A multiple regression analysis produced the following tables. Summary Output                     Regression Statistics                     Multiple R   0.978724022                 R Square   0.957900711                         Adjusted R Square   0.952287472                 Standard Error   67.67055418                 Observations   18                                             ANOVA                         df   SS   MS   F   Significance F Regression   2   1562918.941   781459.5   170.6503   4.80907E-11 Residual   15   68689.55855   4579.304         Total   17   1631608.5                                       Coefficients   Standard Error   t Stat   P-value     Intercept   1959.709718   306.4905312   6.39403   1.21E-05     X1   -0.469657287   0.264557168   -1.77526   0.096144     X2   -2.163344882   0.278361425   -7.77171   1.23E-06     Using α = 0.01 to test the model, these results indicate that ____________. at least one of the regression variables is a significant predictor of y none of the regression variables are significant predictors of y y cannot be sufficiently predicted using these data the y intercept in this model is the best predictor variable y is a good predictor of the regression variables in the 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 18.5789245818.57892458 18.5789245818.57892458 18.5997445918.59974459 0.0025711230.002571123 Residual 88 7.9910450367.991045036 0.998880630.99888063     Total 99 26.5699696226.56996962         Step 2 of 3 :   What is the adjusted coefficient of determination for this model, R2a��2? Round your answer to four decimal places

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 0.73360.7336 R Square 0.53810.5381 Adjusted R Square 0.51800.5180 Standard Error 2140.27632140.2763 Observations 49     ANOVA   dfdf SSSS MSMS F� Significance F� Regression 22 245,430,999.7671245,430,999.7671 122,715,499.8836122,715,499.8836 26.789226.7892 1.9E-081.9E-08 Residual 4646 210,716,007.0084210,716,007.0084 4,580,782.76114,580,782.7611     Total 4848 456,147,006.7755456,147,006.7755             Coefficients Standard Error t� Stat P-value Lower 95%95% Upper 95%95% Intercept 14276.146814276.1468 2,531.84252,531.8425 5.63865.6386 0.0000010040.000001004 9179.81229179.8122 19,372.481419,372.4814 Education (Years) 2349.95952349.9595 338.5500338.5500 6.94126.9412 0.0000000110.000000011 1668.49371668.4937 3031.42533031.4253 Experience (Years) 833.6183833.6183…

Compute the predicted value of IPT for the mean value of PSS (static facial expressions (higher PSS)) The regression formula is: The terms of the equation and their values are:

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7) Below is a multiple regression in which the dependent variable is market value of houses and the independent variables are the age of the house and square footage of the house. The regression was estimated for 42 houses. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total df 2 39 41 0.745495 0.555762 0.532981 7211.848 42 SS 2537650171 2028419591 4566069762 Coefficients Standard Error MS 1.27E+09 52010759 F 24.39544 Significance F 1.3443E-07 Upper 95% t Stat P-value Lower 95% Intercept 47331.38 13884.34664 3.408974 0.001528 19247.6673 House Age -825.161 607.3128421 -1.35871 0.182046 -2053.5662 Square Feet 40.91107 6.696523994 6.109299 3.65E-07 27.3660835 7A. What is the estimated regression equation for determining the market value of houses? 7B. Discuss tests of significance of the regression 7C. What percentage of the variation in the dependent variable, Market Value, is explained by the regression…

The results of the regression analysis to estimate setup costs using the number of setup hours are as follows: SUMMARY OUTPUT                 Regression Statistics       Multiple R 0.965127       R Square 0.931469       Adjusted R Square 0.921679       Standard Error 285.6803       Observations 9                 ANOVA           df SS MS F Regression 1 7765004 7765004 95.14395 Residual 7 571292.5 81613.21   Total 8   8336296                 Coefficients Std Error t Stat P-value Intercept 2498.644 680.6304 3.671073 0.007953 X Variable 1 2.506915 0.257009 9.754176 2.52E–05           What percentage change (rounded to one decimal place) in setup costs can be explained by changes in setup hours? a.95.1% b.96.5% c.25.7% d.none of these e.93.1%

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 33 18.154037618.1540376 6.051345876.05134587 17.6080562317.60805623 4.3383E-034.3383E-03 Residual 55 1.7183457921.718345792 0.343669160.34366916     Total 88 19.8723833919.87238339         Step 2 of 3 :   What is the adjusted coefficient of determination for this model, R2a��2? Round your answer to four decimal places

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…

Analysis of Variance Source DF SS MS Regression 1 Residual Error 13 0.2364 Total 14 11.3240 What is the value for MSR (Mean Square for Regression)?