## 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
0 Comments
## Leave a Reply. |
## AuthorAndrew A. (Andy) Jawlik is the author of the book, Statistics from A to Z -- Confusing Concepts Clarified, published by Wiley. ## Archives
February 2019
## Categories |