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Ott and Longnecker's An Introduction to Statistical Methods and Data Analysis, Seventh Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments.
PART 1: INTRODUCTION
1. Statistics and the Scientific Method
PART 2: COLLECTING DATA
2. Using Surveys and Scientific Studies to Collect Data
PART 3: SUMMARIZING DATA
3. Data Description. Introduction and Abstract of Research Study
4. Probability And Probability Distributions
PART 4: ANALYZING DATA, INTERPRETING THE ANALYSES, AND COMMUNICATING RESULTS
5. Inferences about Population Central Values
6. Inferences Comparing Two Population Central Values
7. Inferences about Population Variances
8. Inferences About More Than Two Population Central Values
9. Multiple Comparisons
10. Categorical Data
PART 5: ANALYZING DATA: REGRESSION METHODS AND MODEL BUILDING
11. Linear Regression and Correlation
12. Multiple Regression and the General Linear Model
13. Further Regression Topics
PART 6: DESIGN OF EXPERIMENTS AND ANALYSIS OF VARIANCE
14. Analysis of Variance for Completely Randomized Designs
15. Analysis of Variance for Blocked Designs
16. Analysis of Covariance
17. Analysis of Variance for Some Fixed-, Random-, and Mixed-Effects Models
18. Split-Plot, Repeated Measures, and Crossover Designs
19. Analysis of Variance for Some Unbalanced Designs
20. Communicating and Documenting the Results of a Study or Experiment
Lyman Ott earned his Bachelor's degree in Mathematics and Education and Master's degree in Mathematics from Bucknell University, and Ph.D in Statistics from the Virginia Polytechnic Institute. After two years working in statistics in the pharmaceutical industry, Dr. Ott became assistant professor in the Statistic Department at the University of Florida in 1968 and was named associate professor in 1972. He joined Merrell-National laboratories in 1975 as head of the Biostatistics Department and then head of the company's Research Data Center. He later became director of Biomedical Information Systems, Vice President of Global Systems and Quality Improvement in Research and Development, and Senior Vice President Business Process Improvement and Biometrics. He retired from the pharmaceutical industry in 1998, and now serves as consultant and Board of Advisors member for Abundance Technologies, Inc. Dr. Ott has published extensively in scientific journals and authored or co-authored seven college textbooks including Basic Statistical Ideas for Managers, Statistics: A Tool for the Social Sciences and An Introduction to Statistical Methods and Data Analysis. He has been a member of the Industrial Research Institute, the Drug Information Association and the Biometrics Society. In addition, he is a Fellow of the American Statistical Association and received the Biostatistics Career Achievement Award from the Pharmaceutical research and Manufacturers of America in 1998. He was also an All-American soccer player in college and is a member of the Bucknell University Athletic Hall of Fame.
Michael Longnecker currently serves as Professor and Associate Department Head at Texas A&M University. He received his B.S. at Michigan Technological University, his first M.S. at Western Michigan University, his second M.S. at Florida State University, and his Ph.D. at Florida State University. He is interested in Nonparametrics, Statistical Process Control, and Statistical Consulting.
