

Exhibit 12-3 You are given the following information about y and x. Dependent Variable Y) Independent Variable ) 12 4 3 6 7 2 6 4 -Refer to Exhibit 12-3. The least squares estimate of b? equals

The standard error is the

Exhibit 12-8 The following information regarding a dependent variable Y and an independent variable X is provided =4 \Sigma=90 \Sigma=340 \Sigma- -)=-156 \Sigma-=234 \Sigma-=1974 =104 -Refer to Exhibit 12-8. The Y intercept is

Larger values of R² imply that the observations are more closely grouped about the

Data and Statistics

Descriptive Statistics: Tabular and Graphical Displays

Descriptive Statistics: Numerical Measures

Introduction to Probability

Discrete Probability Distributions

Continuous Probability Distributions

Sampling and Sampling Distributions

Interval Estimation

Hypothesis Tests

Comparisons Involving Means, Experimental Design, and Analysis of Variance

Comparisons Involving Proportions and a Test of Independence

Multiple Regression

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