Statistics

Front Cover
Applet Correlation
Title Page
Copyright Page
Contents
Preface
Applications Index
Chapter 1 Statistics, Data, and Statistical Thinking
	1.1 The Science of Statistics
	1.2 Types of Statistical Applications
	1.3 Fundamental Elements of Statistics
	1.4 Types of Data
	1.5 Collecting Data: Sampling and Related Issues
	1.6 The Role of Statistics in Critical Thinking and Ethics
	Statistics in Action: Social Media Network Usage—Are You Linked In?
	Using Technology: MINITAB: Accessing and Listing Data
Chapter 2 Methods for Describing Sets of Data
	2.1 Describing Qualitative Data
	2.2 Graphical Methods for Describing Quantitative Data
	2.3 Numerical Measures of Central Tendency
	2.4 Numerical Measures of Variability
	2.5 Using the Mean and Standard Deviation to Describe Data
	2.6 Numerical Measures of Relative Standing
	2.7 Methods for Detecting Outliers: Box Plots and z-Scores
	2.8 Graphing Bivariate Relationships (Optional)
	2.9 Distorting the Truth with Descriptive Statistics
	Statistics in Action: Body Image Dissatisfaction: Real or Imagined?
	Using Technology: MINITAB: Describing Data
	TI-83/TI–84 Plus Graphing Calculator: Describing Data
Chapter 3 Probability
	3.1 Events, Sample Spaces, and Probability
	3.2 Unions and Intersections
	3.3 Complementary Events
	3.4 The Additive Rule and Mutually Exclusive Events
	3.5 Conditional Probability
	3.6 The Multiplicative Rule and Independent Events
	3.7 Some Additional Counting Rules (Optional)
	Bayes’s Rule (Optional)
	Statistics in Action: Lotto Buster! Can You Improve Your Chance of Winning?
	Using Technology: TI-83/TI-84 Plus Graphing Calculator: Combinations and Permutations
Chapter 4 Discrete Random Variables
	4.1 Two Types of Random Variables
	4.2 Probability Distributions for Discrete Random Variables
	4.3 Expected Values of Discrete Random Variables
	4.4 The Binomial Random Variable
	4.5 The Poisson Random Variable (Optional)
	4.6 The Hypergeometric Random Variable (Optional)
	Statistics in Action: Probability in a Reverse Cocaine Sting: Was Cocaine Really Sold?
	Using Technology: MINITAB: Discrete probabilities
	TI-83/TI-84 Plus Graphing Calculator: Discrete Random Variables and Probabilities
Chapter 5 Continuous Random Variables
	5.1 Continuous Probability Distributions
	5.2 The Uniform Distribution
	5.3 The Normal Distribution
	5.4 Descriptive Methods for Assessing Normality
	5.5 Approximating a Binomial Distribution with a Normal Distribution (Optional)
	5.6 The Exponential Distribution (Optional)
	Statistics in Action: Super Weapons Development—Is the Hit Ratio Optimized?
	Using Technology: MINITAB: Continuous Random Variable Probabilities and Normal Probability Plots
	TI–83/TI–84 Plus Graphing Calculator: Normal Random Variable and Normal Probability Plots
Chapter 6 Sampling Distributions
	6.1 The Concept of a Sampling Distribution
	6.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance
	6.3 The Sampling Distribution of x and the Central Limit Theorem
	6.4 The Sampling Distribution of the Sample Proportion
	Statistics in Action: The Insomnia Pill: Is It Effective?
	Using Technology: MINITAB: Simulating a Sampling Distribution
Chapter 7 Inferences Based on a Single Sample: Estimation with Confidence Intervals
	7.1 Identifying and Estimating the Target Parameter
	7.2 Confidence Interval for a Population Mean: Normal (z) Statistic
	7.3 Confidence Interval for a Population Mean: Student’s t-Statistic
	7.4 Large-Sample Confidence Interval for a Population Proportion
	7.5 Determining the Sample Size
	7.6 Confidence Interval for a Population Variance (Optional)
	Statistics in Action: Medicare Fraud Investigations
	Using Technology: MINITAB: Confidence Intervals
	TI-83/TI-84 Plus Graphing Calculator: Confidence Intervals
Chapter 8 Inferences Based on a Single Sample: Tests of Hypothesis
	8.1 The Elements of a Test of Hypothesis
	8.2 Formulating Hypotheses and Setting Up the Rejection Region
	8.3 Observed Significance Levels:p-Values
	8.4 Test of Hypothesis about a Population Mean: Normal (z) Statistic
	8.5 Test of Hypothesis about a Population Mean: Student’s t-Statistic
	8.6 Large-Sample Test of Hypothesis about a Population Proportion
	8.7 Calculating Type II Error Probabilities: More about β (Optional)
	8.8 Test of Hypothesis about a Population Variance (Optional)
	Statistics in Action: Diary of a KLEENEX® User—How Many Tissues in a Box?
	Using Technology: MINITAB: Tests of Hypotheses
	TI-83/TI-84 Plus Graphing Calculator: Tests of Hypotheses
Chapter 9 Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses
	9.1 Identifying the Target Parameter
	9.2 Comparing Two Population Means: Independent Sampling
	9.3 Comparing Two Population Means: Paired Difference Experiments
	9.4 Comparing Two Population Proportions: Independent Sampling
	9.5 Determining the Sample Size
	9.6 Comparing Two Population Variances: Independent Sampling (Optional)
	Statistics in Action: ZixIt Corp. v. Visa USA Inc.—A Libel Case
	Using Technology: MINITAB: Two-Sample Inferences
	TI-83/TI-84 Plus Graphing Calculator: Two Sample Inferences
Chapter 10 Analysis of Variance: Comparing More than Two Means
	10.1 Elements of a Designed Study
	10.2 The Completely Randomized Design: Single Factor
	10.3 Multiple Comparisons of Means
	10.4 The Randomized Block Design
	10.5 Factorial Experiments: Two Factors
	Statistics in Action: Voice versus Face Recognition—Does One Follow the Other?
	Using Technology: MINITAB: Analysis of Variance
	TI–83/TI–84 Plus Graphing Calculator: Analysis of Variance
Chapter 11 Simple Linear Regression
	11.1 Probabilistic Models
	11.2 Fitting the Model: The Least Squares Approach
	11.3 Model Assumptions
	11.4 Assessing the Utility of the Model: Making Inferences about the Slope β1
	11.5 The Coefficients of Correlation and Determination
	11.6 Using the Model for Estimation and Prediction
	11.7 A Complete Example
	Statistics in Action: Can “Dowsers” Really Detect Water?
	Using Technology: MINITAB: Simple Linear Regression
	TI-83/TI-84 Plus Graphing Calculator: Simple Linear Regression
Chapter 12 Multiple Regression and Model Building
	12.1 Multiple-Regression Models
	PART I: First-Order Models with Quantitative Independent Variables
		12.2 Estimating and Making Inferences about the β Parameters
		12.3 Evaluating Overall Model Utility
		12.4 Using the Model for Estimation and Prediction
	PART II: Model Building in Multiple Regression
		12.5 Interaction Models
		12.6 Quadratic and Other Higher Order Models
		12.7 Qualitative (Dummy) Variable Models
		12.8 Models with Both Quantitative and Qualitative Variables (Optional)
		12.9 Comparing Nested Models (Optional)
		12.10 Stepwise Regression (Optional)
	PART III: Multiple Regression Diagnostics
		12.11 Residual Analysis: Checking the Regression Assumptions
		12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation
	Statistics in Action: Modeling Condominium Sales: What Factors Affect Auction Price?
	Using Technology: MINITAB: Multiple Regression
	TI-83/TI-84 Plus Graphing Calculator: Multiple Regression
Chapter 13 Categorical Data Analysis
	13.1 Categorical Data and the Multinomial Experiment
	13.2 Testing Categorical Probabilities: One-Way Table
	13.3 Testing Categorical Probabilities: Two-Way (Contingency) Table
	13.4 A Word of Caution about Chi-Square Tests
	Statistics in Action: The Case of the Ghoulish Transplant Tissue
	Using Technology: MINITAB: Chi-Square Analyses
	TI-83/TI-84 Plus Graphing Calculator: Chi-Square Analyses
Appendix A: Summation Notation
Appendix B: Tables
	Table I Binomial Probabilities
	Table II Normal Curve Areas
	Table III Critical Values of t
	Table IV Critical Values of x2
	Table V Percentage Points of the F-Distribution, α = .10
	Table VI Percentage Points of the F-Distribution, α = .05
	Table VII Percentage Points of the F-Distribution, α = .025
	Table VIII Percentage Points of the F-Distribution, α = .01
	Table IX Critical Values of TL and TU for the Wilcoxon Rank Sum Test: Independent Samples
	Table X Critical Values of T0 in the Wilcoxon Paired Difference Signed Rank Test
	Table XI Critical Values of Spearman’s Rank Correlation Coefficient
	Table XII Critical Values of the Studentized Range, α = .05
	Table XIII Critical Values of the Studentized Range, α = .01
Appendix C: Calculation Formulas for Analysis of Variance
Short Answers to Selected Odd-Numbered Exercises
Index
Credits
Making Connections Using Data
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