# Interpreting Regression Coefficients

2017-05-15

When we read the list of coefficients, here is how we interpret them:

• The intercept is the starting point – so if you knew no other information it would be the best guess.

• Each coefficient multiplies the corresponding column to refine the prediction from the estimate. It tells us how much one unit in each column shifts the prediction.

• When you use a categorical variable, in R the intercept represents the default position for a given value in the categorical column. Every other value then gets a modifier to the base prediction.

Linear regression is easy, but the real value here is Steph’s explanation of logistic regression coefficients.

## Python and R Data Reshaping

2019-09-10

John Mount takes us through a couple of data shaping packages: The advantages of data_algebra and cdata are: – The user specifies their desired transform declaratively by example and in data. What one does is: work an example, and then write down what you want (we have a tutorial on this here).– The transform systems can print what a transform is going to […]