For the regression output below: Testing at a 0.05 level of significance, the t-statistic associated with the parameter estimate b shows which of the following? DEPENDENT VARIABLE Y OBSERVATIONS. 18 VARIABLE INTERCEPT The estimated h parameter is not a R-SQUARE 0.3066 PARAMETER ESTIMATE 15.48 21.30 the significar F-RATIO 7.076 STANDARD ERROR 5.09 8.03 P-VALUE ON F 0.0171 ONLE T-RATIO 3.04 2.66 P-VALUE 0.0008 0.0171
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- For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracyThe local utility company surveys 12 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis appears below: Bill 13.45 +4.39*Size Coefficients Estimate Std. Error (Intercept) 13.45 Size 4.39 0.54 0.2 We are 90% confident that the mean annual electric bill increases by between 4.028✔ dollars and 4.753 x dollars for every additional square foot in home size. Round your answers to three decimal places and enter in increasing order.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…
- 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…The local utility company surveys 12 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis appears below: Bill = 15.9 + 4.45*Size Coefficients Estimate Std. Error (Intercept) 15.9 0.3 Size 4.45 0.57 We are 98% confident that the mean annual electric bill increases by between dollars and dollars for every additional square foot in home size.The local utility company surveys 11 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis appears below: Bill = 12.4 +4.54*Size Coefficients Estimate Std. Error (Intercept) 12.4 Size 4.54 0.6 0.85 dollars and We are 80% confident that the mean annual electric bill increases by between dollars for every additional square foot in home size. Round your answers to three decimal places and enter in increasing order.
- The local utility company surveys 12 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis appears below: Bill 13.45 + 4.39*Size Coefficients Estimate Std. Error (Intercept) 13.45 Size 4.39 0.54 0.2 We are 90% confident that the mean annual electric bill increases by between dollars and dollars for every additional square foot in home size. Round your answers to three decimal places and enter in increasing order.As a marketing manager for TriFood, you want to determine whether store Sales (# sold in one month) of TriPower bars are related to price (in cents) of TriPower bars and in-store promotional expenditures (in dollars) for TriPower bars. You conduct a multiple regression analysis with store Sales (Y) as the response variable, and Price (X1) and Promotion (X2) as explanatory variables. Use the pictured Excel regression output below to answer the questions. a) Write the estimated multiple regression equation. b) Should one interpret the estimated value for the intercept (yes or no)? c) Interpret the value for Standard Error under Regression Statistics. d) Interpret the value for R square. e) State the hypotheses for assessing the statistical significance of the overall regression equation. f) Interpret the estimated coefficient for price. g) An external consultant to TriFoods believes that for every $1 increase in promotional expenditures, sales will increase by 4.7 units. Test the…Consider the following computer output from a multiple regression analysis relating the price of a used car to the variables: age of car, mileage, and safety rating. Coefficients Coefficients Standard Error t Stat P-value Intercept 49314.99 6450.95 7.645 0.0000 Age (Year) - 25890.57 3156.87 - 8.201 0.0000 Mileage - 1584.68 127.77 - 12.402 0.0000 (in Thousands) Safety Rating - 1762.27 2658.52 -0.663 0.5102 Does the sign of the coefficient for the variable safety rating make sense? Answer E Tables Keypa Keyboard Shorte O Yes, because it is expected that as safety rating increases then the price should decrease. O No, because it is expected that as safety rating increases then the price should also increase. O No, because it is expected that as safety rating increases then the price should decrease. O Yes, because it is expected that as safety rating increases then the price should also increase.
- As a marketing manager for TriFood, you want to determine whether store Sales (# sold in one month) of TriPower bars are related to price (in cents) of TriPower bars and in-store promotional expenditures (in dollars) for TriPower bars. You conduct a multiple regression analysis with store Sales (Y) as the response variable, and Price (X1) and Promotion (X2) as explanatory variables. Use the pictured Excel regression output below to answer the questions. a) Interpret the value for R square. Interpret the estimated coefficient for price. b) State the hypotheses for assessing the statistical significance of the overall regression equation. Does the model overall fit the data (yes or no?) f) An external consultant to TriFoods believes that for every $1 increase in promotional expenditures, sales will increase by 4.7 units. Test the consultant's hypothesis at a 5% significance level using both approaches (tcalc vs tcrit and p-value vs a).A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-1.044 b=20.533 r2=0.857476 r=-0.926 Assume the correlation is significant, and use this to predict the number of situps a person who watches 10.5 hours of TV can do (to one decimal place)The station manager of a local television station is interested in predicting the amount of television (in hours) that a person in the viewing area will watch. The explanatory variables are age (in years), education (highest level obtained, in years) and family size (number of people in household). The multiple regression output is shown below: Summary measures Multiple R R-Square Adj R-Square 0.6644 StErr of Estimate 0.5598 ANOVA Table Source df SS MS F p-value Explained 3 13.9682 4.6561 0.0000 Unexplained 18 5.6413 0.3134 Regression coefficients Coefficient Std Err t-value p-value Constant 1.683 1.1696 1.4389 0.1674 Age -0.0498 0.0199 -2.5018 0.0222 Education 0.2135 0.0503…
