Checking the data and assumptions before the final analysis.

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Presentation transcript:

Checking the data and assumptions before the final analysis. Cleaning up your act Checking the data and assumptions before the final analysis. 4/5/2019 AGR206

Topics Detection of problems: Fixing problems: Population represented by sample. Missing data. Normality of errors. Linearity and Lack of Fit. Homogeneity of variance. Outliers. Univariate Multivariate Multicollinearity. Fixing problems: Transformations 4/5/2019 AGR206

Population and sample. Scope of regression. 4/5/2019 AGR206

Normality. Usually, it is assumed that errors are normally distributed. In JMP, obtain errors (residuals) and then use the Analyze Distributions platform. Example: xmpl_Pyield.jmp 4/5/2019 AGR206

Linearity/Lack of Fit Lack of Fit compares the variance within replicated X values to the variance around the model. 4/5/2019 AGR206

Lack of Fit ANOVA Table Example: xmpl_Pyield.jmp 4/5/2019 AGR206

Homogeneity of Variance Variance of errors are assumed equal. Plot errors vs. Yhat and X’s. Use UnEqual variance test in the Fit Y by X platform. Example: xmpl_Pyield 4/5/2019 AGR206

Outliers Univariate: Multivariate: Check the studentized residual. Compare to t(n-p, 0.001). Or correct by Bonferroni. Check X dimension: hii<2(m+1)/n hii is the leverage m is the number of X variables n is the number of observations Multivariate: Mahalanobis squared distance ~ 2(m) if variables are normal. 4/5/2019 AGR206

Outliers Example: xmpl_UVoutl.jmp Example: xmpl_MVoutl.jmp 4/5/2019 AGR206