Introduction to Statistics and Data Analysis

ISBN-10: 0840054904
ISBN-13: 9780840054906
Edition: 4th 2012
List price: $285.95 Buy it from $9.43
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Book details

List price: $285.95
Edition: 4th
Copyright year: 2012
Publisher: Brooks/Cole
Publication date: 1/1/2011
Binding: Hardcover
Pages: 944
Size: 8.50" wide x 11.00" long x 1.50" tall
Weight: 4.532
Language: English

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. A faculty member at Cal Poly from 1979 until 2009, Roxy 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. Roxy 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. Roxy served for five years as the Chief Reader for the Advanced Placement 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, Roxy is also co-editor of "Statistical Case Studies: A Collaboration Between Academe and Industry" and a member of the editorial board for "Statistics: A Guide to the Unknown, 4th Edition." Outside the classroom, Roxy likes to travel and spends her spare time reading mystery novels. She also collects Navajo rugs and heads to Arizona and New Mexico whenever she can find the time.

Chris Olsen taught statistics at George Washington High School in Cedar Rapids, Iowa, for over 25 years as well as at Cornell College and Grinnell College. Chris is a past member (twice) of the AP Statistics Test Development Committee and has been a table leader at the AP Statistics reading for 11 years. He is a long-time consultant to the College Board and has led workshops and institutes for AP Statistics teachers in the United States and internationally. Chris was the Iowa recipient of the Presidential Award for Excellence in Science and Mathematics Teaching in 1986, a regional awardee of the IBM Computer Teacher of the Year in 1988, and received the Siemens Award for Advanced Placement in mathematics in 1999. Chris is a frequent contributor to and is moderator of the AP Teacher Community online. He is currently a member of the editorial board of "Teaching Statistics." Chris graduated from Iowa State University with a major in mathematics. While acquiring graduate degrees at the University of Iowa, he concentrated on statistics, computer programming, and psychometrics. In his spare time he enjoys reading and hiking. He and his wife have a daughter, Anna, a Caltech graduate in Civil Engineering. She is cofounder and principal engineer at Geosynergy, specializing in the quantification of uncertainty in seismic risk.

Jay Devore is Professor Emeritus of Statistics at California Polytechnic State University. He earned his undergraduate degree in Engineering Science from the University of California at Berkeley, spent a year at the University of Sheffield in England, and finished his Ph.D. in statistics at Stanford University. Jay previously taught at the University of Florida and at Oberlin College and has had visiting appointments at Stanford, Harvard, the University of Washington, New York University, and Columbia University. From 1998 to 2006, he served as Chair of the Statistics Department. In addition to this book, Jay has written several widely used engineering statistics texts and a book in applied mathematical statistics. He recently coauthored a text in probability and stochastic processes. He is the recipient of a distinguished teaching award from Cal Poly, is a Fellow of the American Statistical Association , and has served several terms as an Associate Editor of the "Journal of the American Statistical Association." In his spare time, he enjoys reading, cooking and eating good food, tennis, and travel to faraway places. He is especially proud of his wife, Carol, a retired elementary school teacher, his daughter Allison, who has held several high-level positions in nonprofit organizations in Boston and New York City, and his daughter Teresa, an ESL teacher in New York City.

The Role Of Statistics And The Data Analysis Process
Why Study Statistics
The Nature and Role of Variability
Statistics and the Data Analysis Process
Types of Data and Some Simple Graphical Displays
Collecting Data Sensibly
Statistical Studies: Observation and Experimentation
Sampling
Simple Comparative Experiments
More on Experimental Design
More on Observational Studies: Designing Surveys (Optional)
Interpreting and Communicating the Results of Statistical Analyses
Graphical Methods For Describing Data
Displaying Categorical Data: Comparative Bar Charts and Pie Charts
Displaying Numerical Data: Stem-and-Leaf Displays
Displaying Numerical Data: Frequency Distributions and Histograms
Displaying Bivariate Numerical Data
Interpreting and Communicating the Results of Statistical Analyses
Numerical Methods For Describing Data
Describing the Center of a Data Set
Describing Variability in a Data Set
Summarizing a Data Set: Boxplots
Interpreting Center and Variability: Chebyshev's Rule, the Empirical Rule, and z Scores
Interpreting and Communicating the Results of Statistical Analyses
Summarizing Bivariate Data
Correlation
Linear Regression: Fitting a Line to Bivariate Data
Assessing the Fit of a Line
Nonlinear Relationships and Transformations
Logistic Regression (Optional)
Interpreting and Communicating the Results of Statistical Analyses
Probability
Chance Experiments and Events
Definition of Probability
Basic Properties of Probability
Conditional Probability
Independence
Some General Probability Rules
Estimating Probabilities Empirically Using Simulation
Random Variables And Probability Distributions
Random Variables
Probability Distributions for Discrete Random Variables
Probability Distributions for Continuous Random Variables
Mean and Standard Deviation of a Random Variable
Binomial and Geometric Distributions
Normal Distributions
Checking for Normality and Normalizing Transformations
Using the Normal Distribution to Approximate a Discrete Distribution
Sampling Variability And Sampling Distribution
Statistics and Sampling Variability
The Sampling Distribution of a Sample Mean
The Sampling Distribution of a Sample Proportion
Estimation Using A Single Sample
Point Estimation
Large-Sample Confidence Interval for a Population Proportion
Confidence Interval for a Population Mean
Interpreting and Communicating the Results of Statistical Analyses
Hypothesis Testing Using A Single Sample
Hypotheses and Test Procedures
Errors in Hypotheses Testing
Large-Sample Hypothesis Tests for a Population Proportion
Hypotheses Tests for a Population Mean
Power and Probability of Type II Error
Interpreting and Communicating the Results of Statistical Analyses
Comparing Two Populations Or Treatments
Inferences Concerning the Difference Between Two Population or Treatment Means Using Independent Samples
Inferences Concerning the Difference Between Two Population or Treatment Means Using Paired Samples
Large Sample Inferences Concerning a Difference Between Two Population or Treatment Proportions
Interpreting and Communicating the Results of Statistical Analyses
The Analysis Of Categorical Data And Goodness-Of-Fit Tests
Chi-Square Tests for Univariate Data
Tests for Homogeneity and Independence in a Two-way Table
Interpreting and Communicating the Results of Statistical Analyses
Simple Linear Regression And Correlation: Inferential Methods
Simple Linear Regression Model
Inferences About the Slope of the Population Regression Line
Checking Model Adequacy
Inferences Based on the Estimated Regression Line (Optional)
Inferences About the Population Correlation Coefficient (Optional)
Interpreting and Communicating the Results of Statistical Analyses
Multiple Regression Analysis
Multiple Regression Models
Fitting a Model and Assessing Its Utility
Inferences Based on an Estimated Model (online)
Other Issues in Multiple Regression (online)
Interpreting and Communicating the Results of Statistical Analyses (online)
Activity 14.1: Exploring the Relationship Between Number of Predictors and Sample Size
Analysis Of Variance
Single-Factor ANOVA and the F TeSt. Multiple Comparisons
The F Test for a Randomized Block Experiment (online)
Two-Factor ANOVA (online)
Interpreting and Communicating the Results of Statistical Analyses (online)
Nonparametric (Distribution-Free Statistical Methods (Online)
Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Independent Samples (Optional)
Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Paired Samples
Distribution-Free ANOVA

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