# Regression Analysis

- Page ID
- 223

Regression analysis is a set of statistical processes for estimating the relationships among variables.

- Multiple Comparison
- Multiple comparison refers to the situation where a family of statistical inferences are considered simultaneously.

- Extra Sum of Squares
- When there are many predictors, it is often of interest to see if one or a few of the predictors can do the job of estimation of the mean response and prediction of new observations well enough. This can be put in the framework of comparison between a reduced regression model involving a subset of the variables versus the full regression model involving all the variables.

- Nonparametric Inference - Kernel Density Estimation
- The non-parametric estimation of a pdf f of a distribution on the real line. The kernel density estimator is a non-parametric estimator because it is not based on a parametric model.

- Simple linear regression
- Analysis of variance approach to regression
- Diagnostics for residuals(continued)
- General Linear Test
- Least squares principle
- Multiple Linear Regression (continued)
- Regression diagnostics for one predictor
- Regression through the origin
- Simple Linear Regression (with one predictor)
- Simultaneous Inference
- Some basic facts about vectors and matrices
- Test for Lack of Fit