|
- How should outliers be dealt with in linear regression analysis?
What statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis? Are there any special considerations for multilinear regression?
- regression - When should I use lasso vs ridge? - Cross Validated
Ridge regression is useful as a general shrinking of all coefficients together It is shrinking to reduce the variance and over fitting It relates to the prior believe that coefficient values shouldn't be too large (and these can become large in fitting when there is collinearity) Lasso is useful as a shrinking of a selection of the coefficients
- Multivariable vs multivariate regression - Cross Validated
Multivariable regression is any regression model where there is more than one explanatory variable For this reason it is often simply known as "multiple regression" In the simple case of just one explanatory variable, this is sometimes called univariable regression Unfortunately multivariable regression is often mistakenly called multivariate regression, or vice versa Multivariate
- regression - When is R squared negative? - Cross Validated
Also, for OLS regression, R^2 is the squared correlation between the predicted and the observed values Hence, it must be non-negative For simple OLS regression with one predictor, this is equivalent to the squared correlation between the predictor and the dependent variable -- again, this must be non-negative
- When conducting multiple regression, when should you center your . . .
In some literature, I have read that a regression with multiple explanatory variables, if in different units, needed to be standardized (Standardizing consists in subtracting the mean and dividin
- regression - Difference between forecast and prediction . . . - Cross . . .
I was wondering what difference and relation are between forecast and prediction? Especially in time series and regression? For example, am I correct that: In time series, forecasting seems to mea
- Whats the difference between correlation and simple linear regression . . .
Note that one perspective on the relationship between regression correlation can be discerned from my answer here: What is the difference between doing linear regression on y with x versus x with y?
- regression - Trying to understand the fitted vs residual plot? - Cross . . .
A good residual vs fitted plot has three characteristics: The residuals "bounce randomly" around the 0 line This suggests that the assumption that the relationship is linear is reasonable The res
|
|
|