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Unformatted text preview: quadratic model Y = β + β 1 X + β 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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 Spring '12
 smith
 Regression Analysis, Quadratic equation, quadratic term

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