Introduction to Statistical Investigations AP^{®} Edition is the first AP^{®} Statistics book designed from the ground up to follow the GAISE recommendations endorsed by the American Statistical Association. Piloted by 22 AP^{®} Statistics teachers and used by more than 1000 students over a 2-year period, this text is unlike any other AP^{®} Statistics text currently available.
To ensure maximum coverage of necessary concepts, every chapter includes content written specifically for the AP^{®} Statistics course. Along with AP^{®}-exam tips and AP^{®}-style exercises found throughout the text, a chapter on preparing for the AP^{®} exam is included. Two full-length practice exams (one in the text and one online) as well as a comprehensive set of 300 author videos round out the program.
The authors introduce a spiral approach to statistical process through a six-step process of conducting statistical investigations. Students begin to develop an understanding of important and challenging concepts such as the p-value from the beginning. Then deepen their understanding as they encounter such ideas repeatedly in new scenarios where they reconsider and apply the six-step statistical investigation method allowing them to revisit, at deeper and deeper levels each time, the core ideas of statistical inference.
PRELIMINARIES: Introduction to Statistical Investigations
SECTION P.1 Introduction to the Six-Step Method
SECTION P.2 Exploring Data
SECTION P.3 Exploring Random Processes
UNIT 1
CHAPTER 1 Significance: How Strong Is the Evidence?
SECTION 1.1 Introduction to Chance Models
SECTION 1.2 Measuring the Strength of Evidence
SECTION 1.3 Alternative Measure of Strength of Evidence
SECTION 1.4 What Impacts Strength of Evidence?
SECTION 1.5 Inference for a Single Proportion: Theory-Based Approach
SECTION 1.6 Sampling Distribution of a Sample Proportion
SECTION 1.7 One-Proportion z-Test for a Population Proportion
CHAPTER 2 Generalization: How Broadly Do the Results Apply?
SECTION 2.1 Sampling from a Finite Population
SECTION 2.1 Part 2 More on Simple Random Samples
SECTION 2.1 Part 3 Stratified and Cluster Random Sampling
SECTION 2.2 Inference for a Single Quantitative Variable
SECTION 2.2 Part 2 Sampling Distribution of a Sample Mean
SECTION 2.2 Part 3 One-Sample t-Test for a Population Mean
SECTION 2.3 Errors and Significance
CHAPTER 3 Estimation: How Large Is the Effect?
SECTION 3.1 Statistical Inference: Confidence Intervals
SECTION 3.2 2SD and Theory-Based Confidence Intervals for a Single Proportion
SECTION 3.2 Part 2 One-Proportion z-Interval for a Single Proportion
SECTION 3.3 2SD and Theory-Based Confidence Intervals for a Single Mean
SECTION 3.3 Part 2 One-Sample t-Interval for a Single Mean
SECTION 3.4 Factors that Affect the Width of a Confidence Interval
SECTION 3.5: Cautions When Conducting Inference
CHAPTER 4 Causation: Can We Say What Caused the Effect?
SECTION 4.1 Association and Confounding
SECTION 4.2 Observational Studies versus Experiments
SECTION 4.3 Design of Experiments: Completely Randomized Design
SECTION 4.4 Design of Experiments: Randomized Block Design
UNIT 2
CHAPTER 5 Comparing Two Proportions
SECTION 5.1 Comparing Two Groups: Categorical Response
SECTION 5.2 Comparing Two Proportions: Simulation-Based Approach
SECTION 5.3 Comparing Two Proportions: Theory-Based Approach
SECTION 5.4 Confidence Interval and Significance Test for a Difference Between Two Proportions
CHAPTER 6 Comparing Two Means
SECTION 6.1 Comparing Two Groups: Quantitative Response
SECTION 6.1 Part 2 Comparing Distributions for a Quantitative Response Variable
SECTION 6.2 Comparing Two Means: Simulation-Based Approach
SECTION 6.3 Comparing Two Means: Theory-Based Approach
SECTION 6.4 Confidence Interval and Significance Test for a Difference Between Two Independent Means
CHAPTER 7 Paired Data: One Quantitative Variable
SECTION 7.1 Paired Designs
SECTION 7.2 Simulation-Based Approach to Analyzing Paired Data
SECTION 7.3 Theory-Based Approach to Analyzing Data from Paired Samples
SECTION 7.4 Confidence Interval and Significance Test for a Difference Between Two Means (Paired Data)
UNIT 3
CHAPTER 8 Comparing More Than Two Proportions
SECTION 8.1 Comparing Multiple Proportions: Simulation-Based Approach
SECTION 8.2 Comparing Multiple Proportions: Theory-Based Approach
SECTION 8.3 Chi-Square Test for Homogeneity of Proportions and Independence
SECTION 8.4 Chi-Square Goodness-of-Fit Test
CHAPTER 9 Comparing More Than Two Means (Online at www.wiley.com/go/highschool)
CHAPTER 10 Two Quantitative Variables
SECTION 10.1 Two Quantitative Variables: Scatterplots and Correlation
SECTION 10.2 Inference for the Correlation Coefficient: Simulation-Based Approach
SECTION 10.3 Least Squares Regression
SECTION 10.3 Part 2: More on Least Squares Regression
SECTION 10.3 Part 3: Transformations to Achieve Linearity
SECTION 10.4 Inference for the Regression Slope: Simulation-Based Approach
SECTION 10.5 Inference for the Regression Slope: Theory-Based Approach
SECTION 10.6: Confidence Interval and Significance Test for the Slope of a Regression Line
UNIT 4
CHAPTER 11 Randomness in Data: Modeling Randomness
ECTION 11.1 Basics of Probability
SECTION 11.2 Probability Rules
SECTION 11.3 Conditional Probabilities and Independence
SECTION 11.4 Discrete Random Variables
SECTION 11.5 Random Variable Rules
SECTION 11.6 Binomial and Geometric Random Variables
SECTION 11.7 Continuous Random Variables and Normal Distributions
SECTION 11.8 Revisiting Theory-Based Approximations to Sampling Distributions
CHAPTER 12 Preparing for the AP Statistics Exam: Putting it all Together
SECTION 12.1 Exploring Data
SECTION 12.2 Sampling and Experimentation
SECTION 12.3 Anticipating Patterns
SECTION 12.4 Statistical Inference
FULL LENGTH AP® PRACTICE EXAMS
Practice Exam A
Practice Exam B (online at www.wiley.com/go/highschool)
Instructor resources
Student resources
Statistics: Unlocking the Power of Data, 2nd Edition continues to utilize these intuitive methods like randomization and bootstrap intervals to introduce the fundamental idea of statistical inference. These methods are brought to life through authentically relevant examples, enabled through easy to use statistical software, and are accessible at very early stages of a course. The program includes the more traditional methods like t-tests, chi-square texts, etc. but only after students have developed a strong intuitive understanding of inference through randomization methods. The focus throughout is on data analysis and the primary goal is to enable students to effectively collect data, analyze data, and interpret conclusions drawn from data. The program is driven by real data and real applications.
Instructor resources
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Introductory Statistics, 9th Edition with the online learning environment of WileyPLUS Learning Space provides the necessary practice, support, concept mastery and individualized instruction that ensure success in a High School Statistics course. With its Collaborative E-Textbook, My Notes personalization capability and Course Stream communication tool, WileyPLUS Learning Space will transform any course into a vibrant, collaborative learning community.
This WileyPLUS Learning Space course includes ORION Adaptive Practice Algebra Refresher Module that gives students the opportunity to master algebra skills at home, allowing instructors to focus class time on Introductory Statistics. Based on cognitive science, ORION helps students build proficiency on topics while using their study time most effectively. Easy-to-use analytics highlight student strengths and weaknesses to give them the best chance of succeeding in the course.
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