Basic Statistical Methods and Models for the Sciences

Basic Statistical Methods and Models for the Sciences

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The use of statistics in biology, medicine, engineering, and the sciences has grown dramatically in recent years and having a basic background in the subject has become a near necessity for students and researchers in these fields. Although many introductory statistics books already exist, too often their focus leans towards theory and few help readers gain effective experience in using a standard statistical software package. Designed to be used in a first course for graduate or upper-level undergraduate students, Basic Statistical Methods and Models builds a practical foundation in the use of statistical tools and imparts a clear understanding of their underlying assumptions and limitations. Without getting bogged down in proofs and derivations, thorough discussions help readers understand why the stated methods and results are reasonable. The use of the statistical software Minitab is integrated throughout the book, giving readers valuable experience with computer simulation and problem-solving techniques. The author focuses on applications and the models appropriate to each problem while emphasizing Monte Carlo methods, the Central Limit Theorem, confidence intervals, and power functions. The text assumes that readers have some degree of maturity in mathematics, but it does not require the use of calculus. This, along with its very clear explanations, generous number of exercises, and demonstrations of the extensive uses of statistics in diverse areas applications make Basic Statistical Methods and Models highly accessible to students in a wide range of more

Product details

  • Hardback | 296 pages
  • 162.1 x 240.8 x 20.6mm | 589.68g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 158488147X
  • 9781584881476

Review quote

"Rosenblatt writes for introductory (non-calculus-based) courses in statistics that offer a clear understanding of statistical procedures together with underlying assumptions and limitations. The author brings a fresh approach to the understanding of statistical concepts by integrating throughout Minitab software, providing valuable insight into computer simulation and problem-solving techniquesRosenblatt clearly treats the subject matter by carefully wording the explanations and by having readers work with computer-generated data with properties specified by readers. Numerous solved examples; exercises; epilogue with extensions of topics covered. An interesting and useful book. Recommended. - CHOICE "This text attempts to address the needs of those who use statistics but are not statisticians. Writing such a text poses two challenges. The first challenge is to present mathematically complex ideas in such a way as to engender an intuitive understanding of the concepts without relying on mathematical detail or rigor. The second is to ground these concepts in application, to show how and why they are important from a practical standpointthe book is successful on both points" - TECHNOMETRICSshow more

Table of contents

INTRODUCTION Scientific Method The Aims of Medicine, Science, and Engineering The Roles of Models and Data Deterministic and Statistical Models Probability Theory and Computer Simulation Definition: Monte Carlo Simulation CLASSES OF MODELS AND STATISTICAL INFERENCE Statistical Models - the Frequency Interpretation Some Useful Statistical Models Narrowing Down the Class of Potential Models SAMPLING AND DESCRIPTIVE STATISTICS Representative and Random Samples Descriptive Statistics of Location Descriptive Statistics of Variability Other Descriptive Statistics SURVEY OF BASIC PROBABILITY Introduction Probability and its Basic Rules Discrete Uniform Models and Counting Conditional Probability Statistical Independence Systematic Approach to Probability Problems Random Variables, Expectation and Variance The Central Limit Theorem and its Applications INTRODUCTION TO STATISTICAL ESTIMATION Methods of Estimation Distribution of Sample Percentiles Adequacy of Estimators Confidence Limits and Confidence Intervals Confidence Limits and Interval for Binomial p Comparing Estimators The Bootstrap TESTING HYPOTHESES Introduction Some Commonly Used Statistical Tests Types I and II Errors and (Discriminating) Power The Simulation Approach to Estimating Power Some Final Issues and Comments BASIC REGRESSION AND ANALYSIS OF VARIANCE Introduction Simple Linear Regression Multiple Linear Regression The Analysis of Variance EPILOGUE BIBLIOGRAPHY SELECTED ANSWERS AND SOLUTIONS INDEXshow more

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