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Stay on current Cengage siteOtt 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.

Introduction. Why Study Statistics? Some Current Applications of Statistics. A Note to the Student. Summary. Exercises.

PART 2: COLLECTING DATA.

2. Using Surveys and Scientific Studies to Collect Data.

Introduction and Abstract of Research Study. Observational Studies. Sampling Designs for Surveys. Experimental Studies. Designs for Experimental Studies. Research Study: Exit Polls versus Election Results. Summary. Exercises.

PART 3: SUMMARIZING DATA.

3. Data Description.

Introduction and Abstract of Research Study. Calculators, Computers, and Software Systems. Describing Data on a Single Variable: Graphical Methods. Describing Data on a Single Variable: Measures of Central Tendency. Describing Data on a Single Variable: Measures of Variability. The Boxplot. Summarizing Data from More Than One Variable: Graphs and Correlation. Research Study: Controlling for Student Background in the Assessment of Teaching. Summary and Key Formulas. Exercises.

4. Probability And Probability Distributions.

Introduction and Abstract of Research Study. Finding the Probability of an Event. Basic Event Relations and Probability Laws. Conditional Probability and Independence. Bayes' Formula. Variables: Discrete and Continuous. Probability Distributions for Discrete Random Variables. Two Discrete Random Variables: The Binomial and the Poisson. Probability Distributions for Continuous Random Variables. A Continuous Probability Distribution: The Normal Distribution. Random Sampling. Sampling Distributions. Normal Approximation to the Binomial. Evaluating Whether or Not a Population Distribution Is Normal. Research Study: Inferences about Performance Enhancing Drugs among Athletes. Minitab Instructions. Summary and Key Formulas. Exercises.

PART 4: ANALYZING DATA, INTERPRETING THE ANALYSES, AND COMMUNICATING RESULTS.

5. Inferences about Population Central Values.

Introduction and Abstract of a Research Study. Estimation of μ. Choosing the Sample Size for Estimating μ. A Statistical Test for μ. Choosing the Sample Size for μ. The Level of Significance of a Statistical Test. Inferences about μ for a Normal Population, σ Unknown. Inferences about μ when Population in Nonnormal and n is small: Bootstrap Methods. Inferences about the Median. Research Study: Percent Calories from Fat. Summary and Key Formulas. Exercises.

6. Inferences Comparing Two Population Central Values.

Introduction and Abstract of a Research Study. Inferences about μ1 − μ2: Independent Samples. A Nonparametric Alternative: The Wilcoxon Rank Sum Test. Inferences about μ1 − μ2: Paired Data. A Nonparametric Alternative: The Wilcoxon Signed-Rank Test. Choosing Sample Sizes for Inferences about μ1 − μ2. Research Study: Effects of Oil Spill on Plant Growth. Summary. Exercises.

7. Inferences about Population Variances.

Introduction and Abstract of a Research Study. Estimation and Tests for a Population Variance. Estimation and Tests for Comparing Two Population Variances. Tests for Comparing t > 2 Population Variances. Research Study: Evaluation of Methods for Detecting E. coli. Summary and Key Formulas. Exercises.

8. Inferences About More Than Two Population Central Values

Introduction and Abstract of a Research Study. A Statistical Test About More Than Two Population Means: An Analysis of Variance. The Model for Observations in a Completely Randomized Design. Checking on the AOV Conditions. An Alternative Analysis: Transformations of the Data. A Nonparametric Alternative: The Kruskal-Wallis Test. Research Study: Effect on Timing on the Treatment of Port-Wine Stains with Lasers. Summary and Key Formulas. Exercises.

9. Multiple Comparisons.

Introduction and Abstract of Research Study. Linear Contrasts. Which Error Rate Is Controlled? Fisher's Least Significant Difference. Tukey's W Procedure. Student-Neuman-Keuls Procedure. Dunnett's Procedure: Comparison of Treatments to a Control. Scheffé's S Method. A Nonparametric Multiple-Comparison Procedure. Research Study: Are Interviewers' Decisions Affected by Different Handicap Types? Summary and Key Formulas. Exercises.

10. Categorical Data.

Introduction and Abstract of Research Study. Inferences about a Population Proportion . Inferences about the Difference between Two Population Proportions, 1 − 2. Inferences about Several Proportions: Chi-Square Goodness-of-Fit Test. Tests for Independence and Homogeneity. Measuring Strength of Relaxation. Odds and Odd Ratios. Combining Sets of 2 2 Contingency Tables (optional). Research Study: Does Gender Bias Exist in the Selection of Students for Vocational Education? Summary and Key Formulas. Exercises.

PART 5: ANALYZING DATA: REGRESSION METHODS AND MODEL BUILDING.

11. Linear Regression and Correlation.

Introduction and Abstract of Research Study. Estimating Model Parameters. Inferences about Regression Parameters. Predicting New y Values Using Regression. Examining Lack of Fit in Linear Regression. The Inverse Regression Problem (Calibration). Correlation. Research Study: Two Methods for Detecting E. coli. Summary and Key Formulas. Exercises.

12. Multiple Regression and the General Linear Model.

Introduction and Abstract of Research Study. The General Linear Model. Estimating Multiple Regression Coefficients. Inferences in Multiple Regression. Testing a Subset of Regression Coefficients. Forecasting Using Multiple Regression. Comparing the Slopes of Several Regression Lines. Logistic Regression. Some Multiple Regression Theory (Optional). Research Study: Designing an Electric Drill. Summary and Key Formulas. Exercises.

13. Further Regression Topics.

Introduction and Abstract of Research Study. Selecting the Variables (Step 1). Formulating the Model (Step 2). Checking Model Assumptions (Step 3). Research Study: Construction Costs for Nuclear Power Plants. Summary and Key Formulas. Exercises.

PART 6: DESIGN OF EXPERIMENTS AND ANALYSIS OF VARIANCE.

14. Analysis of Variance for Completely Randomized Designs.

Introduction and Abstract of Research Study. Completely Randomized Design with Single Factor. Factorial Treatment Structure. Factorial Treatment Structures with an Unequal Number of Replications. Estimation of Treatment Differences and Comparisons of Treatment Means. Determining the Number of Replications. Research Study: Development of a Low-Fat Processed Meat. Summary and Key Formulas. Exercises.

15. Analysis of Variance for Blocked Designs.

Introduction and Abstract of Research Study. Randomized Complete Block Design. Latin Square Design. Factorial Treatment Structure in a Randomized Complete Block Design. A Nonparametric Alternative—Friedman's Test. Research Study: Control of Leatherjackets. Summary and Key Formulas. Exercises.

16. Analysis of Covariance.

Introduction and Abstract of Research Study. A Completely Randomized Design with One Covariate. The Extrapolation Problem. Multiple Covariates and More Complicated Designs. Research Study: Evaluations of Cool-Season Grasses for Putting Greens. Summary. Exercises.

17. Analysis of Variance for Some Fixed-, Random-, and Mixed-Effects Models.

Introduction and Abstract of Research Study. A One-Factor Experiment with Random Treatment Effects. Extensions of Random-Effects Models. Mixed-Effects Models. Rules for Obtaining Expecting Mean Squares. Nested Factors. Research Study: Factors Affecting Pressure Drops Across Expansion Joints . Summary. Exercises.

18. Split-Plot, Repeated Measures, and Crossover Designs.

Introduction and Abstract of Research Study. Split-Plot Designs. Single-Factor Experiments with Repeated Measures on One of the Factors. Two-Factor Experiments with Repeated Measures on One of the Factors. Crossover Design. Research Study: Effects of Oil Spill on Plant Growth. Summary. Exercises.

19. Analysis of Variance for Some Unbalanced Designs.

Introduction and Abstract of Research Study. A Randomized Block Design with One or More Missing Observations. A Latin Square Design with Missing Data. Balanced Incomplete Block (BIB) Designs. Research Study: Evaluation of the Consistency of Property Assessment. Summary and Key Formulas. Exercises.

PART 7: COMMUNICATING AND DOCUMENTING THE RESULTS OF ANALYSES

20. Communicating and Documenting the Results of a Study or Experiment.

Introduction. The Difficulty of Good Communication. Communication Hurdles: Graphical Distortions. Communication Hurdles: Biased Samples. Communication Hurdles: Sample Size. The Statistical Report. Documentation and Storage of Results. Summary. Exercises.

- The text contains numerous new examples.
- The book contains code for R, a free data analysis software program.
- Simplification of multiple comparison procedures by deleting sections 9.4 and 9.6.
- An exact test is included for testing a single proportion.
- McNemar's test for paired proportions is included.
- Spearman's nonparametric measure of correlation is included.
- The use of AIC and BIC in variable selection is included.
- More than 300 new exercises.

- A special section titled "What is Statistics?" opens the text by explaining why students should study statistics and presenting an engaging discussion of several major studies illustrating the use of statistics to solve a variety of important real-life problems.
- The text emphasizes step-by-step learning and practical skill-building by providing frequent opportunities for students to interpret results and draw conclusions from studies that illustrate key concepts, providing an immediate, effective way to review and apply the material.
- A uniquely effective Four-Step Process to Problem-Solving and Understanding the Collected Data, developed by the authors, helps students of all backgrounds master the essentials of statistics by 1)gathering data, 2) summarizing data, 3) analyzing data, and 4) communicating the results of data analyses.
- Computer output from Minitab®, SAS, and SPSS is provided in numerous examples and exercises to familiarize students with the use of more sophisticated graphical illustrations of statistical results.

**R. Lyman Ott**

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 T. Longnecker**

Texas A&M University

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.

"This book actually has everything I've been looking for content-wise in a text of this nature."

"It is better than other books; that is why I have been using this book for twenty five years."