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The formula for a multiple linear regression is: = the predicted value of the dependent variable = the y-intercept (value of y when all other parameters are set to 0)
Multiple linear regression is a method we can use to quantify the relationship between two or more predictor variables and a response variable. This tutorial explains how to perform multiple linear regression by hand. Example: Multiple Linear Regression by Hand
Multiple linear regression answers several questions. Is at least one of the variables X i useful for predicting the outcome Y? Which subset of the predictors is most important? How good is a linear model for these data? Given a set of predictor values, what is a likely value for Y, and how accurate is this prediction? The estimates β ^
Learn about Multiple Regression, the basic condition for it and its formula with assumptions behind the theory, its advantages, disadvantages and examples.
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.
In simple linear regression, a criterion variable is predicted from one predictor variable. In multiple regression, the criterion is predicted by two or more variables.
The multiple regression equation can be used to estimate systolic blood pressures as a function of a participant's BMI, age, gender and treatment for hypertension status. For example, we can estimate the blood pressure of a 50 year old male, with a BMI of 25 who is not on treatment for hypertension as follows: