Running a basic multiple regression analysis in SPSS is simple. For a thorough analysis, however, we want to make sure we satisfy the main assumptions, which are linearity: each predictor has a linear relation with our outcome variable; normality: the prediction errors are normally distributed in the population; homoscedasticity: the variance of the errors is constant in the population. […]
Correlation B Coefficients Intercept Regression Residuals R-Square Simple linear regression is a technique that predicts a metric variable from a linear relation with another metric variable.Remember that “metric variables” refers to variables measured at interval or ratio level. The point here is that calculations -like addition and subtraction- are meaningful on metric variables (“salary” or […]
The vast majority of statistical tests really just compare several populations on one variable or compare several variables in one population. Now, if we add to this comparison the number of variables or populations and the measurement levels of our variables then choosing the appropriate statistical test suddenly becomes remarkably easy.* The overviews below also nicely visualize how basic tests relate […]
