321w3p2 - Ex/ A simple wage regression wi = 0 1 educi ui ,...

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Ex/ A simple wage regression w i = 0   1 educ i u i , for i=1,. ..,n (2) where wage, w, is measured in $/hr, and educ is years of education.
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Regression Line for Education on Wage wage w = 0   1 educ = population regression line slope = 1 = w educ w 13 w 12 0 12 13 educ
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Scatter plot and True Regression Line for Education & Wage wage ° ° ° ° ° w = 0   1 educ = unknown population regression line w 2 w 1 − 0   1 educ 1 w 1 0 u 1 = w 1 − 0   1 educ 1 ° ° u 2 = w 2 − 0   1 educ 2 but we don't actually know u, educ 2 educ 1 educ because we don't know betas.
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Estimating the Coefficients of a Simple Linear Regression Model Ordinary Least Squares (OLS) Estimator that estimator which yeilds a regression line that is closest to the observed data, where closeness is measured by the sum of squared residuals. let 1 = an estimate of 1 , & 0 = an est. of 0 Then our fitted (predicted) value of Y i is: Y i = 0 1 X i (3)
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This note was uploaded on 01/26/2012 for the course ECON 401 taught by Professor Burbidge,john during the Fall '08 term at Waterloo.

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321w3p2 - Ex/ A simple wage regression wi = 0 1 educi ui ,...

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