3. Regression Analysis in Excel and R
One of the obstacles to moving smoothly between Excel and R is the array of differences in how the two applications combine the results of a regression analysis. For example, Excel’s principal regression function is LINEST( ), which returns the elements of the regression equation as well as eight other statistics that enable the user to assess the equation’s accuracy and its reliability. One of R’s principal regression functions is lm, an abbreviation of linear model. As it’s normally used, lm returns some useful information that LINEST( ) doesn’t, but lm for some ...
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