Multiple regression is a statistical method for examining the relationship between numerous independent variables and a single dependent variable. The goal of multiple regression analysis is to predict the value of a single dependent variable by using known independent variables.
There are various different forms of multiple regression analyses (for example, standard, hierarchical, setwise, and stepwise), but just two will be discussed here (standard and stepwise). The sort of analysis performed is determined by the researcher's study issue.
When you want to know how strong the association is between two or more independent variables and one dependent variable, you can use multiple linear regression (e.g. how rainfall, temperature, and amount of fertiliser added affect crop growth).
For example, if you're using multiple regression to predict blood pressure (the dependent variable) from independent variables like height, weight, age, and weekly activity hours, you should also include sex as an independent variable.
It's also common to use it to forecast the value of one dependent variable based on the values of two or more independent variables. Multiple regression is used when there are two or more independent variables.
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