## Statistics Tip of the Week: Establishing Correlation is a Prerequisite for Linear Regression2/1/2017 Establishing Correlation is a prerequisite for Linear Regression. We can't use Linear Regression unless there is a Linear Correlation. The following compare-and-contrast table may help in understanding both concepts. Correlation analysis There is no looking into the future. The purpose of Linear Regression, on the other hand, is to define a Model (a linear equation) which can be used to describes the present or past situation. It uses Sample data to infer a property of the source Population or Process. predict the results of Designed Experiments.Regression also uses r, but employs a variety of other Statistics.Correlation mainly uses the Correlation Coefficient, r. Linear Correlation is limited to 2 Variables, which can be plotted on a 2-dimensional x-y graph. Linear Regression can go to 3 or more Variables/ dimensions.Correlation analysis and Linear Regression both attempt to determine whether 2 Variables vary in synch. In Linear Regression, that line is the whole point. We calculate a best-fit line through the data: y = a + bx.In Correlation, we ask to what degree the plotted data forms a shape that seems to follow an imaginary line that would go through it. But we don't try to specify that line. , Regression does.Correlation Analysis does not attempt to identify a Cause-Effect relationship
2 Comments
Zvondiwa Mberengwa
8/13/2019 04:06:03 am
Thanks for clarifying the concepts. Please assist me with Correlation matrix.
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Andrew Jawlik
8/13/2019 07:57:16 am
Not sure what you mean by Correlation Matrix. My book has 2 chapters on Correlation, and these are explained in 2 videos on my You Tube channel (the channel has the same name as the title of the book)
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## AuthorAndrew A. (Andy) Jawlik is the author of the book, Statistics from A to Z -- Confusing Concepts Clarified, published by Wiley. ## Archives
March 2020
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