Explain how using multiple linear regression controls for confounding.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Explain how using multiple linear regression controls for confounding.
we know that multiple linear regression is the extension of simple linear regression .as simple linear regression contain only one independent variable. and multiple regression contain more than one independent variable. in the multiple linear regression there is some interaction effect of independent variable to the dependent variable and in this way multiple variables gives the coefficients related to the each variable which suggest the change in dependent variable and also gives the idea of interaction which causes confounding.
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