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Logs In Regression - Statistics Department

Statistics 621 Robert StineFall, 2001 1 Logs Transformation in a Regression EquationLogs as the PredictorThe interpretation of the slope and intercept in a Regression change when thepredictor (X) is put on a log scale. In this case, the intercept is the expected valueof the response when the predictor is 1, and the slope measures the expectedchange in the response when the predictor increases by a fixed properties of the Regression equation are most clear in the context of anexample, such as the display example from the casebook. In that example, theestimated least squares Regression equation isSales = 84 + 139 log(Feet)To interpret the intercept 84 in this equation, we need to remove the term involvingthe slope.

a “new” Accord (foolish using only data from used Accords) as Log(Value for Age=0) = 3.03 so that the value itself would be about e3.03 = $20.7 thousand, which you can check from the plot. For the slope, again look for the percentage change, but now in the response. In this case, changes of the age in years will produce percentage changes ...

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