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Chapter 2.2 - Sample Spaces And The Algebra Of SetsChapter 2.3 - The Probability FunctionChapter 2.4 - Conditional ProbabilityChapter 2.5 - IndependenceChapter 2.6 - CombinatoricsChapter 2.7 - Combinatorial ProbabilityChapter 3.2 - Binomial And Hypergeometric ProbabilitiesChapter 3.3 - Discrete Random VariablesChapter 3.4 - Continuous Random VariablesChapter 3.5 - Expected Values
Chapter 3.6 - The VarianceChapter 3.7 - Joint DensitiesChapter 3.8 - Transforming And Combining Random VariablesChapter 3.9 - Further Properties Of The Mean And VarianceChapter 3.10 - Order StatisticsChapter 3.11 - Conditional DensitiesChapter 3.12 - Moment-generating FunctionsChapter 4.2 - The Poisson DistributionChapter 4.3 - The Normal DistributionChapter 4.4 - The Geometric DistributionChapter 4.5 - The Negative Binomial DistributionChapter 4.6 - The Gamma DistributionChapter 5.2 - Estimating Parameters: The Method Of Maximum Likelihood And The Method Of MomentsChapter 5.3 - Interval EstimationChapter 5.4 - Properties Of EstimatorsChapter 5.5 - Minimum-variance Estimators: The Cramer-rao Lower BoundChapter 5.6 - Sufficient EstimatorsChapter 5.7 - ConsistencyChapter 5.8 - Bayesian EstimationChapter 6.2 - The Decision RuleChapter 6.3 - Testing Binomial Data—h0: P = PoChapter 6.4 - Type I And Type Ii ErrorsChapter 6.5 - A Notion Of Optimality: The Generalized Likelihood RatioChapter 7.3 - Deriving The Distribution Of Y−μChapter 7.4 - Drawing Inferences About μChapter 7.5 - Drawing Inferences About Σ2Chapter 8.2 - Classifying DataChapter 9.2 - Testing H0: Μx = ΜyChapter 9.3 - Testing H0: Σ2 X = Σ2 Y—the F TestChapter 9.4 - Binomial Data: Testing H0: Px = PyChapter 9.5 - Confidence Intervals For The Two-sample ProblemChapter 10.2 - The Multinomial DistributionChapter 10.3 - Goodness-of-fit Tests: All Parameters KnownChapter 10.4 - Goodness-of-fit Tests: Parameters UnknownChapter 10.5 - Contingency TablesChapter 11.2 - The Method Of Least SquaresChapter 11.3 - The Linear ModelChapter 11.4 - Covariance And CorrelationChapter 11.5 - The Bivariate Normal DistributionChapter 12.2 - The F TestChapter 12.3 - Multiple Comparisons: Tukey’s MethodChapter 12.4 - Testing Subhypotheses With ContrastsChapter 12.5 - Data TransformationsChapter 12.A.2 - The Distribution Of Sstr/(k−1)/sse/(n−k) When H1 Is TrueChapter 13.2 - The F Test For A Randomized Block DesignChapter 13.3 - The Paired T TestChapter 14.2 - The Sign TestChapter 14.3 - Wilcoxon TestsChapter 14.4 - The Kruskal-wallis TestChapter 14.5 - The Friedman TestChapter 14.6 - Testing For Randomness
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