The number that you really need to be looking at.
How large is large? Your regression software compares the t statistic on your variable with values in the Student's t distribution to determine the P value, which is If a coefficient is large compared to its standard error, then it is probably different from 0. With which the regression coefficient is measured. It can be thought of as a measure of the precision The standard error is an estimate of the standard deviation of the coefficient, the amount it varies across cases. The t statistic is the coefficient divided by its standard error. Variable is having absolutely no effect (has a coefficient of 0) and youĪre looking for a reason to reject this theory. The null (default) hypothesis is always that each independent If alternatively any apparent differences from 0 are just due to randomĬhance. When running your regression, you are trying to discover whether theĬoefficients on your independent variables are really different fromĠ (so the independent variables are having a genuine effect on your dependent variable) or Measure to tell you how strongly each independent variable is associated Independent variables (betas) and the constant (alpha)-you need some Prediction components of your equation-the coefficients on your One, decrease by 294.1955 when mpg goes up by one, and is predicted to beġ1905.42 when both mpg and foreign are zero.Ĭoming up with a prediction equation like this is only a usefulĮxercise if the independent variables in your dataset have someĬorrelation with your dependent variable. Is predicted to increase 1767.292 when the foreign variable goes up by In the Stata regression shown below, the prediction equation is price = That describe the size of the effect the independent variables are havingĪnd A is the value Y is predicted to have when all the Independent variables you are using to predict it, b1, b2Īnd so on are the coefficients or multipliers Where Y is the dependent variable you are trying to predict, Remember that regression analysis is used to produce an equation that will predict a dependent variable using one or more independent variables. For assistance in performing regression in particular software packages, there are some resources at UCLA Statistical Computing Portal.
You may wish to read our companion page Introduction to Regression first. This guide assumes that you have at least a little familiarity with the concepts of linear multiple regression, and are capable of performing a regression in some software package such as Stata, SPSS or Excel.