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- Why are regression problems called regression problems?
I was just wondering why regression problems are called "regression" problems What is the story behind the name? One definition for regression: "Relapse to a less perfect or developed state "
- 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 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
- correlation - What is the difference between linear regression on y . . .
The Pearson correlation coefficient of x and y is the same, whether you compute pearson(x, y) or pearson(y, x) This suggests that doing a linear regression of y given x or x given y should be the
- regression - Converting standardized betas back to original variables . . .
I have a problem where I need to standardize the variables run the (ridge regression) to calculate the ridge estimates of the betas I then need to convert these back to the original variables scale
- 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
- regression - Interpreting the residuals vs. fitted values plot for . . .
None of the three plots show correlation (at least not linear correlation, which is the relevant meaning of 'correlation' in the sense in which it is being used in "the residuals and the fitted values are uncorrelated")
- regression - Whats the difference between multiple R and R squared . . .
In linear regression, we often get multiple R and R squared What are the differences between them?
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