A final step in regression analysis is an examination of the residuals in a residual plot. This allows you to test the assumptions of the regression model itself by looking for patterns in the residuals. The residual plot for the problem is as follows: FIGURE. RESIDUAL PLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM Percent Under 21 Residual Plot 2 1.5 1 0.5 半 to 16 10 12 14 18 20 -0.5 -1 -1.5 Percent under 21 Which statement offers the best interpretation of the residual plot? O It appears that the residual plot exhibits a pattern whereby a linear model may not be adequate or the best fit, indicating that an assumption of the linear model may have been violated. O It appears that the residual plot exhibits a good pattern of constant variance, indicating that the equal variance assumption of the model is supported. aEcumption of the model is not supported. Residuals -00

The dependent variable in the regression in our cost driver analysis is which of the following? Company sales Total overhead cost for the entire period of time Total overhead cost per month

6 9 13 Yi 7 18 9. 26 23 a. Which of the following scatter diagrams accurately represents the data? A. 24+ 24+ 20- 20- 16+ 16+ 12+ 12+ 8+ 8+ 4- -4 4 12 16 24 28 x -4 4 8. 12 16 20 24 28 x -4t -4t С. D. 24 -----24+ 20- 20- 16- 16- 20 B. 20 00 00 O.

please answer in text form and in proper format answer with must explanation , calculation for each part and steps clearly

When running a ols regression, if my control variables are insignificant via T-test should I keep them in the regression? Are they significant?

please answer in text form and in proper format answer with must explanation , calculation for each part and steps clearly

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 0.7339 R Square 0.5386 Adjusted R Square 0.5185 Standard Error 2137.5200 Observations 49 ANOVA SS df Regression 2 245,370,679.3850 122,685,339.6925 26.8517 MS F Significance F 1.9E-08 Total Residual 46 210,173,612.6150 48 455,544,292.0000 4,568,991.5786 Coefficients Standard Error Intercept Education (Years) 14290.37278 2350.8671 2,528.5819 338.1140 Experience (Years) 829.3167 392.5627 t Stat P-value 5.6515 0.000000961 9200.6014 6.9529 0.000000011 2.1126 0.040093183 Lower 95 % Upper 95% 19,380.1442 1670.2789 3031.4553 39.129 1619.5044 Step 2 of 2: How much would you expect your salary to increase if you had one more year of education?

Explain the OLS Estimator in Multiple Regression in detail?

You estimated a regression with the following output.         Source |       SS           df       MS      Number of obs   =       223 -------------+----------------------------------   F(1, 221)       =  17592.99        Model |   182392130         1   182392130   Prob > F        =    0.0000     Residual |  2291176.96       221  10367.3166   R-squared       =    0.9876 -------------+----------------------------------   Adj R-squared   =    0.9875        Total |   184683307       222  831906.786   Root MSE        =    101.82   ------------------------------------------------------------------------------            Y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval] -------------+----------------------------------------------------------------            X |   11.97037   .0902481   132.64   0.000     11.79252    12.14823        _cons |   74.40159   10.96696     6.78   0.000     52.78839    96.01479…

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