Biostatistics for the biological and health sciences

Biostatistics for the biological and health sciences

For courses in Biostatistics. Real-world applications connect statistical concepts to everyday life. Biostatistics for the Biological and Health Sciences uses a variety of real-world applications to bring statistical theories and methods to life. Through these examples and a friendly writing style,...

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Detalles Bibliográficos
Otros Autores: Triola, Marc M., author (author), Roy, Jason, author, Triola, Mario F., author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Harlow, England : Pearson Education Limited [2019]
Edición:Second edition
Materias:
Medical statistics.
Biometry.
Libros electrónicos.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009767231906719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • About the Authors
  • Contents
  • Preface
  • Acknowledgments
  • 1. Introduction to Statistics
  • 1-1. Statistical and Critical Thinking
  • 1-2. Types of Data
  • 1-3. Collecting Sample Data
  • 2. Exploring Data with Tables and Graphs
  • 2-1. Frequency Distributions for Organizing and Summarizing Data
  • 2-2. Histograms
  • 2-3. Graphs That Enlighten and Graphs That Deceive
  • 2-4. Scatterplots, Correlation, and Regression
  • 3. Describing, Exploring, and Comparing Data
  • 3-1. Measures of Center
  • 3-2. Measures of Variation
  • 3-3. Measures of Relative Standing and Boxplots
  • 4. Probability
  • 4-1. Basic Concepts of Probability
  • 4-2. Addition Rule and Multiplication Rule
  • 4-3. Complements, Conditional Probability, and Bayes' Theorem
  • 4-4. Risks and Odds
  • 4-5. Rates of Mortality, Fertility, and Morbidity
  • 4-6. Counting
  • 5. Discrete Probability Distributions
  • 5-1. Probability Distributions
  • 5-2. Binomial Probability Distributions
  • 5-3. Poisson Probability Distributions
  • 6. Normal Probability Distributions
  • 6-1. The Standard Normal Distribution
  • 6-2. Real Applications of Normal Distributions
  • 6-3. Sampling Distributions and Estimators
  • 6-4. The Central Limit Theorem
  • 6-5. Assessing Normality
  • 6-6. Normal as Approximation to Binomial
  • 7. Estimating Parameters and Determining Sample Sizes
  • 7-1. Estimating a Population Proportion
  • 7-2. Estimating a Population Mean
  • 7-3. Estimating a Population Standard Deviation or Variance
  • 7-4. Bootstrapping: Using Technology for Estimates
  • 8. Hypothesis Testing
  • 8-1. Basics of Hypothesis Testing
  • 8-2. Testing a Claim About a Proportion
  • 8-3. Testing a Claim About a Mean
  • 8-4. Testing a Claim About a Standard Deviation or Variance
  • 9. Inferences from Two Samples
  • 9-1. Two Proportions
  • 9-2. Two Means: Independent Samples.
  • 9-3. Two Dependent Samples (Matched Pairs)
  • 9-4. Two Variances or Standard Deviations
  • 10. Correlation and Regression
  • 10-1. Correlation
  • 10-2. Regression
  • 10-3. Prediction Intervals and Variation
  • 10-4. Multiple Regression
  • 10-5. Dummy Variables and Logistic Regression
  • 11. Goodness-of-Fit and Contingency Tables
  • 11-1. Goodness-of-Fit
  • 11-2. Contingency Tables
  • 12. Analysis of Variance
  • 12-1. One-Way ANOVA
  • 12-2. Two-Way ANOVA
  • 13. Nonparametric Tests
  • 13-1. Basics of Nonparametric Tests
  • 13-2. Sign Test
  • 13-3. Wilcoxon Signed-Ranks Test for Matched Pairs
  • 13-4. Wilcoxon Rank-Sum Test for Two Independent Samples
  • 13-5. Kruskal-Wallis Test for Three or More Samples
  • 13-6. Rank Correlation
  • 14. Survival Analysis
  • 14-1. Life Tables
  • 14-2. Kaplan-Meier Survival Analysis
  • Appendix A: Tables
  • Appendix B: Data Sets
  • Appendix C: Websites and Bibliography of Books
  • Appendix D: Answers to Odd-Numbered Section Exercises
  • Credits
  • Index
  • Back Cover.

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