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Quantitative Research Methods: Regression and Correlation

Pearson's Correlation (r) - Quick Introduction

The correlation coefficient (r) tells you the strength of the relationship between two variables.  The value of r has a range of -1 to 1 (0 indicates no relationship).  Values of r closer to -1 or 1 indicate a stronger relationship and values closer to 0 indicate a weaker relationship.  The coefficient is affected by a variety of factors, so it's always best to also plot your two variables as a scatterplot.  

Spurious Relationships

A relationship between two variables might be correlated - like ice cream sales and murders committed on a particular day.  This does not mean that people buying more ice cream CAUSES murders to increase.  A relationship like this is called a spurious relationship or a spurious correlation.  

Calculating r

Correlation does NOT equal Causation

Regression - Quick Introduction

Regression is a statistical method that tries to uncover the association between variables.  There are assumptions that must be met before running a regression and it's very important to understand how to properly interpret a regression equation.  There are methods for how to find which predictors are best such as the bootstrap method, and there are others who will choose predictors based on theory.  

Linear Regression

Multiple Regression


If you do a subject search for Regression Analysis you'll see that the library has over 200 books about regression.  Select books are listed below.  Also, note that econometrics texts will often include regression analysis and other related methods.  


Search for ebooks using Quicksearch.  Use keywords to search for e-books about Regression.