Now that you've got a good sense of how to 'speak' R, let's use it with linear regression to make distinctive predictions.

R 2 (R-squared) is a statistical measure of the goodness of fit of a linear regression model (from 0.00 to 1.00), also known as the coefficient of determination.

Both Josh Barro and Matt Yglesias took issue with one of the most common interpretations of RR’s work – the existence of a sort of economic tipping point for countries with debt-to-GDP ratios ...

Sometimes, a model uses the square, square-root or any other power of one or more independent variables to predict the dependent one, which makes it a non-linear regression.

You will likely find approach (2) to be the most useful in practice because, in many cases, you will want to change the granularity of your categorical variables. A regression equation with a zillion ...

When multiple variables are associated with a response, the interpretation of a prediction equation is seldom simple.

The following statements fit a quadratic regression model to the data. This enables you to estimate the linear relationship between iron content and corrosion resistance and test for the presence of a ...

Parametric versus Semi/nonparametric Regression Models Course Topics Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the ...