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- regression - What does it mean to regress a variable against another . . .
Those words connote causality, but regression can work the other way round too (use Y to predict X) The independent dependent variable language merely specifies how one thing depends on the other Generally speaking it makes more sense to use correlation rather than regression if there is no causal relationship
- 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
- 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 - How to interpret this shape of QQ plot of standardized . . .
I am running linear regression for a continuous variable (not standardized) with age and 2 other numeric continuous variables (not standardized), 2 categorical variables with 3 levels each and 1
- regression - Maximum likelihood method vs. least squares method - Cross . . .
What is the main difference between maximum likelihood estimation (MLE) vs least squares estimaton (LSE) ? Why can't we use MLE for predicting $y$ values in linear
- 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
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