![]() Assumption #3: There needs to be a linear relationship between the dependent and independent variables.Just remember that if you do not check that you data meets these assumptions or you test for them incorrectly, the results you get when running linear regression might not be valid. However, don’t worry because even when your data fails certain assumptions, there is often a solution to overcome this (e.g., transforming your data or using another statistical test instead). In fact, do not be surprised if your data fails one or more of these assumptions since this is fairly typical when working with real-world data rather than textbook examples, which often only show you how to carry out linear regression when everything goes well. When moving on to assumptions #3, #4, #5, #6 and #7, we suggest testing them in this order because it represents an order where, if a violation to the assumption is not correctable, you will no longer be able to use linear regression. In this guide, we show you the linear regression procedure and Stata output when both your dependent and independent variables were measured on a continuous level.įortunately, you can check assumptions #3, #4, #5, #6 and #7 using Stata. ![]() In case you are unsure, examples of categorical variables include gender (e.g., 2 groups: male and female), ethnicity (e.g., 3 groups: Caucasian, African American and Hispanic), physical activity level (e.g., 4 groups: sedentary, low, moderate and high), and profession (e.g., 5 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 2 groups) or one-way ANOVA (for 3 groups or more).
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