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Statistics for the Life Sciences, 5th edition

  • Myra L. Samuels, 
  • Jeffrey A. Witmer, 
  • Andrew Schaffner

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Overview

Statistics for the Life Sciences uses authentic examples and exercises from a variety of life sciences to give statistical concepts relevance, so you can connect concepts with situations outside of class. The text emphasizes understanding ideas rather than memorizing and is ideal for a variety of scientific fields.

Published by Pearson (July 14th 2021) - Copyright © 2016

ISBN-13: 9780137515011

Subject: Advanced Statistics

Category: Biostatistics

Table of contents

Table of Contents

UNIT I: DATA AND DISTRIBUTIONS

  1. Introduction
    • 1.1 Statistics and the Life Sciences
    • 1.2 Types of Evidence
    • 1.3 Random Sampling
  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
  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)
  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
  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

UNIT II: INFERENCE FOR MEANS

  1. 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
  2. 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
  3. 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

UNIT III: INFERENCE FOR CATEGORICAL DATA

  1. 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
  2. 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

UNIT IV: MODELING RELATIONSHIPS

  1. 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
  2. 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
  3. A Summary of Inference Methods
    • 13.1 Introduction
    • 13.2 Data Analysis Examples

Chapter Appendices

Chapter Notes

Statistical Tables

Answers to Selected Exercises

Your questions answered

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