Question 1

What do we mean when we say that a simple linear regression model is 'statistically' useful?

Accepted Answer

A)  The model is 'practically' useful for predicting Y. 
B)  The model is an excellent predictor of Y. 
C)  All the statistics computed from the sample make sense. 
D)  The model is a better predictor of Y than the sample mean, $\bar { Y }$ . 
A)  The model is 'practically' useful for predicting Y. 
B)  The model is an excellent predictor of Y. 
C)  All the statistics computed from the sample make sense. 
D)  The model is a better predictor of Y than the sample mean, $\bar { Y }$ .

Question 2

Instruction 12.8
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 MicrosoftS1U1P1 S1S1P0 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.
 $\text { Regression statistics }$
$\begin{array}{|l|l|}
\hline\text { MultipleR } & 0.8857 \
\hline \text { R Square } & 0.7845 \
\hline \begin{array}{l}
\text { Adjusted R } \
\text { Square }
\end{array} & 0.7801 \
\hline \text { Standard Error } & 1.3704 \
\hline \text { Observations } & 51\
\hline
\end{array}$

$\begin{array}{l}
\text { ANOVA }\
\begin{array}{|l|l|l|l|l|l|l|}
\hline & d f & 55 & \text { MS } & F & \begin{array}{l}
\text { Significance } \
F
\end{array} & \
\hline \text { Regression } & 1 & 335.0472 & 335.0473 & 178.3859 & & \
\hline \text { Residual } & & & 1.8782 & & & \
\hline \text { Total } & 50 & 427.0798 & & & & \
\hline
\end{array}
\end{array}$

$\begin{array}{|l|l|l|l|l|l|l|}
\hline & \text { Coefficients } & \begin{array}{l}
\text { Standard } \
\text { Error }
\end{array} & \text { tStat } & \text { p-value } & \text { Lower 95\% } & \text { Upper 95\% } \
\hline \text { Intercept } & -1.8940 & 0.4018 & -4.7134 & 2.051 \mathrm{E}-05 & -2.7015 & -1.0865 \
\hline \text { Hours } & 0.9795 & 0.0733 & 13.3561 & 5.944 \mathrm{E}-18 & 0.8321 & 1.1269 \
\hline
\end{array}$ Note: 2.051E-05 = 2.051 * 10S1S1U1P1-S1S1P0S1U1P10.5S1S1P0 and 5.944E-18 = 5.944 * 10S1S1U1P1-S1S1P0S1U1P118S1S1P0.
-Referring to Instruction 12.8,the error sum of squares (SSE)of the above regression is

Accepted Answer

A)  427.079804. 
B)  92.0325465. 
C)  1.878215. 
D)  335.047257. 
A)  427.079804. 
B)  92.0325465. 
C)  1.878215. 
D)  335.047257.

Question 3

Instruction 12.14
The managing partner of an advertising agency believes that his company's sales are related to the industry sales. He uses MicrosoftS1U1P1 S1S1P0 Excel's Data Analysis tool to analyse the last four years of quarterly data with the following results:
 $\text { Regression statistics }$
$\begin{array}{|l|l|}
\hline \text { Multiple R } & 0.802 \
\hline \text { R Square } & 0.643 \
\hline \begin{array}{l}
\text { Adjusted R } \
\text { Square }
\end{array} & 0.618 \
\hline \begin{array}{l}
\text { Standard Error } \
\text { SYX }
\end{array} & 0.9224 \
\hline \text { Observations } & 16 \
\hline
\end{array}$

$\text { ANOVA }$ 
$\begin{array}{|l|l|l|l|l|l|}
\hline & \text { df } & \text { SS } & \text { MS } & \text { F } & \text { Sig.F } \
\hline \text { Regression } & 1 & 21.497 & 21.497 & 25.27 & 0.000 \
\hline \text { Error } & 14 & 11.912 & 0.851 & & \
\hline \text { Total } & 15 & 33.409 & & & \
\hline
\end{array}$

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

$\begin{array}{|l|l|}
\hline\text { Durbin-Watson } & 1.59 \
\text { Statistic } &\
\hline
\end{array}$
-Referring to Instruction 12.14,the correlation coefficient is ____________.

Accepted Answer

The answer of Instruction 12.14
The managing partner of an advertising...

Question 4

The sample correlation coefficient between X and Y is 0.375.It has been found out that the p-value is 0.256 when testing H0: ρ = 0 against the two-sided alternative H1: ρ ≠ 0.To test H0: ρ = 0 against the one-sided alternative H1: ρ > 0 at a significance level of 0.2,the p-value is

Accepted Answer

A)  0.256 / 2. 
B)  (0.256)2. 
C)  1 - 0.256. 
D)  1 - 0.256 / 2. 
A)  0.256 / 2. 
B)  (0.256)2. 
C)  1 - 0.256. 
D)  1 - 0.256 / 2.

Question 5

Instruction 12.23
The managing partner of an advertising agency believes that his company's sales are related to the industry sales. He uses MicrosoftS1U1P1 S1S1P0 Excel's Data Analysis tool to analyse the last four years of quarterly data with the following results:
 $\text { Regression statistics }$
$\begin{array}{|l|l|}
\hline \text { Multiple R } & 0.802 \
\hline \text { R Square } & 0.643 \
\hline \begin{array}{l}
\text { Adjusted R } \
\text { Square }
\end{array} & 0.618 \
\hline \begin{array}{l}
\text { Standard Error } \
\text { SYX }
\end{array} & 0.9224 \
\hline \text { Observations } & 16 \
\hline
\end{array}$

$\text { ANOVA }$ 
$\begin{array}{|l|l|l|l|l|l|}
\hline & \text { df } & \text { SS } & \text { MS } & \text { F } & \text { Sig.F } \
\hline \text { Regression } & 1 & 21.497 & 21.497 & 25.27 & 0.000 \
\hline \text { Error } & 14 & 11.912 & 0.851 & & \
\hline \text { Total } & 15 & 33.409 & & & \
\hline
\end{array}$

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

$\begin{array}{|l|l|}
\hline\text { Durbin-Watson } & 1.59 \
\text { Statistic } &\
\hline
\end{array}$

-Referring to Instruction 12.23,the partner wants to test for autocorrelation using the Durbin-Watson statistic.Using a level of significance of 0.05,the decision he should make is

Accepted Answer

A)  the test is unable to make a definite conclusion. 
B)  there is evidence of autocorrelation. 
C)  there is no evidence of autocorrelation. 
D)  there is not enough information to perform the test. 
A)  the test is unable to make a definite conclusion. 
B)  there is evidence of autocorrelation. 
C)  there is no evidence of autocorrelation. 
D)  there is not enough information to perform the test.

Question 6

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,there is a 95% probability 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 7

Instruction 12.2
A chocolate bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses six country towns and cities and offers the chocolate bar at different prices. Using chocolate bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:
 $$\begin{array} { | l | l | l | } 
\hline \text { City } & \text { Price(\$) } & \text { Syles } \
\hline \text { Toowoomba } & 1.30 & 100 \
\hline \text { Broken Hill } & 1.60 & 50 \
\hline \text { Bendigo } & 1.80 & 50 \
\hline \text { Kalgoorlie } & 2.00 & 40 \
\hline \text { Launceston } & 2.40 & 38 \
\hline \text { Port Augusta } & 2.50 & 32 \
\hline
\end{array}$$
-Referring to Instruction 12.2,what is the standard error of the regression slope estimate, $S _ { b _ { 1 } }$ ?

Accepted Answer

A)  0.885 
B)  12.650 
C)  16.299 
D)  0.784 
A)  0.885 
B)  12.650 
C)  16.299 
D)  0.784

Question 8

Based on the residual plot below,you will conclude that there might be a violation of which of the following assumptions?

Accepted Answer

A)  Homoscedasticity 
B)  Independence of errors 
C)  Linearity of the relationship 
D)  Normality of errors 
A)  Homoscedasticity 
B)  Independence of errors 
C)  Linearity of the relationship 
D)  Normality of errors

Question 9

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,which of the following is the correct alternative hypothesis for testing whether there is a linear relationship between revenue and number of downloads?

Accepted Answer

A)  H1: b1 = 0 
B)  H1: $\beta$1 $\neq$0 
C)  H1: b1 $\neq$ 0 
D)  H1: $\beta$1 = 0 
A)  H1: b1 = 0 
B)  H1: $\beta$1 $\neq$0 
C)  H1: b1 $\neq$ 0 
D)  H1: $\beta$1 = 0

Question 10

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.
 $$\begin{array} { | l | l | l | } 
\hline \text { Student } & \text { Coop jobs } & \text { job Offer } \
\hline 1 & 1 & 4 \
\hline 2 & 2 & 6 \
\hline 3 & 1 & 3 \
\hline 4 & 0 & 1 \
\hline
\end{array}$$
-Referring to Instruction 12.3,the regression sum of squares (SSR)is____________.

Accepted Answer

The answer of Instruction 12.3
The director of cooperative education at...

Question 11

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% confidence-interval estimate for the mean number of job offers received by students who have had exactly one cooperative education job.The t critical value she would use is_____________.

Accepted Answer

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

Question 12

The width of the prediction interval for the predicted value of Y is dependent on

Accepted Answer

A)  the sample size. 
B)  the standard error of the estimate. 
C)  the value of X for which the prediction is being made. 
D)  All of the above. 
A)  the sample size. 
B)  the standard error of the estimate. 
C)  the value of X for which the prediction is being made. 
D)  All of the above.

Question 13

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 construct a 95% prediction interval for the number of job offers received by a student who has had exactly two cooperative education jobs.The prediction interval is from __________ to __________.

Accepted Answer

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

Question 14

Instruction 12.34
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:
 $$\begin{array} { | l | c | } 
\hline \text { Customers } & \text { Sales (Thousands of Dollars) } \
\hline 907 & 11.20 \
\hline 926 & 11.05 \
\hline 713 & 8.21 \
\hline 741 & 9.21 \
\hline 780 & 9.42 \
\hline 898 & 10.08 \
\hline 510 & 6.73 \
\hline 529 & 7.02 \
\hline 460 & 6.12 \
\hline 872 & 9.52 \
\hline 650 & 7.53 \
\hline 603 & 7.25 \
\hline
\end{array}$$
-Referring to Instruction 12.34,the average weekly sales will increase by an estimated $0.01 for each additional purchasing customer.

Accepted Answer

A) True 
 B)False

Question 15

Instruction 12.27
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.
 $$\begin{array} { | l | l | l | } 
\hline \text { Student } & \text { Coop jobs } & \text { job Offer } \
\hline 1 & 1 & 4 \
\hline 2 & 2 & 6 \
\hline 3 & 1 & 3 \
\hline 4 & 0 & 1 \
\hline
\end{array}$$
-Referring to Instruction 12.27,the director of cooperative education 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____________

Accepted Answer

The answer of Instruction 12.27
The director of cooperative education at...

Question 16

Instruction 12.19
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 12.19,which of the following assumptions appears to have been violated?

Accepted Answer

A)  Independence of errors 
B)  Normality of error 
C)  Homoscedasticity 
D)  None of the above 
A)  Independence of errors 
B)  Normality of error 
C)  Homoscedasticity 
D)  None of the above

Question 17

Instruction 12.31
An investment specialist claims that if one holds a portfolio that moves in opposite direction to the market index like the All Ordinaries Index, then it is possible to reduce the variability of the portfolio's return. In other words, one can create a portfolio with positive returns but less exposure to risk. A sample of 26 years of the All Ordinaries index and a portfolio consisting of stocks of private prisons, which are believed to be negatively related to the All Ordinaries index, is collected. A regression analysis was performed by regressing the returns of the prison stocks portfolio (Y) on the returns of All Ordinaries index (X) to prove that the prison stocks portfolio is negatively related to the All Ordinaries index at a 5% level of significance. The results are given in the following MicrosoftS1U1P1 S1S1P0 Excel output.
 $$\begin{array} { | l | l | l | l | l | } 
\hline & \text { Coefflelents } & \text { Standard Error } & \text { tStat } & p \text {-vahse } \
\hline \text { Intercept } & 4.866004258 & 0.35743609 & 13.61363441 & 8.7932 \mathrm { E } - 13 \
\hline \text { S\&P } & - 0.502513506 & 0.071597152 & - 7.01862425 & 2.94942 \mathrm { E } - 07 \
\hline
\end{array}$$
-Referring to Instruction 12.31,to test whether the prison stocks portfolio is negatively related to the All Ordinaries index,the p-value of the associated test statistic is

Accepted Answer

A)  2.94942E-07. 
B)  (2.94942E-07)2. 
C)  2.94942E-07 / 2. 
D)  8.7832E-13. 
A)  2.94942E-07. 
B)  (2.94942E-07)2. 
C)  2.94942E-07 / 2. 
D)  8.7832E-13.

Question 18

Instruction 12.25
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 12.25,the Durbin-Watson statistic is inappropriate for this data set.

Accepted Answer

AU: Please

Question 19

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 is the p-value for testing whether there is a linear relationship between revenue and the number of downloads at a 5% level of significance?

Accepted Answer

Virtually

Question 20

Instruction 12.23
The managing partner of an advertising agency believes that his company's sales are related to the industry sales. He uses MicrosoftS1U1P1 S1S1P0 Excel's Data Analysis tool to analyse the last four years of quarterly data with the following results:
 $\text { Regression statistics }$
$\begin{array}{|l|l|}
\hline \text { Multiple R } & 0.802 \
\hline \text { R Square } & 0.643 \
\hline \begin{array}{l}
\text { Adjusted R } \
\text { Square }
\end{array} & 0.618 \
\hline \begin{array}{l}
\text { Standard Error } \
\text { SYX }
\end{array} & 0.9224 \
\hline \text { Observations } & 16 \
\hline
\end{array}$

$\text { ANOVA }$ 
$\begin{array}{|l|l|l|l|l|l|}
\hline & \text { df } & \text { SS } & \text { MS } & \text { F } & \text { Sig.F } \
\hline \text { Regression } & 1 & 21.497 & 21.497 & 25.27 & 0.000 \
\hline \text { Error } & 14 & 11.912 & 0.851 & & \
\hline \text { Total } & 15 & 33.409 & & & \
\hline
\end{array}$

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

$\begin{array}{|l|l|}
\hline\text { Durbin-Watson } & 1.59 \
\text { Statistic } &\
\hline
\end{array}$

-Referring to Instruction 12.23,if the Durbin-Watson statistic has a value close to 4,which assumption is violated?

Accepted Answer

A)  Homoscedasticity 
B)  Normality of the errors 
C)  Independence of errors 
D)  None of the above 
A)  Homoscedasticity 
B)  Normality of the errors 
C)  Independence of errors 
D)  None of the above

What do we mean when we say that a simple linear regression model is 'statistically' useful?

The sample correlation coefficient between X and Y is 0.375.It has been found out that the p-value is 0.256 when testing H₀: ρ = 0 against the two-sided alternative H₁: ρ ≠ 0.To test H₀: ρ = 0 against the one-sided alternative H₁: ρ > 0 at a significance level of 0.2,the p-value is

Based on the residual plot below,you will conclude that there might be a violation of which of the following assumptions?

The width of the prediction interval for the predicted value of Y is dependent on

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

Filters

Exam 12: Simple Linear Regression

What do we mean when we say that a simple linear regression model is 'statistically' useful?

The sample correlation coefficient between X and Y is 0.375.It has been found out that the p-value is 0.256 when testing H0: ρ = 0 against the two-sided alternative H1: ρ ≠ 0.To test H0: ρ = 0 against the one-sided alternative H1: ρ > 0 at a significance level of 0.2,the p-value is

Based on the residual plot below,you will conclude that there might be a violation of which of the following assumptions?

The width of the prediction interval for the predicted value of Y is dependent on

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

Filters

The sample correlation coefficient between X and Y is 0.375.It has been found out that the p-value is 0.256 when testing H₀: ρ = 0 against the two-sided alternative H₁: ρ ≠ 0.To test H₀: ρ = 0 against the one-sided alternative H₁: ρ > 0 at a significance level of 0.2,the p-value is