This generalizes to any number of independent chi-square random variables.Ī method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation If two independent random variables X1 and X2 are distributed as chi-square with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chi-square random variable with u = + v v 1 2 degrees of freedom. In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error). Key Statistics Terms and definitions covered in this textbookĪ fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each Chapter 7: Scatterplots, Association, and Correlation.Chapter 6: The Standard Deviation as a Ruler and the Normal Model.Chapter 5: Understanding and Comparing Distributions.
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Chapter 4: Displaying and Summarizing Quantitative Data.Chapter 3: Displaying and Describing Categorical Data.Chapter 21: More About Tests and Intervals.Chapter 20: Testing Hypotheses About Proportions.Chapter 19: Confidence Intervals for Proportions.
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Chapter 14: From Randomness to Probability.Chapter 13: Experiments and Observational Studies.Chapter 10: Re-expressing Data: Get It Straight!.