![]() We want to minimize this distance between our points and the regression line to have the best fit of our observed points. Lastly, $\epsilon$, is the error term of the regression formula, which is distance of each point ($i$ ) to the predicted regression line. The same goes for $sex(x_2)$, $education(x_3)$, and $language(x_4)$ which are the remaining independent variables, sex, education, and language, that are multiplied by the calculated coefficients in the model. Next, $age(x_1)$, is the variable age multiplied by the calculated regression coefficient that is added to $\beta_0$. We can think of $\beta_0$ as our starting wage value of the observations in the dataset. This is equal to $\beta_0$, the intercept of the model where our regression line intersects with the y axis when $x$ is zero. $_i$, is our dependent variable of the model that we are predicting with four independent variables of a specific observation $i$.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |