Search Results

In the section on Ridge Regression (source: Elements of Statistical Learning by Hastie, Tibshirani, Friedman) : When there are many correlated variables in a linear regression model, their coefficients … We will now look at Ridge Regression, Lasso Regression, and Elastic Net, which implement three different ways to constrain the weights. …

Keeping in mind the widespread misunderstanding that linear regression can only model linear relationships/missing the fact that linear regression refers to linearity in parameters and can accommodate … That is, (3.36) is simply a multiple linear regression model with $X_1=horsepower$ and $X_2=horsepower^2$. …

It is a logistic regression. All coefficients are statistically significant at the 5% level. I calculated the VIF for the set of predictors in the multiple regression model. …

We can't include it in the regression as is, but you could create a dummy variable for all but one value of kettlehole and put those in your regression to effectively obtain the desired relationship within … In so doing, you've effectively run a Fixed Effects Regression. Entity fixed effects can control for variables that are constant over time but differ across entities (i.e. kettleholes). …

No. If your regressors are perfectly collinear, OLS estimation is impossible. In contrast, the more multicollinear (high collinearity but not perfect) your regressors the more inefficient your estimat …

In this article Chen & Pearl make the following 2 statements: "Identification techniques are available for models in which X is far from satisfying $E(u_i|X_i)=0$" in response to Stock & Watson's sen …

I've upvoted a couple answers that already provide many of the ingredients to the answer. I'll provide what I view as a more direct answer. Suppose you find a dataset with observations on 2 fields: x …

The first question you should ask yourself is whether the outcome variable is nominal (no natural ordering of the categories as in "red","blue","green") or ordinal (with natural ordering as in "low"," …

+1 to Kjetil b halvorsen. His answers are enlightening and this one is no exception. I do think that there is something additional to be contributed here because the question asks about "treating reg …

Then you build a sample using this DGP and run a regression on this sample (can be linear regression or Huber or any other type). … This clarification of how regression simulation works should answer all your questions. …

The Penn State STAT200 course states that assuming that the errors are independent is equivalent to assuming that residuals $\hat{\epsilon}$ and predicted values $\hat{y}$ are not related, which I tak …

Having consulted a number of sources, I still can't find a complete proof that Regression Sum of Squares ($SS_{regression}$) and ($SS_{residual}$) are independent random variables. …

In your case, where you are building a causal model, running univariate regressions before running multiple regression has a completely different goal. Let me expand on the latter. … It will even give you hints as to how to alter your causal graph, because the result of this regression suggests that there must be a path between A and E that is not d-separated by D. …

Nonlinear vs. generalized linear model: How do you refer to logistic, Poisson, etc. regression? …

.)=0 it means that you are assuming that the true population regression is: which, when y is binary, is equivalent to assuming that: which, in words, means you are assuming that the P(y=1|x) is a …