Correlation is the relationship or association between two variables. There are multiple ways to measure correlation, but the most common is Pearson's correlation coefficient (r), which tells you the strength of the linear 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. Because Pearson's coefficient only picks up on linear relationships, and there are many other ways for variables to be associated, it's always best to plot your variables on a scatter plot, so that you can visually inspect them for other types of correlation.

- CorrelationPenn State University tutorial
- Correlation and CausationAustralian Bureau of Statistics Article

It's important to remember that correlation does not always indicate causation. Two variables can be correlated without either variable causing the other. For instance, ice cream sales and drownings might be correlated, but that doesn't mean that ice cream *causes* drownings—instead, both ice cream sales and drownings increase when the weather is hot. Relationships like this are called spurious correlations.

- SpuriousnessHarvard Business Review article.
- New Evidence for Theory of The StorkA satirical article demonstrating the dangers of confusing correlation with causation.

Regression is a statistical method for estimating the relationship between two or more variables. In theory, regression can be used to predict the value of one variable (the dependent variable) from the value of one or more other variables (the independent variable/s or predictor/s). There are many different types of regression, depending on the number of variables and the properties of the data that one is working with, and each makes assumptions about the relationship between the variables. (For instance, most types of regression assume that the variables have a linear relationship.) Therefore, it is important to understand the assumptions underlying the type of regression that you use and how to properly interpret its results. Because regression will always output a relationship, whether or not the variables are truly causally associated, it is also important to carefully select your predictor variables.

- A Refresher on Regression AnalysisHarvard Business Review article.

Simple linear regression estimates a linear relationship between one dependent variable and one independent variable.

- Simple Linear Regression TutorialPenn State University Tutorial
- Statistics 101: Linear Regression, The Very BasicsYouTube video from Brandon Foltz.

Multiple linear regression estimates a linear relationship between one dependent variable and two or more independent variables.

- Multiple Linear Regression TutorialPenn State University Tutorial
- Multiple Regression BasicsNYU course materials.
- Statistics 101: Multiple Linear Regression, The Very BasicsYouTube video from Brandon Foltz.

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.

- Interaction Effects in Multiple Regression by James Jaccard (Editor); Robert TurrisiCall Number: HA31.3 .J33 2003ISBN: 0761927425Publication Date: 2003-03-05
- Regression Models for Categorical Dependent Variables Using Stata, Second Edition by Jeremy Freese; J. Scott LongCall Number: QA278.2 .L66 2006XISBN: 1597180114Publication Date: 2005-11-15
- Regression Models for Time Series Analysis by Benjamin Kedem; Konstantinos FokianosCall Number: QA280 .K428 2002ISBN: 0471363553Publication Date: 2002-08-19

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

- Applied Econometrics Using the SASĀ® System by Vivek AjmaniISBN: 9780470129494Publication Date: 2009-06-15
- Handbook of Regression Analysis by Samprit Chatterjee; Jeffrey S. SimonoffISBN: 9780470887165Publication Date: 2012-12-26
- Regression Analysis by Rudolf J. Freund; William J. Wilson; Ping SaISBN: 9780080522975Publication Date: 2006-05-30
- Applied Logistic Regression by David W. Hosmer; Stanley Lemeshow; Rodney X. SturdivantISBN: 9781118548356Publication Date: 2013-02-26
- Multivariate Nonparametric Regression and Visualization by Jussi KlemeläISBN: 9781118593509Publication Date: 2014-05-05

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