w intentionally left blank] Coefficients Standard Error Intercept 12.924 4.425 x1 -3.682 2.630 x2 45.216 12.560 Refer to Exhibit 3. The test statistic used to determine if there is a relationship among the variables equals: Select one: a. .2 b. -1.4 c. .77 d. 5
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Below you are given a partial Excel output based on a sample of 16 observations.
| ANOVA | ||||
|---|---|---|---|---|
| df | SS | MS | F |
|
| Regression | 4,853 | 2,426.5 | ||
| Residual | 485.3 | |||
| [row intentionally left blank] | ||||
| Coefficients | Standard Error |
|||
| Intercept | 12.924 | 4.425 | ||
| x1 | -3.682 | 2.630 | ||
| x2 | 45.216 | 12.560 |
Refer to Exhibit 3. The test statistic used to determine if there is a relationship among the variables equals:
.2
-1.4
.77
5
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- A researcher conducted a repeated measures study comparing three treatment conditions. Refer to attached images and tale to answer a to d. Mean Std. Deviation N Treatment I 1.00 1.414 5 Treatment II 5.00 2.345 5 Treatment III 6.00 1.581 5 In APA format, report the F-ratio related to the treatment effect: Is this treatment effect significant? What is the partial η2 value for the treatment effect? Is this a weak, moderate, or strong effect?Consider the following ANOVA table for a multiple regression model relating housing prices (in thousands of dollars) to the number of bedrooms in the house and the size of the lot on which the house was built (in square feet). There were 7575 total observations. Estimated Price=25,356.83+2776.31(Bedrooms)+0.2478(Lot Size)Estimated Price=25,356.83+2776.31(Bedrooms)+0.2478(Lot Size) ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 302,698.1946302,698.1946 151,349.0973151,349.0973 16.033416.0334 1.7407E-061.7407E-06 Residual 7272 679,651.5550679,651.5550 9439.60499439.6049 Total 7474 982,349.7496982,349.7496 Compute the adjusted coefficient of determination for this regression model. Round your answer to four decimal places.Consider the following ANOVA table for a multiple regression model relating housing prices (in thousands of dollars) to the number of bedrooms in the house and the size of the lot on which the house was built (in square feet). There were 9090 total observations. Estimated Price=20,160.07+2188.83(Bedrooms)+0.2139(Lot Size)Estimated Price=20,160.07+2188.83(Bedrooms)+0.2139(Lot Size) ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 306,443.7975306,443.7975 153,221.8988153,221.8988 21.727421.7274 2.2211E-082.2211E-08 Residual 8787 613,525.5190613,525.5190 7052.01757052.0175 Total 8989 919,969.3165919,969.3165 What percent of variation in housing prices is explained by the number of bedrooms and lot size? Round your answer to two decimal places
- Based on the ANOVA table given, is there enough evidence at the 0.050.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? ANOVA Source df SS MS F Significance F Regression 3 212.987150 70.995717 1.092713 0.448523 Residual 4 259.887850 64.971963 Total 7 472.875000Use the following information from a multiple regression analysis. n=15 b1=2 b2=6 Sb1=1.4 Sb2=0.5 a. Which variable has the largest slope, in units of a t statistic? b. Construct a 90% confidence interval estimate of the population slope, β1. c. At the 0.10 level of significance, determine whether each independent variable makes a significant contribution to the regression model. On the basis of these results, indicate the independent variables to include in this model.Use the following ANOVA table for regression to answer the questions. Analysis of Variance Source DF SS MS F P Regression 1 3375.0 3375.0 20.5 0.000 Residual Error 174 28640.4 164.6 Total 175 32015.4 The -statistic is _________________.The -value is ____________________. Choose the conclusion of this test using a 5% significance level. -Reject H0. The model is not effective. -Do not reject . We did not find evidence that the model is not effective. -Reject H0. The model is effective. -Do not reject . We did not find evidence that the model is effective.
- 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 For x1= 360 and x2 = 220, the…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 These results indicate that…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…
- 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 The regression equation for…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…Consider the following ANOVA table for a simple linear regression. Source Sum Degrees of freedom Mean Square F Regression 6789.5 1 6789.5 181.6 Error 336.5 Total 7126.0 10 What is the value of the mean square error for this regression analysis?