Chapman and Hall/CRC
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 disciplines.
"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 techniques…Rosenblatt 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.
"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 standpoint…the book is successful on both points…"
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
Probability and its Basic Rules
Discrete Uniform Models and Counting
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
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
Simple Linear Regression
Multiple Linear Regression
The Analysis of Variance
SELECTED ANSWERS AND SOLUTIONS