Use the following analyses when you have a continuous response variable.
 Regression
 Model the relationship between categorical or continuous predictors and one response, and use the model to predict response values for new observations. Easily include interaction and polynomial terms, transform the response, or use stepwise regression if needed. In Minitab, choose .
 Best subsets
 Compare all possible models using a specified set of predictors, and display the best fitting models that contain one predictor, two predictors, and so on. In Minitab, choose .
 Fitted line plot
 Plot the relationship between one predictor and one response. In Minitab, choose .
 Nonlinear regression

Model the relationship between predictors and a response when quadratic or cubic terms are not adequate. Use when you can specify a nonlinear relationship, such as nonlinear growth or decay, to describe the relationship. In Minitab, choose .
 Stability study
 Plan a stability study and create a custom worksheet for data collection. In Minitab, choose .
 Estimate the shelf life of a drug product with a linear model. In Minitab, choose .
 Orthogonal regression
 Model the relationship between one response and one predictor when the measurements of both the response and the predictor include random error. In Minitab, choose .
 Partial least squares

Determine whether a set of predictors are related to the responses. Use when you have predictors that are highly collinear or when you have more predictors than observations. In Minitab, choose .