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Correlation and Regression Introduction

An Introduction to Correlation and Regression Analysis

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Correlation and Regression Analysis:

 

Correlation and regression analysis are essential tools in statistical analysis. Put simply, correlation measures the relationship between two variables, while regression measures one variable’s effect on another. This blog post will briefly examine correlation and regression analysis, when you might use them, and how to interpret the results.

Types of Correlation Analysis

 

There are three main types of correlation analysis: Pearson’s correlation, Spearman’s rank correlation, and Kendell’s tau

Pearson’s correlation, the relationship between two variables, is measured using a linear equation. 
Spearman’s rank correlation is used when you want to measure the relationship between two ranked variables.
Kendell’s tau is used to measure the relationship between two variables that are ordinal (i.e., data that can be put in order, such as “very good,” “good,” “average,” “poor,” etc.).


When to Use Correlation Analysis

 

Correlation analysis is typically used to see if there is a relationship between two variables. For example, you might want to see if there is a relationship between how much sleep people get and how productive they are at work. Or, you might want to see if there is a relationship between how often people exercise and how many sick days they take.

How to Interpret the Results of Correlation Analysis

 

The results of correlation analysis will give you a number called the correlation coefficient. This number will range from -1 to 1. A positive number indicates a positive relationship (i.e., as one variable increases, so does the other). In contrast, a negative number indicates a negative relationship (i.e., as one variable increases, the other decreases). The closer the number is to 1 or -1, the stronger the relationship between the two variables.

Correlation and Regression Examples
Correlation and Regression Examples

 

An easy way to remember this is with the phrase, “the closer to 1, the stronger the relation.” For example, a correlation coefficient of .9 would indicate a very strong positive relationship between two variables. In comparison, a correlation coefficient of -.2 would indicate a very weak negative relationship between two variables.

Conclusion:

 

Correlation and regression analysis are powerful tools that can be used to uncover relationships between variables. In this blog post, we briefly looked at what these analyses are, when you might use them, and how to interpret the results. To learn more about statistical analysis, check out our courses, especially our Lean Six Sigma Green Belt Course where we introduce this concept.

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Reagan Pannell

Reagan Pannell is a highly accomplished professional with 15 years of experience in building lean management programs for corporate companies. With his expertise in strategy execution, he has established himself as a trusted advisor for numerous organisations seeking to improve their operational efficiency.

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