Statistics for the Life Sciences
by Myra L. Samuels, Jeffrey A. Witmer, Andrew Schaffner
5th Edition
Publisher: Pearson
Book Details
| Print ISBN | 9780321989581 |
| eText ISBN | 9780134150413 |
| Publisher | Pearson |
| Publishing Year | 2015 |
| Edition | 5th Edition |
| Language | English |
Statistics for the Life Sciences, 5th Edition, is a textbook that introduces statistical methods and inference procedures for life science applications. Written by Myra L. Samuels, Jeffrey A. Witmer, and Andrew Schaffner and published by Pearson, the volume requires no prerequisites beyond elementary algebra.
The text examines descriptive statistics, probability distributions, statistical inference for means and proportions, and categorical data analysis. It also presents methods for modeling relationships through analysis of variance, correlation, and linear regression.
In the 2015 edition, randomization tests motivate the inference procedures introduced throughout the book. A concluding summary of inference methods pairs with data analysis examples to support student review.
Table of Contents
Chapter 1: Introduction
- • 1.1 Statistics and the Life Sciences
- • 1.2 Types of Evidence
- • 1.3 Random Sampling
Chapter 2: Description of Samples and Populations
- • 2.1 Introduction
- • 2.2 Frequency Distributions
- • 2.3 Descriptive Statistics: Measures of Center
- • 2.4 Boxplots
- • 2.5 Relationships Between Variables
- • 2.6 Measures of Dispersion
- • 2.7 Effect of Transformation of Variables
- • 2.8 Statistical Inference
- • 2.9 Perspective
Chapter 3: Probability and the Binomial Distribution
- • 3.1 Probability and the Life Sciences
- • 3.2 Introduction to Probability
- • 3.3 Probability Rules (Optional)
- • 3.4 Density Curves
- • 3.5 Random Variables
- • 3.6 The Binomial Distribution
- • 3.7 Fitting a Binomial Distribution to Data (Optional)
Chapter 4: The Normal Distribution
- • 4.1 Introduction
- • 4.2 The Normal Curves
- • 4.3 Areas under a Normal Curve
- • 4.4 Assessing Normality
- • 4.5 Perspective
Chapter 5: Sampling Distributions
- • 5.1 Basic Ideas
- • 5.2 The Sample Mean
- • 5.3 Illustration of the Central Limit Theorem
- • 5.4 The Normal Approximation to the Binomial Distribution
- • 5.5 Perspective
- • Unit I Highlights and Study
Chapter 6: Confidence Intervals
- • 6.1 Statistical Estimation
- • 6.2 Standard Error of the Mean
- • 6.3 Confidence Interval for μ
- • 6.4 Planning a Study to Estimate μ
- • 6.5 Conditions for Validity of Estimation Methods
- • 6.6 Comparing Two Means
- • 6.7 Confidence Interval for (μ1 - μ2)
- • 6.8 Perspective and Summary
Chapter 7: Comparison of Two Independent Samples
- • 7.1 Hypothesis Testing: The Randomization Test
- • 7.2 Hypothesis Testing: The t Test
- • 7.3 Further Discussion of the t Test
- • 7.4 Association and Causation
- • 7.5 One-Tailed t Tests
- • 7.6 More on Interpretation of Statistical Significance
- • 7.7 Planning for Adequate Power
- • 7.8 Student's t: Conditions and Summary
- • 7.9 More on Principles of Testing Hypotheses
- • 7.10 The Wilcoxon-Mann-Whitney Test
Chapter 8: Comparison of Paired Samples
- • 8.1 Introduction
- • 8.2 The Paired-Sample t Test and Confidence Interval
- • 8.3 The Paired Design
- • 8.4 The Sign Test
- • 8.5 The Wilcoxon Signed-Rank Test
- • 8.6 Perspective
- • Unit II Highlights and Study
Chapter 9: Categorical Data: One-Sample Distributions
- • 9.1 Dichotomous Observations
- • 9.2 Confidence Interval for a Population Proportion
- • 9.3 Other Confidence Levels (Optional)
- • 9.4 Inference for Proportions: The Chi-Square Goodness-of-Fit Test
- • 9.5 Perspective and Summary
Chapter 10: Categorical Data: Relationships
- • 10.1 Introduction
- • 10.2 The Chi-Square Test for the 2 × 2 Contingency Table
- • 10.3 Independence and Association in the 2 × 2 Contingency Table
- • 10.4 Fisher's Exact Test
- • 10.5 The r × k Contingency Table
- • 10.6 Applicability of Methods
- • 10.7 Confidence Interval for Difference Between Probabilities
- • 10.8 Paired Data and 2 × 2 Tables
- • 10.9 Relative Risk and the Odds Ratio
- • 10.10 Summary of Chi-Square Test
- • Unit III Highlights and Study
Chapter 11: Comparing the Means of Many Independent Samples
- • 11.1 Introduction
- • 11.2 The Basic One-Way Analysis of Variance
- • 11.3 The Analysis of Variance Model
- • 11.4 The Global F Test
- • 11.5 Applicability of Methods
- • 11.6 One-Way Randomized Blocks Design
- • 11.7 Two-Way ANOVA
- • 11.8 Linear Combinations of Means
- • 11.9 Multiple Comparisons
- • 11.10 Perspective
Chapter 12: Linear Regression and Correlation
- • 12.1 Introduction
- • 12.2 The Correlation Coefficient
- • 12.3 The Fitted Regression Line
- • 12.4 Parametric Interpretation of Regression: The Linear Model
- • 12.5 Statistical Inference Concerning β1
- • 12.6 Guidelines for Interpreting Regression and Correlation
- • 12.7 Precision in Prediction
- • 12.8 Perspective
- • 12.9 Summary of Formulas
- • Unit IV Highlights and Study
Chapter 13: A Summary of Inference Methods
- • 13.1 Introduction
- • 13.2 Data Analysis Examples
Chapter Appendices: Chapter Appendices
Chapter Notes: Chapter Notes
Chapter Tables: Statistical Tables
Chapter Answers: Answers to Selected Exercises
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