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Conduct multiple regression

 

This function is applicable to any multivariate dataset or two-way table that can be regarded as a multivariate dataset. Follow the following steps:

1. The dependent variable in a larger font and underlined;

2. The retained independent variables;

3. Variable vectors to facilitate visualization of the relationships among the variables, particularly those between the dependent variable and the independent variables. For interpretation;

4. Total variation of the dependent variable explained by the multiple regression, on the top of the biplot; and

5. Numerical results, including the partial regression coefficient, t-statistics, type I error probability of the partial regression coefficients, and simple r-square values (variation of the dependent variable explained by each independent variable). This information is also printed to the log file for your reference.

Note: The simple r-square values are more correct when the multiple regression was preceded by the execution of the "find associates" function).

 

Follow this link for an example: QTL identification based on multiple linear regression.