8. Calculating SSR, SSE, SST, and R-squared Suppose you are interested in studying the effects of education on wages. You gather four data points and use ordinary least squares (OLS) to estimate the following simple linear model: wage = Bo + Bieduc + u where wage - hourly wage in dollars educ - years of formal education After running your regression, you decide to examine how the fitted values of wages from your regression compare to the actual wages in your data set. These data are summarized in the following table: residuals obsno twage, wage, u = wage, - twage 1 15 14.7 0.3 2 19 19.9 -0.9 3 26 25.1 0.9 4 30 30.3 -0.3 Based on the data in the table, the explained sum of squares (SSE) is Based on the data in the table, the residual sum of squares (SSR) is Based on the data in the table, the total sum of squares (SST) is While you are skeptical of your OLS regression due to the low number of data points, you decide to calculate the R-squared of the regression to understand how well the independent variable educ explains the dependent variable wage. The resulting Re is

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8. Calculating SSR, SSE, SST, and R-squared
Suppose you are interested in studying the effects of education on wages. You gather four data points and use ordinary least squares (OLS) to
estimate the following simple linear model:
wage 3 Во + Bіeduc + u
where
wage = hourly wage in dollars
educ = years of formal education
After running your regression, you decide to examine how the fitted values of wages from your regression compare to the actual wages in your data
set. These data are summarized in the following table:
residuals
obsno
wage,
wage;
wage; – wage,
1
15
14.7
0.3
2
19
19.9
-0.9
3
26
25.1
0.9
4.
30
30.3
-0.3
Based on the data in the table, the explained sum of squares (SSE) is
Based on the data in the table, the residual sum of squares (SSR) is
Based on the data in the table, the total sum of squares (SST) is
While you are skeptical of your OLS regression due to the low number of data points, you decide to calculate the R-squared of the regression to
understand how well the independent variable educ explains the dependent variable wage. The resulting R² is
Transcribed Image Text:8. Calculating SSR, SSE, SST, and R-squared Suppose you are interested in studying the effects of education on wages. You gather four data points and use ordinary least squares (OLS) to estimate the following simple linear model: wage 3 Во + Bіeduc + u where wage = hourly wage in dollars educ = years of formal education After running your regression, you decide to examine how the fitted values of wages from your regression compare to the actual wages in your data set. These data are summarized in the following table: residuals obsno wage, wage; wage; – wage, 1 15 14.7 0.3 2 19 19.9 -0.9 3 26 25.1 0.9 4. 30 30.3 -0.3 Based on the data in the table, the explained sum of squares (SSE) is Based on the data in the table, the residual sum of squares (SSR) is Based on the data in the table, the total sum of squares (SST) is While you are skeptical of your OLS regression due to the low number of data points, you decide to calculate the R-squared of the regression to understand how well the independent variable educ explains the dependent variable wage. The resulting R² is
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