Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience.   Regression Statistics Multiple R 0.73650.7365 R Square 0.54240.5424 Adjusted R Square 0.52250.5225 Standard Error 2124.60962124.6096 Observations 4949     ANOVA   dfdf SSSS MSMS F� Significance F� Regression 22 246,127,958.1791246,127,958.1791 123,063,979.0896123,063,979.0896 27.262927.2629 1.6E-081.6E-08 Residual 4646 207,642,442.8821207,642,442.8821 4,513,966.14964,513,966.1496     Total 4848 453,770,401.0612453,770,401.0612             Coefficients Standard Error t� Stat P-value Lower 95%95% Upper 95%95% Intercept 14256.268814256.2688 2,513.30952,513.3095 5.67235.6723 0.0000008950.000000895 9197.23929197.2392 19,315.298419,315.2984 Education (Years) 2353.85412353.8541 336.0719336.0719 7.00407.0040 0.0000000090.000000009 1677.37651677.3765 3030.33173030.3317 Experience (Years) 832.8371832.8371 390.1917390.1917 2.13442.1344 0.0381716160.038171616 47.421947.4219 1618.25231618.2523     Step 1 of 2 :   What would be your expected salary with no education and no experience?

A business is evaluating their advertising budget, and wishes to determine the relationship between advertising dollars spent and changes in revenue. Below is the output from their regression.  SUMMARY OUTPUT                         Regression Statistics           Multiple R 0.95           R Square 0.90           Adjusted R Square 0.82           Standard Error 0.82           Observations 8                         ANOVA               df SS MS F Significance F   Regression 3 23.188 7.729 11.505 0.020   Residual 4 2.687 0.672       Total 7 25.875                         Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept 83.91 2.03 41.36 0.00 78.28 89.54 TV ($k) 1.96 0.48 4.10 0.01…

A regression analysis was performed and the summary output is shown below.   Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 2020     ANOVA   dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181     Total 1919 2078.6512078.651           Step 1 of 2:  How many independent variables are included in the regression model

A regression analysis was performed and the summary output is shown below.   Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 20     ANOVA   dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181     Total 1919 2078.6512078.651           Step 2 of 2:  Which measure is appropriate for determining the proportion of variation in the dependent variable explained by the set of independent variable(s) in this model?

The owner of a new pizzeria in town wants to study the relationship between weekly revenue and advertising expenditures. All measures are recorded in thousands of dollars. The summary output for the regression model is given below.ANOVA   dfdf SSSS MSMS F� Significance F� Regression 11 20.2147598620.21475986 20.2147598620.21475986 20.9811354520.98113545 0.0013268390.001326839 Residual 99 8.6712580048.671258004 0.963473110.96347311     Total 1010 28.8860178628.88601786         Step 3 of 3:  Which statistic is most appropriate for the pizzeria owner to determine the usefulness of the regression model and why?

Big Time Corporation wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the following information (note: only a portion of the regression analysis results is presented here) SUMMARY OUTPUT Regression Statistics Multiple R 0.998041188 R Square 0.996086212 Adjusted R Square 0.994781616 Standard Error 917.1714273 Observations 5 ANOVA df SS MS F Significance F Regression 1 642276389.7 642276389.7 763.5208906 0.000104039 Residual 3 2523610.281 841203 427 Total 4 644800000 Coefficients Standard Errort Stat P-value Intercept 6679.617454 931.8558049 7.168080532 0.005592948 3714.03639 X Variable 9.796772265 0.354545968 27.63188178 0.000104039 8.66844876 What is variable cost per machine hour (rounded to the nearest cent)? OA. $6,679.62 OB. $917.17 OC. $9.80 OD. $.99 Lower 95% Upper 95.0% 9645.198517 10.92509577

The owner of a new pizzeria in town wants to study the relationship between weekly revenue and advertising expenditures. All measures are recorded in thousands of dollars. The summary output for the regression model is given below.ANOVA   dfdf SSSS MSMS F� Significance F� Regression 11 15.1540376815.15403768 15.1540376815.15403768 16.0586340516.05863405 0.0102486910.010248691 Residual 55 4.7183457924.718345792 0.943669160.94366916     Total 66 19.8723834719.87238347         Step 2 of 3 :   What is the adjusted coefficient of determination for this model, R2a��2? Round your answer to four decimal places

Do the following plots show 1. Constant variability  2. Nearly normal residuals 3.  Independent observations for SLR (conditions for linear regression)

Rewrite the regression model to include coefficients from your regression analysis output and then answer the following question   What would be the company's loss if the significant variable(s) change per unit?     SUMMARY OUTPUT                                   Regression Statistics               Multiple R 0.93082               R Square 0.866425               Adjusted R Square 0.85833               Standard Error 4108.993               Observations 36                                 ANOVA                   df SS MS F Significance F       Regression 2 3.61E+09 1.81E+09 107.0261 3.75E-15       Residual 33 5.57E+08 16883824           Total 35 4.17E+09                                 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 3996.678 6603.651 0.605223 0.549171 -9438.55 17431.91 -9438.55 17431.91 X Variable 1 43.5364 3.589484 12.12887 1.05E-13 36.23354 50.83926 36.23354 50.83926 X Variable 2…

The ANOVA summary table to the right is for a multiple regression model with six independent variables. Complete parts (a) through (e). a. Determine the regression mean square (MSR) and the mean square error (MSE). (Round to four decimal places as needed.) (Round to four decimal places as needed.) MSR= MSE = b. Compute the overall FSTAT test statistic. FSTAT (Round to two decimal places as needed.) C Source Regression Error Total Degrees of Sum of Freedom Squares 240 190 430 6 26 32