7 Assumptions of Linear regression — Data Science with Python & R

7 Assumptions of Linear regression using Stata

There are seven “assumptions” that underpin linear regression. If any of these seven assumptions are not met, you cannot analyse your data using linear because you will not get a valid result. Since assumptions #1 and #2 relate to your choice of variables, they cannot be tested for using Stata. However, you should decide whether your study meets these assumptions before moving on.

• Assumption #1: Your dependent variable should be measured at the continuous level. Examples of such continuous variables include height (measured in feet and inches), temperature (measured in oC), salary (measured in US dollars), revision time (measured in hours), intelligence (measured using IQ score), reaction time (measured in milliseconds), test performance (measured from 0 to 100), sales (measured in number of transactions per month), and so forth. If you are unsure whether your dependent variable is continuous (i.e., measured at the interval or ratio level), see our Types of Variable guide.
• Assumption #2: Your independent variable should be measured at the continuous or categorical level. However, if you have a categorical independent variable, it is more common to use an independent t-test (for 2 groups) or one-way ANOVA (for 3 groups or more). In case you are unsure, examples of categorical variables include gender (e.g., 2 groups: male and female), ethnicity (e.g., 3 groups: Caucasian…