An introduction to statisical methods and data analysis [electronic resource] / R. Lymann Ott, Michael Longnecker.

Includes bibliographical references and index.

Statistics and the Scientific Method -- COLLECTING DATA -- Using Surveys and Experimental Studies to Gather Data -- SUMMARIZING DATA -- Data Description -- Probability and Probability Distributions -- ANALYZING DATA, INTERPRETING THE ANALYSES, AND COMMUNICATING RESULTS -- Inferences about Population Central Values -Inferences Comparing Two Population Central Values -- Inferences about Population Variances -- Inferences About More Than Two Population Central Values -- Multiple Comparisons -- Categorical Data -- ANALYZING DATA: REGRESSION METHODS AND MODEL BUILDING -- Linear Regression and Correlation -- Multiple Regression and the General Linear Model -- Further Regression Topics -- DESIGN OF EXPERIMENTS AND ANALYSIS OF VARIANCE -- Analysis of Variance for Completely Randomized Designs -- Analysis of Variance for Blocked Designs -- Analysis of Covariance -- Analysis of Variance for Some Fixed-, Random-, and Mixed-Effects Models -- Split-Plot, Repeated Measures, and Crossover Designs -- Analysis of Variance for Some Unbalanced Designs -- COMMUNICATING AND DOCUMENTING THE RESULTS OF ANALYSES -- Communicating and Documenting the Results of a Study or Experiment.

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.