Introduction to Statistics for Biology  book cover
SAVE
$17.39
3rd Edition

Introduction to Statistics for Biology




ISBN 9781584886525
Published May 17, 2007 by Chapman and Hall/CRC
296 Pages 78 B/W Illustrations

 
SAVE ~ $17.39
was $86.95
USD $69.56

Prices & shipping based on shipping country


Preview

Book Description

Even though an understanding of experimental design and statistics is central to modern biology, undergraduate and graduate students studying biological subjects often lack confidence in their numerical abilities. Allaying the anxieties of students, Introduction to Statistics for Biology, Third Edition provides a painless introduction to the subject while demonstrating the importance of statistics in contemporary biological studies.

New to the Third Edition

  • More detailed explanation of the ideas of elementary probability to simplify the rationale behind hypothesis testing, before moving on to simple tests
  • An emphasis on experimental design and data simulation prior to performing an experiment
  • A general template for carrying out statistical tests from hypothesis to interpretation
  • Worked examples and updated Minitab analyses and graphics
  • A CD-ROM that contains a free trial version of Minitab

    Using Minitab throughout to present practical examples, the authors emphasize the interpretation of computer output. With its nontechnical approach and practical advice, this student-friendly introductory text lays the foundation for the advanced study of statistical analysis.
  • Table of Contents

    PREFACE

    HOW LONG IS A WORM?
    Introduction
    Sampling a Population
    The Normal Distribution
    Probability
    Continuous Measurements-Worms Again
    Expressing Variability

    CONFIDENCE INTERVALS
    The Importance of Confidence Intervals
    Calculating Confidence Intervals
    Another Way of Looking At It
    Your First Statistical Test
    One- and Two-Tailed Tests
    The Other Side of the Coin-Type II Errors
    Recap-Hypothesis Testing
    A Complication
    Testing Fish with t
    Minitab Does a One-Sample t-Test
    95% CI for Worms
    Anatomy of Test Statistics

    COMPARING THINGS: TWO SAMPLE TESTS
    A Simple Case
    Matched-Pairs t-Test
    Another Example-Testing Twin Sheep
    Independent Samples: Comparing Two Populations
    Calculation of Independent Samples t-Test
    One- and Two-Tailed Tests-A Reminder
    Minitab Carries Out a Two-Sample t-Test
    Pooling the Variances?

    PLANNING AN EXPERIMENT
    Principles of Sampling
    Principles of Experimental Design
    Recording Data and Simulating an Experiment
    Simulating Your Experiment

    PARTITIONING VARIATION AND CONSTRUCTING A MODEL
    It's Simple
    … But Not That Simple
    The Example: Field Margins in Conservation
    The Idea of a Statistical Model
    Laying Out the Experiment
    Sources of Variation: Random Variation
    The Model

    ANALYZING YOUR RESULTS: IS THERE ANYTHING THERE?
    Is Spider Abundance Affected by Treatment?
    Why Not Use Multiple t-Tests?
    ANOVA for a Wholly Randomized Design
    Comparing the Sources of Variation
    The Two Extremes of Explanation: All or Nothing
    The ANOVA Table
    Testing Our Hypothesis
    Including Blocks: Randomized Complete Block Designs
    Analyzing the Spider Data Set in Minitab
    The Assumptions behind ANOVA and How to Test Them
    Another Use for the F-Test: Testing Homogeneity of Variance

    INTERPRETING YOUR ANALYSIS: FROM HYPOTHESIS TESTING TO BIOLOGICAL MEANING
    Treatment Means and Confidence Intervals
    Difference between Two Treatment Means
    Getting More Out of an Experiment: Factorial Designs and Interactions
    Getting More Out of the Analysis: Using the Factorial Design to Ask More Relevant Questions
    Interactions
    Adding Blocking to the Factorial Analysis
    How to Interpret Interaction Plots: The Plant Hormone Experiment
    Loss of Data and Unbalanced Experiments
    Limitations of ANOVA and the General Linear Model (GLM)

    RELATING ONE VARIABLE TO ANOTHER
    Correlation
    Calculating the Correlation Coefficient, and a New Idea: Covariance
    Regression
    Linear Regression
    The Model
    Interpreting Hypothesis Tests in Regression
    A Further Example of Linear Regression
    Assumptions
    The Importance of Plotting Observations
    Confidence Intervals
    Standard Error of Prediction (Prediction Interval)
    Caution in the Interpretation of Regression and Correlation

    CATEGORICAL DATA
    The Chi-Squared Goodness-of-Fit Test
    A More Interesting Example: Testing Genetic Models
    Contingency Analysis: Chi-Squared Test of Proportions
    A Further Example of a Chi-Squared Contingency Test
    Beyond Two-Dimensional Tables: The Likelihood Ratio Chi-Square

    NONPARAMETRIC TESTS
    Introduction
    Basic Ideas
    A Taxonomy of Tests
    Single-Sample Tests
    Matched-Pairs Tests
    Independent Samples
    Two Quantitative Variables: Spearman's Rank Correlation
    Why Bother with Parametric Tests?

    MANAGING YOUR PROJECT
    Choosing a Topic and a Supervisor
    Common Mistakes
    General Principles of Experimental Design and Execution
    Analyzing Your Data and Writing the Report
    Structure
    The First Draft
    Illustrating Results
    What It Is All About: Getting Through Your Project

    APPENDIX A: AN INTRODUCTION TO MINITAB
    Conventions Used in This Book
    Starting Up
    Help
    Data Entry
    Looking at the Worms Data
    Updating Graphs
    Stacking and Unstacking-A Useful Trick
    Looking Up Probabilities
    Report Writer
    The Minitab Command Line
    Saving Your Session

    APPENDIX B: STATISTICAL POWER AND SAMPLE SIZE

    APPENDIX C: STATISTICAL TABLES

    APPENDIX D: REFERENCES AND FURTHER READING

    APPENDIX E: STATISTICAL TESTS

    INDEX

    ...
    View More

    Reviews

    "With its non-technical approach and practical advice, this accessible to students introductory text lays the foundation for more advanced study of statistical analysis and biometry. I strongly recommend this excellent text to all undergraduate students in the biological sciences."
    Journal of the Royal Statistical Society

    Support Material

    Ancillaries