Results Linear Regression Model Summary - bmi-y Model R R² Adjusted R² RMSE Ho 0.000 0.000 0.000 5.655 H₁ 0.081 0.007 0.002 5.651 ANOVA Model Sum of Squares df Mean Square F P H₁ Regression 41.910 1 Residual Total 6322.327 198 41.910 1.313 0.253 31.931 6364.236 199 Note. The intercept model is omitted, as no meaningful information can be shown. Coefficients Model Unstandardized Standard Error Standardized t р Ho (Intercept) 30.634 0.400 76.608 < .001 H₁ (Intercept) Income-x 31.728 -3.638x10-7 1.035 3.175x10-7 30.653 < .001 -0.081 -1.146 0.253
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Using the F-statistic with the significance level (α) of 5%, assess the significance of the regression model from the attached image.
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- 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…please do all parts! The estimated regression equation for this data set is y=4.4878+1.9549x. Part A: (in image) Part B: is the linear function is the appropriate regression function for this data set? Part C: do the residuals have a constant variance? Part D: are the residuals independent? Part E: are the error terms are normally distributed? y x22 821 818 846 2241 2254 2276 3258 3268 32
- A real estate research firm has developed a regression model relating list price (Y in $1,000) with two independent variables. The two independent variables are number of bedrooms and size of the property. Part of the regression results are shown below. ANOVA df SS MS Regression 2 154881.37 77440.69 Residual 57 899723.61 15784.62 Coefficients Standard Error t Stat Intercept 74.298 91.326 # Bedrooms 73.634 25.271 Acres 41.458 38.630 What has been the sample size? What is the value of the F test statistic for testing whether the regression model is significant? What is the rejection rule for testing whether the regression model is significant at the 0.05 level of significance? What is the value of the t test statistic for testing whether the variable Acres is significant?A manager at a local bank analyzed the relationship between monthly salary and three independent variables: Length of service (measured in months), Gender (0 = female, 1= male), and Job type (0 = clerical, 1 = technical). The following ANOVA summarizes the %3D regression results. ANOVA Source of Variation df Sum of Squares Mean Square F Regression 3 1,004,346.771 334,782.257 5.96 Residual 26 1,461,134.596 56,197.48445 Total 29 2,465,481.367 Coefficients Standard Error t-Stat p-value Intercept 784.92 322.25 2.44 0.02 Service 9.19 3.20 2.87 0.01 Gender 222.78 89.00 2.50 0.02 Job -28.21 89.61 -0.31 0.76 The level of significance is 0.05. In the regression model, which of the following are dummy variables?The regression equation is Health Index= y + a Age + B Blood sugar + 8 Blood Pressure Coef 20986 339.28 209.2 207.2 Coef Constant Age Blood sugar Blood pressure SE 2912 71.95 179.3 225.4 ETO 7.21 4.72 0.92 P 0.002 0.009 0.308 **
- student used multiple regression analysis to study how family spending (y) is influenced by income(x1), family size (x2), and additionsto savings(x3). The variables y, x1, and x3 are measured in thousandsof dollars. The following results were obtained. anova df ss regression 3 45.9634 residual 11 2.6218 total coefficient standard error intercept 0.0136 x1 0.7992 0.074 x2 0.2280 0.190 x3 -0.5796 0.920 d. Carry out a test to see if x3 and y are significantly related. Use a 5% level of significance.A manager at a local bank analyzed the relationship between monthly salary and three independent variables: Length of service (measured in months), Gender (0 = female, 1 = male), and Job type (0 = clerical, 1 = technical). The following ANOVA summarizes the regression results.ANOVA Source of Variation df Sum of Squares Mean Square F Regression 3 1,004,346.771 334,782.257 5.96 Residual 26 1,461,134.596 56,197.48445 Total 29 2,465,481.367 Coefficients Standard Error t-Stat p-value Intercept 784.92 322.25 2.44 0.02 Service 9.19 3.20 2.87 0.01 Gender 222.78 89.00 2.50 0.02 Job −28.21 89.61 −0.31 0.76 The level of significance is 0.05. Based on the hypothesis tests for the individual regression coefficients, ________. Multiple Choice all the regression coefficients are not equal to zero "Job" is the only significant variable in the model only months of service and gender are significantly related to monthly salary "Service"…A student collected concentration versus absorbance data for a series of standards and produced a standard curve. Which value of 2 would reflect the linear regression curve the student produced? Absorbance 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 ² = 0.10 ² = 0.76 ² = 0.98 2 = 0.45 1 Absorbance vs. Concentration 2 3 Concentration (ppm) 4 5 6
- s is the typical amount by which the (BMI Change, Depression Score Change) value what is predicted using the least squares regression line.A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression analysis shows the following results. Coefficients Standard Error t-Stat p-value Intercept -12.201 6.560 -1.860 0.100 Number of contacts 2.195 0.176 12.505 0.000 ANOVA df SS MS F Significance F Regression 1.00 |13,555.42 |13,555.42 156.38 0.00 Residual 8.00 693.48 86.68 Total 9.00 14,248.90 Assume that X = 33.4 and E(X – X) 2814.4. Rounding to one decimal place, the 95% confidence interval for 30 calls isph/mod/quiz/attempt.php?attempt3932918&lcmid%3D166208page%3D27 University 53 The following excel printout provides information to estimate overhead costs using linear regression: Upper 95% 9289.88697 8.27042244 897.781429 36.5701299 Standard Error t Stat P-value Lower 95% Coefficients 6035.987027 1411.05464 4.277642 0.002696 2782.0871 Intercept DLH ut of 4.558482698 1.609683731 2.831912 0.022085 0.846543 # setups 771.1028938 54.93418317 14.03685 6.44E-07 644.42436 29.9411124 2.874675342 10.41548 6.26E-06 23.312095 # moves estion Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.996584412 0.99318049 0.990623174 347.9563597 12 a. Write the multiple regression model (round to nearest cent). Answer: 54 b. What is the estimate of overhead if the department has 1,205 DLH, 55 setups and 125 moves? Answer: out of question