eBook Probability and Statistical Inference 10th Edition By Robert V. Hogg, Elliot A. Tanis, Dale L. Zimmerman
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eBook Probability and Statistical Inference 10th Edition By Robert V. Hogg, Elliot A. Tanis, Dale L. Zimmerman

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563
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About this ebook
The 10th Edition of Probability and Statistical Inference by Robert V. Hogg, Elliot A. Tanis, and Dale L. Zimmerman is structured into 9 primary chapters that bridge foundational calculus-based probability theory with mathematical statistics and inferential data analysis.
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The textbook core topics are categorized below by chapter:
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Part I: Probability Theory & Distributions (Chapters 1–5)
  • Chapter 1: Probability
    Properties of probability, methods of enumeration (combinatorics), conditional probability, independent events, Bayes' theorem, and the law of total probability.
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  • Chapter 2: Discrete Distributions
    Random variables of the discrete type, mathematical expectation, special expectations (mean, variance, skewness), the binomial distribution, the hypergeometric distribution, the negative binomial distribution, and the Poisson distribution.
    Bartleby.com +3
  • Chapter 3: Continuous Distributions
    Random variables of the continuous type, probability density functions (pdf), cumulative distribution functions (cdf), the exponential distribution, gamma distribution, chi-square distribution, and the normal distribution.
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  • Chapter 4: Bivariate Distributions
    Bivariate, marginal, and conditional distributions, mathematical expectations, correlation coefficients, conditional variance, and the bivariate normal distribution.
    جامعة الملك سعود +1
  • Chapter 5: Distributions of Functions of Random Variables
    Functions of one or more random variables, the moment-generating function technique, transformations of random variables, Chebyshev’s inequality, convergence in probability/distribution, and the Central Limit Theorem (CLT).
    جامعة الملك سعود +3
Part II: Statistical Inference & Methods (Chapters 6–9)
  • Chapter 6: Point Estimation
    Descriptive and order statistics, point estimation principles, maximum likelihood estimators (MLE) and their distributions, the method of moments, and sufficient statistics.
  • Chapter 7: Interval Estimation
    Constructing confidence intervals for population means and proportions, confidence intervals for regression coefficients, distribution-free confidence intervals for percentiles, and resampling methods (such as bootstrapping).
  • Chapter 8: Tests of Statistical Hypotheses
    Formulation of hypotheses, Type I and Type II errors, significance testing for means and proportions, the Wilcoxon tests, the power of a test, best critical regions (Neyman-Pearson Lemma), and likelihood ratio tests.
  • Chapter 9: More About Tests (Often incorporates advanced applications)
    Chi-square goodness-of-fit and contingency tables, Analysis of Variance (one-way and two-way ANOVA), general factorial and
    2k2 to the k-th power
    2
    experimental designs, simple linear regression analysis, Bayesian estimation/inference methods, and statistical quality control.
    Hasthika +4

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Topics

Bivariate Distributions Continuous Distributions Discrete Distributions Distributions of Functions of Random Variables Interval Estimation maximum likelihood estimators (MLE) and their distributions Point Estimation Descriptive and order statistics point estimation principles Statistical Inference & Methods Tests of Statistical Hypotheses
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