Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

Zheng Yuan, Yuhong Yang, Combining Linear Regression Models: When and How?, Journal of the American Statistical Association, Vol. 100, No. 472 (Dec., 2005), pp. 1202-1214 ...

For time-series analysis, it is possible to develop a linear regression model that simply fits a line to the variable's historical performance and extrapolates that into the future.

Before building my model, I want to step back to offer an easy-to-understand definition of linear regression and why it’s vital to analyzing data. What is linear regression?

We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...

Course Topics Ordinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be continuous ...

Learn how ARIMA models use time series data for accurate short-term forecasting. Discover its pros, cons, and essential tips for financial predictions.