Whilst there are a number of ways to check whether a linear relationship exists between your two variables, we suggest creating a scatterplot using Minitab, where you can plot the dependent variable against your independent variable. Assumption #3: There needs to be a linear relationship between the dependent and independent variables.Assumptions #3, #4, #5, #6 and #7 are explained below: However, there are possible solutions to correct such violations (e.g., transforming your data) such that you can still use a linear regression. In fact, do not be surprised if your data violates one or more of these assumptions. You have to check that your data meets these assumptions because if it does not, the results you get when running a linear regression might not be valid. In this guide, we show you the linear regression procedure and Minitab output when both your dependent and independent variables were measured at the continuous level.Īssumptions #3, #4, #5, #6 and #7 relate to the nature of your data and can be checked using Minitab. In case you are unsure, examples of categorical variables include gender (e.g., two groups: male and female), ethnicity (e.g., three groups: Caucasian, African American and Hispanic), physical activity level (e.g., four groups: sedentary, low, moderate and high), and profession (e.g., five groups: surgeon, doctor, nurse, dentist, therapist). However, if you have a categorical independent variable, it is more common to use an independent t-test (for two groups) or one-way ANOVA (for three groups or more).
#MINITAB 18 PREDICT HOW TO#
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