Test for Normal Distribution

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Presentation transcript:

Test for Normal Distribution

Test of Normality - 1 Graphical Test Histogram Check shape: skewness, outliers Normal Probability Plot Check shape: straight, convex, S-shaped

Construction of a Normal Probability Plot Alternative estimates of the cumulative relative frequency of an observation pi = (i - 0.5)/ n pi = i / (n+1) pi = (i - 0.375) / (n+0.25) Estimate of the percentile | Normal Standardized Q(pi) = NORMSINV(pi) Q(pi) = NORMINV(pi, mean, stand. dev.)

Non-Normal Populations Flat Skewed Expected | Normal Data Expected | Normal Data

Test of Normality - 2 Test Statistics Stand. Dev. Skewness Kurtosis

The Jarque-Bera Test If the population is normal and the data are random, then: follows approximately c2 with the # 0f degrees of freedom 2. Reject H0 if JB > 6