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Lecture 8

Lecture 8 - Feb 1.doc

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McGill University
PSYC 305
Heungsun Hwang

PSYC305 Lecture 8 - Feb. 1 One-Way ANOVA: • Assume all three populations all have the same variances Assessing Normality: • Look at descriptive statistics Skewness ≃ 0 • • > 0 - positively/right skew • < 0 - negative/left skew • Look at their standard errors • If it is normally distribution, there is no skewness If it is skewed, technically you can’t apply the one-way ANOVA • • Construct charts • Separate histograms for each group to assess normality • It doesn’t have to be perfect, just roughly symmetric • Evaluate a normal quantile plot (or normal probability plot) Sort observations from smallest to largest • • Calculate the z-scores of the sorted observations • Plot the observations against the corresponding z-scores • If the data are closed to normal, then the points will lie close to some straight line Statistical tests of normality: • • The Kolmogorov-Smirnov (K-S) test • The Shapiro-Wilk test • Compares sample scores to a set of scores generated from a normal distri- bution with the sample means and standard deviation •If the test is non-significant (p > .05), the distribution of the sample is the same as a normal distribution • Limitation of the normality tests • It is very easy to give significant results when sample size is large • You use these tests, but plot your data as well ← ← Histogram & Normal Quantile Plot - Price Promotion Data: Assessing Homogeneity of Variance: Serious violation of this assumption tends to inflate the observed value of the
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