You run the regression y₁ = B₁ + B₁ x ₁₁ + B³ 2 x2i Во 1 1i + ε i • variance of ε turns out to be i The proportional to 4x . Write down a 1i transformed, homoskedastic equation.

In an OLS regression, which value represents the "best" R2 in terms of explained variance in the dependent variable?  A. 2.53 B. 16.22 C. .001 D. 0.53

Consider the OLS estimator 3;. Under the Gauss-Markov assumptions, O the estimator is the best linear unbiased estimator. O the estimator is asymptotically normally distributed. O the estimator has the properties stated in the other three possible answers. O the estimator is consistent.

1. R-squaredSuppose regression of y on an intercept and x with 50 observations yields total sum of squares 100 andexplained sum of squares 36.(a) What is ?^2?(b) What is the correlation coefficient between y and x?(c) What is the standard error of the residual?

IV. 得分 What information can be obtained from this summary output? a to enter = 0.05, a to remove = 0.05 Analysis of Variance Source DF Adi sS Adi MS F-Value P-Value www Regression 4 37260200 9315050 45. 23 0. 000 0. 000 0. 000 Poten 1 4727687 4727687 22. 95 AdvExp 4630364 4630364 22. 48 Share 1 3009401 3009401 14. 61 0.001 Accounts 1 2129972 2129972 10. 34 0.004 Error 20 4119349 205967 Total 24 41379549 R-sq R-sq (adi) R-sq (pred) 453. 836 90. 04% 88. 05% 85. 97% Coefficients Term Coef SE Coef T-Value P-Value VIF Constant -1442 424 -3. 40 0,003 Poten 0. 03822 0. 00798 4. 79 0. 000 1. 83 AdvExp 0. 1750 0. 0369 4. 74 0. 000 1. 15 Share 190. 1 49. 7 3. 82 0.001 1.74 Accounts 9. 21 2. 87 3. 22 0. 004 1. 99 Fits and Diagnostics for Unusual Observations Std Obs Sales Fit Resid Resid www 10 4876 3942 934 2. 14 R

OA linear regression model is Units 3,414-0.839xWeek. For week 45, what is the forecast for the number of units? Round your answer to the nearest whole number. OO units

1- the locus of conditional means of Y fort he fixed values of X is the. Please select one; a) intercept line b) linear regression line c) population regression line d) conditional expectation function m

Please no written by hand The assumption of normally distributed errors means that... A. errors can be ignored when doing regression modelling. B. the OLS estimators can also be assumed to be normally distributed since they are a linear functions of the errors. C. the OLS estimators can also be assumed to be normally distributed since they are BLUE. D. the OLS estimators can also be assumed to be normally distributed since they are minimum variance. E. the regression model will not be subject to specification error.

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 Residual 46 210,173,612.6150 Total 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 6.9529 0.000000011 2.1126 0.040093183 Lower 95 % Upper 95 % 9200.6014 19,380.1442 1670.2789 3031.4553 39.129 1619.5044 Step 1 of 2: What would be your expected salary with no education and no experience?

Consider the output here from a regression in R. What is 3₂? Coefficients: Estimate (Intercept) 1.708 5.404 -1.478 9.531 X1 X2 X3 Std. Error 0.555 2.792 0.6 2.758