Statistics for the Life Sciences, Fifth Edition, Global Edition

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. Cover 1 Title Page 2 Copyrigth Page 3 Contents 4 Preface 7 Acknowledgments 10 Unit I Data and Distributions 12 1 Introduction 12 1.1 Statistics and the Life Sciences 12 1.2 Types of Evidence 18 1.3 Random Sampling 27 2 Description of Samples and Populations 38 2.1 Introduction 38 2.2 Frequency Distributions 40 2.3 Descriptive Statistics: Measures of Center 51 2.4 Boxplots 56 2.5 Relationships between Variables 63 2.6 Measures of Dispersion 70 2.7 Effect of Transformation of Variables* 78 2.8 Statistical Inference 83 2.9 Perspective 89 3 Probability and the Binomial Distribution 94 3.1 Probability and the Life Sciences 94 3.2 Introduction to Probability 94 3.3 Probability Rules* 105 3.4 Density Curves 110 3.5 Random Variables 113 3.6 The Binomial Distribution 119 3.7 Fitting a Binomial Distribution to Data* 127 4 The Normal Distribution 133 4.1 Introduction 133 4.2 The Normal Curves 135 4.3 Areas under a Normal Curve 137 4.4 Assessing Normality 144 4.5 Perspective 154 5 Sampling Distributions 157 5.1 Basic Ideas 157 5.2 The Sample Mean 161 5.3 Illustration of the Central Limit Theorem* 171 5.4 The Normal Approximation to the Binomial Distribution* 174 5.5 Perspective 180 Unit I Highlights and Study 182 Unit II Inference for Means 187 6 Confidence Intervals 187 6.1 Statistical Estimation 187 6.2 Standard Error of the Mean 188 6.3 Confidence Interval for μ 193 6.4 Planning a Study to Estimate μ 204 6.5 Conditions for Validity of Estimation Methods 207 6.6 Comparing Two Means 216 6.7 Confidence Interval for (μ1 — μ2) 222 6.8 Perspective and Summary 228 7 Comparison of Two Independent Samples 234 7.1 Hypothesis Testing: The Randomization Test 234 7.2 Hypothesis Testing: The t Test 240 7.3 Further Discussion of the t Test 252 7.4 Association and Causation 260 7.5 One-Tailed t Tests 268 7.6 More on Interpretation of Statistical Significance 279 7.7 Planning for Adequate Power* 286 7.8 Student’s t: Conditions and Summary 292 7.9 More on Principles of Testing Hypotheses 296 7.10 The Wilcoxon-Mann-Whitney Test 302 8 Comparison of Paired Samples 318 8.1 Introduction 318 8.2 The Paired-Sample t Test and Confidence Interval 321 8.3 The Paired Design 330 8.4 The Sign Test 336 8.5 The Wilcoxon Signed-Rank Test 342 8.6 Perspective 347 Unit II Highlights and Study 357 Unit III Inference for Categorical Data 366 9 Categorical Data: One-Sample Distributions 366 9.1 Dichotomous Observations 366 9.2 Confidence Interval for a Population Proportion 371 9.3 Other Confidence Levels* 377 9.4 Inference for Proportions: The Chi-Square Goodness-of-Fit Test 379 9.5 Perspective and Summary 389 10 Categorical Data: Relationships 394 10.1 Introduction 394 10.2 The Chi-Square Test for the 2 X 2 Contingency Table 398 10.3 Independence and Association in the 2 X 2 Contingency Table 405 10.4 Fisher’s Exact Test* 413 10.5 The r X k Contingency Table 418 10.6 Applicability of Methods 424 10.7 Confidence Interval for Difference Between Probabilities 428 10.8 Paired Data and 2 X 2 Tables* 430 10.9 Relative Risk and the Odds Ratio* 433 10.10 Summary of Chi-Square Test 441 Unit III Highlights and Study 446 Unit IV Modeling Relationships 453 11 Comparing the Means of Many Independent Samples 453 11.1 Introduction 453 11.2 The Basic One-Way Analysis of Variance 457 11.3 The Analysis of Variance Model 466 11.4 The Global F Test 468 11.5 Applicability of Methods 473 11.6 One-Way Randomized Blocks Design 477 11.7 Two-Way ANOVA 489 11.8 Linear Combinations of Means* 498 11.9 Multiple Comparisons* 506 11.10 Perspective 516 12 Linear Regression and Correlation 522 12.1 Introduction 522 12.2 The Correlation Coefficient 524 12.3 The Fitted Regression Line 536 12.4 Parametric Interpretation of Regression: The Linear Model 548 12.5 Statistical Inference Concerning β1 554 12.6 Guidelines for Interpreting Regression and Correlation 560 12.7 Precision in Prediction* 572 12.8 Perspective 575 12.9 Summary of Formulas 586 Unit IV Highlights and Study 595 13 A Summary of Inference Methods 604 13.1 Introduction 604 13.2 Data Analysis Examples 606 Chapter Appendices** 620 Chapter Notes** 627 Answers to Selected Exercises 629 Credits 637 Index 638 A 638 B 638 C 638 D 639 E 640 F 640 G 641 H 641 I 641 L 641 M 641 N 642 O 642 P 643 Q 643 R 643 S 644 T 645 U 646 V 646 W 646 X 646 Z 646 Index of Examples 647 A 647 B 647 C 647 D 647 E 647 F 647 G 647 H 647 I 647 K 647 L 647 M 648 N 648 O 648 P 648 R 648 S 648 T 648 U 648 V 648 W 648 Y 648 This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. 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. With a strong emphasis on real data, exploratory data analysis, interpretation of results and checking assumptions, this text clearly conveys the key concepts of statistics as applied to life sciences while incorporating tools and themes of modern data analysis