Q&A Hero

Q: A multiple regression analysis produced the following tables. Predictor Coefficients Standard Error t Statistic p-value Intercept -139.609 2548.989 -0.05477 0.957154 x1 24.24619 22.25267 1.089586 0.295682 x2 32.10171 17.44559 1.840105 0.08869 Source df SS MS F p-value Regression 2 302689 151344.5 1.705942 0.219838 Residual 13 1153309 88716.07 Total 15 1455998 These results indicate that ____________. a) none of the predictor variables are significant at the 5% level b) each predictor variable is significant at the 5% level c) x1 is the only predictor variable significant at the 5% level d) x2 is the only predictor variable significant at the 5% level e) all variables are significant at 5% level

Q: A multiple regression analysis produced the following tables. Predictor Coefficients Standard Error t Statistic p-value Intercept -139.609 2548.989 -0.05477 0.957154 x1 24.24619 22.25267 1.089586 0.295682 x2 32.10171 17.44559 1.840105 0.08869 Source df SS MS F p-value Regression 2 302689 151344.5 1.705942 0.219838 Residual 13 1153309 88716.07 Total 15 1455998 Using a = 0.01 to test the null hypothesis H0: b2 = 0, the critical t value is ____. a) 1.174 b) 2.093 c) 2.131 d) 4.012 e) 3.012

Q: A multiple regression analysis produced the following tables. Predictor Coefficients Standard Error t Statistic p-value Intercept -139.609 2548.989 -0.05477 0.957154 x1 24.24619 22.25267 1.089586 0.295682 x2 32.10171 17.44559 1.840105 0.08869 Source df SS MS F p-value Regression 2 302689 151344.5 1.705942 0.219838 Residual 13 1153309 88716.07 Total 15 1455998 Using a = 0.01 to test the null hypothesis H0: b 1 = b 2 = 0, the critical F value is ____. a) 5.99 b) 5.70 c) 1.96 d) 4.84 e) 6.70

Q: A multiple regression analysis produced the following tables. Predictor Coefficients Standard Error t Statistic p-value Intercept -139.609 2548.989 -0.05477 0.957154 x1 24.24619 22.25267 1.089586 0.295682 x2 32.10171 17.44559 1.840105 0.08869 Source df SS MS F p-value Regression 2 302689 151344.5 1.705942 0.219838 Residual 13 1153309 88716.07 Total 15 1455998 The sample size for this analysis is ____________. a) 17 b) 13 c) 16 d) 11 e) 15

Q: A multiple regression analysis produced the following tables. Predictor Coefficients Standard Error t Statistic p-value Intercept -139.609 2548.989 -0.05477 0.957154 x1 24.24619 22.25267 1.089586 0.295682 x2 32.10171 17.44559 1.840105 0.08869 Source df SS MS F p-value Regression 2 302689 151344.5 1.705942 0.219838 Residual 13 1153309 88716.07 Total 15 1455998 The regression equation for this analysis is ____________. a) y = 302689 + 1153309 x1 + 1455998 x2 b) y = -139.609 + 24.24619 x1 + 32.10171 x2 c) y = 2548.989 + 22.25267 x1 + 17.44559 x2 d) y = -0.05477 + 1.089586 x1 + 1.840105 x2 e) y = 0.05477 + 1.089586 x1 + 1.840105 x2

Q: A multiple regression analysis produced the following tables. Predictor Coefficients Standard Error t Statistic p-value Intercept 624.5369 78.49712 7.956176 6.88E-06 x1 8.569122 1.652255 5.186319 0.000301 x2 4.736515 0.699194 6.774248 3.06E-05 Source df SS MS F p-value Regression 2 1660914 830457.1 58.31956 1.4E-06 Residual 11 156637.5 14239.77 Total 13 1817552 The adjusted R2 is ____________. a) 0.9138 b) 0.9408 c) 0.8981 d) 0.8851 e) 0.8891

Q: A multiple regression analysis produced the following tables. Predictor Coefficients Standard Error t Statistic p-value Intercept 624.5369 78.49712 7.956176 6.88E-06 x1 8.569122 1.652255 5.186319 0.000301 x2 4.736515 0.699194 6.774248 3.06E-05 Source df SS MS F p-value Regression 2 1660914 830457.1 58.31956 1.4E-06 Residual 11 156637.5 14239.77 Total 13 1817552 The coefficient of multiple determination is ____________. a) 0.0592 b) 0.9138 c) 0.1149 d) 0.9559 e) 1.0000

Q: A multiple regression analysis produced the following tables. Predictor Coefficients Standard Error t Statistic p-value Intercept 624.5369 78.49712 7.956176 6.88E-06 x1 8.569122 1.652255 5.186319 0.000301 x2 4.736515 0.699194 6.774248 3.06E-05 Source df SS MS F p-value Regression 2 1660914 830457.1 58.31956 1.4E-06 Residual 11 156637.5 14239.77 Total 13 1817552 For x1= 30 and x2 = 100, the predicted value of y is ____________. a) 753.77 b) 1,173.00 c) 1,355.26 d) 615.13 e) 6153.13

Q: A multiple regression analysis produced the following tables. Predictor Coefficients Standard Error t Statistic p-value Intercept 624.5369 78.49712 7.956176 6.88E-06 x1 8.569122 1.652255 5.186319 0.000301 x2 4.736515 0.699194 6.774248 3.06E-05 Source df SS MS F p-value Regression 2 1660914 830457.1 58.31956 1.4E-06 Residual 11 156637.5 14239.77 Total 13 1817552 These results indicate that ____________. a) none of the predictor variables are significant at the 5% level b) each predictor variable is significant at the 5% level c) x1 is the only predictor variable significant at the 5% level d) x2 is the only predictor variable significant at the 5% level e) the intercept is not significant at 5% level

Q: The following ANOVA table is from a multiple regression analysis. Source df SS MS F p Regression 3 1500 Error 26 Total 2300 The adjusted R2 value is __________. a) 0.65 b) 0.39 c) 0.61 d) 0.53 e) 0.78

Q: The following ANOVA table is from a multiple regression analysis. Source df SS MS F p Regression 3 1500 Error 26 Total 2300 The R2 value is __________. a) 0.65 b) 0.53 c) 0.35 d) 0.43 e) 1.37

Q: The following ANOVA table is from a multiple regression analysis. Source df SS MS F p Regression 3 1500 Error 26 Total 2300 The value of the standard error of the estimate se is __________. a) 30.77 b) 5.55 c) 4.03 d) 3.20 e) 0.73

Q: The following ANOVA table is from a multiple regression analysis. Source df SS MS F p Regression 3 1500 Error 26 Total 2300 The observed F value is __________. a) 16.25 b) 30.77 c) 500 d) 0.049 e) 0.039

Q: The following ANOVA table is from a multiple regression analysis. Source df SS MS F p Regression 3 1500 Error 26 Total 2300 The MSE value is __________. a) 31 b) 500 c) 16 d) 2300 e) 8.7

Q: The following ANOVA table is from a multiple regression analysis. Source df SS MS F p Regression 3 1500 Error 26 Total 2300 The SSE value is __________. a) 30 b) 1500 c) 500 d) 800 e) 2300

Q: The following ANOVA table is from a multiple regression analysis. Source df SS MS F p Regression 3 1500 Error 26 Total 2300 The MSR value is __________. a) 1500 b) 50 c) 2300 d) 500 e) 31

Q: The following ANOVA table is from a multiple regression analysis.SourcedfSSMSFpRegression31500Error26Total2300The number of independent variables in the analysis is __________.a) 30b) 26c) 1d) 3e) 2

Q: The following ANOVA table is from a multiple regression analysis.SourcedfSSMSFpRegression31500Error26Total2300The sample size for the analysis is __________.a) 30b) 26c) 3d) 29e) 31

Q: The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.SourcedfSSMSFpRegression700ErrorTotal1000The adjusted R2 value is __________.a) 0.80b) 0.70c) 0.66d) 0.76e) 0.30

Q: The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.SourcedfSSMSFpRegression700ErrorTotal1000The R2 value is __________.a) 0.80b) 0.70c) 0.66d) 0.76e) 0.30

Q: The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.SourcedfSSMSFpRegression700ErrorTotal1000The value of the standard error of the estimate se is __________.a) 13.23b) 3.16c) 17.32d) 26.46e) 10.00

Q: The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.SourcedfSSMSFpRegression700ErrorTotal1000The observed F value is __________.a) 17.50b) 2.33c) 0.70d) 0.43e) 0.50

Q: The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.SourcedfSSMSFpRegression700ErrorTotal1000The MSE value is __________.a) 8.57b) 8.82c) 10.00d) 75.00e) 20.00

Q: The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.SourcedfSSMSFpRegression700ErrorTotal1000The MSR value is __________.a) 700.00b) 350.00c) 233.33d) 175.00e) 275.00

Q: The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.SourcedfSSMSFpRegression700ErrorTotal1000The number of degrees of freedom for error is __________.a) 1b) 4c) 34d) 30e) 35

Q: The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.SourcedfSSMSFpRegression700ErrorTotal1000The number of degrees of freedom for regression is __________.a) 1b) 4c) 34d) 30e) 35

Q: In regression analysis, outliers may be identified by examining the ________. a) coefficient of determination b) coefficient of correlation c) p-values for the partial coefficients d) residuals e) R-squared value

Q: A multiple regression analysis produced the following tables. These results indicate that ____________. a) none of the predictor variables are significant at the 10% level b) each predictor variable is significant at the 10% level c) x1 is significant at the 10% level d) x2 is significant at the 10% level e) the intercept is not significant at 10% level

Q: A multiple regression analysis produced the following tables. Using a = 0.05 to test the null hypothesis H0: b2 = 0, the correct decision is ____. a) fail to reject the null hypothesis b) reject the null hypothesis c) fail to reject the alternative hypothesis d) reject the alternative hypothesis e) there is not enought information provided to make a decision

Q: A multiple regression analysis produced the following tables. Using a = 0.05 to test the null hypothesis H0: b1 = 0, the correct decision is ____. a) fail to reject the null hypothesis b) reject the null hypothesis c) fail to reject the alternative hypothesis d) reject the alternative hypothesis e) there is not enought information provided to make a decision

Q: A multiple regression analysis produced the following tables. Using a = 0.01 to test the model, these results indicate that ____________. a) at least one of the regression variables is a significant predictor of y b) none of the regression variables are significant predictors of y c) y cannot be sufficiently predicted using these data d) y is a good predictor of the regression variables in the model e) the y intercept in this model is the best predictor variable

Q: A multiple regression analysis produced the following tables.PredictorCoefficientsStandard Errort Statisticp-valueIntercept752.0833336.31582.2362410.042132x111.873755.320472.2317110.042493x21.9081830.6627422.8792260.01213SourcedfSSMSFp-valueRegression2203693.3101846.76.7454060.010884Residual12181184.115098.67Total14384877.4These results indicate that ____________.a) none of the predictor variables are significant at the 5% levelb) each predictor variable is significant at the 5% levelc) x1 is the only predictor variable significant at the 5% leveld) x2 is the only predictor variable significant at the 5% levele) the intercept is not significant at the 5% level

Q: A multiple regression analysis produced the following tables.PredictorCoefficientsStandard Errort Statisticp-valueIntercept752.0833336.31582.2362410.042132x111.873755.320472.2317110.042493x21.9081830.6627422.8792260.01213SourcedfSSMSFp-valueRegression2203693.3101846.76.7454060.010884Residual12181184.115098.67Total14384877.4Using a = 0.10 to test the null hypothesis H0: b2 = 0, the critical t value is ____.a) 1.345b) 1.356c) 1.761d) 2.782e) 1.782

Q: A multiple regression analysis produced the following tables.PredictorCoefficientsStandard Errort Statisticp-valueIntercept752.0833336.31582.2362410.042132x111.873755.320472.2317110.042493x21.9081830.6627422.8792260.01213SourcedfSSMSFp-valueRegression2203693.3101846.76.7454060.010884Residual12181184.115098.67Total14384877.4Using a = 0.05 to test the null hypothesis H0: b1 = b2 = 0, the critical F value is ____.a) 3.74b) 3.89c) 4.75d) 4.60e) 2.74

Q: A multiple regression analysis produced the following tables.PredictorCoefficientsStandard Errort Statisticp-valueIntercept616.6849154.55343.9901080.000947x1-3.338332.333548-1.430580.170675x21.7800750.3356055.304075.83E-05SourcedfSSMSFp-valueRegression212178360891.4814.761170.000286Residual1561876.684125.112Total17183659.6These results indicate that ____________.a) none of the predictor variables are significant at the 5% levelb) each predictor variable is significant at the 5% levelc) x1 is significant at the 5% leveld) x2 is significant at the 5% levele) the intercept is not significant at 5% level

Q: A multiple regression analysis produced the following tables.PredictorCoefficientsStandard Errort Statisticp-valueIntercept616.6849154.55343.9901080.000947x1-3.338332.333548-1.430580.170675x21.7800750.3356055.304075.83E-05SourcedfSSMSFp-valueRegression212178360891.4814.761170.000286Residual1561876.684125.112Total17183659.6Using a = 0.05 to test the null hypothesis H0: b1 = 0, the critical t value is ____.a) 1.753b) 2.110c) 2.131d) 1.740e) 2.500

Q: A multiple regression analysis produced the following tables.PredictorCoefficientsStandard Errort Statisticp-valueIntercept616.6849154.55343.9901080.000947x1-3.338332.333548-1.430580.170675x21.7800750.3356055.304075.83E-05SourcedfSSMSFp-valueRegression212178360891.4814.761170.000286Residual1561876.684125.112Total17183659.6Using a = 0.01 to test the null hypothesis H0: b 1 = b 2 = 0, the critical F value is ____.a) 8.68b) 6.36c) 8.40d) 6.11e) 3.36

Q: A multiple regression analysis produced the following tables. The sample size for this analysis is ____________. a) 12 b) 15 c) 17 d) 18 e) 24

Q: A multiple regression analysis produced the following tables.The regression equation for this analysis is ____________.a) y = 1959.71 + 0.46 x1 + 2.16 x2b) y = 1959.71 - 0.46 x1 + 2.16 x2c) y = 1959.71 - 0.46 x1 - 2.16 x2d) y =1959.71 + 0.46 x1 - 2.16 x2e) y =- 0.46 x1 " 2.16 x2

Q: A multiple regression analysis produced the following tables. For x1= 360 and x2 = 220, the predicted value of y is ____________. a) 1314.70 b) 1959.71 c) 1077.58 d) 2635.19 e) 2265.57

Q: A multiple regression analysis produced the following tables. Predictor Coefficients Standard Error t Statistic p-value Intercept 616.6849 154.5534 3.990108 0.000947 x1 -3.33833 2.333548 -1.43058 0.170675 x2 1.780075 0.335605 5.30407 5.83E-05 Source df SS MS F p-value Regression 2 121783 60891.48 14.76117 0.000286 Residual 15 61876.68 4125.112 Total 17 183659.6 The sample size for this analysis is ____________. a) 19 b) 17 c) 34 d) 15 e) 18

Q: A multiple regression analysis produced the following tables. Predictor Coefficients Standard Error t Statistic p-value Intercept 616.6849 154.5534 3.990108 0.000947 x1 -3.33833 2.333548 -1.43058 0.170675 x2 1.780075 0.335605 5.30407 5.83E-05 Source df SS MS F p-value Regression 2 121783 60891.48 14.76117 0.000286 Residual 15 61876.68 4125.112 Total 17 183659.6 The regression equation for this analysis is ____________. a) y = 616.6849 + 3.33833 x1 + 1.780075 x2 b) y = 154.5535 - 1.43058 x1 + 5.30407 x2 c) y = 616.6849 - 3.33833 x1 - 1.780075 x2 d) y = 154.5535 + 2.333548 x1 + 0.335605 x2 e) y = 616.6849 - 3.33833 x1 + 1.780075 x2

Q: The multiple regression formulas used to estimate the regression coefficients are designed to ________________. a) minimize the total sum of squares (SST) b) minimize the sum of squares of error (SSE) c) maximize the standard error of the estimate d) maximize the p-value for the calculated F value e) minimize the mean error

Q: A real estate appraiser is developing a regression model to predict the market value of single family residential houses as a function of heated area, number of bedrooms, number of bathrooms, age of the house, and central heating (yes, no). The "central heating" variable in this model is _______. a) a response variable b) an independent variable c) a quantitative variable d) a dependent variable e) a constant

Q: A real estate appraiser is developing a regression model to predict the market value of single family residential houses as a function of heated area, number of bedrooms, number of bathrooms, age of the house, and central heating (yes, no). The response variable in this model is _______. a) heated area b) number of bedrooms c) market value d) central heating e) residential houses

Q: A human resources analyst is developing a regression model to predict electricity production plant manager compensation as a function of production capacity of the plant, number of employees at the plant, and plant technology (coal, oil, and nuclear). The "number of employees at the plant" variable in this model is ______. a) a qualitative variable b) a dependent variable c) a response variable d) an indicator variable e) an independent variable

Q: A human resources analyst is developing a regression model to predict electricity production plant manager compensation as a function of production capacity of the plant, number of employees at the plant, and plant technology (coal, oil, and nuclear). The "plant technology" variable in this model is ______. a) a response variable b) a dependent variable c) a quantitative variable d) an independent variable e) a constant

Q: A human resources analyst is developing a regression model to predict electricity production plant manager compensation as a function of production capacity of the plant, number of employees at the plant, and plant technology (coal, oil, and nuclear). The response variable in this model is ______. a) plant manager compensation b) plant capacity c) number of employees d) plant technology e) nuclear

Q: A market analyst is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighborhood (urban, suburban, and rural). The "income" variable in this model is ____. a) an indicator variable b) a response variable c) a qualitative variable d) a dependent variable e) an independent variable

Q: A market analyst is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighborhood (urban, suburban, and rural). The "neighborhood" variable in this model is ______. a) an independent variable b) a response variable c) a quantitative variable d) a dependent variable e) a constant

Q: A market analyst is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighborhood (urban, suburban, and rural). The response variable in this model is _____. a) family size b) expenditures on groceries c) household income d) suburban e) household neighborhood

Q: A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch), production plant (Kingsland, and Yorktown), and production shift (day, and evening). In this model, "batch size" is ______. a) a response variable b) an indicator variable c) a dependent variable d) a qualitative variable e) an independent variable

Q: A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch), production plant (Kingsland, and Yorktown), and production shift (day, and evening). In this model, "shift" is ______. a) a response variable b) an independent variable c) a quantitative variable d) a dependent variable e) a constant

Q: A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch), production plant (Kingsland, and Yorktown), and production shift (day, and evening). The response variable in this model is ______. a) batch size b) production shift c) production plant d) total cost e) variable cost

Q: Minitab and Excel output for a multiple regression model show the t tests for the regression coefficients but do not provide a t test for the regression constant.

Q: Minitab and Excel output for a multiple regression model show the F test for the overall model, but do not provide the t tests for the regression coefficients.

Q: The value of adjusted R2 always goes up when a nontrivial explanatory variable is added to a regression model.

Q: The value of R2 always goes up when a nontrivial explanatory variable is added to a regression model.

Q: In a multiple regression model, the proportion of the variation of the dependent variable, y, accounted for the independent variables in the regression model is given by the coefficient of multiple correlation.

Q: The standard error of the estimate of a multiple regression model is computed by taking the square root of the mean squares of error.

Q: The standard error of the estimate of a multiple regression model is essentially the standard deviation of the residuals for the regression model.

Q: If we reject H0: 1= 2=0 using the F-test, then we should conclude that both slopes are different from zero.

Q: In a multiple regression analysis with N observations and k independent variables, the degrees of freedom for the residual error is given by (N - k).

Q: In a multiple regression analysis with N observations and k independent variables, the degrees of freedom for the residual error is given by (N - k - 1).

Q: The mean square error (MSerr) is calculated by dividing the sum of squares error (SSerr) by the number of error degrees of freedom (dferr).

Q: The mean square error (MSerr) is calculated by dividing the sum of squares error (SSerr) by the number of observations in the data set (N).

Q: The F value that is used to test for the overall significance of a multiple regression model is calculated by dividing the sum of mean squares regression (SSreg) by the sum of squares error (SSerr).

Q: The F value that is used to test for the overall significance of a multiple regression model is calculated by dividing the mean square regression (MSreg) by the mean square error (MSerr).

Q: The F test is used to determine whether the overall regression model is significant.

Q: Multiple t-tests are used to determine whether the independent variables in the regression model are significant.

Q: A slope in a multiple regression model is known as a partial slope because it ignores the effects of other explanatory variables.

Q: In the model y = b 0 + b 1x1 + b 2x2 + b 3x3 + e, e is a constant.

Q: In a multiple regression model, the partial regression coefficient of an independent variable represents the increase in the y variable when that independent variable is increased by one unit if the values of all other independent variables are held constant.

Q: Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie's dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept -0.14156 0.292143 -0.48455 0.653331 x 0.105195 0.013231 7.950352 0.001356 Source df SS MS F Se = 0.237 Regression 1 3.550325 3.550325 63.20809 r2 = 0.940483 Residual 4 0.224675 0.056169 Total 5 3.775 For a city with a population of 500,000, Annie's model predicts annual sales of ________________. a) $70,780 b) $5,259 c) $170,780 d) $52,597 e) $152,597

Q: Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie's dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept -0.14156 0.292143 -0.48455 0.653331 x 0.105195 0.013231 7.950352 0.001356 Source df SS MS F Se = 0.237 Regression 1 3.550325 3.550325 63.20809 r2 = 0.940483 Residual 4 0.224675 0.056169 Total 5 3.775 Using a = 0.05, Annie should ________________. a) increase the sample size b) not reject H0: b1 = 0 c) reject H0: b1 = 0 d) suspend judgment e) reject H0: b0 = 0

Q: Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie's dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept -0.14156 0.292143 -0.48455 0.653331 x 0.105195 0.013231 7.950352 0.001356 Source df SS MS F Se = 0.237 Regression 1 3.550325 3.550325 63.20809 r2 = 0.940483 Residual 4 0.224675 0.056169 Total 5 3.775 Annie's sample size is __________. a) 2 b) 4 c) 6 d) 8 e) 10

Q: Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie's dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept -0.14156 0.292143 -0.48455 0.653331 x 0.105195 0.013231 7.950352 0.001356 Source df SS MS F Se = 0.237 Regression 1 3.550325 3.550325 63.20809 r2 = 0.940483 Residual 4 0.224675 0.056169 Total 5 3.775 The numerical value of the correlation coefficient between the historic city tour sales and the size of city population is __________. a) 0.969785 b) 0.940483 c) 0.224675 d) -0.14156 e) 1.000000

Q: Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie's dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept -0.14156 0.292143 -0.48455 0.653331 x 0.105195 0.013231 7.950352 0.001356 Source df SS MS F Se = 0.237 Regression 1 3.550325 3.550325 63.20809 r2 = 0.940483 Residual 4 0.224675 0.056169 Total 5 3.775 Annie's regression model can be written as: __________. a) y = 7.950352 - 0.48455x b) y = -0.48455 + 7.950352x c) y = -0.14156 + 0.105195x d) y = 0.105195 - 0.14156x e) y = 0.105195 + 0.14156x

Q: Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 39.14942 22.30182 1.755436 0.109712 x 1.792312 0.407507 4.398234 0.001339 Source df SS MS F Se = 29.51443 Regression 1 16850.99 16850.99 19.34446 r2 = 0.682478 Residual 9 7839.915 871.1017 Total 10 24690.91 For a household with $50,000 annual income, Abby's model predicts monthly grocery expenditures of ________________. a) $150.35 b) $50.35 c) $1,959.29 d) $128.65 e) $1286.50

Q: Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 39.14942 22.30182 1.755436 0.109712 x 1.792312 0.407507 4.398234 0.001339 Source df SS MS F Se = 29.51443 Regression 1 16850.99 16850.99 19.34446 r2 = 0.682478 Residual 9 7839.915 871.1017 Total 10 24690.91 Using a = 0.05, Abby should ________________. a) reject H0: b1 = 0 b) not reject H0: b1 = 0 c) increase the sample size d) suspend judgment e) reject H0: b0 = 0