You cannot test the first two of these assumptions with Minitab because they relate to your study design and choice of variables. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for linear regression to give you a valid result. In this guide, we show you how to carry out linear regression using Minitab, as well as interpret and report the results from this test.

If your dependent variable is dichotomous, you could use a binomial logistic regression. Alternatively, if you just want to establish whether a linear relationship exists, but are not making predictions, you could use Pearson's correlation.

Note: If you have two or more independent variables, rather than just one, you need to use multiple regression. Alternatively, you could use linear regression to understand whether cholesterol concentration (a fat in the blood linked to heart disease) can be predicted based on time spent exercising (i.e., the dependent variable would be 'cholesterol concentration', measured in mmol/L, and the independent variable would be 'time spent exercising', measured in hours). We will refer to these as dependent and independent variables throughout this guide.įor example, you could use linear regression to understand whether test anxiety can be predicted based on revision time (i.e., the dependent variable would be 'test anxiety', measured using an anxiety index, and the independent variable would be 'revision time', measured in hours). The dependent variable can also be referred to as the outcome, target or criterion variable, whilst the independent variable can also be referred to as the predictor, explanatory or regressor variable.

Linear regression, also known as simple linear regression or bivariate linear regression, is used when we want to predict the value of a dependent variable based on the value of an independent variable. Linear regression using Minitab Introduction

Multiple regression in minitab express