Understanding advanced statistical methods
 Responsibility
 Peter H. Westfall, Kevin S.S. Henning.
 Publication
 Boca Raton : CRC Press, [2013]
 Copyright notice
 ©2013
 Physical description
 xxv, 543 pages : illustrations ; 26 cm.
 Series
 Texts in statistical science.
Access
Available online
Science Library (Li and Ma)
Stacks
Call number  Status 

QA276 .W4537 2013  Checked out  Overdue Request 
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Creators/Contributors
 Author/Creator
 Westfall, Peter H., 1957
 Contributor
 Henning, Kevin S. S.
Contents/Summary
 Contents

 Introduction: Probability, Statistics, and Science Reality, Nature, Science, and Models Statistical Processes: Nature, Design and Measurement, and Data Models Deterministic Models Variability Parameters Purely Probabilistic Statistical Models Statistical Models with Both Deterministic and Probabilistic Components Statistical Inference Good and Bad Models Uses of Probability Models Random Variables and Their Probability Distributions Introduction Types of Random Variables: Nominal, Ordinal, and Continuous Discrete Probability Distribution Functions Continuous Probability Distribution Functions Some CalculusDerivatives and Least Squares More CalculusIntegrals and Cumulative Distribution Functions Probability Calculation and Simulation Introduction Analytic Calculations, Discrete and Continuous Cases SimulationBased Approximation Generating Random Numbers Identifying Distributions Introduction Identifying Distributions from Theory Alone Using Data: Estimating Distributions via the Histogram Quantiles: Theoretical and DataBased Estimates Using Data: Comparing Distributions via the QuantileQuantile Plot Effect of Randomness on Histograms and qq Plots Conditional Distributions and Independence Introduction Conditional Discrete Distributions Estimating Conditional Discrete Distributions Conditional Continuous Distributions Estimating Conditional Continuous Distributions Independence Marginal Distributions, Joint Distributions, Independence, and Bayes' Theorem Introduction Joint and Marginal Distributions Estimating and Visualizing Joint Distributions Conditional Distributions from Joint Distributions Joint Distributions When Variables Are Independent Bayes' Theorem Sampling from Populations and Processes Introduction Sampling from Populations Critique of the Population Interpretation of Probability Models The Process Model versus the Population Model Independent and Identically Distributed Random Variables and Other Models Checking the iid Assumption Expected Value and the Law of Large Numbers Introduction Discrete Case Continuous Case Law of Large Numbers Law of Large Numbers for the Bernoulli Distribution Keeping the Terminology Straight: Mean, Average, Sample Mean, Sample Average, and Expected Value Bootstrap Distribution and the PlugIn Principle Functions of Random Variables: Their Distributions and Expected Values Introduction Distributions of Functions: The Discrete Case Distributions of Functions: The Continuous Case Expected Values of Functions and the Law of the Unconscious Statistician Linearity and Additivity Properties Nonlinear Functions and Jensen's Inequality Variance Standard Deviation, Mean Absolute Deviation, and Chebyshev's Inequality Linearity Property of Variance Skewness and Kurtosis Distributions of Totals Introduction Additivity Property of Variance Covariance and Correlation Central Limit Theorem Estimation: Unbiasedness, Consistency, and Efficiency Introduction Biased and Unbiased Estimators Bias of the PlugIn Estimator of Variance Removing the Bias of the PlugIn Estimator of Variance The Joke Is on Us: The Standard Deviation Estimator Is Biased after All Consistency of Estimators Efficiency of Estimators Likelihood Function and Maximum Likelihood Estimates Introduction Likelihood Function Maximum Likelihood Estimates Wald Standard Error Bayesian Statistics Introduction: Play a Game with Hans! Prior Information and Posterior Knowledge Case of the Unknown Survey Bayesian Statistics: The Overview Bayesian Analysis of the Bernoulli Parameter Bayesian Analysis Using Simulation What Good Is Bayes? Frequentist Statistical Methods Introduction LargeSample Approximate Frequentist Confidence Interval for the Process Mean What Does Approximate Really Mean for an Interval Range? Comparing the Bayesian and Frequentist Paradigms Are Your Results Explainable by Chance Alone? Introduction What Does by Chance Alone Mean? The pValue The Extremely Ugly "pv <= 0.05" Rule of Thumb ChiSquared, Student's t, and FDistributions, with Applications Introduction Linearity and Additivity Properties of the Normal Distribution Effect of Using an Estimate of s ChiSquared Distribution Frequentist Confidence Interval for s Student's tDistribution Comparing Two Independent Samples Using a Confidence Interval Comparing Two Independent Homoscedastic Normal Samples via Hypothesis Testing FDistribution and ANOVA Test FDistribution and Comparing Variances of Two Independent Groups Likelihood Ratio Tests Introduction Likelihood Ratio Method for Constructing Test Statistics Evaluating the Statistical Significance of Likelihood Ratio Test Statistics Likelihood Ratio GoodnessofFit Tests CrossClassification Frequency Tables and Tests of Independence Comparing NonNested Models via the AIC Statistic Sample Size and Power Introduction Choosing a Sample Size for a Prespecified Accuracy Margin Power Noncentral Distributions Choosing a Sample Size for Prespecified Power Post Hoc Power: A Useless Statistic Robustness and Nonparametric Methods Introduction Nonparametric Tests Based on the Rank Transformation Randomization Tests Level and Power Robustness Bootstrap Percentilet Confidence Interval Final Words Index Vocabulary, Formula Summaries, and Exercises appear at the end of each chapter.
 (source: Nielsen Book Data)9781466512108 20160611
 Publisher's Summary
 Providing a muchneeded bridge between elementary statistics courses and advanced research methods courses, Understanding Advanced Statistical Methods helps students grasp the fundamental assumptions and machinery behind sophisticated statistical topics, such as logistic regression, maximum likelihood, bootstrapping, nonparametrics, and Bayesian methods. The book teaches students how to properly model, think critically, and design their own studies to avoid common errors. It leads them to think differently not only about math and statistics but also about general research and the scientific method. With a focus on statistical models as producers of data, the book enables students to more easily understand the machinery of advanced statistics. It also downplays the "population" interpretation of statistical models and presents Bayesian methods before frequentist ones. Requiring no prior calculus experience, the text employs a "justintime" approach that introduces mathematical topics, including calculus, where needed. Formulas throughout the text are used to explain why calculus and probability are essential in statistical modeling. The authors also intuitively explain the theory and logic behind real data analysis, incorporating a range of application examples from the social, economic, biological, medical, physical, and engineering sciences. Enabling your students to answer the why behind statistical methods, this text teaches them how to successfully draw conclusions when the premises are flawed. It empowers them to use advanced statistical methods with confidence and develop their own statistical recipes. Ancillary materials are available on the book's website.
(source: Nielsen Book Data)9781466512108 20160611  Supplemental links
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Bibliographic information
 Publication date
 2013
 Copyright date
 2013
 Series
 Chapman & Hall/CRC texts in statistical science series
 Note
 Includes index.
 ISBN
 9781466512108 (hardback)
 1466512105 (hardback)