Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience.Regression StatisticsMultiple R0.7352R Square0.5405Adjusted R Square0.5205Standard Error2131.1820Observations49ANOVAdfRegression 2 245,793,126.4218SSMSFSignificance F122,896,563.2109 27.05821.7E-08ResidualTotal46 208,929,085.537448 454,722,211.95924,541,936.6421Coefficients Standard Errort StatP-valueLower 95 %Upper 95 %InterceptEducation (Years)14268.682362352.26982,521.08445.65970.0000009349194.002719,343.3621337.11156.9777 0.000000011673.69953030.8401Experience (Years)832.2096391.39872.12620.0388847144.36491620.0543Step 2 of 2: How much would you expect your salary to increase if you stayed at the company for another year?

The objective of the question is to determine the expected increase in salary with an additional…

Answered: Consider the following computer output…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The owner of a new pizzeria in town wants to study the relationship between weekly revenue and advertising expenditures. All measures are recorded in thousands of dollars. The summary output for the regression model is given below.ANOVA dfdf SSSS MSMS F� Significance F� Regression 33 18.154037618.1540376 6.051345876.05134587 17.6080562317.60805623 4.3383E-034.3383E-03 Residual 55 1.7183457921.718345792 0.343669160.34366916 Total 88 19.8723833919.87238339 Step 2 of 3 : What is the adjusted coefficient of determination for this model, R2a��2? Round your answer to four decimal places
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 is
Use the following ANOVA table for regression to answer the questions. Analysis of Variance Source DF SS MS F P Regression 1 291.4 291.4 2.01 0.158 Residual Error 174 25230.0 145.0 Total 175 25521.4 Give the F-statistic and p-value.Enter the exact answers.The F-statistic is =The p-value is =
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 month
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.73650.7365 R Square 0.54240.5424 Adjusted R Square 0.52250.5225 Standard Error 2124.60962124.6096 Observations 4949 ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 246,127,958.1791246,127,958.1791 123,063,979.0896123,063,979.0896 27.262927.2629 1.6E-081.6E-08 Residual 4646 207,642,442.8821207,642,442.8821 4,513,966.14964,513,966.1496 Total 4848 453,770,401.0612453,770,401.0612 Coefficients Standard Error t� Stat P-value Lower 95%95% Upper 95%95% Intercept 14256.268814256.2688 2,513.30952,513.3095 5.67235.6723 0.0000008950.000000895 9197.23929197.2392 19,315.298419,315.2984 Education (Years) 2353.85412353.8541 336.0719336.0719 7.00407.0040 0.0000000090.000000009 1677.37651677.3765 3030.33173030.3317 Experience (Years) 832.8371832.8371…
The owner of a new pizzeria in town wants to study the relationship between weekly revenue and advertising expenditures. All measures are recorded in thousands of dollars. The summary output for the regression model is given below.ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 20.2147598620.21475986 20.2147598620.21475986 20.9811354520.98113545 0.0013268390.001326839 Residual 99 8.6712580048.671258004 0.963473110.96347311 Total 1010 28.8860178628.88601786 Step 3 of 3: Which statistic is most appropriate for the pizzeria owner to determine the usefulness of the regression model and why?
In the following regression, X = total assets ($ billions), Y = total revenue ($ billions), and n = 64 large banks. R2 0.519 Std. Error 6.977 n 64 ANOVA table Source SS df MS F p-value Regression 3,260.0981 1 3,260.0981 66.97 1.90E-11 Residual 3,018.3339 62 48.6828 Total 6,278.4320 63 Regression output confidence interval variables coefficients std. error t Stat p-value Lower 95% Upper 95% Intercept 6.5763 1.9254 3.416 .0011 2.7275 10.4252 X1 0.0452 0.0055 8.183 1.90E-11 0.0342 0.0563 (a) Write the fitted regression equation. yˆy^ = Not attempted + Not attempted X (b-1) State the degrees of freedom for a two-tailed test for zero slope, and use Appendix D to find the critical value at α = .05. (Round t critical value to 3 decimal places.) Degrees of freedom Not attempted tcrit ± Not attempted…
Big Time Corporation wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the following information (note: only a portion of the regression analysis results is presented here) SUMMARY OUTPUT Regression Statistics Multiple R 0.998041188 R Square 0.996086212 Adjusted R Square 0.994781616 Standard Error 917.1714273 Observations 5 ANOVA df SS MS F Significance F Regression 1 642276389.7 642276389.7 763.5208906 0.000104039 Residual 3 2523610.281 841203 427 Total 4 644800000 Coefficients Standard Errort Stat P-value Intercept 6679.617454 931.8558049 7.168080532 0.005592948 3714.03639 X Variable 9.796772265 0.354545968 27.63188178 0.000104039 8.66844876 What is variable cost per machine hour (rounded to the nearest cent)? OA. $6,679.62 OB. $917.17 OC. $9.80 OD. $.99 Lower 95% Upper 95.0% 9645.198517 10.92509577
The owner of a new pizzeria in town wants to study the relationship between weekly revenue and advertising expenditures. All measures are recorded in thousands of dollars. The summary output for the regression model is given below.ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 15.1540376815.15403768 15.1540376815.15403768 16.0586340516.05863405 0.0102486910.010248691 Residual 55 4.7183457924.718345792 0.943669160.94366916 Total 66 19.8723834719.87238347 Step 2 of 3 : What is the adjusted coefficient of determination for this model, R2a��2? Round your answer to four decimal places
Does Job stress influence overall life satisfaction? A sample of 7,814 people were surveyed, and this is the result of the regression analysis: Descriptive Statistics Mean Std. Deviation N Life satisfaction 7.97 1.194 7817 Job stress 3.16 1.667 7817 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .228a .052 .052 1.162 .052 427.062 1 7815 .000 a. Predictors: (Constant), Job stress b. Dependent Variable: Life satisfaction ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 577.130 1 577.130 427.062 .000b Residual 10561.161 7815 1.351 Total 11138.291 7816 a. Dependent Variable: Life satisfaction b. Predictors: (Constant), Job stress Coefficientsa Model Unstandardized…
4. A simple linear regression is fit to a dataset. Unfortunately, the corresponding ANOVA table is not complete because some quantities in the table are missing due to unknown digital errors. Calculate the missing values, denoted by "?", in the ANOVA table based on the other available values. Is this regression model significant (α = 0.05)? Source Regression Residual Total df ? ? 21 SS ? ? ? MS = SS/df ? 1.13 F-Ratio 430.65 p-value ?
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Coefficients 1186 213.48 Coefficients - 130.46 294.11 Standard Error Does the sign of the coefficient for the variable safety rating make sense? 123.87 21.89 14.26 356.37 t Stat P-value 9.575 0.0000 9.752 0.0000 -9.149 0.0000 0.825 0.4128

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