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Regression that includes two or more non-constant independent variables.

1 vote

Interpreting and comparing linear and quadratic regression

As was pointed out in the comments you need to include all of your variables in the model to understand importance. A simple and effective way to understand a variable's importance with respect to the …
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0 votes

Multicollinearity and correlation in multiple regression

Consider the definition of the VIF: $$ \text{VIF} = \frac{1}{1-R^2_j} $$ Where $R^2_j$ is the coefficient of determination of predictor variable $x_j$. Now $$ R^2_j = 1 - \frac{\sum_i e_i}{\sum_i(y_i …
Chris's user avatar
  • 711
0 votes

Multi-Class Classification for Regression

Bin $t$ into ten groups: $0 \leq t_1 \leq 10, 10 < t_2 \leq 20, ...$ (IE if a value is between 0 and 10 inclusive, it gets labeled "1") and predict via a classifier.
Chris's user avatar
  • 711