Residuals I assume that you have a basic understanding of the concept of residuals in regression analysis. Here is the Wikipedia explanation: " If one runs a regression on some data, then the …

Often times a statistical analyst is handed a set dataset and asked to fit a model using a technique such as linear regression. Very frequently the dataset is accompanied with a disclaimer similar...

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 regression when either …

I am currently working to build a model using a multiple linear regression. After fiddling around with my model, I am unsure how to best determine which variables to keep and which to remove. My m...

For your first question, I don't think that a linear regression model assumes that your dependent and independent variables have to be normal. However, there is an assumption about the normality of …

There are two overall approaches to model development that tend to work well. (1) Have an adequate sample size and fit the entire pre-specified model, and (2) used penalized maximum likelihood …

To your other two points: Linear regression is in its basic form the same in statsmodels and in scikit-learn. However, the implementation differs which might produce different results in edge cases, and …

First, OLS regression makes no assumptions about the data, it makes assumptions about the errors, as estimated by residuals. Second, transforming data to make in fit a model is, in my opinion, the wrong …

I'm not used to using variables in the date format in R. I'm just wondering if it is possible to add a date variable as an explanatory variable in a linear regression model. If it's possible, how c...

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 an …