Introduction to the Practice of Statistics, 10th Edition
- Length: 784 pages
- Edition: 10
- Language: English
- Publisher: W. H. Freeman
- Publication Date: 2021-01-22
- ISBN-10: 1319244440
- ISBN-13: 9781319244446
- Sales Rank: #685362 (See Top 100 Books)
Now available with Macmillan’s new online learning tool Achieve, Introduction to the Practice of Statistics, 10th edition, prepares students for the application of statistics in the real world by using current examples and encouraging exploration into data analysis and interpretation. The text enforces statistical thinking by providing learning objectives and linked exercises to help students master core statistics concepts and think beyond the calculations.
Achieve for Introduction to the Practice of Statistics integrates outcome-based learning objectives and a wealth of examples with assessment in an easy-to-use interface. Students are provided with rich digital resources that solidify conceptual understanding, as well as homework problems with hints, answer-specific feedback, and a fully worked solution.
About this Book Cover Title Page Copyright Brief Contents About the Authors Contents To Teachers: About This Book Preface To Students: What Is Statistics? Applications Data Table Index Beyond the Basics Index Part I Looking at Data Chapter 1 Looking at Data—Distributions Introduction 1.1 Data Key characteristics of a data set Section 1.1 Summary Section 1.1 Exercises 1.2 Measuring Center: The Median Categorical variables: Bar graphs and pie charts Quantitative variables: Stemplots and histograms Histograms Examining distributions Dealing with outliers Time plots Section 1.2 Summary Section 1.2 Exercises 1.3 Describing Distributions with Numbers Measuring center: The mean Measuring center: The median Comparing the mean and the median Measuring spread: The quartiles The five-number summary and boxplots The 1.5 × IQR rule for suspected outliers Measuring spread: The standard deviation Properties of the standard deviation Choosing measures of center and spread Changing the unit of measurement Section 1.3 Summary Section 1.3 Exercises 1.4 Density Curves and Normal Distributions Density curves Measuring center and spread for density curves Normal distributions The 68–95–99.7 rule Standardizing observations Normal distribution calculations Using the standard Normal table Inverse Normal calculations Normal quantile plots Beyond the Basics Section 1.4 Summary Section 1.4 Exercises Chapter 1 Exercises Chapter 2 Looking at Data—Relationships Introduction 2.1 Relationships Examining relationships Section 2.1 Summary Section 2.1 Exercises 2.2 Scatterplots Interpreting scatterplots The log transformation Adding categorical variables to scatterplots Scatterplot smoothers Categorical explanatory variables Section 2.2 Summary Section 2.2 Exercises 2.3 Correlation The correlation r Properties of correlation Section 2.3 Summary Section 2.3 Exercises 2.4 Least-Squares Regression Fitting a line to data Prediction The least-squares regression line Facts about least-squares regression Correlation and regression Interpretation of r2 Section 2.4 Summary Section 2.4 Exercises 2.5 Cautions about Correlation and Regression Extrapolation Residuals The distribution of the residuals Outliers and influential observations Beware of the lurking variable Beware of correlations based on averaged data Beware of restricted ranges Section 2.5 Summary Section 2.5 Exercises 2.6 Data Analysis for Two-Way Tables The two-way table Joint distribution Marginal distributions Describing relations in two-way tables Conditional distributions Simpson’s paradox Section 2.6 Summary Section 2.6 Exercises 2.7 The Question of Causation Explaining association Establishing causation Section 2.7 Summary Section 2.7 Exercises Chapter 2 Exercises Chapter 3 Producing Data Introduction 3.1 Sources of Data Anecdotal data Available data Sample surveys and experiments Section 3.1 Summary Section 3.1 Exercises 3.2 Design of Experiments Comparative experiments Randomization Randomized comparative experiments How to randomize Randomization using software Randomization using random digits Cautions about experimentation Matched pairs designs Block designs Section 3.2 Summary Section 3.2 Exercises 3.3 Sampling Design Simple random samples How to select a simple random sample Stratified random samples Multistage random samples Cautions about sample surveys Section 3.3 Summary Section 3.3 Exercises 3.4 Ethics Institutional review boards Informed consent Confidentiality Clinical trials Behavioral and social science experiments Section 3.4 Summary Section 3.4 Exercises Chapter 3 Exercises Part II Probability and Inference Chapter 4 Probability: The Study of Randomness Introduction 4.1 Randomness The language of probability Thinking about randomness The uses of probability Section 4.1 Summary Section 4.1 Exercises 4.2 Probability Models Sample spaces Probability rules Assigning probabilities: Finite number of outcomes Assigning probabilities: Equally likely outcomes Independence and the multiplication rule Applying the probability rules Section 4.2 Summary Section 4.2 Exercises 4.3 Random Variables Discrete random variables Continuous random variables Normal distributions as probability distributions Section 4.3 Summary Section 4.3 Exercises 4.4 Means and Variances of Random Variables The mean of a random variable Statistical estimation and the law of large numbers Thinking about the law of large numbers Rules for means The variance of a random variable Rules for variances and standard deviations Section 4.4 Summary Section 4.4 Exercises 4.5 General Probability Rules General addition rules Conditional probability General multiplication rules Tree diagrams Bayes’s rule Independence again Section 4.5 Summary Section 4.5 Exercises Chapter 4 Exercises Chapter 5 Sampling Distributions Introduction 5.1 Toward Statistical Inference Sampling variability Sampling distributions Bias and variability Sampling from large populations Why randomize? Section 5.1 Summary Section 5.1 Exercises 5.2 The Sampling Distribution of a Sample Mean The mean and standard deviation of x The central limit theorem A few more facts related to the sampling distribution of x Section 5.2 Summary Section 5.2 Exercises 5.3 Sampling Distributions for Counts and Proportions The binomial distributions for sample counts Binomial distributions in statistical sampling Finding binomial probabilities Binomial mean and standard deviation Sample proportions Normal approximation for counts and proportions The continuity correction Binomial formula The Poisson distributions for sample counts Section 5.3 Summary Section 5.3 Exercises Chapter 5 Exercises Chapter 6 Introduction to Inference Introduction Overview of inference 6.1 Estimating with Confidence Statistical confidence Confidence intervals Confidence interval for a population mean How confidence intervals behave Choosing the sample size Some cautions Section 6.1 Summary Section 6.1 Exercises 6.2 Tests of Significance The reasoning of significance tests Stating hypotheses Test statistics P-values Statistical significance Tests for a population mean Two-sided significance tests and confidence intervals The P-value versus a statement of significance Section 6.2 Summary Section 6.2 Exercises 6.3 Use and Abuse of Tests Choosing a level of significance What statistical significance does not mean Don’t ignore lack of significance Statistical inference is not valid for all sets of data Beware of searching for significance Section 6.3 Summary Section 6.3 Exercises 6.4 Inference as a Decision Two types of error Error probabilities The common practice of testing hypotheses Section 6.4 Summary Section 6.4 Exercises Chapter 6 Exercises Chapter 7 Inference for Means Introduction 7.1 Inference for the Mean of a Population The t distributions One-sample t confidence interval The one-sample t test Using software Matched pairs t procedures Robustness of the t procedures Inference for non-normal populations Beyond the Basics: The bootstrap Section 7.1 Summary Section 7.1 Exercises 7.2 Comparing Two Means The two-sample z statistic The two-sample t procedures The two-sample t confidence interval The two-sample t significance test Robustness of the two-sample procedures Inference for small samples The pooled two-sample t procedures Section 7.2 Summary Section 7.2 Exercises 7.3 Sample Size Calculations Sample size for confidence intervals Power of a significance test Section 7.3 Summary Section 7.3 Exercises Chapter 7 Exercises Chapter 8 Inference for Proportions Introduction 8.1 Inference for a Single Proportion Large-sample confidence interval for a single proportion Beyond the Basics: Plus four confidence interval for a single proportion Significance test for a single proportion Choosing a sample size for a confidence interval Choosing a sample size for a significance test Section 8.1 Summary Section 8.1 Exercises 8.2 Comparing Two Proportions Large-sample confidence interval for a difference in proportions Beyond the Basics: Plus four confidence interval for a difference in proportions Significance test for a difference in proportions Choosing a sample size for two sample proportions Use the margin of error Use the power of the significance test Beyond the Basics: Relative risk Section 8.2 Summary Section 8.2 Exercises Chapter 8 Exercises Part III Topics in Inference Chapter 9 Inference for Categorical Data Introduction 9.1 Sources of Data The hypothesis: No association Expected cell counts The chi-square test Computations Computing conditional distributions The chi-square test and the z test Section 9.1 Summary Section 9.1 Exercises 9.2 Goodness of Fit Section 9.2 Summary Section 9.2 Exercises Chapter 9 Exercises Chapter 10 Inference for Regression Introduction 10.1 Simple Linear Regression Statistical model for linear regression Preliminary data analysis and inference considerations Revisiting the simple linear regression model Estimating the regression parameters Estimating the regression parameters Confidence intervals and significance tests Confidence intervals for mean response Prediction intervals Transforming variables Section 10.1 Summary Section 10.1 Exercises 10.2 More Detail about Simple Linear Regression Analysis of variance for regression The ANOVA F test Calculations for regression inference Inference for correlation Section 10.2 Summary Section 10.2 Exercises Chapter 10 Exercises Chapter 11 Multiple Regression Introduction 11.1 Inference for Multiple Regression Population multiple regression equation Data for multiple regression Multiple linear regression model Estimation of the multiple regression parameters Confidence intervals and significance tests for regression coefficients ANOVA table for multiple regression Squared multiple correlation R2 Section 11.1 Summary Section 11.1 Exercises 11.2 A Case Study Preliminary analysis Relationships between pairs of variables Fitting a multiple regression model Interpretation of results Examining the residuals Refining the model Considering other sets of explanatory variables Test for a collection of regression coefficients Section 11.2 Summary Section 11.2 Exercises Chapter 11 Exercises Chapter 12 One-Way Analysis of Variance Introduction 12.1 Inference for One-Way Analysis of Variance The one-way ANOVA setting Comparing means The two-sample t statistic An overview of ANOVA The ANOVA model Estimates of population parameters Testing hypotheses in one-way ANOVA The ANOVA table The F test Software Section 12.1 Summary Section 12.1 Exercises 12.2 Comparing the Means Contrasts Multiple comparisons Simultaneous confidence intervals Power of the one-way ANOVA F test Section 12.2 Summary Section 12.2 Exercises Chapter 12 Exercises Chapter 13 Two-Way Analysis of Variance Introduction 13.1 The Two-Way ANOVA Modell Advantages of two-way ANOVA The two-way ANOVA model Main effects and interactions Section 13.1 Summary Section 13.1 Exercises 13.2 Inference for Two-Way ANOVA The two-way ANOVA table Carrying out a two-way ANOVA Section 13.2 Summary Section 13.2 Exercises Chapter 13 Exercises Companion Chapters Chapter 14 Logistic Regression Introduction 14.1 The Logistic Regression Model Binomial distributions and odds Odds for two groups Model for logistic regression Fitting and interpreting the logistic regression model Section 14.1 Summary Section 14.1 Exercises 14.2 A Case Study Confidence intervals and significance tests Inference for multiple logistic regression Section 14.2 Summary Section 14.2 Exercises Chapter 14 Exercises Notes and Data Sources Chapter 15 Nonparametric Rank Tests Introduction 15.1 Inference for One-Way Analysis of Variance The rank transformation The Wilcoxon rank sum test The Normal approximation What hypotheses does Wilcoxon test? Ties Nonparametric rank and t procedures Section 15.1 Summary Section 15.1 Exercises 15.2 The Wilcoxon Signed Rank Test The Normal approximation Ties Testing a hypothesis about the median of a distribution Section 15.2 Summary Section 15.2 Exercises 15.3 The Kruskal-Wallis Test* Hypotheses and assumptions The Kruskal-Wallis test Section 15.3 Summary Section 15.3 Exercises Chapter 15 Exercises Notes and Data Sources Chapter 16 Bootstrap Methods and Permutation Tests Introduction Software 16.1 The Bootstrap Idea The big idea: Resampling and the bootstrap distribution Thinking about the bootstrap idea Using software Section 16.1 Summary Section 16.1 Exercises 16.2 First Steps in Using the Bootstrap Bootstrap t confidence intervals Bootstrapping to compare two groups Section 16.2 Summary Section 16.2 Exercises 16.3 How Accurate Is a Bootstrap Distribution? Bootstrapping small samples Bootstrapping a sample median Section 16.3 Summary Section 16.3 Exercises 16.4 Bootstrap Confidence Intervals Bootstrap percentile confidence intervals A more accurate bootstrap confidence interval: BCa Confidence intervals for the correlation Section 16.4 Summary Section 16.4 Exercises 16.5 Significance Testing Using Permutation Tests Using software Permutation tests in practice Permutation tests in other settings Section 16.5 Summary Section 16.5 Exercises Chapter 16 Exercises Notes and Data Sources Chapter 17 Statistics for Quality: Control and Capability Introduction 17.1 Processes and Statistical Process Control Describing processes Statistical process control x charts for process monitoring s charts for process monitoring Section 17.1 Summary Section 17.1 Exercises 17.2 Using Control Charts x and R charts Additional out-of-control rules Setting up control charts Comments on statistical control Don’t confuse control with capability! Section 17.2 Summary Section 17.2 Exercises 17.3 Process Capability Indexes The capability indexes Cp and Cpk Cautions about capability indexes Section 17.3 Summary Section 17.3 Exercises 17.4 Control Charts for Sample Proportions Control limits for p charts Section 17.4 Summary Section 17.4 Exercises Chapter 17 Exercises Notes and Data Sources Tables Answers to Odd-Numbered Exercises Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Notes and Data Sources Index Formulas and Key Ideas Back Cover
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