Introduction to Statistics and Data Analysis - Softcover

Devore, Jay L.; Peck, Roxy; Olsen, Chris

Softcover

ISBN 10: 0840068395 ISBN 13: 9780840068392

Publisher: Brooks/Cole, 2011

Synopsis

Roxy Peck, Chris Olsen, and Jay Devore's new edition uses real data and attention-grabbing examples to introduce students to the study of statistics and data analysis. Traditional in structure yet modern in approach, this text guides students through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation - including the frequent substitution of words for symbols - helps students grasp concepts and cement their comprehension. Hands-on activities and interactive applets allow students to practice statistics firsthand. "Introduction to Statistics and Data Analysis, 4th edition, International Edition", includes updated coverage of the graphing calculator as well as expanded coverage of probability. CourseSmart goes beyond traditional expectations - providing instant, online access to the textbooks and course materials you need and at a lower cost to your students. To request an electronic sample of this Cengage Learning title, go to our associated website.

About this title

Review:

1. 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. 2. 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. 3. 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. 4. 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. 5. 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. 6. PROBABILITY. Chance Experiments and Events. Definition of Probability. Basic Properties of Probability. Conditional Probability. Independence. Some General Probability Rules. Estimating Probabilities Empirically Using Simulation. 7. 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. 8. SAMPLING VARIABILITY AND SAMPLING DISTRIBUTION. Statistics and Sampling Variability. The Sampling Distribution of a Sample Mean. The Sampling Distribution of a Sample Proportion. 9. 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. 10. 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. 11. 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. 12. 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. 13. 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. 14. 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. 15. 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). 16. 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.

About the Author:

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 Cal Poly Statistics Department. In addition to this book, Jay has written several other widely used statistics texts for engineers and scientists 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, a high school teacher in Brooklyn.

About this edition

• Publisher: Brooks/Cole
• Publication date: 2011
• ISBN 10: 0840068395
• ISBN 13: 9780840068392
• Binding: Paperback
• Edition number: 4
• Number of pages: 880