LinearRegression3

# 9252012 p kolm 9 parallels with the two variable case

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Unformatted text preview: ith the Two-Variable Case b0 is the intercept b1,..., bk are the slope parameters u is the error term (or disturbance) As before: We make a zero conditional mean assumption, E (u | x1, x 2,¼, x k ) = 0 We solve for b0, b1,..., bk by minimizing the sum of squared residuals o That’s why we call it ordinary least-squares (OLS) VER. 9/25/2012. © P. KOLM 10 Example Multiple regression model: wage = b0 + b1educ + b2exper + u Fitted regression model: ˆ ˆ ˆ ˆ wage = b0 + b1educ + b2exper + u Interpretation: ˆ b1 is the estimated increase in the wage for a unit increase in educ holding exper constant VER. 9/25/2012. © P. KOLM 11 Example (Excel Output) SUMMARY OUTPUT Regression Statistics Multiple R 0.368583072 R Square 0.135853481 Adjusted R Square 0.13399909 Standard Error 376.2948248 Observations 935 ANOVA df Regression Residual Total Intercept educ exper 2 932 934 SS 20747023.1 131969145.1 152716168.2 Coefficients Standard Error -272.5278605 107.2627094 76.21638857 6.296603998 17.63776991 3.1617754 MS F 10373511.55 73.26040306 141597.7952 t Stat -2.540751226 12.10436429 5.578438593 P-value 0.01122266 1.98778E-31 3.18016E-08 Significance F 2.81657E-30 Lower 95% Upper 95% -483.0322737 -62.02344727 63.85922421 88.57355293 11.43274601 23.84279381 VER. 9/25/2012. © P. KOLM 12 Example (Matlab Output) Ordinary Least- squares Estimates Dependent Variable = R-squared = 0.1359 Rbar-squared = 0.1340 sigma^2 = 141597.7952 Durbin-Watson = 1.8348 Nobs, Nvars = 935, wage 3 *************************************************************** Variable Coefficient t-statistic t-probability intercept -272.527860 -2.540751 0.011223 educ 76.216389 12.104364 0.000000 exper 17.637770 5.578439 0.000000 VER. 9/25/2012. © P. KOLM 13 Example - Interpretation The fitted regression line is: wage = -272.5 + 76.22educ + 17.64exper Holding experience fixed, a one year increase in education increases the monthly wage by 76.22 dollars Holding education fixed, a one year increase in experience increases the monthly wage by 17.64 dollars When education and experience are zero, wages are predicted to be -\$272.5 VER. 9/25/2012. © P. KOLM 14 Classical Linear Regression Assumptions (Multivariable Case) Population model is linear in parameters: y = b0 + b1x1 + b2x 2 +¼+ bk x k + u [MLR.1] {(x i1, xi 2,¼, x ik , yi ) : i = 1,2,¼, n} is a random sample from the population model, so that yi =b0 + b1x i 1 + b2x i 2 +¼+ bk x ik + ui [MLR.2] E (u | x1, x 2,¼x k ) = 0 , implying that all of the explanatory variables are [MLR.3] uncorrelated with the error None of the x ’s is constant, and there are no exact linear relationships among them1 [MLR.4] Homoskedasticity: Assume Var (u | x 1, x 2,..., x k ) = s 2 [MLR.5] Normality: u N (0, s 2 ) [MLR.6] (needed for hypothesis testing, etc.) → MLR.1-MLR.5 are known as the Gauss-Markov assumptions → MLR.1-MLR.6 are called the classical linear model assumptions (CLM) We now take a look at the main results for the multivariate case VER. 9/25/2012. © P. KOLM 15 Main Results for the Multivariate Case: Unbiasedness OLS is unbiased, that is ˆ E...
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## This document was uploaded on 02/17/2014 for the course COURANT G63.2751.0 at NYU.

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