Lecture is about assessing Normality of Data, by matching DATA CDF with NORMAL CDF
1. Plot data CDF, Normal CDF and assess fit visually.
2. Standardization of data by subtracting mean, dividing by STD - then plot CDF and match to Standard Normal
3. Linearizing using a normal probability plot, so that normal CDF is linear.
4. Calculate the maximum difference between Normal CDF and Data CDF -- The Kolmogorov-Smirnoff statistics
5. Evaluating significance of KS statistics using simulations in EXCEL, and using POPTOOLS.
POPTOOLS website - download free EXCEL add-in to enable easy simulations in EXCEL
E4M L10 Testing Data for Normality - Video Lecture: Match data CDF to Normal CDF visually and via Kolmogorov Smirnov Statistic. Simulations to assess significance (asadzaman.net)
E4M L10 Testing Data for Normality - 1 hr 35 min YouTube version (same lecture)