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An introduction to multiple linear regression
Multiple Linear Regression | A Quick and Simple Guide
This chapter describes how to compute multiple linear regression with interaction effects. Previously, we have described how to build a multiple linear regression model Chapter ref linear-regression for predicting a continuous outcome variable y based on multiple predictor variables x. The above equation, also known as additive model , investigates only the main effects of predictors. It assumes that the relationship between a given predictor variable and the outcome is independent of the other predictor variables James et al. Bruce and Bruce Considering our example, the additive model assumes that, the effect on sales of youtube advertising is independent of the effect of facebook advertising.
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Regression formula is used to assess the relationship between dependent and independent variable and find out how it affects the dependent variable on the change of independent variable and represented by equation Y is equal to aX plus b where Y is the dependent variable, a is the slope of regression equation, x is the independent variable and b is constant. Regression analysis widely used statistical methods to estimate the relationships between one or more independent variables and dependent variables. Regression is a powerful tool as it is used to assess the strength of the relationship between two or more variables, and then it would be used for modeling the relationship between those variables in the future. Regression analysis, as mentioned earlier, is majorly used to find equations that will fit the data.
Multiple linear regression MLR , also known simply as multiple regression, is a statistical technique that uses several explanatory variables to predict the outcome of a response variable. The goal of multiple linear regression MLR is to model the linear relationship between the explanatory independent variables and response dependent variable. In essence, multiple regression is the extension of ordinary least-squares OLS regression because it involves more than one explanatory variable. Simple linear regression is a function that allows an analyst or statistician to make predictions about one variable based on the information that is known about another variable. Linear regression can only be used when one has two continuous variables—an independent variable and a dependent variable.