Preliminary Edition of Statistics

Roxy Peck is emerita associate dean of the College of Science and Mathematics and professor of statistics emerita at California Polytechnic State University, San Luis Obispo. As a faculty member at Cal Poly from 1979 until 2009, Dr. Peck served for six years as chair of the statistics department before becoming associate dean, a position she held for 13 years. She received an M.S. in mathematics and a Ph.D. in applied statistics from the University of California, Riverside. Dr. Peck is nationally known in the area of statistics education, and she was presented with the Lifetime Achievement Award in Statistics Education at the U.S. Conference on Teaching Statistics in 2009. In 2003, she received the American Statistical Association�s Founder�s Award, recognizing her contributions to K-12 and undergraduate statistics education. She is a fellow of the American Statistical Association and an elected member of the International Statistics Institute. Dr. Peck served for five years as the chief reader for the Advanced Placement (AP) Statistics Exam and has chaired the American Statistical Association�s Joint Committee with the National Council of Teachers of Mathematics on Curriculum in Statistics and Probability for Grades K-12 and the Section on Statistics Education. In addition to her texts in introductory statistics, Dr. Peck is co-editor of STATISTICAL CASE STUDIES: A COLLABORATION BETWEEN ACADEME AND INDUSTRY and is a member of the editorial board for STATISTICS: A GUIDE TO THE UNKNOWN, 4TH EDITION. Outside of the classroom, she likes to travel and spends her spare time reading mystery novels. Dr. Peck also collects Navajo rugs and travels to Arizona and New Mexico whenever she can find the time.

Section I: COLLECTING DATA.
1. Collecting Data in Reasonable Ways.
Statistical Studies: Observation and Experimentation. Collecting Data: Planning an Observational Study. Collecting Data: Planning an Experiment. The Importance of Random Selection and Random Assignment: What Types of Conclusions are Reasonable?
Section II: DESCRIBING DATA DISTRIBUTIONS.
2. Graphical Methods for Describing Data Distributions.
Selecting an Appropriate Graphical Display. Displaying Categorical Data: Bar Charts and Comparative Bar Charts. Displaying Numerical Data: Dotplots, Stem-and-Leaf Displays, and
Histograms. Displaying Bivariate Numerical Data: Scatterplots and Time-Series Plots. Graphical Displays in the Media.
3. Numerical Methods for Describing Data Distributions.
Selecting Appropriate Numerical Summaries. Describing Center and Spread for Data Distributions that are Approximately Symmetric. Describing Center and Spread for Data Distributions that are Skewed or Have Outliers. Summarizing a Data Set: Boxplots. Measures of Relative Standing: z-scores and Percentiles.
4. Describing Bivariate Numerical Data.
Correlation. Linear Regression: Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Describing Linear Relationships and Making Predictions--Putting it all Together. Bonus Material on Logistic Regression (Online).
Section III: A FOUNDATION FOR INFERENCE: REASONING ABOUT PROBABILITY.
5. Probability.
Interpreting Probabilities. Computing Probabilities. Probabilities of More Complex Events: Unions, Intersections and Complements. Conditional Probability. Probability as a Basis for Making Decisions. Estimating Probabilities Empirically and Using Simulation (Optional)
6. Random Variables and Probability Distributions.
Random Variables. Probability Distributions for Discrete Random Variables. Probability Distributions for Continuous Random Variables. The Mean and Standard Deviation of a Random Variable. The Normal Distribution. Checking for Normality. The Binomial and Geometric Distributions (Optional). Using the Normal Distribution to Approximate a Discrete
Distribution (Optional). Counting Rules, The Poisson Distribution (Online).
Section IV: LEARNING FROM SAMPLE DATA.
7. An Overview of Statistical Inference--Learning from Data.
Statistical Inference--What We Can Learn From Data. Selecting an Appropriate Method--Four Key Questions. A Five-Step Process for Statistical Inference.
8. Sampling Variability and Sampling Distributions.
Statistics and Sampling Variability. The Sampling Distribution of a Sample Proportion. How Sampling Distributions Support Learning From Data.
9. Estimating a Population Proportion.
Selecting an Estimator. Estimating a Population Proportion--Margin of Error. A Large-Sample Confidence Interval for a Population Proportion. Choosing a Sample Size to Achieve a Desired Margin of Error.
10. Asking and Answering Questions about a Population Proportion.
Hypotheses and Possible Conclusions. Potential Errors in Hypothesis Testing. The Logic of Hypothesis Testing--An Informal Example. A Procedure for Carrying Out a Hypothesis Test. Large-Sample Hypothesis Tests for a Population Proportion.
11. Asking and Answering Questions about the Difference between Two Population Proportions.
Estimating the Difference between Two Population Proportions. Testing Hypotheses about the Difference between Two Population Proportions.
12. Asking and Answering Questions about a Population Mean.
Sampling Distribution of the Sample Mean. A Confidence Interval for a Population Mean. Testing Hypotheses about a Population Mean.
13. Asking and Answering Questions about the Difference between Two Population Means.
Testing Hypotheses about the Difference between Two Population Means Using Independent Samples. Testing Hypotheses about the Difference between Two Population Means Using Paired Samples. Estimating the Difference between Two Population Means.
Section V: ADDITIONAL OPPORTUNITIES TO LEARN FROM DATA.
14. Learning from Experiment Data.
Variability and Random Assignment. Testing Hypotheses about Differences in Treatment Effects. Estimating a Difference in Treatment Effects.
15. Learning from Categorical Data.
Chi-Square Tests for Univariate Categorical Data. Tests for Homogeneity and Independence in a Two-Way Table.
16. Understanding Relationships--Numerical Data Part 2 (Online).
The Simple Linear Regression Model. Inferences Concerning the Slope of the Population Regression Line. Checking Model Adequacy.
17. Asking and Answering Questions about More Than Two Means (Online).
The Analysis of Variance--Single-Factor ANOVA and the F Test. Multiple Comparisons.
Appendix: ANOVA Computations.