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Unformatted text preview: quadratic model Y = + 1 X + 2 X 2 is a highly significant model that explains over 80% of variability in Y . Use of Transformations Consider the following data set relating Salary to Years of Experience The full data set consists of 50 values. SLR of Salary (Y) on Experience (X) Residual Plot exhibits heteroscedasticity Define the new variable lnY SLR of ln Y vs X Heteroscedasticity is not evident Exponential relationship between Y and X The loglinear equation ln Y = 9.84 + 0.05 X can easily be transformed to the exponential equation Y = e 9.84+0.05X = e 9.84 e 0.05x =18769e 0.05X showing that Y is an exponential function of X. Conclusion Heteroscedasticity may be reduced or eliminated by taking a transformation of the dependent variable. Logarithmic and square root transformations are commonly used....
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This note was uploaded on 02/28/2012 for the course MANAGEMENT MGSC 272 taught by Professor Smith during the Spring '12 term at McGill.
 Spring '12
 smith

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