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For introductory undergraduate or graduate courses in statistics aimed at life science majors.
Bringing Statistics to Life
The Fifth Edition of Statistics for the Life Sciences uses authentic examples and exercises from a wide variety of life science domains to give statistical concepts personal relevance, enabling students to connect concepts with situations they will encounter outside the classroom. The emphasis on understanding ideas rather than memorizing formulas makes the text ideal for students studying a variety of scientific fields: animal science, agronomy, biology, forestry, health, medicine, nutrition, pharmacy, physical education, zoology and more. In the fifth edition, randomization tests have been moved to the fore to motivate the inference procedures introduced in the text. There are no prerequisites for the text except elementary algebra.
Contenu
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
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
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
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
UNIT III: INFERENCE FOR CATEGORICAL DATA
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
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
UNIT IV: MODELING RELATIONSHIPS
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.