With linear regression with no constraints, R2 R 2 must be positive (or zero) and equals the square of the correlation coefficient, r r. A negative R2 R 2 is only possible with linear …

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."

Q&A for people interested in statistics, machine learning, data analysis, data mining, and data visualization

Nov 14, 2010 · 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 …

Nov 24, 2015 · For the top set of points, the red ones, the regression line is the best possible regression line that also passes through the origin. It just happens that that regression line is …

The biggest challenge this presents from a purely practical point of view is that, when used in regression models where predictions are a key model output, transformations of the …

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 …

Where β∗ β ∗ are the estimators from the regression run on the standardized variables and β^ β ^ is the same estimator converted back to the original scale, Sy S y is the sample standard …

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 …

Jun 19, 2016 · A regression model is often used for extrapolation, i.e. predicting the response to an input which lies outside of the range of the values of the predictor variable used to fit the …