linear regression

Hierarchical Linear Model

Linear regression probably is the most familiar technique in data analysis, but its application is often hamstrung by model assumptions. For instance, if the data has a hierarchical structure, quite often the assumptions of linear regression are feasible only at local levels. We will investigate an extension of the linear model to bi-level hierarchies.

Prediction Interval for MLR

A tutorial on the prediction interval for a multiple linear regression model.

Confidence Interval for MLR

A tutorial on the confidence interval for a multiple linear regression model.

Significance Test for MLR

A tutorial on the significance test for a multiple linear regression model.

A tutorial on the adjusted coefficient of determination for a multiple linear regression model.

Multiple Coefficient of Determination

A tutorial on the coefficient of determination for a multiple linear regression model.

Estimated Multiple Regression Equation

A tutorial on estimated regression equation for a multiple linear regression model.

Multiple Linear Regression

A multiple linear regression (MLR) model that describes a dependent variable y by independent variables x1, x2, ..., xp (p > 1) is expressed by the equation as follows, where the numbers α and βk (k = 1, 2, ..., p) are the parameters, and ϵ is the error term.

Normal Probability Plot of Residuals

A tutorial on the normal probability plot for the residual of a simple linear regression model.

Standardized Residual

A tutorial on the standardized residual of a simple linear regression model.