book

A First Course in Statistics, 12th Edition

Name: A First Course in Statistics, 12th Edition
ISBN: 9780136759089

by James T. McClave, Terry T. Sincich

January 2016

Beginner

627 pages

36h 45m

English

Pearson

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Available in MyStatLabTM for Your Introductory Statistics Courses
Applet Correlation
A First Course in Statistics
A First Course in Statistics
Contents
Preface
New in the 12th EditionContent-Specific Changes to This EditionHallmark StrengthsGet the most out of MyStatLab™Resources for SuccessReviewers of Previous EditionsOther Contributors
Applications Index
1 Statistics, Data, and Statistical Thinking
Contents Where We’re Going
1.1 The Science of Statistics
1.2 Types of Statistical Applications

1.3 Fundamental Elements of Statistics
Problem Problem Problem
1.4 Types of Data
Problem
1.5 Collecting Data: Sampling and Related Issues
Problem
1.6 The Role of Statistics in Critical Thinking and Ethics
Problem Problem
Chapter Notes
Key Terms Key Ideas Types of Statistical ApplicationsDescriptiveInferentialTypes of DataData Collection MethodsTypes of Random SamplesProblems with Nonrandom SamplesExercises 1.1–1.36Understanding the Principles Applet Exercise 1.1 Applet Exercise 1.2Applying the Concepts—Basic Applying the Concepts–Intermediate Applying the Concepts—Advanced Critical Thinking Challenges References
2 Methods for Describing Sets of Data
Contents Where We’ve Been Where We’re Going
2.1 Describing Qualitative Data
Problem Exercises 2.1–2.24Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
2.2 Graphical Methods for Describing Quantitative Data
Dot PlotsStem-and-Leaf DisplayHistogramsProblem Exercises 2.25–2.48Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
2.3 Numerical Measures of Central Tendency
Problem Problem Problem Problem Problem Problem Exercises 2.49–2.72Understanding the Principles Learning the Mechanics Applet Exercise 2.1 Applet Exercise 2.2 Applet Exercise 2.3Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
2.4 Numerical Measures of Variability
Problem Problem Exercises 2.73–2.92Understanding the Principles Learning the Mechanics Applet Exercise 2.4 Applet Exercise 2.5 Applet Exercise 2.6Applying the Concepts—Basic Applying the Concepts—Intermediate
2.5 Using the Mean and Standard Deviation to Describe Data
Problem Problem Problem Exercises 2.93–2.113Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
2.6 Numerical Measures of Relative Standing
Problem Problem Exercises 2.114–2.131Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
2.7 Methods for Detecting Outliers: Box Plots and z-Scores
Problem Problem Problem Exercises 2.132–2.153Understanding the Principles Learning the Mechanics Applet Exercise 2.7Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
2.8 Graphing Bivariate Relationships (Optional)
Problem Exercises 2.154–2.169Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
2.9 Distorting the Truth with Descriptive Statistics
Graphical DistortionsMisleading Numerical Descriptive StatisticsProblem Problem Exercises 2.170–2.173Applying the Concepts—Intermediate
Chapter Notes
Key Terms Key Symbols Key Ideas Describing Qualitative DataGraphing Quantitative DataRules for Describing Quantitative DataRules for Detecting Quantitative OutliersGuide to Selecting the Data Description MethodSupplementary Exercises 2.174–2.208Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenge References Using Technology MINITAB: Describing DataGraphing DataNumerical Descriptive StatisticsTI-83/TI–84 Plus Graphing Calculator: Describing DataHistogram from Raw DataHistogram from a Frequency TableOne-Variable Descriptive StatisticsSorting Data (to Find the Mode)Box PlotScatterplots
3 Probability
Contents Where We’ve Been Where We’re Going
3.1 Events, Sample Spaces, and Probability
Problem Problem Problem Problem Problem Problem Problem Exercises 3.1–3.37 Understanding the Principles Learning the Mechanics Applet Exercise 3.1 Applet Exercise 3.2Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
3.2 Unions and Intersections
Problem Look Back Problem
3.3 Complementary Events
Problem
3.4 The Additive Rule and Mutually Exclusive Events
Problem Problem Exercises 3.38–3.67Understanding the Principles Learning the Mechanics Applet Exercise 3.3 Applet Exercise 3.4Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
3.5 Conditional Probability
Problem Problem Problem
3.6 The Multiplicative Rule and Independent Events
Problem Problem Problem Problem Statistics in Action Revisited The Probability of Winning Cash 3 or Play 4Exercises 3.68–3.103Understanding the Principles Learning the Mechanics Applet Exercise 3.5Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
Chapter Notes
Key Terms Key Symbols Key Ideas Probability Rules for k Sample Points, S1,S2,S3,…,SkCombinations RuleGuide to Selecting Probability RulesSupplementary Exercises 3.104–3.146Understanding the Principles Learning the Mechanics Applet Exercise 3.6Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenges References
4 Random Variables and Probability Distributions
Contents Where We’ve Been Where We’re Going
4.1 Two Types of Random Variables
Problem Problem Problem Exercises 4.1–4.16Understanding the Principles Applying the Concepts—Basic Applying the Concepts—Intermediate
4.2 Probability Distributions for Discrete Random Variables
Problem Problem Problem Problem Problem Exercises 4.17–4.47Understanding the Principles Learning the Mechanics Applet Exercise 4.1 Applet Exercise 4.2Applying the Concepts—Basic Applying the Concepts—Intermediate
4.3 The Binomial Random Variable
Problem Problem Problem Problem Using Tables and Technology for Binomial ProbabilitiesProblem Exercises 4.48–4.72Understanding the Principles Learning the Mechanics Applet Exercise 4.3 Applet Exercise 4.4 Applet Exercise 4.5Applying the Concepts—Basic Apply the Concepts—Intermediate Applying the Concepts—Advanced
4.4 Probability Distributions for Continuous Random Variables
Problem
4.5 The Normal Distribution
Problem Problem Problem Problem Problem Problem Problem Problem Problem Exercises 4.73–4.102Understanding the Principles Learning the Mechanics Applet Exercise 4.6Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
4.6 Descriptive Methods for Assessing Normality
Problem Statistics in Action Revisited Assessing whether the Normal Distribution Is Appropriate for Modeling the Super Weapon Hit DataExercises 4.103–4.124Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 4.7 Approximating a Binomial Distribution with a Normal Distribution (Optional)Problem Exercises 4.125–4.142Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 4.8 Sampling DistributionsProblem Problem Exercises 4.143–4.151Understanding the Principles Learning the Mechanics 4.9 The Sampling Distribution of x¯ and the Central Limit TheoremProblem Problem Problem Exercises 4.152–4.175Understanding the Principles Learning the Mechanics Applet Exercise 4.7 Applet Exercise 4.8Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
Chapter Notes
Key Terms Key Symbols Key Ideas Properties of Discrete Probability DistributionsProperties of Continuous Probability DistributionsMethods for Assessing NormalityNormal Approximation to BinomialKey Formulas Guide to Selecting a Probability DistributionGenerating the Sampling Distribution of x ¯ Supplementary Exercises 4.176–4.220Understanding the Principles Apply the Concepts–Basic Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenge References Using Technology MINITAB: Binomial Probabilities, Normal Probability, and Simulated Sampling DistributionBinomial ProbabilitesNormal ProbabilitiesNormal Probability PlotTI-83/TI-84 Plus Graphing Calculator: Discrete Random Variables, Binomial, and Normal ProbabilitiesCalculating the Mean and Standard Deviation of a Discrete Random VariableCalculating Binomial ProbabilitiesI. P(x=k)II. P(x≤k)III. P(x<k),P(x>k),P(x≥k)Graphing the Area under the Standard Normal CurveFinding Normal Probabilities without a GraphExampleFinding Normal Probabilities with a GraphExampleGraphing a Normal Probability PlotSimulating a Sampling Distribution
5 Inferences Based on a Single Sample Estimation with Confidence Intervals
Contents Where We’ve Been Where We’re Going
5.1 Identifying and Estimating the Target Parameter
5.2 Confidence Interval for a Population Mean: Normal (z) Statistic
Problem Problem Problem Exercises 5.1–5.28Understanding the Principles Learning the Mechanics Applet Exercise 5.1 Applet Exercise 5.2Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
5.3 Confidence Interval for a Population Mean: Student’s t-Statistic
Problem 1Solution to Problem 1Problem 2Solution to Problem 2Problem Problem Statistics in Action Revisited Estimating the Mean OverpaymentExercises 5.29–5.51Understanding the Principles Applet Exercise 5.3 Applet Exercise 5.4Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
5.4 Large-Sample Confidence Interval for a Population Proportion
Problem Problem Look Ahead Problem Exercises 5.52–5.73Understanding the Principles Applet Exercise 5.5 Applet Exercise 5.6Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
5.5 Determining the Sample Size
Estimating a Population MeanProblem Estimating a Population ProportionProblem Exercises 5.74–5.98Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
5.6 Confidence Interval for a Population Variance (Optional)
Problem Problem Exercises 5.99–5.117Understanding the Principles Learning the Mechanics Applying the Concepts—Basics Applying the Concepts—Intermediate
Chapter Notes
Key Terms Key Symbols Key Ideas Population Parameters, Estimators, & Standard ErrorsDetermining the Sample Size n:Key Words for Identifying the Target Parameter:Commonly Used z-values for a Large-Sample Confidence Interval for μ or p:Illustrating the Notion of “95% Confidence”Guide to Forming a Confidence IntervalSupplementary Exercises 5.118–5.152Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenge References
6 Inferences Based on a Single Sample Tests of Hypothesis
Contents Where We’ve Been Where We’re Going
6.1 The Elements of a Test of Hypothesis
6.2 Formulating Hypotheses and Setting Up the Rejection Region
Problem Problem Problem Exercises 6.1–6.21Understanding the Principles Learning the Mechanics Applet Exercise 6.1Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
6.3 Observed Significance Levels: p-Values
Problem Exercises 6.22–6.30Learning the Mechanics
6.4 Test of Hypothesis about a Population Mean: Normal (z) Statistic
Problem Problem Problem Exercises 6.31–6.50Understanding the Principles Learning the Mechanics Applet Exercise 6.2 Applet Exercise 6.3 Applet Exercise 6.4Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
6.5 Test of Hypothesis about a Population Mean: Student’s t-Statistic
Problem Problem Exercises 6.51–6.72Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
6.6 Large-Sample Test of Hypothesis about a Population Proportion
Problem Problem Small samplesExercises 6.73–6.93Understanding the Principles Learning the Mechanics Applet Exercise 6.5 Applet Exercise 6.6Applying the Concepts—Basic Applying the Concepts—IntermediateApplying the Concepts—Advanced
6.7 Test of Hypothesis about a Population Variance (Optional)
Problem Problem Exercises 6.94–6.114Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
6.8 A Nonparametric Test about a Population Median (Optional)
Problem Exercises 6.115–6.131Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate
Chapter Notes
Key Terms Key Symbols Key Ideas Key Words for Identifying the Target ParameterElements of a Hypothesis TestForms of Alternative HypothesisUsing p-Values to DecideGuide to Selecting a One-Sample Hypothesis TestSupplementary Exercises 6.132–6.169Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenges References
7 Comparing Population Means
Contents Where We’ve Been Where We’re Going
7.1 Identifying the Target Parameter
7.2 Comparing Two Population Means: Independent Sampling
Large SamplesProblem Problem Problem Small SamplesProblem Exercises 7.1–7.28Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
7.3 Comparing Two Population Means: Paired Difference Experiments
Problem Exercises 7.29–7.52Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
7.4 Determining the Sample Size
Problem Problem Exercises 7.53–7.65Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate
7.5 A Nonparametric Test for Comparing Two Populations: Independent Samples (Optional)
Problem Exercises 7.66–7.85Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts-Intermediate
7.6 A Nonparametric Test for Comparing Two Populations: Paired Difference Experiment (Optional)
Problem Exercises 7.86–7.102Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
7.7 Comparing Three or More Population Means: Analysis of Variance (Optional)
Problem Problem Exercises 7.103–7.121Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
Chapter Notes
Key Terms Key Symbols Key Ideas Key Words for Identifying the Target ParameterDetermining the Sample SizeConditions Required for Inferences about μ1−μ2 Large samples:Small samples:*Conditions Required for ANOVALarge or small samples:Conditions Required for Inferences about μd Large samples:Small samples:Using a Confidence Interval for (μ1−μ2) to Determine whether a Difference ExistsGuide to Comparing Population MeansSupplementary Exercises 7.122–7.147Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenge
References
8 Comparing Population Proportions
Contents Where We’ve Been Where We’re Going
8.1 Comparing Two Population Proportions: Independent Sampling
Problem Problem Exercises 8.1–8.23Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
8.2 Determining the Sample Size
Problem Exercises 8.24–8.33 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate
8.3 Testing Category Probabilities: Multinomial Experiment
Problem Problem Exercises 8.34–8.53 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
8.4 Testing Categorical Probabilities: Two-Way (Contingency) Table
Problem Contingency Tables with Fixed MarginalsExercises 8.54–8.78 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
Chapter Notes
Key Terms Key Symbols/Notation Key Ideas Multinomial DataProperties of a Multinomial ExperimentOne-Way TableTwo-Way (Contingency) TableChi-Square (χ2) StatisticChi-square tests for independenceConditions Required for Valid χ2 TestsCategorical Data Analysis GuideSupplementary Exercises 8.79–8.107 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenge References
9 Simple Linear Regression
Contents Where We’ve Been Where We’re Going
9.1 Probabilistic Models
Problem Exercises 9.1–9.14Understanding the Principles Learning the Mechanics Applying the Concepts—Basic
9.2 Fitting the Model: The Least Squares Approach
Problem Exercises 9.15–9.36Understanding the Principles Learning the Mechanics Applet Exercise 9.1Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
9.3 Model Assumptions
Problem Exercises 9.37–9.52Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
9.4 Assessing the Utility of the Model: Making Inferences about the Slope β1
Problem Exercises 9.53–9.76Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
9.5 The Coefficients of Correlation and Determination
Coefficient of CorrelationProblem Coefficient of DeterminationProblem Exercises 9.77–9.100Understanding the Principles Learning the Mechanics Applet Exercise 9.2Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
9.6 Using the Model for Estimation and Prediction
Problem Problem Exercises 9.99–9.119Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced
9.7 A Complete Example
Exercises 9.120–9.123Applying the Concepts—Intermediate
9.8 A Nonparametric Test for Correlation (Optional)
Problem Exercises 9.124—9.139Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate
Chapter Notes
Key Terms Key Symbols/Notation Key Ideas Simple Linear Regression VariablesMethod of least squares propertiesFirst-order (straight-line) modelPractical interpretation of y-interceptPractical interpretation of slopeCoefficient of correlation, rCoefficient of determination, r2Practical interpretation of model standard deviation sComparing Intervals in Step 5Nonparametric Test for Rank CorrelationKey Formulas Guide to Simple Linear RegressionSupplementary Exercises 9.140–9.163Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenge References
Appendix A: Summation Notation
Problem Problem Problem
Appendix B: Tables
Appendix C: Calculation Formulas for Analysis of Variance (Independent Sampling)
Short Answers to Selected Odd Exercises
Index
Photo Credits
Chapter 1Chapter 2Chapter 3Chapter 4Chapter 5Chapter 6Chapter 7Chapter 8Chapter 9
Chapter 9
Selected Formulas

Content preview from A First Course in Statistics, 12th Edition

Preface

A First Course in Statistics, 12^th edition, is an introductory text designed for one-semester courses that emphasizes inference and sound decision-making through extensive coverage of data collection and analysis. As in earlier editions, the 12th edition text stresses the development of statistical thinking, the assessment of credibility, and value of the inferences made from data, both by those who consume and those who produce them. It assumes a mathematical background of basic algebra.

The text incorporates the following features, developed from the American Statistical Association’s (ASA) Guidelines for Assessment and Instruction in Statistics Education (GAISE) Project:

Emphasize statistical literacy and develop statistical thinking ...

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A First Course in Statistics, 12th Edition

by James T. McClave, Terry T. Sincich

Preface

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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