regression - What's the difference between multiple R and R …
Nov 3, 2017 · In linear regression, we often get multiple R and R squared. What are the differences between them?
regression - Converting standardized betas back to original …
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 - 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 …
When conducting multiple regression, when should you center …
Jun 5, 2012 · 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 …
How to derive the standard error of linear regression coefficient
another way of thinking about the n-2 df is that it's because we use 2 means to estimate the slope coefficient (the mean of Y and X) df from Wikipedia: "...In general, the degrees of freedom of …
regression - Trying to understand the fitted vs residual plot?
Dec 23, 2016 · 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 …
Interpreting Z-Scores of Linear Regression Coefficients
Jul 11, 2022 · Well, under the hypothetical scenario that the true regression coefficient is equal to 0, statisticians have figured out how likely a given Z-score is (using the normal distribution …
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 …
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 - Linear vs Nonlinear Machine Learning Algorithms
Jan 6, 2021 · Three linear machine learning algorithms: Linear Regression, Logistic Regression and Linear Discriminant Analysis. Five nonlinear algorithms: Classification and Regression …