Use Linear Regression to describe the relationship between a set of predictors and a continuous response using the ordinary least squares method. You can include interaction and polynomial …

Use the regression equation to describe the relationship between the response and the terms in the model. The regression equation is an algebraic representation of the regression line.

Regression results identify the direction, size, and statistical significance of the relationship between a predictor and response. The sign of each coefficient indicates the direction of the …

When calculating a regression equation to model data, Minitab estimates the coefficients for each predictor variable based on your sample and displays these estimates in a coefficients table.

The chemist performs a multiple regression analysis to fit a model with the predictors and eliminate the predictors that do not have a statistically significant relationship with the response.

Complete the following steps to interpret a regression model. Key output includes the p-value, the coefficients, R 2, and the residual plots.

Fit Regression Model and Linear Regression perform the same analysis from different menus. Use these analyses to describe the relationship between a set of predictors and a continuous …

A regression coefficient describes the size and direction of the relationship between a predictor and the response variable. Coefficients are the numbers by which the values of the term are …

Easily include interaction and polynomial terms, transform the response, or use stepwise regression if needed. In Minitab, choose Stat > Regression > Regression > Fit Regression …

To remove highly correlated predictors from a regression equation, Minitab does the following steps: Minitab performs the SWEEP method on the correlation matrix, R, treating X 1 …