Given the following details about a multiple regression model based on a sample of 45 observations: bo = 17, b = 4, b2 = -3, b3 = 8, sum of squares due to error =385.5,and sum of squares due to regression = 780.2 Find the mean square due to regression (MSR): Select one: a. 291.43 Ob. 195.05 c. 388.57 d. 260.07
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- 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…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. Intercept Age (Year) Mileage (in Thousands) Safety Rating Does the sign of the coefficient for the variable age make sense? Answer Coefficients 23287.65 7671.28 -551.27 Coefficients 894.16 Standard Error 3327.39 8976.75 45.10 96.36 t Stat 6.999 0.855 - 12.223 9.280 P-value 0.0000 0.3965 0.0000 0.0000 O No, because it is expected that as age increases then the price should decrease. O Yes, because it is expected that as age increases then the price should also increase. O No, because it is expected that as age increases then the price should also increase. O Yes, because it is expected that as age increases then the price should decrease. Tables Key Keyboard ShorChoose the answer
- Given data below where x=neckline and y=waistline n=15 Answer the following: a. Standard Error of estimate b. If the neckline is 47 cm, estimate the waistline (in cm) using the regression model.Given are five observations for two variables, x and y. Use the estimated regression equation: y = 73.9561 - 3.1463x (NEED ANSWERS FOR A, B, C, D, E)41
- You may need to use the appropriate technology to answer this question. Following is a portion of the computer output for a regression analysis relating y = maintenance expense (dollars per month) to x = usage (hours per week) of a particular brand of computer terminal. Analysis of Variance SOURCE DF Adj SS Adj MS Regression 1 1575.76 1575.76 Error 8 349.14 43.64 Total 9 1924.90 Predictor Coef SE Coef Constant 6.1092 0.9361 X 0.8951 0.1490 Regression Equation Y = 6.1092 + 0.8951 X #1) Write the estimated regression equation. ŷ = #2) Find the value of the test statistic. (Round your answer to two decimal places.)Find the p-value. (Round your answer to three decimal places.) #3)Use the estimated regression equation to predict monthly maintenance expense (in dollars per month) for any terminal that is used 15 hoursper week. (Round your answer to the nearest cent.) $ _____per monthThe following data are the monthly salaries and the grade point averages for students who obtained a bachelor's degree in mathematics. GPA: 2.6, 3.4, 3.6, 3.2, 3.5, and 2.9; Monthly Salary: 330o, 3600, 4000, 3500, 3900, and 3600. Plot the scatter diagram showing the deviations about the estimated regression line and the line y = y(bar).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 following regression model was fitted to sample data with 12 observations: = 30 +4.50x. What is the residual for an observation (x = 2, y = 40)? 0.50 -1 1 -0.50Pls answer within 30 minutes to 1hr.In a regression analysis, if SSE = 200 and SSR = 300, then the coefficient of determination is O a. 0.600 O b. 1.500 O c. 0.400 d. 0.667
