$\begingroup$ Your model is misspecified; you need a different kind of model. If your data cannot go below 0, and your model can, your model does not reflect the reality of your data. The …

From experience, exclusion should be limited to instances of incorrect data entry. Reweighting outliers in the linear regression model is a very good compromise method. The application of …

Because the variance of the sampling distribution of the regression coefficient would be larger (by a factor of the VIF) if it were correlated with other variables in the model, the p-values would …

Sep 23, 2020 · You can use Linear Regression to model any linear/non-linear relationship using basis expansion (slides from Elements of Statistical Learning). In your case you could …

Jul 19, 2020 · Edit: I checked with some linear regressions and Wald test for hypothesis (predictor_name = 0) gives almost exactly same P-value as the linear regression fitted …

Jun 19, 2016 · A regression model is often used for extrapolation, i.e. predicting the response to an input which lies outside of the range of the values of the predictor variable used to fit the …

Check different kind of models. Another model might be better to explain your data (for example, non-linear regression, etc). You would still have to check that the assumptions of this "new …

In regression we can get biased estimators of slopes by doing stepwise regression. A variable is more likely to be kept in a stepwise regression if the estimated slope is further from 0 and …

Sep 20, 2018 · The most common question is "what does the state variable represent?" The answer to that depends on the model, but most DLMs can be thought of as a regression with a …

The assignment is to calculate the linear regression analysis/regression equation for a data set containing years and the percentage of unemployment in the population at that time. While I'm …