Assumption #1: Your dependent variable should be measured at the continuous level.

However, you should decide whether your study meets these assumptions before moving on. Since assumptions #1 and #2 relate to your choice of variables, they cannot be tested for using Stata. If any of these eight assumptions are not met, you cannot analyze your data using multiple regression because you will not get a valid result. There are eight 'assumptions' that underpin multiple regression. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for multiple regression to give you a valid result.

This 'quick start' guide shows you how to carry out multiple regression using Stata, as well as how to interpret and report the results from this test. For example, you might want to know how much of the variation in exam anxiety can be explained by coursework mark, revision time, lecture attendance and IQ score 'as a whole', but also the 'relative contribution' of each independent variable in explaining the variance. Multiple regression also allows you to determine the overall fit (variance explained) of the model and the relative contribution of each of the independent variables to the total variance explained. If you have a dichotomous dependent variable you can use a binomial logistic regression. Alternately, you could use multiple regression to determine if income can be predicted based on age, gender and educational level (i.e., the dependent variable would be 'income', and the three independent variables would be 'age', 'gender' and 'educational level').

For example, you could use multiple regression to determine if exam anxiety can be predicted based on coursework mark, revision time, lecture attendance and IQ score (i.e., the dependent variable would be 'exam anxiety', and the four independent variables would be 'coursework mark', 'revision time', 'lecture attendance' and 'IQ score'). Multiple regression (an extension of simple linear regression) is used to predict the value of a dependent variable (also known as an outcome variable) based on the value of two or more independent variables (also known as predictor variables). Multiple Regression Analysis using Stata Introduction

Stata 13 datasets