Based on the below data what will be the value of multiple R?
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- A researcher wants to predict the effect of the number of times a person eats every day and the number of times they exercise on BMI. What statistical test would work best? A. Pearson's R B. Spearman Rho C. Linear regression D. Multiple regressionThe managing partner of an advertising agency believes that his company's sales are related to the industry sales. He uses Microsoft Excel’s Data Analysis tool to analyze the last 4 years of quarterly data (i.e., n = 16) with the following results: Regression StatisticsMultiple R 0.802R Square 0.643Adjusted R Square 0.618Standard Error SYX 0.9224Observations 16 ANOVA df SS MS F Sig.FRegression 1 21.497 21.497 25.27 0.000Error 14 11.912 0.851Total 15 33.409 Predictor Coef StdError tStat P-valueIntercept 3.962 1.440 2.75 0.016Industry 0.040451 0.008048 5.03 0.000 Durbin-Watson Statistic 1.59 Referring to Table 13-5, the prediction for a quarter in which X = 80 is Y -hat = ________.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 results of the regression analysis to estimate maintenance costs using the number of machine hours are as follows: SUMMARY OUTPUT Regression Statistics Multiple R 0.965203 R Square 0.931617 Adjusted R Square 0.914522 Standard Error 4361.72 Observations 6 ANOVA df SS MS F Regression 1 1.04E+09 1.04E+09 54.49443 Residual 4 76098414 19024604 Total 5 1.11E+09 Coefficients Std Error t Stat P-value Intercept 8707.49 3961.828 2.197847 0.092875 X Variable 1 9.220886 1.249098 7.382034 0.001795 The cost formula to estimate maintenance cost would be a.none of these b.$8707.49 + $9.22X c.$8707.49 + ($9.22 × 0.93) d.$8707.49 × 0.93 e.$8707.49 - $9.22XThe service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 20.2 4.357 4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of…The service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 20.2 4.357 4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of…
- Using the regression line attached. Based on only the above plot, one can conclude: a) height causes an increase in weight b) weight causes an increase in height c) taller people are more likely to weigh more than shorter people, at least in the sample on which this data is based d) a statistically significant predictive relationship between height and weight e) c and dWhich of the following best describes a regression coefficient in a bivariate setting? a. The change in Y predicted by a unit change in X b. The slope of a line that minimizes the sum of squared residuals c. The correlation coefficient multiplied by SDy/SDx d. All of the aboveIn a regression analysis involving 18 observations and four independent variables, the following information was obtained. Multiple R = .6000 R Square = .3600 Standard Error = 4.8000 Based on the above information, fill in all the blanks in the following ANOVA table. Is the model significant? ANOVA df SS MS F Regression ? ? ? ? Error ? ? ? Total ? ?
- Regression analysis was applied between sales data (y) and advertising data (x) and the following information was obtained. SSR = 800SST = 1250Sample Size (n) = 12 The standard error of the estimate isIn a regression model involving 44 observations, the following estimated regression equation was obtained. For this model Model SS = 600 and Residual SS = 400. The computed F statistics for testing the overall significance of the above model isA study investigated the link between speed of an automobile at the time of a crash, and the cost of the insurance claim. Below is the residual plot and the output from Excel.SUMMARY OUTPUT Regression Statistics Multiple R 0.17652 R Square 0.03116 Adjusted R Square 0.03116 Standard Error 0.42014 Observations 225 ANOVA df SS MS F Significance F Regression 1 1.266 1.266 7.1717 0.00795 Residual 223 39.363 0.1765 Total 224 40.629 Coefficients Standard Error t-Stat P-value Lower 95% Upper 95% Intercept 0.843 2.2068 0.382 0.703 -3.5066 5.1926 X Variable 1 0.003807 0.001422 2.678 0.00795 0.001 0.0066 Are there any assumptions for regression that you feel ought to be examined? The residuals to do not average zero since the middle is about 50Normality because there are a lot of outliers on the low end Linearity because of the rainbow type curveIndependence because there are more high speed crashes than low speed crashesConstant Variance because some…