Introduction to Probability and Statistics - 14th Edition - by Mendenhall, William - ISBN 9781133103752
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Introduction to Probability and Statist...
14th Edition
Mendenhall, William
Publisher: Cengage Learning
ISBN: 9781133103752

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Chapter 3.4 - Numerical Measures For Quantitave Bivariate DataChapter 4 - Probability And Probability DistributionsChapter 4.3 - Calculating Probabilities Using Simple EventsChapter 4.4 - Useful Counting Rules (optional)Chapter 4.6 - Independence, Conditional Probabiity, And The Multiplication RuleChapter 4.7 - Bayes’ Rule (optional)Chapter 4.8 - Discrete Random Variables And Their Probability DistributionsChapter 5 - Several Useful Discrete DistributionsChapter 5.2 - The Binomial Probability DistributionChapter 5.3 - The Poisson Probability DistributionChapter 5.4 - The Hypergeometric Probability DistributionChapter 6 - The Normal Probability DistributionChapter 6.3 - Tabulated Areas Of The Normal Probability DistributionChapter 6.4 - The Normal Approximation To The Binomial Probability DistributionChapter 7 - Sampling DistributionsChapter 7.2 - Sampling Plans And Experimental DesignsChapter 7.5 - The Sampling Distribution Of The Sample MeanChapter 7.6 - The Sampling Distribution Of The Sample ProportionChapter 7.7 - A Sampling Application: Statistical Process Control (optional)Chapter 8 - Large-sample EstimationChapter 8.4 - Point EstimationChapter 8.5 - Interval EstimationChapter 8.6 - Estimating The Difference Between Two Population MeansChapter 8.7 - Estimating The Difference Between Two Binomial ProportionsChapter 8.9 - Choosing The Sample SizeChapter 9 - Large-sample Tests Of HypothesesChapter 9.3 - A Large-sample Test About A Population MeanChapter 9.4 - A Large-sample Test Of Hypothesis For The Difference Between Two Population MeansChapter 9.5 - A Large-sample Test Of Hypothesis For A Binomial ProportionChapter 9.6 - A Large-sample Test Of Hypothesis For The Difference Between Two Binomial ProportionsChapter 10 - Inference From Small SamplesChapter 10.3 - Small-sample Inferences Concerning A Population MeanChapter 10.4 - Small-sample Inferences For The Difference Between Two Population Means:independent Random SamplesChapter 10.5 - Small-sample Inferences For The Difference Between Two Means: A Paired-difference TestChapter 10.6 - Inferences Concerning A Population VarianceChapter 10.7 - Comparing Two Population VariancesChapter 11 - The Analysis Of VarianceChapter 11.5 - The Analysis Of Variance For A Completely Randomized DesignChapter 11.6 - Ranking Population MeansChapter 11.8 - The Analysis Of Variance For A Randomized Block DesignChapter 11.10 - The Analysis Of Variance For An A X B Factorial ExperimentChapter 12 - Linear Regression And CorrelationChapter 12.4 - An Analysis Of Variance For Linear RegressionChapter 12.5 - Testing The Usefulness Of The Linear Regression ModelChapter 12.6 - Diagnostic Tools For Checking The Regression AssumptionsChapter 12.7 - Estimation And Prediction Using The Fitted LineChapter 12.8 - Correlation AnalysisChapter 13 - Multiple Regression AnalysisChapter 13.4 - A Polynomial Regression ModelChapter 13.5 - Using Quantitative And Qualitative Predictor Variables In A Regression ModelChapter 14 - Analysis Of Categorical DataChapter 14.3 - Testing Specified Cell Probabilities: The Goodness-of-fit TestChapter 14.4 - Contingency Tables: A Two-way ClassificationChapter 14.5 - Comparing Several Multinomial Populations: A Two-way Classification With Fixed Row Or Column TotalsChapter 15 - Nonparametric StatisticsChapter 15.2 - The Wilcoxon Rank Sum Test: Independent Random SamplesChapter 15.3 - The Sign Test For A Paired ExperimentChapter 15.5 - The Wilcoxon Signed-rank Test For A Paired ExperimentChapter 15.6 - The Kruskal–wallis H-test For Completely Randomized DesignsChapter 15.7 - The Friedman Fr-test For Randomized Block DesignsChapter 15.8 - Rank Correlation Coefficient

Book Details

Used by hundreds of thousands of students, INTRODUCTION TO PROBABILITY AND STATISTICS, Fourteenth Edition, blends proven coverage with new innovations to ensure you gain a solid understanding of statistical concepts--and see their relevance to your everyday life. The new edition retains the text's straightforward presentation and traditional outline for descriptive and inferential statistics while incorporating modern technology--including computational software and interactive visual tools--to help you master statistical reasoning and skillfully interpret statistical results. Drawing from decades of classroom teaching experience, the authors clearly illustrate how to apply statistical procedures as they explain how to describe real sets of data, what statistical tests mean in terms of practical application, how to evaluate the validity of the assumptions behind statistical tests, and what to do when statistical assumptions have been violated. Statistics can be an intimidating course, but with this text you will be well prepared. With its thorough explanations, insightful examples, practical exercises, and innovative technology features, this text equips you with a firm foundation in statistical concepts, as well as the tools to apply them to the world around you.

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