Multiple regression
Multiple Regression is one of the most widely used statistical tools, the reason for this is convincing: it is remarkably efficient for answering questions involving many variables.
Application
This technique is applicable to many subject fields, from agriculture to medicine.
Example
The data in this example is obtained from a study of the relationship between the amount of rainfall on the yield of corn in the USA.
The raw data was presented in the following format:
|
Year |
Rainfall (in/year) |
Yield (bu/acre) |
1890
1891
•
•
•
1927
|
9.6
12.9
•
•
•
10.4
|
24.5
33.7
•
•
•
32.6
|
Summary of statistical findings
There is strong evidence that the relationship between yield and rainfall is not linear. The term in the regression model which describes the curvature is significant (p=0.014). A graphical representation of corn yield vs rainfall can be seen as follows:
