2. Ordinary least squares residuals for simple linear regressions Suppose you ha

Question

2. Ordinary least squares residuals for simple linear regressions Suppose you have gathered data on wages and education level for n individuals. You would like to use your data to construct an ordinary least squares (OLS) simple linear regression model to study the effects of education on salary. Your OLS regression line is: ^ wage,- + educi where wage - yearly salary educ-years of education Given the OLS regression line, which of the following best represents the residual for the ith observation? The difference between the actual wage and the wage predicted by the OLS regression line O The square of the wage predicted by the OLS regression line O The actual wage plus the wage predicted by the OLS regression line The square of the actual wage Since you are using OLS to obtain the slope and intercept parameter estimates, you know that your choice of Po and Pi

Explanation / Answer

- the difference between the actual wage and the wage predicted by OLS line

Residual is the difference of actual wage and estimated wage.

- minimises the sum of squares of the differences between actual wage and wage predicted by the ols regression line for all observations in the sample.

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