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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 with multiple dependent variables? - Cross Validated
Is it possible to have a (multiple) regression equation with two or more dependent variables? Sure, you could run two separate regression equations, one for each DV, but that doesn't seem like it
- 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
- 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
- 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 - Interpret log-linear with dummy variable - Cross Validated
I have the following model: ln(y) = b0 + B1 X1 + B2 ln(X2) + B3 X3 My X1 is a dummy that can take the values 0, 1 and 2 The coefficient for the dummy 1 is -0 500 My question is how do I interpret
- 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
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