Contents - Index
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.