Summary: A thorough grounding in statistics is necessary for a career in any experimental science, but many students find themselves intimidated by the subject. Hampton and Havel have written this text with these students in mind. While providing the theory and assumptions necessary for a deep understanding of statistics, they make it approachable and keep it relevant to the interests of biology students. Their examples and exercises show how to choose the appropriate statist
ical method for a particular hypothesis and how to execute that method using problems encountered by real-world biologists. The second edition has been ambitiously updated and reorganized, facilitating clearer connections between topics and improving clarity of those that are logically distinct. A wide range of descriptive and inferential methods is covered, including: normal, binomial, and Poisson frequency distributions; sampling distributions; one- and two-sample t-tests; the Mann-Whitney and Wilcoxon signed-ranks tests; ANOVA; randomized block and factorial designs; correlations and regression analysis; and the chi-square test and other analyses of frequencies. The accompanying CD contains large data sets (in both ASCII and Excel formats), allowing students and instructors to save time and focus on concepts rather than data entry.
Summary: A thorough grounding in statistics is necessary for a career in any experimental science, but many students find themselves intimidated by the subject. Hampton and Havel have written this text with these students in mind. While providing the theory and assumptions necessary for a deep understanding of statistics, they make it approachable and keep it relevant to the interests of biology students. Their examples and exercises show how to choose the appropriate statistical method for a particular hypothesis and how to execute that method using problems encountered by real-world biologists. The second edition has been ambitiously updated and reorganized, facilitating clearer connections between topics and improving clarity of those that are logically distinct. A wide range of descriptive and inferential methods is covered, including: normal, binomial, and Poisson frequency distributions; sampling distributions; one- and two-sample t-tests; the Mann-Whitney and Wilcoxon signed-ranks tests; ANOVA; randomized block and factorial designs; correlations and regression analysis; and the chi-square test and other analyses of frequencies. The accompanying CD contains large data sets (in both ASCII and Excel formats), allowing students and instructors to save time and focus on concepts rather than data entry. ...show less
Edition/Copyright:2ND 06 Cover: Paperback Publisher:Waveland Press, Inc. Published: 08/22/2005 International: No
View Table of Contents
1. Some Basic Concepts
What Is Statistics? / Populations and Samples / Randomness / Independence / Other Types of Samples
2. Data Measurement and Management of Numbers
Variables and Data / Scales of Measurement / Data Management
3. Frequency Distributions and Graphic Presentation of Data
Frequency Distributions of Discrete Variables / Frequency Distributions of Continuous Variables / Histograms and Their Interpretation / Cumulative Frequency Distributions / Other Handy Graph Types
4. Descriptive Statistics: Measures of Central Tendency and Dispersion
Sample Statistics and Population Parameters / Measures of Central Tendency / Measures of Dispersion / Descriptive Statistics from a Computer / Visualizing the Location of the Mean and Standard Deviation
5. Probability and Discrete Probability Distributions
Probability and Probability Distributions / The Binomial Distribution / The Poisson Distribution
6. The Normal Distribution
The Normal Distribution and Its Properties / The Standard Normal Distribution and Z Scores / Testing for Normality / Normal Approximation of the Binomial Distribution / Discrete Variables and the Normal Distribution / Parametric and Nonparametric Statistics
7. Statistical Inference I: Estimation and Sampling Distributions
An Introduction to Statistical Inference / Estimating a Population Mean: The Central Limit Theorem / Estimating a Population Mean: Standard Error of the Mean / Confidence Interval of ? When ? is Known / Confidence Interval of ? When ? is Unknown: The t Distribution / Reporting a Sample Mean and Its Variation
8. Statistical Inference II: Hypothesis Testing and the One-Sample t-Test
Statistical Hypothesis Testing and the Scientific Method / Test of a Hypothesis Concerning a Single Population Mean: The One-Sample t-Test / One-Tailed and Two-Tailed Hypothesis Tests / Statistical Decision Making and Its Potential Errors / Steps in Testing a Hypothesis
9. Inferences Concerning Two Populations and Paired Comparisons
The t-Test for Two Independent Samples / Confidence Interval for the Difference between Two Population Means / A Nonparametric Test for Two Independent Samples: The Mann-Whitney Test / Tests for Two Related Samples / The Paired t-Test / Nonparametric Tests for Two Related Samples / Power of the Test: How Large a Sample Is Sufficient? / Review: Which Statistical Test Is Appropriate? / Comparisons of Variances from Two Key Samples
The Rationale of ANOVA: An Illustration / The Assumptions of ANOVA / Fixed-Effects ANOVA (Model I) / Testing the Assumptions of ANOVA / Remedies for Failed Assumptions
11. Other ANOVA Designs
The Randomized Block Design / The Factorial Design / The Friedman Test / Other ANOVA Designs
12. Modeling One Measurement Variable against Another: Regression Analysis
Regression versus Correlation / Simple Linear Regression Fundamentals / Estimating the Regression Function and the Regression Line / Calculating the Estimated Regression Equation / Testing the Significance of the Regression Equation / The Confidence Interval for ? / The Coefficient of Determination (r2) / Predicting y from x / Dealing with Several Values of y for Each Value of x / Checking Assumptions and Remedies for Their Failure / Advanced Regression Techniques
13. Association between Two Measurement Variables: Correlation
The Pearson Correlation Coefficient / A Correlation Matrix / Nonparametric Correlation Analysis (Spearman's r)
14. Analysis of Frequencies
The Chi-Square Goodness-of-Fit Test / The Chi-Square Test for Association / The Fisher Exact Probability Test / The McNemar Test for the Significance of Changes / Graphic Displays of Frequency Data
15. Choice of Tests and a View of Some Other Procedures
Choice of the Appropriate Statistical Test / Experimental Design / A View of Some Other Statistical Procedures
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