Q&A Hero

Q: Inspection of the following table of correlation coefficients for variables in a multiple regression analysis reveals that the first independent variable entered by the forward selection procedure will be ___________. y x1 x2 x3 x4 x5 y 1 x1 -0.44008 1 x2 0.566053 -0.51728 1 x3 0.064919 -0.22264 -0.00734 1 x4 -0.35711 0.028957 -0.49869 0.260586 1 x5 0.426363 -0.20467 0.078916 0.207477 0.023839 1 a) x1 b) x2 c) x3 d) x4 e) x5

Q: Inspection of the following table of correlation coefficients for variables in a multiple regression analysis reveals that the first independent variable entered by the forward selection procedure will be ___________. y x1 x2 x3 x4 x5 y 1 x1 -0.1661 1 x2 0.231849 -0.51728 1 x3 0.423522 -0.22264 -0.00734 1 x4 -0.33227 0.028957 -0.49869 0.260586 1 x5 0.199796 -0.20467 0.078916 0.207477 0.023839 1 a) x2 b) x3 c) x4 d) x5 e) x1

Q: An "all possible regressions" search of a data set containing "k" independent variables will produce __________ regressions. a) 2k -1 b) 2k - 1 c) k2 - 1 d) 2k - 1 e) 2k

Q: An "all possible regressions" search of a data set containing 8 independent variables will produce ______ regressions. a) 8 b) 15 c) 256 d) 64 e) 255

Q: An "all possible regressions" search of a data set containing 5 independent variables will produce ______ regressions. a) 31 b) 10 c) 25 d) 32 e) 24

Q: An "all possible regressions" search of a data set containing 7 independent variables will produce ______ regressions. a) 13 b) 127 c) 48 d) 64 e) 97

Q: Which of the following iterative search procedures for model-building in a multiple regression analysis adds variables to model as it proceeds, but does not reevaluate the contribution of previously entered variables? a) Backward elimination b) Stepwise regression c) Forward selection d) All possible regressions e) Forward elimination

Q: Which of the following iterative search procedures for model-building in a multiple regression analysis starts with all independent variables in the model and then drops non-significant independent variables is a step-by-step manner? a) Backward elimination b) Stepwise regression c) Forward selection d) All possible regressions e) Backward selection

Q: Which of the following iterative search procedures for model-building in a multiple regression analysis reevaluates the contribution of variables previously include in the model after entering a new independent variable? a) Backward elimination b) Stepwise regression c) Forward selection d) All possible regressions e) Backward selection

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 weekly household expenditures on groceries (in $'s), and her independent variables are annual household income (in $1,000's) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table. Coefficients Standard Error t Statistic p-value Intercept 19.68247 10.01176 1.965934 0.077667 x1 (income) 1.735272 0.174564 9.940612 1.68E-06 x2 (neighborhood) 49.12456 7.655776 6.416667 7.67E-05 For two households, one suburban and one rural, Abby's model predicts ________. a) equal weekly expenditures for groceries b) the suburban household's weekly expenditures for groceries will be $49 more c) the rural household's weekly expenditures for groceries will be $49 more d) the suburban household's weekly expenditures for groceries will be $8 more e) the rural household's weekly expenditures for groceries will be $49 less

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 weekly household expenditures on groceries (in $'s), and her independent variables are annual household income (in $1,000's) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table. Coefficients Standard Error t Statistic p-value Intercept 19.68247 10.01176 1.965934 0.077667 x1 (income) 1.735272 0.174564 9.940612 1.68E-06 x2 (neighborhood) 49.12456 7.655776 6.416667 7.67E-05 For a suburban household with $90,000 annual income, Abby's model predicts weekly grocery expenditure of ________________. a) $156.19 b) $224.98 c) $444.62 d) $141.36 e) $175.86

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 weekly household expenditures on groceries (in $'s), and her independent variables are annual household income (in $1,000's) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table. Coefficients Standard Error t Statistic p-value Intercept 19.68247 10.01176 1.965934 0.077667 x1 (income) 1.735272 0.174564 9.940612 1.68E-06 x2 (neighborhood) 49.12456 7.655776 6.416667 7.67E-05 For a rural household with $90,000 annual income, Abby's model predicts weekly grocery expenditure of ________________. a) $156.19 b) $224.98 c) $444.62 d) $141.36 e) $175.86

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 weekly household expenditures on groceries (in $'s), and her independent variables are annual household income (in $1,000's) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table. Coefficients Standard Error t Statistic p-value Intercept 19.68247 10.01176 1.965934 0.077667 X1 (income) 1.735272 0.174564 9.940612 1.68E-06 X2 (neighborhood) 49.12456 7.655776 6.416667 7.67E-05 Abby's model is ________________. a) y = 19.68247 + 10.01176 x1 + 1.965934 x2 b) y = 1.965934 + 9.940612 x1 + 6.416667 x2 c) y = 10.01176 + 0.174564 x1 + 7.655776 x2 d) y = 19.68247 - 1.735272 x1 + 49.12456 x2 e) y = 19.68247 + 1.735272 x1 + 49.12456 x2

Q: Alan Bissell, a market analyst for City Sound Online Mart, is analyzing sales from heavy metal song downloads. Alan's dependent variable is annual heavy metal song download sales (in $1,000,000's), and his independent variables are website visitors (in 1,000's) and type of download format requested (0 = MP3, 1 = other). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 1.7 0.384212 4.424638 0.00166 x1(website visitors) 0.04 0.014029 2.851146 0.019054 x2(download format) -1.5666667 0.20518 -7.63558 3.21E-05 For the same number of website visitors, what is difference between the predicted sales for MP3 versus "˜other" heavy metal song downloads a) $1,566,666 higher sales for "˜other" formats b) the same sales for both formats c) $1,566,666 lower sales for the "˜other" format d) $1,700,000 higher sales for the MP3 format e) $ 1,700,000 lower sales for the "˜other" format

Q: Alan Bissell, a market analyst for City Sound Online Mart, is analyzing sales from heavy metal song downloads. Alan's dependent variable is annual heavy metal song download sales (in $1,000,000's), and his independent variables are website visitors (in 1,000's) and type of download format requested (0 = MP3, 1 = other). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 1.7 0.384212 4.424638 0.00166 x1(website visitors) 0.04 0.014029 2.851146 0.019054 x2(download format) -1.5666667 0.20518 -7.63558 3.21E-05 For a "˜other" download formats with 10,000 website visitors, Alan's model predicts annual sales of heavy metal song downloads of ________________. a) $2,100,000 b) $524,507 c) $533,333 d) $729,683 e) $210,000

Q: Alan Bissell, a market analyst for City Sound Online Mart, is analyzing sales from heavy metal song downloads. Alan's dependent variable is annual heavy metal song download sales (in $1,000,000's), and his independent variables are website visitors (in 1,000's) and type of download format requested (0 = MP3, 1 = other). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 1.7 0.384212 4.424638 0.00166 x1(website visitors) 0.04 0.014029 2.851146 0.019054 x2(download format) -1.5666667 0.20518 -7.63558 3.21E-05 For an MP3 sales with 10,000 website visitors, Alan's model predicts annual sales of heavy metal dong downloads of ________________. a) $2,100,000 b) $524,507 c) $533,333 d) $729,683 e) $21,000,000

Q: Alan Bissell, a market analyst for City Sound Online Mart, is analyzing sales from heavy metal song downloads. Alan's dependent variable is annual heavy metal song download sales (in $1,000,000's), and his independent variables are website visitors (in 1,000's) and type of download format requested (0 = MP3, 1 = other). Regression analysis of the data yielded the following tables. Coefficients Standard Error t Statistic p-value Intercept 1.7 0.384212 4.424638 0.00166 x1(website visitors) 0.04 0.014029 2.851146 0.019054 x2(download format) -1.5666667 0.20518 -7.63558 3.21E-05 Alan's model is ________________. a) y = 1.7 + 0.384212 x1 + 4.424638 x2 + 0.00166 x3 b) y = 1.7 + 0.04 x1 + 1.5666667 x2 c) y = 0.384212 + 0.014029 x1 + 0.20518 x2 d) y = 4.424638 + 2.851146 x1 - 7.63558 x2 e) y = 1.7 + 0.04 x1 - 1.5666667 x2

Q: Hope Hernandez is the new regional Vice President for a large gasoline station chain. She wants a regression model to predict sales in the convenience stores. Her data set includes two qualitative variables: the gasoline station location (inner city, freeway, and suburbs), and curb appeal of the convenience store (low, medium, and high). The number of dummy variables needed for Hope's regression model is ______. a) 2 b) 4 c) 6 d) 8 e) 9

Q: Hope Hernandez is the new regional Vice President for a large gasoline station chain. She wants a regression model to predict sales in the convenience stores. Her data set includes two qualitative variables: the gasoline station location (inner city, freeway, and suburbs), and curb appeal of the convenience store (low, medium, and high). The number of dummy variables needed for "curb appeal" in Hope's regression model is ______. a) 1 b) 2 c) 3 d) 4 e) 5

Q: Yvonne Yang, VP of Finance at Discrete Components, Inc. (DCI), wants a regression model which predicts the average collection period on credit sales. Her data set includes two qualitative variables: sales discount rates (0%, 2%, 4%, and 6%), and total assets of credit customers (small, medium, and large). The number of dummy variables needed for "total assets of credit customer" in Yvonne's regression model is ________. a) 1 b) 2 c) 3 d) 4 e) 7

Q: Yvonne Yang, VP of Finance at Discrete Components, Inc. (DCI), wants a regression model which predicts the average collection period on credit sales. Her data set includes two qualitative variables: sales discount rates (0%, 2%, 4%, and 6%), and total assets of credit customers (small, medium, and large). The number of dummy variables needed for "sales discount rate" in Yvonne's regression model is ________. a) 1 b) 2 c) 3 d) 4 e) 7

Q: If a qualitative variable has 4 categories, how many dummy variables must be created and used in the regression analysis?a) 3b) 4c) 5d) 6e) 7

Q: In multiple regression analysis, qualitative variables are sometimes referred to as ___. a) dummy variables b) quantitative variables c) dependent variables d) performance variables e) cardinal variables

Q: After a transformation of the y-variable values into log y, and performing a regression analysis produced the following tables. Coefficients Standard Error t Statistic p-value Intercept 2.005349 0.097351 20.59923 4.81E-18 x 0.027126 0.009518 2.849843 0.008275 df SS MS F p-value Regression 1 0.196642 0.196642 8.121607 0.008447 Residual 26 0.629517 0.024212 Total 27 0.826159 For x1= 10, the predicted value of y is ____________. a) 155.79 b) 1.25 c) 2.42 d) 189.06 e) 18.90

Q: A local parent group was concerned with the increasing school cost for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.CoefficientsStandard Errort Statisticp-valueIntercept707.9144435.11831.6269470.114567x12.90330781.628020.0355680.971871x1211.912973.8062113.1298780.003967dfSSMSFp-valueRegression2320551531602757747.345571.49E-09Residual279140128338523.3Total2941195281For a child in grade 10 (x1= 10) the predicted value of y is ____________.a) 707.91b) 1,117.38c) 856.08d) 2,189.54e) 1,928.24

Q: A local parent group was concerned with the increasing school cost for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.CoefficientsStandard Errort Statisticp-valueIntercept707.9144435.11831.6269470.114567x12.90330781.628020.0355680.971871x1211.912973.8062113.1298780.003967dfSSMSFp-valueRegression2320551531602757747.345571.49E-09Residual279140128338523.3Total2941195281For a child in grade 5 (x1= 2), the predicted value of y is ____________.a) 707.91b) 1,020.26c) 781.99d) 840.06e) 1078.32

Q: A local parent group was concerned with the increasing school cost for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.CoefficientsStandard Errort Statisticp-valueIntercept707.9144435.11831.6269470.114567x12.90330781.628020.0355680.971871x1211.912973.8062113.1298780.003967dfSSMSFp-valueRegression2320551531602757747.345571.49E-09Residual279140128338523.3Total2941195281These results indicate that ____________.a) none of the predictor variables is 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) x12 is the only predictor variable significant at the 5% levele) each predictor variable is insignificant at the 5% level

Q: A local parent group was concerned with the increasing school cost for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.CoefficientsStandard Errort Statisticp-valueIntercept707.9144435.11831.6269470.114567x12.90330781.628020.0355680.971871x1211.912973.8062113.1298780.003967dfSSMSFp-valueRegression2320551531602757747.345571.49E-09Residual279140128338523.3Total2941195281Using a = 0.05 to test the null hypothesis H0: b2 = 0, the critical t value is ____.a) 1.311b) 1.699c) 1.703d) 2.052e) 2.502

Q: A local parent group was concerned with the increasing school cost for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.CoefficientsStandard Errort Statisticp-valueIntercept707.9144435.11831.6269470.114567x12.90330781.628020.0355680.971871x1211.912973.8062113.1298780.003967dfSSMSFp-valueRegression2320551531602757747.345571.49E-09Residual279140128338523.3Total2941195281Using a = 0.05 to test the null hypothesis H0: b1 = 0, the critical t value is ____.a) 1.311b) 1.699c) 1.703d) 2.502e) 2.052

Q: A local parent group was concerned with the increasing school cost for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.CoefficientsStandard Errort Statisticp-valueIntercept707.9144435.11831.6269470.114567x12.90330781.628020.0355680.971871x1211.912973.8062113.1298780.003967dfSSMSFp-valueRegression2320551531602757747.345571.49E-09Residual279140128338523.3Total2941195281Using a = 0.01 to test the null hypothesis H0: b1 = b2 = 0, the critical F value is ____.a) 5.42b) 5.49c) 7.60d) 3.35e) 2.49

Q: A local parent group was concerned with the increasing school cost for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.CoefficientsStandard Errort Statisticp-valueIntercept707.9144435.11831.6269470.114567x12.90330781.628020.0355680.971871x1211.912973.8062113.1298780.003967dfSSMSFp-valueRegression2320551531602757747.345571.49E-09Residual279140128338523.3Total2941195281The sample size for this analysis is ____________.a) 27b) 29c) 30d) 25e) 28

Q: A local parent group was concerned with the increasing school cost for families with school aged children. The parent group was interested in understanding the relationship between the academic grade level for the child and the total costs spent per child per academic year. They performed a multiple regression analysis using total cost as the dependent variable and academic year (x1) as the independent variables. The multiple regression analysis produced the following tables.CoefficientsStandard Errort Statisticp-valueIntercept707.9144435.11831.6269470.114567x12.90330781.628020.0355680.971871x1211.912973.8062113.1298780.003967DfSSMSFp-valueRegression2320551531602757747.345571.49E-09Residual279140128338523.3Total2941195281The regression equation for this analysis is ____________.a) y = 707.9144 + 2.903307 x1 + 11.91297 x12b) y = 707.9144 + 435.1183 x1 + 1.626947 x12c) y = 435.1183 + 81.62802 x1 + 3.806211 x12d) y = 1.626947 + 0.035568 x1 + 3.129878 x12e) y = 1.626947 + 0.035568 x1 - 3.129878 x12

Q: A multiple regression analysis produced the following tables. Coefficients Standard Error t Statistic p-value Intercept 1411.876 762.1533 1.852483 0.074919 x1 35.18215 96.8433 0.363289 0.719218 x12 7.721648 3.007943 2.567086 0.016115 df SS MS F Regression 2 58567032 29283516 57.34861 Residual 25 12765573 510622.9 Total 27 71332605 For x1= 20, the predicted value of y is ____________. a) 5,204.18. b) 2,031.38 c) 2,538.86 d) 6262.19 e) 6,535.86

Q: A multiple regression analysis produced the following tables. Coefficients Standard Error t Statistic p-value Intercept 1411.876 762.1533 1.852483 0.074919 x1 35.18215 96.8433 0.363289 0.719218 x12 7.721648 3.007943 2.567086 0.016115 df SS MS F Regression 2 58567032 29283516 57.34861 Residual 25 12765573 510622.9 Total 27 71332605 For x1= 10, the predicted value of y is ____________. a) 8.88. b) 2,031.38 c) 2,53.86 d) 262.19 e) 2,535.86

Q: A multiple regression analysis produced the following tables. Coefficients Standard Error t Statistic p-value Intercept 1411.876 762.1533 1.852483 0.074919 x1 35.18215 96.8433 0.363289 0.719218 x12 7.721648 3.007943 2.567086 0.016115 df SS MS F Regression 2 58567032 29283516 57.34861 Residual 25 12765573 510622.9 Total 27 71332605 Using a = 0.10 to test the null hypothesis H0: b2 = 0, the critical t value is ____. a) 1.316 b) 1.314 c) 1.703 d) 1.780 e) 1.708

Q: A multiple regression analysis produced the following tables. Coefficients Standard Error t Statistic p-value Intercept 1411.876 762.1533 1.852483 0.074919 x1 35.18215 96.8433 0.363289 0.719218 x12 7.721648 3.007943 2.567086 0.016115 df SS MS F Regression 2 58567032 29283516 57.34861 Residual 25 12765573 510622.9 Total 27 71332605 Using a = 0.10 to test the null hypothesis H0: b1 = 0, the critical t value is ____. a) 1.316 b) 1.314 c) 1.703 d) 1.780 e) 1.708

Q: A multiple regression analysis produced the following tables. Coefficients Standard Error t Statistic p-value Intercept 1411.876 762.1533 1.852483 0.074919 x1 35.18215 96.8433 0.363289 0.719218 x12 7.721648 3.007943 2.567086 0.016115 df SS MS F Regression 2 58567032 29283516 57.34861 Residual 25 12765573 510622.9 Total 27 71332605 Using a = 0.05 to test the null hypothesis H0: b1 = b2 = 0, the critical F value is ____. a) 4.24 b) 3.39 c) 5.57 d) 3.35 e) 2.35

Q: A multiple regression analysis produced the following tables. Coefficients Standard Error t Statistic p-value Intercept 1411.876 762.1533 1.852483 0.074919 x1 35.18215 96.8433 0.363289 0.719218 x12 7.721648 3.007943 2.567086 0.016115 df SS MS F Regression 2 58567032 29283516 57.34861 Residual 25 12765573 510622.9 Total 27 71332605 The sample size for this analysis is ____________. a) 28 b) 25 c) 30 d) 27 e) 2

Q: A multiple regression analysis produced the following tables. Coefficients Standard Error t Statistic p-value Intercept 1411.876 762.1533 1.852483 0.074919 x1 35.18215 96.8433 0.363289 0.719218 x12 7.721648 3.007943 2.567086 0.016115 df SS MS F Regression 2 58567032 29283516 57.34861 Residual 25 12765573 510622.9 Total 27 71332605 The regression equation for this analysis is ____________. a) y = 762.1533 + 96.8433 x1 + 3.007943 x12 b) y = 1411.876 + 762.1533 x1 + 1.852483 x12 c) y = 1411.876 + 35.18215 x1 + 7.721648 x12 d) y = 762.1533 + 1.852483 x1 + 0.074919 x12 e) y = 762.1533 - 1.852483 x1 + 0.074919 x12

Q: The following scatter plot indicates that _________. a) a x2 transform may be useful b) a log y transform may be useful c) a x4 transform may be useful d) no transform is needed e) a x3 transform may be useful

Q: The following scatter plot indicates that _________.a) a log x transform may be usefulb) a log y transform may be usefulc) an x2 transform may be usefuld) no transform is needede) a (- x) transform may be useful

Q: The following scatter plot indicates that _________. a) a log x transform may be useful b) a log y transform may be useful c) a x2 transform may be useful d) no transform is needed e) a 1/x transform may be useful

Q: The following scatter plot indicates that _________. a) a log x transform may be useful b) a y2 transform may be useful c) a x2 transform may be useful d) no transform is needed e) a 1/x transform may be useful

Q: Multiple linear regression models can handle certain nonlinear relationships by ________. a) biasing the sample b) recoding or transforming variables c) adjusting the resultant ANOVA table d) adjusting the observed t and F values e) performing nonlinear regression

Q: The logistic regression model constrains the estimated probabilities to lie between 0 and 100.

Q: We may use logistic regression when the dependent variable is a dummy variable, coded 0 or 1.

Q: If the variance inflation factor is bigger than 10, the regression analysis might suffer from the problem of multicollinearity.

Q: If each pair of independent variables is weakly correlated, there is no problem of multicollinearity.

Q: If two or more independent variables are highly correlated, the regression analysis might suffer from the problem of singular collinearity.

Q: Stepwise regression is one of the ways to prevent the problem of multicollinearity.

Q: If a data set contains k independent variables, the "all possible regression" search procedure will determine 2k " 1 different models.

Q: If a data set contains k independent variables, the "all possible regression" search procedure will determine 2k different models.

Q: If a qualitative variable has c categories, then only (c - 1) dummy variables must be included in the regression model.

Q: If a qualitative variable has c categories, then c dummy variables must be included in the regression model, one for each category.

Q: A qualitative variable which represents categories such as geographical territories or job classifications may be included in a regression model by using indicator or dummy variables.

Q: Qualitative data can be incorporated into linear regression models using indicator variables.

Q: The interaction between two independent variables can be examined by including a new variable, which is the sum of the two independent variables, in the regression model.

Q: If the effect of an independent variable (e.g., square footage) on a dependent variable (e.g., price) is affected by different ranges of values for a second independent variable (e.g., age ), the two independent variables are said to interact.

Q: If a square-transformation is applied to a series of positive numbers, all greater than 1, the numerical values of the numbers in the transformed series will be smaller than the corresponding numbers in the original series.

Q: A logarithmic transformation may be applied to both positive and negative numbers.

Q: Recoding data cannot improve the fit of a regression model.

Q: A linear regression model can be used to explore the possibility that a quadratic relationship may exist between two variables by suitably transforming the independent variable.

Q: A linear regression model cannot be used to explore the possibility that a quadratic relationship may exist between two variables.

Q: The regression model y = b0 + b1 x1 + b2 x21 + e is called a quadratic model.

Q: The regression model y = b0 + b1 x1 + b2 x2 + b3 x3 + e is a third order model.

Q: The regression model y = b0 + b1 x1 + b2 x2 + b3 x1x2 + e is a first order model.

Q: Regression models in which the highest power of any predictor variable is 1 and in which there are no cross product terms are referred to as first-order models.

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

Q: In the multiple regression model y = b 0 + b 1x1 + b 2x2 + b 3x3 + e, the b coefficients of the x variables are called partial regression coefficients.

Q: The model y = b 0 + b 1x1 + b 2x2 + b 3x3 + e is a first-order regression model.

Q: The model y = b 0 + b 1x1 + b 2x2 + e is a second-order regression model.

Q: Regression analysis with two dependent variables and two or more independent variables is called multiple regression.

Q: A multiple regression analysis produced the following output from Excel. The correlation coefficient is ____________. a) 0.9787 b) 0.9579 c) 0.9523 d) 67.671 e) 0.0489

Q: A multiple regression analysis produced the following output from Excel. The coefficient of multiple determination is ____________. a) 0.9787 b) 0.9579 c) 0.9523 d) 67.671 e) 0.0489

Q: A multiple regression analysis produced the following output from Excel. The overall proportion of variation of y accounted by x1 and x2 is _______ a) 0.9787 b) 0.9579 c) 0.9523 d) 67.671 e) 0.0489

Q: A multiple regression analysis produced the following output from Minitab.Regression Analysis: Y versus x1 and x2Predictor Coef SE Coef T PConstant -0.0626 0.2034-0.310.762 x1 1.1003 0.54412.020.058 x2 -0.8960 0.5548-1.610.124S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8%Analysis of VarianceSource DF SSMS F PRegression 2 4.70132.3506730.000Residual Error 18 0.57960.0322Total 20 5.2809The overall proportion of variation of y accounted by x1 and x2 is _______a) 0.179b) 0.89c) 0.878d) 0.203e) 0.5441

Q: A multiple regression analysis produced the following output from Minitab.Regression Analysis: Y versus x1 and x2Predictor Coef SE Coef T PConstant -0.0626 0.2034-0.310.762 x1 1.1003 0.54412.020.058 x2 -0.8960 0.5548-1.610.124S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8%Analysis of VarianceSource DF SSMS F PRegression 2 4.70132.3506730.000Residual Error 18 0.57960.0322Total 20 5.2809These 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) at least one of the variable is 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 The adjusted R2 is ____________. a) 0.2079 b) 0.0860 c) 0.5440 d) 0.7921 e) 1.0000

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 coefficient of multiple determination is ____________. a) 0.2079 b) 0. 0860 c) 0.5440 d) 0.7921 e) 0.5000

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 For x1= 40 and x2 = 90, the predicted value of y is ____________. a) 753.77 b) 1,173.00 c) 1,355.26 d) 3,719.39 e) 1,565.75