Question 1

The Y-intercept (b0) represents the

Accepted Answer

A) predicted value of Y when X = 0. 
B) change in estimated Y per unit change in X. 
C) predicted value of Y. 
D) variation around the sample regression line. 
A) predicted value of Y when X = 0. 
B) change in estimated Y per unit change in X. 
C) predicted value of Y. 
D) variation around the sample regression line.

Question 2

The Regression Sum of Squares (SSR) can never be greater than the Total Sum ofSquares (SST).

Accepted Answer

A) True 
 B)False

Question 3

The Chancellor of a university has commissioned a team to collect data on students' GPAs and the amount of time they spend bar hopping every week (measured in minutes).He wants to know if imposing much tougher regulations on all campus bars to make it more difficult for students to spend time in any campus bar will have a significant impact on general students' GPAs.His team should use a t test on the slope of the population regression.

Accepted Answer

A) True 
 B)False

Question 4

SCENARIO 12-12
The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan
application.Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded.Below is the regression output: \[\begin{array}{l}
\begin{array} { l r } 
\hline  { \text { Regression Statistics } } \
\hline \text { Multiple R } & 0.9447 \
\text { R Square } & 0.8924 \
\text { Adjusted R } & 0.8886 \
\text { Square } & \
\text { Standard } & 0.3342 \
\text { Error } & \
\text { Observations } & 30 \
\hline
\end{array}\
\text { ANOVA }\
\begin{array} { l r r r r r } 
\hline & { \text { df } } & { \text { SS } } & { \text { MS } } & \text { F } & \text { Significance } F \
\hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm { E } - 15 \
\text { Residual } & 28 & 3.1282 & 0.1117 & & \
\text { Total } & 29 & 29.072 & & & \
\hline
\end{array}\
\begin{array} { l r r r r r r } 
\hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \
\text { Applications } & 0.0126 & 0.0008 & 15.2388 & 0.0000 & 0.0109 & 0.0143 \
\text { Recorded } & & & & & & \
\hline
\end{array}
\end{array}\] 12-46 Simple Linear Regression   Simple Linear Regression 12-47
-Referring to Scenario 12-12, the estimated mean amount of time it takes to record one additional loan application is

Accepted Answer

A) 0.4024 fewer hours 
B) 0.4024 more hours 
C) 0.0126 fewer hours 
D) 0.0126 more hours 
A) 0.4024 fewer hours 
B) 0.4024 more hours 
C) 0.0126 fewer hours 
D) 0.0126 more hours

Question 5

SCENARIO 12-12
The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan
application.Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded.Below is the regression output: \[\begin{array}{l}
\begin{array} { l r } 
\hline  { \text { Regression Statistics } } \
\hline \text { Multiple R } & 0.9447 \
\text { R Square } & 0.8924 \
\text { Adjusted R } & 0.8886 \
\text { Square } & \
\text { Standard } & 0.3342 \
\text { Error } & \
\text { Observations } & 30 \
\hline
\end{array}\
\text { ANOVA }\
\begin{array} { l r r r r r } 
\hline & { \text { df } } & { \text { SS } } & { \text { MS } } & \text { F } & \text { Significance } F \
\hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm { E } - 15 \
\text { Residual } & 28 & 3.1282 & 0.1117 & & \
\text { Total } & 29 & 29.072 & & & \
\hline
\end{array}\
\begin{array} { l r r r r r r } 
\hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \
\text { Applications } & 0.0126 & 0.0008 & 15.2388 & 0.0000 & 0.0109 & 0.0143 \
\text { Recorded } & & & & & & \
\hline
\end{array}
\end{array}\] 12-46 Simple Linear Regression   Simple Linear Regression 12-47
-Referring to Scenario 12-12, you can be 95% confident that the mean amount of time needed to record one additional loan application is somewhere between 0.0109 and 0.0143 hours.

Accepted Answer

A) True 
 B)False

Question 6

SCENARIO 12-6
The following Excel tables are obtained when "Score received on an exam (measured in percentage points)" (Y) is regressed on "percentage attendance" (X) for 22 students in a Statistics for Business and Economics course. $$\begin{array}{l}
\begin{array} { l r } 
\hline { \text { Regression Statistics } } \
\hline \text { Multiple R } & 0.142620229 \
\text { R Square } & 0.02034053 \
\text { Standard Error } & 20.25979924 \
\text { Observations } & 22 \
\hline
\end{array}\
\begin{array} { l r r r l } 
\hline & \text { Coefficients } & \text { Standard Error } & \text { T Stat } & \text { P-value } \
\hline \text { Intercept } & 39.39027309 & 37.24347659 & 1.057642216 & 0.302826622 \
\text { Attendance } & 0.340583573 & 0.52852452 & 0.644404489 & 0.526635689 \
\hline
\end{array}
\end{array}$$
-Referring to Scenario 12-6, which of the following statements is true?

Accepted Answer

A) If attendance increases by 0.341%, the estimated mean score received will increase by 1 percentage point. 
B) If attendance increases by 1%, the estimated mean score received will increase by 39.39 percentage points. 
C) If attendance increases by 1%, the estimated mean score received will increase by 0.341 percentage points. 
D) If the score received increases by 39.39%, the estimated mean attendance will go up by 1%. 
A) If attendance increases by 0.341%, the estimated mean score received will increase by 1 percentage point. 
B) If attendance increases by 1%, the estimated mean score received will increase by 39.39 percentage points. 
C) If attendance increases by 1%, the estimated mean score received will increase by 0.341 percentage points. 
D) If the score received increases by 39.39%, the estimated mean attendance will go up by 1%.

Question 7

SCENARIO 12-4
The managers of a brokerage firm are interested in finding out if the number of new clients a broker brings into the firm affects the sales generated by the broker.They sample 12 brokers and determine the number of new clients they have enrolled in the last year and their sales amounts in thousands of dollars.These data are presented in the table that follows. $\begin{array}{lll}
\text { Broker } & \text {Clients} & \text {Sales }\
1 & 27 & 52 \
2 & 11 & 37 \
3 & 42 & 64 \
4 & 33 & 55 \
5 & 15 & 29 \
6 & 15 & 34 \
7 & 25 & 58 \
8 & 36 & 59 \
9 & 28 & 44 \
10 & 30 & 48 \
11 & 17 & 31 \
12 & 22 & 38
\end{array}$
-Referring to Scenario 12-4, suppose the managers of the brokerage firm want to construct a 99%confidence interval estimate for the mean sales made by brokers who have brought into the firm24 new clients.The t critical value they would use is _.

Accepted Answer

The answer of SCENARIO 12-4
The managers of a brokerage firm...

Question 8

SCENARIO 12-11
A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware.Following is the output from a simple linear regression
along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed:
   $\begin{array}{lr}
{\text { Regression Statistics }} \
\hline \text { Multiple R } & 0.8691 \
\hline \text { R Square } & 0.7554 \
\hline \text { Adjusted R Square } & 0.7467 \
\hline \text { Standard Error } & 44.4765 \
\hline \text { Observations } & 30.0000 \
\hline
\end{array}$

$\text { ANOVA }$ 
$\begin{array}{|l|r|r|r|r|r|}
\hline &\text { df } & \text { SS } & \text { MS } & F & \text { Significance } F \ 
\hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \
\hline \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \
\hline \text { Total } & 29 & 226451.3503 & & \
\hline
\end{array}$

Simple Linear Regression 12-41  
-Referring to Scenario 12-11, the Durbin-Watson statistic is inappropriate for this data set.

Accepted Answer

A) True 
 B)False

Question 9

SCENARIO 12-5
The managing partner of an advertising agency believes that his company's sales are related to the industry sales.He uses Microsoft Excel to analyze the last 4 years of quarterly data with the following results: $\begin{array}{ll}
{\text { Regression Statistics }} \
\text { Multiple R } & 0.802 \
\text { R Square } & 0.643 \
\text { Adjusted R Square } & 0.618 \
\text { Standard Error Syv } & 0.9234\
\text { Observations } & 16
\end{array}$
ANOVA
$\begin{array}{lrrrcc} 
& \text { df } & \text { SS } & \text { MS } & \text { F } & \text { Sig.F } \
\text { Regression } & 1 & 21.497 & 21.497 & 25.27 & 0.000 \
\text { Error } & 14 & 11.912 & 0.851 & & \
\text { Total } & 15 & 33.409 & & &
\end{array}$

$\begin{array}{llll}\text { Predictor Coef } & \text { StdError } t \text { Stat } & \text { P-value }\
\hline\text { Intercept }& 3.962 & 1.440 & 2.75 & 0.016 \
\text { Industry } &0.040451 & 0.008048 & 5.03 & 0.000
\end{array}$

$\text { Durbin-Watson Statistic } \quad 1.59$
-Referring to Scenario 12-5, the estimates of the Y-intercept and slope are and_, respectively.

Accepted Answer

3.962 and

Question 10

SCENARIO 12-12
The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan
application.Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded.Below is the regression output: \[\begin{array}{l}
\begin{array} { l r } 
\hline  { \text { Regression Statistics } } \
\hline \text { Multiple R } & 0.9447 \
\text { R Square } & 0.8924 \
\text { Adjusted R } & 0.8886 \
\text { Square } & \
\text { Standard } & 0.3342 \
\text { Error } & \
\text { Observations } & 30 \
\hline
\end{array}\
\text { ANOVA }\
\begin{array} { l r r r r r } 
\hline & { \text { df } } & { \text { SS } } & { \text { MS } } & \text { F } & \text { Significance } F \
\hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm { E } - 15 \
\text { Residual } & 28 & 3.1282 & 0.1117 & & \
\text { Total } & 29 & 29.072 & & & \
\hline
\end{array}\
\begin{array} { l r r r r r r } 
\hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \
\text { Applications } & 0.0126 & 0.0008 & 15.2388 & 0.0000 & 0.0109 & 0.0143 \
\text { Recorded } & & & & & & \
\hline
\end{array}
\end{array}\] 12-46 Simple Linear Regression   Simple Linear Regression 12-47
-Referring to Scenario 12-12, to test the claim that the mean amount of time depends positively on the number of loan applications recorded against the null hypothesis that the mean amount of time does not depend linearly on the number of invoices processed, the p-value of the test statistic is .

Accepted Answer

The answer of SCENARIO 12-12
The manager of the purchasing department...

Question 11

SCENARIO 12-1
A large national bank charges local companies for using their services.A bank official reported the results of a regression analysis designed to predict the bank's charges (Y) -- measured in dollars per month -- for services rendered to local companies.One independent variable used to predict service charges to a company is the company's sales revenue (X) -- measured in millions of dollars.Data for
21 companies who use the bank's services were used to fit the model: $$Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { i } + \varepsilon _ { i }$$
Theresultsofthesimplelinearregressionareprovidedbelow.
$$\bar { I } = - 2,700 + 20 X , S _ { Z X } = 65 \text {, two-tail } p \text { value } = 0.034 \text { (for testing } \beta _ { 1 } \text { ) }$$
-Referring to Scenario 12-1, interpret the p-value for testing whether $\beta$1 exceeds 0.

Accepted Answer

A) There is sufficient evidence (at the $\alpha$= 0.05)to conclude that sales revenue (X)is a useful linear predictor of service charge (Y). 
B) There is insufficient evidence (at the $\alpha$ = 0.10)to conclude that sales revenue (X)is a useful linear predictor of service charge (Y). 
C) Sales revenue (X)is a poor predictor of service charge (Y). 
D) For every $1 million increase in sales revenue, you expect a service charge to increase $0.034. 
A) There is sufficient evidence (at the $\alpha$= 0.05)to conclude that sales revenue (X)is a useful linear predictor of service charge (Y). 
B) There is insufficient evidence (at the $\alpha$ = 0.10)to conclude that sales revenue (X)is a useful linear predictor of service charge (Y). 
C) Sales revenue (X)is a poor predictor of service charge (Y). 
D) For every $1 million increase in sales revenue, you expect a service charge to increase $0.034.

Question 12

SCENARIO 12-12
The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan
application.Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded.Below is the regression output: \[\begin{array}{l}
\begin{array} { l r } 
\hline  { \text { Regression Statistics } } \
\hline \text { Multiple R } & 0.9447 \
\text { R Square } & 0.8924 \
\text { Adjusted R } & 0.8886 \
\text { Square } & \
\text { Standard } & 0.3342 \
\text { Error } & \
\text { Observations } & 30 \
\hline
\end{array}\
\text { ANOVA }\
\begin{array} { l r r r r r } 
\hline & { \text { df } } & { \text { SS } } & { \text { MS } } & \text { F } & \text { Significance } F \
\hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm { E } - 15 \
\text { Residual } & 28 & 3.1282 & 0.1117 & & \
\text { Total } & 29 & 29.072 & & & \
\hline
\end{array}\
\begin{array} { l r r r r r r } 
\hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \
\text { Applications } & 0.0126 & 0.0008 & 15.2388 & 0.0000 & 0.0109 & 0.0143 \
\text { Recorded } & & & & & & \
\hline
\end{array}
\end{array}\] 12-46 Simple Linear Regression   Simple Linear Regression 12-47
-Referring to Scenario 12-12, the p-value of the measured t-test statistic to test whether the number of loan applications recorded affects the amount of time is _.

Accepted Answer

The answer of SCENARIO 12-12
The manager of the purchasing department...

Question 13

SCENARIO 12-10
The management of a chain electronic store would like to develop a model for predicting the weekly sales (in thousands of dollars) for individual stores based on the number of customers who made purchases.A random sample of 12 stores yields the following results: $$\begin{array} { | l | c | } 
\hline \text { Customers } & \begin{array} { l } 
\text { Sales } \
\text { (Thousands of } \
\text { Dollars) }
\end{array} \
\hline 907 & 11.20 \
\hline 926 & 11.05 \
\hline 713 & 8.21 \
\hline 741 & 9.21 \
\hline 780 & 9.42 \
\hline 898 & 10.08 \
\hline 510 & 6.73 \
\hline 529 & 7.02 \
\hline 460 & 6.12 \
\hline 872 & 9.52 \
\hline 650 & 7.53 \
\hline 603 & 7.25 \
\hline
\end{array}$$
-Referring to Scenario 12-10, generate the scatter plot.

Accepted Answer

The answer of SCENARIO 12-10
The management of a chain electronic...

Question 14

SCENARIO 12-10
The management of a chain electronic store would like to develop a model for predicting the weekly sales (in thousands of dollars) for individual stores based on the number of customers who made purchases.A random sample of 12 stores yields the following results: $$\begin{array} { | l | c | } 
\hline \text { Customers } & \begin{array} { l } 
\text { Sales } \
\text { (Thousands of } \
\text { Dollars) }
\end{array} \
\hline 907 & 11.20 \
\hline 926 & 11.05 \
\hline 713 & 8.21 \
\hline 741 & 9.21 \
\hline 780 & 9.42 \
\hline 898 & 10.08 \
\hline 510 & 6.73 \
\hline 529 & 7.02 \
\hline 460 & 6.12 \
\hline 872 & 9.52 \
\hline 650 & 7.53 \
\hline 603 & 7.25 \
\hline
\end{array}$$
-Referring to Scenario 12-10, what are the degrees of freedom of the t test statistic when testing whether the number of customers who make a purchase affects weekly sales?

Accepted Answer

The answer of SCENARIO 12-10
The management of a chain electronic...

Question 15

SCENARIO 12-8
It is believed that GPA (grade point average, based on a four point scale) should have a positive linear relationship with ACT scores.Given below is the Excel output for predicting GPA using ACT scores based a data set of 8 randomly chosen students from a Big-Ten university.
Regressing GPA on ACT $$\begin{array}{l}
\begin{array} { l r } 
\hline  { \text { Regression Statistics } } \
\hline \text { Multiple R } & 0.7598 \
\text { R Square } & 0.5774 \
\text { Adjusted R Square } & 0.5069 \
\text { Standard Error } & 0.2691 \
\text { Qbservations } & 8 \
\hline
\end{array}\\end{array}$$
ANOVA 
$\begin{array}{lrrrcc} 
\hline & \text { df } & \text { SS } & \text { MS } & \text { F } & \text { Significance.F } \
\hline\text { Regression } & 1 & 0.5940&0.5940&8.1986&0.0286 \
\text { Error } & 6 & 0.4347&0.0724\
\text { Total } & 7 &1.0287\\hline
\end{array}$
$$\begin{array}{l}\begin{array} { l c r r r r r } 
\hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & P \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & 0.5681 & 0.9284 & 0.6119 & 0.5630 & - 1.7036 & 2.8398 \
\text { ACT } & 0.1021 & 0.0356 & 2.8633 & 0.0286 & 0.0148 & 0.1895 \
\hline
\end{array}
\end{array}$$
-Referring to Scenario 12-8, what are the decision and conclusion on testing whether there is any linear relationship at 1% level of significance between GPA and ACT scores?

Accepted Answer

A) Do not reject the null hypothesis; hence there is insufficient evidence to show that ACT scores and GPA are linearly related. 
B) Reject the null hypothesis; hence there is insufficient evidence to show that ACT scores and GPA are linearly related. 
C) Do not reject the null hypothesis; hence there is sufficient evidence to show that ACT scores and GPA are linearly related. 
D) Reject the null hypothesis; hence there is sufficient evidence to show that ACT scores and GPA are linearly related. 
A) Do not reject the null hypothesis; hence there is insufficient evidence to show that ACT scores and GPA are linearly related. 
B) Reject the null hypothesis; hence there is insufficient evidence to show that ACT scores and GPA are linearly related. 
C) Do not reject the null hypothesis; hence there is sufficient evidence to show that ACT scores and GPA are linearly related. 
D) Reject the null hypothesis; hence there is sufficient evidence to show that ACT scores and GPA are linearly related.

Question 16

SCENARIO 12-3
The director of cooperative education at a state college wants to examine the effect of cooperative education job experience on marketability in the work place.She takes a random sample of 4 students.For these 4, she finds out how many times each had a cooperative education job and how many job offers they received upon graduation.These data are presented in the table below. $\begin{array} {ccc}\text { Student } & \text { Coop Jobs  } & \text {Job Offer }\ 1 & 1 & 4 \ 2 & 2 & 6 \ 3 & 1 & 3 \ 4 & 0 & 1 \end{array}$
-Referring to Scenario 12-3, the director of cooperative education wanted to test the hypothesis that the population slope was equal to 3.0.For a test with a level of significance of 0.05, the null hypothesis should be rejected if the value of the test statistic is .

Accepted Answer

> 4.3027 o

Question 17

SCENARIO 12-12
The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan
application.Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded.Below is the regression output: \[\begin{array}{l}
\begin{array} { l r } 
\hline  { \text { Regression Statistics } } \
\hline \text { Multiple R } & 0.9447 \
\text { R Square } & 0.8924 \
\text { Adjusted R } & 0.8886 \
\text { Square } & \
\text { Standard } & 0.3342 \
\text { Error } & \
\text { Observations } & 30 \
\hline
\end{array}\
\text { ANOVA }\
\begin{array} { l r r r r r } 
\hline & { \text { df } } & { \text { SS } } & { \text { MS } } & \text { F } & \text { Significance } F \
\hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm { E } - 15 \
\text { Residual } & 28 & 3.1282 & 0.1117 & & \
\text { Total } & 29 & 29.072 & & & \
\hline
\end{array}\
\begin{array} { l r r r r r r } 
\hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \
\text { Applications } & 0.0126 & 0.0008 & 15.2388 & 0.0000 & 0.0109 & 0.0143 \
\text { Recorded } & & & & & & \
\hline
\end{array}
\end{array}\] 12-46 Simple Linear Regression   Simple Linear Regression 12-47
-Referring to Scenario 12-12, the value of the measured t-test statistic to test whether the amount of time depends linearly on the number of loan applications recorded is

Accepted Answer

A) 0.8924 
B) 3.2559 
C) 15.2388 
D) 232.2200 
A) 0.8924 
B) 3.2559 
C) 15.2388 
D) 232.2200

Question 18

Regression analysis is used for prediction, while correlation analysis is used to measure the strength of the association between two numerical variables.

Accepted Answer

A) True 
 B)False

Question 19

SCENARIO 12-10
The management of a chain electronic store would like to develop a model for predicting the weekly sales (in thousands of dollars) for individual stores based on the number of customers who made purchases.A random sample of 12 stores yields the following results: $$\begin{array} { | l | c | } 
\hline \text { Customers } & \begin{array} { l } 
\text { Sales } \
\text { (Thousands of } \
\text { Dollars) }
\end{array} \
\hline 907 & 11.20 \
\hline 926 & 11.05 \
\hline 713 & 8.21 \
\hline 741 & 9.21 \
\hline 780 & 9.42 \
\hline 898 & 10.08 \
\hline 510 & 6.73 \
\hline 529 & 7.02 \
\hline 460 & 6.12 \
\hline 872 & 9.52 \
\hline 650 & 7.53 \
\hline 603 & 7.25 \
\hline
\end{array}$$
-Referring to Scenario 12-10, what is the value of the F test statistic when testing whether the number of customers who make purchases is a good predictor for weekly sales?

Accepted Answer

The answer of SCENARIO 12-10
The management of a chain electronic...

Question 20

SCENARIO 12-1
A large national bank charges local companies for using their services.A bank official reported the results of a regression analysis designed to predict the bank's charges (Y) -- measured in dollars per month -- for services rendered to local companies.One independent variable used to predict service charges to a company is the company's sales revenue (X) -- measured in millions of dollars.Data for
21 companies who use the bank's services were used to fit the model: $$Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { i } + \varepsilon _ { i }$$
Theresultsofthesimplelinearregressionareprovidedbelow.
$$\bar { I } = - 2,700 + 20 X , S _ { Z X } = 65 \text {, two-tail } p \text { value } = 0.034 \text { (for testing } \beta _ { 1 } \text { ) }$$
-Referring to Scenario 12-1, interpret the estimate of $\beta$0 , the Y-intercept of the line.

Accepted Answer

A) All companies will be charged at least $2,700 by the bank. 
B) There is no practical interpretation since a sales revenue of $0 is a nonsensical value. 
C) About 95% of the observed service charges fall within $2,700 of the least squares line. 
D) For every $1 million increase in sales revenue, we expect a service charge to decrease $2,700. 
A) All companies will be charged at least $2,700 by the bank. 
B) There is no practical interpretation since a sales revenue of $0 is a nonsensical value. 
C) About 95% of the observed service charges fall within $2,700 of the least squares line. 
D) For every $1 million increase in sales revenue, we expect a service charge to decrease $2,700.

The Y-intercept (b0) represents the

The Regression Sum of Squares (SSR) can never be greater than the Total Sum ofSquares (SST).

Regression analysis is used for prediction, while correlation analysis is used to measure the strength of the association between two numerical variables.

Defining and Collecting Data

Organizing and Visualizing Variables

Numerical Descriptive Measures

Basic Probability

Discrete Probability Distributions

The Normal Distribution

Sampling Distributions

Confidence Interval Estimation

Fundamentals of Hypothesis Testing: One-Sample Tests

Two-Sample Tests

Chi-Square Tests

Multiple Regression

Business Analytics

Filters

Exam 12: Simple Linear Regression

The Y-intercept (b0) represents the

The Regression Sum of Squares (SSR) can never be greater than the Total Sum ofSquares (SST).

Regression analysis is used for prediction, while correlation analysis is used to measure the strength of the association between two numerical variables.

Defining and Collecting Data

Organizing and Visualizing Variables

Numerical Descriptive Measures

Basic Probability

Discrete Probability Distributions

The Normal Distribution

Sampling Distributions

Confidence Interval Estimation

Fundamentals of Hypothesis Testing: One-Sample Tests

Two-Sample Tests

Chi-Square Tests

Multiple Regression

Business Analytics

Filters