A simple regression has a b0 value of 5 and a b1 value of 3.5.What is the predicted Y for an X value of -2?

A) 13.5 B) 12.0 C) -6.0 D) -2.0 A) 13.5 B) 12.0 C) -6.0 D) -2.0

Instruction 12.29 The managers of a brokerage firm are interested in finding out if the number of new customers a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new customers they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. \[\begin{array} { | l | l | l | } \hline \text { Broker } & \text { Clients } & \text { Sales } \\ \hline 1 & 27 & 52 \\ \hline 2 & 11 & 37 \\ \hline 3 & 42 & 64 \\ \hline 4 & 33 & 55 \\ \hline 5 & 15 & 29 \\ \hline 6 & 15 & 34 \\ \hline 7 & 25 & 58 \\ \hline 8 & 36 & 59 \\ \hline 9 & 28 & 44 \\ \hline 10 & 30 & 48 \\ \hline 11 & 17 & 31 \\ \hline 12 & 22 & 38 \\ \hline \end{array}\] -Referring to Instruction 12.29,the managers of the brokerage firm wanted to test the hypothesis that the number of new customers brought in had a positive impact on the amount of sales generated.The value of the test statistic is ____________

The answer of Instruction 12.29 The managers of a brokerage firm...

Instruction 12.32 It is believed that average grade (based on a four-point scale) should have a positive linear relationship with university entrance exam scores. Given below is the MicrosoftS1U1P1 S1S1P0 Excel output from regressing average grade on university entrance exam scores using a data set of eight randomly chosen students from a large university. $\text { Regressing average grade on university entrance exam score }$ $\text { Regression statistics }$ $\begin{array}{|l|l|} \hline\text { MultipleR } & 0.7598 \\ \hline \text { R Square } & 0.5774 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.5069 \\ \hline \text { Standard Error } & 0.2691 \\ \hline \text { Observations } & 8\\ \hline \end{array}$ $\text { ANOVA }$ $\begin{array}{|l|l|l|l|l|l|} \hline & d f & 55 & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 0.5940 & 0.5940 & 8.1986 & 0.0286 \\ \hline \text { Residual } & 6 & 0.4347 & 0.0724 & & \\ \hline \text { Total } & 7 & 1.0287 & & & \\ \hline \end{array}$ $\begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { tStat } & \text { p-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.5681 & 0.9284 & 0.6119 & 0.5630 & -1.7036 & 2.8398 \\ \hline \begin{array}{l} \text { University } \\ \text { entrance exam } \\ \text { score } \end{array} & 0.1021 & 0.0356 & 2.8633 & 0.0286 & 0.0148 & 0.1895 \\ \hline \end{array}$ -Referring to Instruction 12.32,the value of the measured (observetest statistic of the F test for H0:$\beta$1 = 0 versus H1: $\beta$1 $\neq$ 0

A) is always negative. B) is always positive. C) may be negative. D) has the same sign as the corresponding t test statistic. A) is always negative. B) is always positive. C) may be negative. D) has the same sign as the corresponding t test statistic.

Instruction 12.28 The managers of a brokerage firm are interested in finding out if the number of new customers a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new customers they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. \[\begin{array} { | l | l | l | } \hline \text { Broker } & \text { Clients } & \text { 5les } \\ \hline 1 & 27 & 52 \\ \hline 2 & 11 & 37 \\ \hline 3 & 42 & 64 \\ \hline 4 & 33 & 55 \\ \hline 5 & 15 & 29 \\ \hline 6 & 15 & 34 \\ \hline 7 & 25 & 58 \\ \hline 8 & 36 & 59 \\ \hline 9 & 28 & 44 \\ \hline 10 & 30 & 48 \\ \hline 11 & 17 & 31 \\ \hline 12 & 22 & 38 \\ \hline \end{array}\] -Referring to Instruction 12.28,the managers of the brokerage firm wanted to test the hypothesis that the true slope was equal to 0.The denominator of the test statistic is $S _ { b _ { 1 } }$ .The value of $S _ { b _ { 1 } }$ in this sample is ____________.

The answer of Instruction 12.28 The managers of a brokerage firm...

Instruction 12.39 The managers of a brokerage firm are interested in finding out if the number of new customers a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new customers they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. \[\begin{array} { | l | l | l | } \hline \text { Broker } & \text { Clients } & \text { Sles } \\ \hline 1 & 27 & 52 \\ \hline 2 & 11 & 37 \\ \hline 3 & 42 & 64 \\ \hline 4 & 33 & 55 \\ \hline 5 & 15 & 29 \\ \hline 6 & 15 & 34 \\ \hline 7 & 25 & 58 \\ \hline 8 & 36 & 59 \\ \hline 9 & 28 & 44 \\ \hline 10 & 30 & 48 \\ \hline 11 & 17 & 31 \\ \hline 12 & 22 & 38 \\ \hline \end{array}\] -Referring to Instruction 12.39,suppose the managers of the brokerage firm want to obtain a 99% confidence interval estimate for the mean sales made by brokers who have brought into the firm 24 new customers.The confidence interval is from __________ to __________.

The answer of Instruction 12.39 The managers of a brokerage firm...

Instruction 12.38 The director of cooperative education at a university wants to examine the effect of cooperative education job experience on marketability in the workplace. She takes a random sample of four students. For these four, 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. \[\begin{array} { | l | l | l | } \hline \text { Student } & \text { Coop Jobs } & \text {Job Oifer } \\ \hline 1 & 1 & 4 \\ \hline 2 & 2 & 6 \\ \hline 3 & 1 & 3 \\ \hline 4 & 0 & 1 \\ \hline \end{array}\] -Referring to Instruction 12.38,suppose the director of cooperative education wants to obtain a 95% prediction interval for the number of job offers received by a student who has had exactly two cooperative education jobs.The t critical value she would use is __________.

The answer of Instruction 12.38 The director of cooperative education at...

Exam 12: Simple Linear Regression

Instruction 12.35 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: $\text { Regression statistics }$ MultipleR 0.8691 R Square 0.7554 Adjusted R Square 0.7467 Standard Error 44.4765 Observations 30.0000 $\text { ANOVA }$ df SS MS F Significance F Regression 1 171062.9193 171062.9193 86.4759 0.0000 Residual 28 55388.4309 1978.1582 Total 29 226451.3503 Coefficients Standard Error t Stat p -value Lower 95\% Upper 95\% Intercept -95.0614 26.9183 -3.5315 0.0015 -150.2009 -39.9218 Download 3.7297 0.4011 9.2992 0.0000 2.9082 4.5513 $Instruction 12.35 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: \text { Regression statistics } \begin{array}{|l|l|} \hline \text { MultipleR } & 0.8691 \\ \hline \text { R Square } & 0.7554 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.7467 \\ \hline \text { Standard Error } & 44.4765 \\ \hline \text { Observations } & 30.0000\\ \hline \end{array} \text { ANOVA } \begin{array}{|l|l|l|l|l|l|} \hline & d f & S S & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\ \hline \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\ \hline \text { Total } & 29 & 226451.3503 & & & \\ \hline \end{array} \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { t Stat } & p \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\ \hline \text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\ \hline \end{array} -Referring to Instruction 12.35,there is sufficient evidence that revenue and number of downloads are linearly related at a 5% level of significance.$ $Instruction 12.35 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: \text { Regression statistics } \begin{array}{|l|l|} \hline \text { MultipleR } & 0.8691 \\ \hline \text { R Square } & 0.7554 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.7467 \\ \hline \text { Standard Error } & 44.4765 \\ \hline \text { Observations } & 30.0000\\ \hline \end{array} \text { ANOVA } \begin{array}{|l|l|l|l|l|l|} \hline & d f & S S & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\ \hline \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\ \hline \text { Total } & 29 & 226451.3503 & & & \\ \hline \end{array} \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { t Stat } & p \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\ \hline \text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\ \hline \end{array} -Referring to Instruction 12.35,there is sufficient evidence that revenue and number of downloads are linearly related at a 5% level of significance.$ -Referring to Instruction 12.35,there is sufficient evidence that revenue and number of downloads are linearly related at a 5% level of significance.

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Question 141

A zero population correlation coefficient between a pair of random variables means that there is no linear relationship between the random variables.

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Question 142

onfident that the mean amount of time needed to record one additional loan application is somewhere $\text { Regression statistics }$ MultipleR 0.9447 R Square 0.8924 Adjusted R Square 0.8886 Standard Error 0.3342 Observations 30 $\text { ANOVA }$ df S5 MS F Significance F Regression 1 25.9438 25.9438 232.2200 4.3946-15 Residual 28 3.1282 0.1117 Total 29 29.072 Coefficients Standard Error tStat p-value Lower 95\% Upper 95\% Intercept 0.4024 0.1236 3.2559 0.0030 0.1492 0.6555 Applications Recorded 0.0126 0.0008 15.2388 4.3946- 15 0.0109 0.0143 $\text { Note: } 4.3946 \mathrm { E } - 15 \text { is } 4.3946 \times 10 ^ { - 15 } \text {. }$ $onfident that the mean amount of time needed to record one additional loan application is somewhere \text { Regression statistics } \begin{array}{|l|l|} \hline\text { MultipleR } & 0.9447 \\ \hline \text { R Square } & 0.8924 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.8886 \\ \hline \text { Standard Error } & 0.3342 \\ \hline \text { Observations } & 30 \\ \hline \end{array} \text { ANOVA } \begin{array}{|l|l|l|l|l|l|} \hline & d f & S 5 & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm{E}-15 \\ \hline \text { Residual } & 28 & 3.1282 & 0.1117 & & \\ \hline \text { Total } & 29 & 29.072 & & & \\ \hline \end{array} \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { tStat } & \text { p-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\ \hline \begin{array}{l} \text { Applications } \\ \text { Recorded } \end{array} & 0.0126 & 0.0008 & 15.2388 & \begin{array}{l} 4.3946 \mathrm{E}- \\ 15 \end{array} & 0.0109 & 0.0143 \\ \hline \end{array} \text { Note: } 4.3946 \mathrm { E } - 15 \text { is } 4.3946 \times 10 ^ { - 15 } \text {. } -Referring to Instruction 12.36,the value of the measured t test statistic to test whether the amount of time depends linearly on the number of loan applications recorded is$ -Referring to Instruction 12.36,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

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Question 143

Instruction 12.33 It is believed that the average numbers of hours spent studying per day (HOURS) during undergraduate education should have a positive linear relationship with the starting salary (SALARY, measured in thousands of dollars per month) after graduation. Given below is the Microsoft Excel output for predicting starting salary (Y) using number of hours spent studying per day (X) for a sample of 51 students. NOTE: Only partial output is shown. $\text { Regression statistics }$ Multiple R 0.8857 R Square 0.7845 Adjusted R Square 0.7801 Standard Error 1.3704 Observations 51 $\text { ANOVA }$ df SS MS F Significance F Regression 1 335.0472 335.0473 178.3859 Residual 1.8782 Total 50 427.0798 Coefficients Standard Error t Stat p-value Lower 95\% Upper 95\% Intercept -1.8940 0.4018 -4.7134 2.051-05 -2.7015 -1.0865 Hours 0.9795 0.0733 13.3561 5.944-18 0.8321 1.1269 Note: 2.051E-05 = 2.051 * 10^-^0.5 and 5.944E-18 = 5.944 * 10^-¹⁸. -Referring to Instruction 12.33,the value of the measured t test statistic to test whether average SALARY depends linearly on HOURS is

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Question 144

A simple regression has a b₀ value of 5 and a b₁ value of 3.5.What is the predicted Y for an X value of -2?

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Question 145

Instruction 12.29 The managers of a brokerage firm are interested in finding out if the number of new customers a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new customers they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. Broker Clients 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 -Referring to Instruction 12.29,the managers of the brokerage firm wanted to test the hypothesis that the number of new customers brought in had a positive impact on the amount of sales generated.The value of the test statistic is ____________

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Question 146

To determine whether the linear model is appropriate and to check the equal variance assumption,plot residuals against the independent variable.

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Question 147

onfident that the mean amount of time needed to record one additional loan application is somewhere $\text { Regression statistics }$ MultipleR 0.9447 R Square 0.8924 Adjusted R Square 0.8886 Standard Error 0.3342 Observations 30 $\text { ANOVA }$ df S5 MS F Significance F Regression 1 25.9438 25.9438 232.2200 4.3946-15 Residual 28 3.1282 0.1117 Total 29 29.072 Coefficients Standard Error tStat p-value Lower 95\% Upper 95\% Intercept 0.4024 0.1236 3.2559 0.0030 0.1492 0.6555 Applications Recorded 0.0126 0.0008 15.2388 4.3946- 15 0.0109 0.0143 $\text { Note: } 4.3946 \mathrm { E } - 15 \text { is } 4.3946 \times 10 ^ { - 15 } \text {. }$ $onfident that the mean amount of time needed to record one additional loan application is somewhere \text { Regression statistics } \begin{array}{|l|l|} \hline\text { MultipleR } & 0.9447 \\ \hline \text { R Square } & 0.8924 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.8886 \\ \hline \text { Standard Error } & 0.3342 \\ \hline \text { Observations } & 30 \\ \hline \end{array} \text { ANOVA } \begin{array}{|l|l|l|l|l|l|} \hline & d f & S 5 & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm{E}-15 \\ \hline \text { Residual } & 28 & 3.1282 & 0.1117 & & \\ \hline \text { Total } & 29 & 29.072 & & & \\ \hline \end{array} \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { tStat } & \text { p-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\ \hline \begin{array}{l} \text { Applications } \\ \text { Recorded } \end{array} & 0.0126 & 0.0008 & 15.2388 & \begin{array}{l} 4.3946 \mathrm{E}- \\ 15 \end{array} & 0.0109 & 0.0143 \\ \hline \end{array} \text { Note: } 4.3946 \mathrm { E } - 15 \text { is } 4.3946 \times 10 ^ { - 15 } \text {. } -Referring to Instruction 12.36,the 90% confidence interval for the mean change in the amount of time needed as a result of recording one additional loan application is$ -Referring to Instruction 12.36,the 90% confidence interval for the mean change in the amount of time needed as a result of recording one additional loan application is

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Question 148

Instruction 12.17 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: $\text { Regression statistics }$ MultipleR 0.8691 R Square 0.7554 Adjusted R Square 0.7467 Standard Error 44.4765 Observations 30.0000 $\text { ANOVA }$ df SS MS F Significance F Regression 1 171062.9193 171062.9193 86.4759 0.0000 Residual 28 55388.4309 1978.1582 Total 29 226451.3503 Coefficients Standard Error t Stat p -value Lower 95\% Upper 95\% Intercept -95.0614 26.9183 -3.5315 0.0015 -150.2009 -39.9218 Download 3.7297 0.4011 9.2992 0.0000 2.9082 4.5513 $Instruction 12.17 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: \text { Regression statistics } \begin{array}{|l|l|} \hline \text { MultipleR } & 0.8691 \\ \hline \text { R Square } & 0.7554 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.7467 \\ \hline \text { Standard Error } & 44.4765 \\ \hline \text { Observations } & 30.0000\\ \hline \end{array} \text { ANOVA } \begin{array}{|l|l|l|l|l|l|} \hline & d f & S S & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\ \hline \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\ \hline \text { Total } & 29 & 226451.3503 & & & \\ \hline \end{array} \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { t Stat } & p \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\ \hline \text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\ \hline \end{array} -Referring to Instruction 12.17,what is the standard error of estimate?$ $Instruction 12.17 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: \text { Regression statistics } \begin{array}{|l|l|} \hline \text { MultipleR } & 0.8691 \\ \hline \text { R Square } & 0.7554 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.7467 \\ \hline \text { Standard Error } & 44.4765 \\ \hline \text { Observations } & 30.0000\\ \hline \end{array} \text { ANOVA } \begin{array}{|l|l|l|l|l|l|} \hline & d f & S S & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\ \hline \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\ \hline \text { Total } & 29 & 226451.3503 & & & \\ \hline \end{array} \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { t Stat } & p \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\ \hline \text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\ \hline \end{array} -Referring to Instruction 12.17,what is the standard error of estimate?$ -Referring to Instruction 12.17,what is the standard error of estimate?

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Question 149

Which of the following can be used to check the normality assumption?

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Question 150

Instruction 12.9 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: Customers Sales (Thousands of Dollars) 907 11.20 926 11.05 713 8.21 741 9.21 780 9.42 898 10.08 510 6.73 529 7.02 460 6.12 872 9.52 650 7.53 603 7.25 -Referring to Instruction 12.9,93.98% of the total variation in weekly sales can be explained by the variation in the number of customers who make purchases.

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Question 151

Instruction 12.32 It is believed that average grade (based on a four-point scale) should have a positive linear relationship with university entrance exam scores. Given below is the Microsoft Excel output from regressing average grade on university entrance exam scores using a data set of eight randomly chosen students from a large university. $\text { Regressing average grade on university entrance exam score }$ $\text { Regression statistics }$ MultipleR 0.7598 R Square 0.5774 Adjusted R Square 0.5069 Standard Error 0.2691 Observations 8 $\text { ANOVA }$ df 55 MS F Significance F Regression 1 0.5940 0.5940 8.1986 0.0286 Residual 6 0.4347 0.0724 Total 7 1.0287 Coefficients Standard Error tStat p-value Lower 95\% Upper 95\% Intercept 0.5681 0.9284 0.6119 0.5630 -1.7036 2.8398 University entrance exam score 0.1021 0.0356 2.8633 0.0286 0.0148 0.1895 -Referring to Instruction 12.32,the value of the measured (observetest statistic of the F test for H₀: $\beta$ ₁ = 0 versus H₁: $\beta$ ₁ $\neq$ 0

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Question 152

Instruction 12.3 The director of cooperative education at a university wants to examine the effect of cooperative education job experience on marketability in the workplace. She takes a random sample of four students. For these four, 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. Student Coop jobs job Offer 1 1 4 2 2 6 3 1 3 4 0 1 -Referring to Instruction 12.3,the prediction for the number of job offers for a person with two cooperative jobs is____________.

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Question 153

Instruction 12.28 The managers of a brokerage firm are interested in finding out if the number of new customers a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new customers they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. Broker Clients 5les 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 -Referring to Instruction 12.28,the managers of the brokerage firm wanted to test the hypothesis that the true slope was equal to 0.The denominator of the test statistic is $S _ { b _ { 1 } }$ .The value of $S _ { b _ { 1 } }$ in this sample is ____________.

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Question 154

Instruction 12.39 The managers of a brokerage firm are interested in finding out if the number of new customers a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new customers they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. Broker Clients Sles 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 -Referring to Instruction 12.39,suppose the managers of the brokerage firm want to obtain a 99% confidence interval estimate for the mean sales made by brokers who have brought into the firm 24 new customers.The confidence interval is from to .

(Short Answer)

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Question 155

Instruction 12.4 The managers of a brokerage firm are interested in finding out if the number of new customers a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new customers they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. Broker Clients 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 -Referring to Instruction 12.4,the regression sum of squares (SSR)is____________

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Question 156

Instruction 12.35 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: $\text { Regression statistics }$ MultipleR 0.8691 R Square 0.7554 Adjusted R Square 0.7467 Standard Error 44.4765 Observations 30.0000 $\text { ANOVA }$ df SS MS F Significance F Regression 1 171062.9193 171062.9193 86.4759 0.0000 Residual 28 55388.4309 1978.1582 Total 29 226451.3503 Coefficients Standard Error t Stat p -value Lower 95\% Upper 95\% Intercept -95.0614 26.9183 -3.5315 0.0015 -150.2009 -39.9218 Download 3.7297 0.4011 9.2992 0.0000 2.9082 4.5513 $Instruction 12.35 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: \text { Regression statistics } \begin{array}{|l|l|} \hline \text { MultipleR } & 0.8691 \\ \hline \text { R Square } & 0.7554 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.7467 \\ \hline \text { Standard Error } & 44.4765 \\ \hline \text { Observations } & 30.0000\\ \hline \end{array} \text { ANOVA } \begin{array}{|l|l|l|l|l|l|} \hline & d f & S S & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\ \hline \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\ \hline \text { Total } & 29 & 226451.3503 & & & \\ \hline \end{array} \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { t Stat } & p \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\ \hline \text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\ \hline \end{array} -Referring to Instruction 12.35,what are the lower and upper limits of the 95% confidence interval estimate for population slope?$ $Instruction 12.35 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: \text { Regression statistics } \begin{array}{|l|l|} \hline \text { MultipleR } & 0.8691 \\ \hline \text { R Square } & 0.7554 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.7467 \\ \hline \text { Standard Error } & 44.4765 \\ \hline \text { Observations } & 30.0000\\ \hline \end{array} \text { ANOVA } \begin{array}{|l|l|l|l|l|l|} \hline & d f & S S & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 171062.9193 & 171062.9193 & 86.4759 & 0.0000 \\ \hline \text { Residual } & 28 & 55388.4309 & 1978.1582 & & \\ \hline \text { Total } & 29 & 226451.3503 & & & \\ \hline \end{array} \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { t Stat } & p \text {-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & -95.0614 & 26.9183 & -3.5315 & 0.0015 & -150.2009 & -39.9218 \\ \hline \text { Download } & 3.7297 & 0.4011 & 9.2992 & 0.0000 & 2.9082 & 4.5513 \\ \hline \end{array} -Referring to Instruction 12.35,what are the lower and upper limits of the 95% confidence interval estimate for population slope?$ -Referring to Instruction 12.35,what are the lower and upper limits of the 95% confidence interval estimate for population slope?

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Question 157

Instruction 12.18 The manager of the purchasing department of a large savings 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: $\text { Regression statistics }$ MultipleR 0.9447 R Square 0.8924 Adjusted R Square 0.8886 Standard Error 0.3342 Observations 30 $\text { ANOVA }$ df S5 MS F Significance F Regression 1 25.9438 25.9438 232.2200 4.3946-15 Residual 28 3.1282 0.1117 Total 29 29.072 Coefficients Standard Error tStat p-value Lower 95\% Upper 95\% Intercept 0.4024 0.1236 3.2559 0.0030 0.1492 0.6555 Applications Recorded 0.0126 0.0008 15.2388 4.3946- 15 0.0109 0.0143 Note: 4.3946E-15 is 4.3946 x 10^-15. $Instruction 12.18 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: \text { Regression statistics } \begin{array}{|l|l|} \hline\text { MultipleR } & 0.9447 \\ \hline \text { R Square } & 0.8924 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.8886 \\ \hline \text { Standard Error } & 0.3342 \\ \hline \text { Observations } & 30 \\ \hline \end{array} \text { ANOVA } \begin{array}{|l|l|l|l|l|l|} \hline & d f & S 5 & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm{E}-15 \\ \hline \text { Residual } & 28 & 3.1282 & 0.1117 & & \\ \hline \text { Total } & 29 & 29.072 & & & \\ \hline \end{array} \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { tStat } & \text { p-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\ \hline \begin{array}{l} \text { Applications } \\ \text { Recorded } \end{array} & 0.0126 & 0.0008 & 15.2388 & \begin{array}{l} 4.3946 \mathrm{E}- \\ 15 \end{array} & 0.0109 & 0.0143 \\ \hline \end{array} Note: 4.3946E-15 is 4.3946 x 10-15. Note: 4.3946E-15 is 4.3946 × 10-15. -Referring to Instruction 12.18,the error sum of squares (SSE)of the above regression is$ $Instruction 12.18 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: \text { Regression statistics } \begin{array}{|l|l|} \hline\text { MultipleR } & 0.9447 \\ \hline \text { R Square } & 0.8924 \\ \hline \begin{array}{l} \text { Adjusted R } \\ \text { Square } \end{array} & 0.8886 \\ \hline \text { Standard Error } & 0.3342 \\ \hline \text { Observations } & 30 \\ \hline \end{array} \text { ANOVA } \begin{array}{|l|l|l|l|l|l|} \hline & d f & S 5 & M S & F & \begin{array}{l} \text { Significance } \\ F \end{array} \\ \hline \text { Regression } & 1 & 25.9438 & 25.9438 & 232.2200 & 4.3946 \mathrm{E}-15 \\ \hline \text { Residual } & 28 & 3.1282 & 0.1117 & & \\ \hline \text { Total } & 29 & 29.072 & & & \\ \hline \end{array} \begin{array}{|l|l|l|l|l|l|l|} \hline & \text { Coefficients } & \begin{array}{l} \text { Standard } \\ \text { Error } \end{array} & \text { tStat } & \text { p-value } & \text { Lower 95\% } & \text { Upper 95\% } \\ \hline \text { Intercept } & 0.4024 & 0.1236 & 3.2559 & 0.0030 & 0.1492 & 0.6555 \\ \hline \begin{array}{l} \text { Applications } \\ \text { Recorded } \end{array} & 0.0126 & 0.0008 & 15.2388 & \begin{array}{l} 4.3946 \mathrm{E}- \\ 15 \end{array} & 0.0109 & 0.0143 \\ \hline \end{array} Note: 4.3946E-15 is 4.3946 x 10-15. Note: 4.3946E-15 is 4.3946 × 10-15. -Referring to Instruction 12.18,the error sum of squares (SSE)of the above regression is$ Note: 4.3946E-15 is 4.3946 × 10^-¹⁵. -Referring to Instruction 12.18,the error sum of squares (SSE)of the above regression is

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Question 158

The least squares method minimises which of the following?

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Question 159

Instruction 12.38 The director of cooperative education at a university wants to examine the effect of cooperative education job experience on marketability in the workplace. She takes a random sample of four students. For these four, 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. Student Coop Jobs Job Oifer 1 1 4 2 2 6 3 1 3 4 0 1 -Referring to Instruction 12.38,suppose the director of cooperative education wants to obtain a 95% prediction interval for the number of job offers received by a student who has had exactly two cooperative education jobs.The t critical value she would use is __________.

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Question 160

Showing 141 - 160 of 207

A zero population correlation coefficient between a pair of random variables means that there is no linear relationship between the random variables.

A simple regression has a b₀ value of 5 and a b₁ value of 3.5.What is the predicted Y for an X value of -2?

To determine whether the linear model is appropriate and to check the equal variance assumption,plot residuals against the independent variable.

Which of the following can be used to check the normality assumption?

The least squares method minimises which of the following?

Defining and Collecting Data

Organising and Visualising Data

Numerical Descriptive Measures

Basic Probability

Some Important Discrete Probability Distributions

The Normal Distribution and Other Continuous Distributions

Sampling Distributions

Confidence Interval Estimation

Fundamentals of Hypothesis Testing: One-Sample Tests

Hypothesis Testing: Two-Sample Tests

Analysis of Variance

Introduction to Multiple Regression

Time-Series Forecasting and Index Numbers

Chi-Square Tests

Multiple Regression Model Building

Decision Making

Statistical Applications in Quality Management

Further Non-Parametric Tests

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