Based on the following table, what is the sample regression equation? Standard Coefficients Error t Stat p-value Intercept 10,667.2167 7,538.0564 1.311 0.1927 Cost 0.1512 0.941 3.917 0.0002 Grad 184.4909 59.5840 2.574 0.0114 Debt 120.8303 110.0560 1.207 0.2300 Multiple Choice Earnings = 10,667.2167 + 0.1512Cost + 184.4909Grad + 120.8303Debt Earnings = 10,667.2167 – 0.151Cost + 184.4909Grad + 120.830Debt Earnings = 10,667.2167 – 0.151Cost + 184.4909Grad – 120.830Debt Earnings = 10,667.2167 + 0.151Cost + 184.4909Grad – 120.830 Debt

pare percentage differences between 2 or more categories of an independent variable), the following statistical test should be used: a. Simple linear regression b. Pearson correlation coefficient c. T test d. Chi-square test

A company would like to know if efficiency can be improved through a training course for employees. A sample of employees is taken. The time taken for an employee to complete a task before training and after training is observed.

In an ANOVA table for a multiple regression analysis, the global test of significance is based on the _________. Select one: a. Regression mean square divided by the mean square error b. Treatment mean square and block mean square c. Treatment mean square divided by the error variation d. Block and error variation

Refer to the ANOVA table for this regression.  Source SS d.f. MS Regression 1,164,578 5 232,916 Residual 1,500,689 45 33,349 Total 2,665,267 50    (a) State the degrees of freedom for the F test for overall significance.

In a regression analysis, three independent variables are used in the equation based on a sample of 47 observations. In the ANOVA table for a multiple regression analysis, what are the degrees of freedom associated with the F-statistic?   Multiple Choice   3 and 46   4 and 47   3 and 43   2 and 46

3.Which model do you think is the “best” reduced model? Discuss why you choose this model. Analysis of Variance Table (Step back model) Response: rent           Df Sum Sq Mean Sq F value    Pr(>F)    age        1  21000   21000 17.1136 0.0003079 *** sqft       1  35364   35364 28.8196 1.134e-05 *** sd         1   5961    5961  4.8576 0.0362339 *  unts       1   8678    8678  7.0722 0.0130049 *  gar        1  33364   33364 27.1899 1.713e-05 *** cp         1   7641    7641  6.2269 0.0189934 *  Residuals 27  33131    1227                      --- Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1   Step forward model: Response: rent           Df Sum Sq Mean Sq F value    Pr(>F)    age        1  21000   21000 16.6255  0.000406 *** sqft       1  35364   35364 27.9976 1.757e-05 *** sd         1   5961    5961  4.7191  0.039517 *  unts       1   8678    8678  6.8705  0.014697 *  gar        1  33364   33364 26.4144 2.599e-05 *** cp         1   7641    7641  6.0493  0.021176 *…

Using the following information:    Coefficients Intercept -12.8094 Independent variable 2.1794 ANOVA   df SS MS F Regression 1 12,323.80 12,323.80 90.0481 Residual 8 1,454.602 136.8550   Total 9 13,778.4     What is the correlation coefficient?   Multiple Choice   0.8944   0.9457   −0.9457   0.8878