Create residual plots and select 'Residuals versus fits' (with regular residuals). IQ and physical characteristics (residual plots and normality tests) Perform a linear regression analysis of PIQ on Brain and Height. If the p-value of this test is less than your chosen α, you can reject your null hypothesis and conclude that the population is nonnormal. Minitab Help 7: MLR Estimation, Prediction & Model Assumptions. If this observed difference is adequately large, the test will reject the null hypothesis of population normality. Kolmogorov-Smirnov normality test This test compares the ECDF (empirical cumulative distribution function) of your sample data with the distribution expected if the data were normal. This test is similar to the Shapiro-Wilk normality test. The Ryan-Joiner statistic assesses the strength of this correlation if it is less than the appropriate critical value, you will reject the null hypothesis of population normality. If the correlation coefficient is near 1, the population is likely to be normal. Ryan-Joiner normality test This test assesses normality by calculating the correlation between your data and the normal scores of your data. ![]() If the observed difference is adequately large, you will reject the null hypothesis of population normality. Anderson-Darling test This test compares the ECDF (empirical cumulative distribution function) of your sample data with the distribution expected if the data were normal. The following are types of normality tests that you can use to assess normality.
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