Introduction to Probability and Statistics (Metric Version)

Description

Used by hundreds of thousands of students since its first edition, Introduction to Probability and Statistics: Metric Version, 15th Edition, continues to blend the best of its proven, error-free coverage with new innovations. Written for the traditional Introductory Statistics course, the book takes advantage of modern technology–including computational software and graphing calculators–to facilitate statistical reasoning as well as the interpretation of statistical results. In addition to showing how to apply statistical procedures, the authors explain how to describe real sets of data meaningfully, what the statistical tests mean in terms of their practical applications, how to evaluate the validity of the assumptions behind statistical tests, and what to do when statistical assumptions have been violated. The new edition strives to simplify the language of the exposition, examples and exercises, while retaining the statistical integrity that has made this text a market leader–and builds upon this tradition of excellence with new technology integration. Cover Brief Contents Contents Preface Introduction: What Is Statistics? The Population and the Sample Descriptive and Inferential Statistics Achieving the Objective of Inferential Statistics: The Necessary Steps Keys for Successful Learning Chapter 1: Describing Data with Graphs 1.1 Variables and Data 1.2 Graphs for Categorical Data 1.3 Graphs for Quantitative Data 1.4 Relative Frequency Histograms Chapter Review Technology Today Reviewing What You've Learned Case Study: How Is Your Blood Pressure? Chapter 2: Describing Data with Numerical Measures Introduction 2.1 Measures of Center 2.2 Measures of Variability 2.3 Understanding and Interpreting the Standard Deviation 2.4 Measures of Relative Standing Chapter Review Technology Today Reviewing What You've Learned Case Study: The Boys of Summer Chapter 3: Describing Bivariate Data Introduction 3.1 Describing Bivariate Categorical Data 3.2 Describing Bivariate Quantitative Data Chapter Review Technology Today Reviewing What You've Learned Case Study: Are Your Clothes Really Clean? Chapter 4: Probability Introduction 4.1 Events and the Sample Space 4.2 Calculating Probabilities Using Simple Events 4.3 Useful Counting Rules 4.4 Rules for Calculating Probabilities 4.5 Bayes' Rule Chapter Review Reviewing What You've Learned Case Study: Probability and Decision Making in the Congo Chapter 5: Discrete Probability Distributions 5.1 Discrete Random Variables and Their Probability Distributions 5.2 The Binomial Probability Distribution 5.3 The Poisson Probability Distribution 5.4 The Hypergeometric Probability Distribution Chapter Review Technology Today Reviewing What You've Learned Case Study: A Mystery: Cancers Near a Reactor Chapter 6: The Normal Probability Distribution 6.1 Probability Distributions for Continuous Random Variables 6.2 The Normal Probability Distribution 6.3 The Normal Approximation to the Binomial Probability Distribution Chapter Review Technology Today Reviewing What You've Learned Case Study: "Are You Going to Curve the Grades?" Chapter 7: Sampling Distributions Introduction 7.1 Sampling Plans and Experimental Designs 7.2 Statistics and Sampling Distributions 7.3 The Central Limit Theorem and the Sample Mean 7.4 Assessing Normality 7.5 The Sampling Distribution of the Sample Proportion 7.6 A Sampling Application: Statistical Process Control (Optional) Chapter Review Technology Today Reviewing What You've Learned Case Study: Sampling the Roulette at Monte Carlo Chapter 8: Large-Sample Estimation 8.1 Where We've Been and Where We're Going 8.2 Point Estimation 8.3 Interval Estimation 8.4 Estimating the Difference between Two Population Means 8.5 Estimating the Difference between Two Binomial Proportions 8.6 One-Sided Confidence Bounds 8.7 Choosing the Sample Size Chapter Review Technology Today Reviewing What You've Learned Case Study: How Reliable Is That Poll? CBS News: How and Where America Eats Chapter 9: Large-Sample Tests of Hypotheses Introduction 9.1 A Statistical Test of Hypothesis 9.2 A Large-Sample Test about a Population Mean 9.3 A Large-Sample Test of Hypothesis for the Difference between Two Population Means 9.4 A Large-Sample Test of Hypothesis for a Binomial Proportion 9.5 A Large-Sample Test of Hypothesis for the Difference between Two Binomial Proportions 9.6 Concluding Comments on Testing Hypotheses Chapter Review Technology Today Reviewing What You've Learned Case Study: An Aspirin a Day . . . ? Chapter 10: Inference from Small Samples Introduction 10.1 Student's t Distribution 10.2 Small-Sample Inferences Concerning a Population Mean 10.3 Small-Sample Inferences for the Difference between Two Population Means: Independent Random Samples 10.4 Small-Sample Inferences for the Difference between Two Means: A Paired-­Difference Test 10.5 Inferences Concerning a Population Variance 10.6 Comparing Two Population Variances 10.7 Revisiting the Small-Sample Assumptions Chapter Review Technology Today Reviewing What You've Learned Case Study: School Accountability-Are We Doing Better? Chapter 11: The Analysis of Variance 11.1 The Design of an Experiment 11.2 The Completely Randomized Design: A One-Way Classification 11.3 Ranking Population Means 11.4 The Randomized Block Design: A Two-Way Classification 11.5 The a x b Factorial Experiment: A Two-Way Classification 11.6 Revisiting the Analysis of Variance Assumptions 11.7 A Brief Summary Chapter Review Technology Today Reviewing What You've Learned Case Study: How to Save Money on Groceries! Chapter 12: Simple Linear Regression and Correlation Introduction 12.1 Simple Linear Regression 12.2 An Analysis of Variance for Linear Regression 12.3 Testing the Usefulness of the Linear Regression Model 12.4 Diagnostic Tools for Checking the Regression Assumptions 12.5 Estimation and Prediction Using the Fitted Line 12.6 Correlation Analysis Chapter Review Technology Today Reviewing What You've Learned Case Study: Is Your Car "Made in the U.S.A."? Chapter 13: Multiple Linear Regression Analysis Introduction 13.1 The Multiple Regression Model 13.2 Multiple Regression Analysis 13.3 A Polynomial Regression Model 13.4 Using Quantitative and Qualitative Predictor Variables in a Regression Model 13.5 Testing Sets of Regression Coefficients 13.6 Other Topics in Multiple Linear Regression 13.7 Steps to Follow When Building a Multiple Regression Model Chapter Review Technology Today Reviewing What You've Learned Case Study: "Made in the U.S.A."-Another Look Chapter 14: Analysis of Categorical Data 14.1 The Multinomial Experiment and the Chi-Square Statistic 14.2 Testing Specified Cell Probabilities: The Goodness-of-Fit Test 14.3 Contingency Tables: A Two-Way Classification 14.4 Comparing Several Multinomial Populations: A Two-Way Classification with Fixed Row or Column Totals 14.5 Other Topics in Categorical Data Analysis Chapter Review Technology Today Reviewing What You've Learned Case Study: Who Is the Primary Breadwinner in Your Family? Chapter 15: Nonparametric Statistics Introduction 15.1 The Wilcoxon Rank Sum Test: Independent Random Samples 15.2 The Sign Test for a Paired Experiment 15.3 A Comparison of Statistical Tests 15.4 The Wilcoxon Signed-Rank Test for a Paired Experiment 15.5 The Kruskal-Wallis H-Test for Completely Randomized Designs 15.6 The Friedman Fr-Test for Randomized Block Designs 15.7 Rank Correlation Coefficient 15.8 Summary Chapter Review Technology Today Reviewing What You've Learned Case Study: Amazon HQ2 Appendix I: Tables Data Sources Answers to Selected Exercises Index