Data Assumptions: Its about the residuals, and not the variables’ raw data

June 3, 2013

Normality, or normal distributions is a very familiar term but what does it really mean and what does it refer to…   In linear models such as ANOVA and Regression (or any regression-based statistical procedures), an important assumptions is “normality”. The question is whether it refers to the outcome (dependent variable “Y”), or the predictor (independent variable “X”). We should remember that the true answer is “none of the above”.    In linear models where we look at the relationship between dependent and independent variables, our [READ MORE]

Building statistical models: Linear (OLS) regression

October 17, 2012

Everyday, researchers around the world collect sample data to build statistical models to be as representative of the real world as possible so these models can be used to predict changes and outcomes in the real world. Some models are very complex, while others are as basic as calculating a mean score by summating several observations and then dividing the score by the number of observations. This mean score is a hypothetical value so it is just a simple model to describe the data.   The extent to which a statistical model (e.g. the mean score) represents the randomly collected [READ MORE]