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Multiple regression fits the model to the data to find the values for the parameters that will make the predictions of the model come as close as possible to the actual data. Prism offers two forms of mulitiple regrssion

Like simple linear regression, it does so by finding the values of the parameters (regression coefficients) in the model that minimize the sum of the squares of the discrepancies between the actual and predicted Y values. Like simple linear regression, multiple regression is a least-squares method.

Like linear regression, multiple regression can be done using fairly simple calculations. Unlike nonlinear regression, multiple linear regression does not require an iterative approach so does not require initial estimated values for the parameters.


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