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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 …
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 …
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.