BIO SCI 100 Study Guide - Midterm Guide: Central Limit Theorem, Box Plot, Histogram
25 Mar 2017
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R expects objects to be same size , -if 1 object is a multiple of another, r thinks mistake made, *can only remove one set of outliers. =quartile(first cell of range:last cell of range,1): top of 3rdquartile, =quartile(first cell of range:last cell of range,1), difference is iqr (multiple by 1. 5 for whiskers). Command>quantile(x: extreme outliers: 3 times instead of 1. 5 (should be marked with*). Most important command for boxplot: boxplot(datap2) = not gonna work (uppercase. B). boxplot(datap2)=gives outliers: central limit theorem: sampling dist of any stat is normal if sample size is large enough. = rcommand: qqnorm() -(visualization of what"s happening w/ normal distribution).
