Student Solutions Manual for Statistics for the Life Sciences
Häftad, Engelska, 2015
1 349 kr
Produktinformation
- Utgivningsdatum2015-09-09
- Mått100 x 100 x 100 mm
- Vikt100 g
- FormatHäftad
- SpråkEngelska
- Upplaga5
- FörlagPearson Education (US)
- ISBN9780321989697
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Myra L. Samuels (late) was an Associate Professor of Biostatistics and Epidemiology in Purdue's Department of Veterinary Pathobiology and Associate Director of Statistical Consulting in the Department of Statistics. She received her PhD in Statistics from the University of California–Berkeley, under Jerzy Neyman, and taught at Purdue for 24 years. Her research was oriented toward issues in biostatistics and included both conceptual issues in mathematical statistics and collaborations on applications. Myra was a member of the American Statistical Association, the Biometric Society, and the Society for Clinical Trials. Dr. Samuels passed away in 1992. Jeff Witmer is Professor of Mathematics at Oberlin College. He received his PhD in Statistics from the University of Minnesota and taught at the University of Florida before coming to Oberlin. He is a Fellow of the American Statistical Association and an elected member of the International Statistics Institute. Andrew Schaffner is Professor of Statistics at California Polytechnic State University–San Luis Obispo and faculty statistician for the Environmental Biotechnology Institute. He received his PhD in Statistics from the University of Washington. His research involves statistical applications in environmental monitoring.
- Table of Contents UNIT I: DATA AND DISTRIBUTIONS Introduction 1.1 Statistics and the Life Sciences1.2 Types of Evidence1.3 Random SamplingDescription of Samples and Populations 2.1 Introduction2.2 Frequency Distributions2.3 Descriptive Statistics: Measures of Center2.4 Boxplots2.5 Relationships Between Variables2.6 Measures of Dispersion2.7 Effect of Transformation of Variables2.8 Statistical Inference2.9 PerspectiveProbability and the Binomial Distribution 3.1 Probability and the Life Sciences3.2 Introduction to Probability3.3 Probability Rules (Optional)3.4 Density Curves3.5 Random Variables3.6 The Binomial Distribution3.7 Fitting a Binomial Distribution to Data (Optional)The Normal Distribution 4.1 Introduction4.2 The Normal Curves4.3 Areas under a Normal Curve4.4 Assessing Normality4.5 PerspectiveSampling Distributions 5.1 Basic Ideas5.2 The Sample Mean5.3 Illustration of the Central Limit Theorem5.4 The Normal Approximation to the Binomial Distribution5.5 PerspectiveUnit I Highlights and StudyUNIT II: INFERENCE FOR MEANS Confidence Intervals 6.1 Statistical Estimation6.2 Standard Error of the Mean6.3 Confidence Interval for μ6.4 Planning a Study to Estimate μ6.5 Conditions for Validity of Estimation Methods6.6 Comparing Two Means6.7 Confidence Interval for (μ1 - μ2)6.8 Perspective and SummaryComparison of Two Independent Samples 7.1 Hypothesis Testing: The Randomization Test7.2 Hypothesis Testing: The t Test7.3 Further Discussion of the t Test7.4 Association and Causation7.5 One-Tailed t Tests7.6 More on Interpretation of Statistical Significance7.7 Planning for Adequate Power7.8 Student's t: Conditions and Summary7.9 More on Principles of Testing Hypotheses7.10 The Wilcoxon-Mann-Whitney TestComparison of Paired Samples 8.1 Introduction8.2 The Paired-Sample t Test and Confidence Interval8.3 The Paired Design8.4 The Sign Test8.5 The Wilcoxon Signed-Rank Test8.6 PerspectiveUnit II Highlights and StudyUNIT III: INFERENCE FOR CATEGORICAL DATA Categorical Data: One-Sample Distributions 9.1 Dichotomous Observations9.2 Confidence Interval for a Population Proportion9.3 Other Confidence Levels (Optional)9.4 Inference for Proportions: The Chi-Square Goodness-of-Fit Test9.5 Perspective and SummaryCategorical Data: Relationships 10.1 Introduction10.2 The Chi-Square Test for the 2 × 2 Contingency Table10.3 Independence and Association in the 2 × 2 Contingency Table10.4 Fisher's Exact Test10.5 The r × k Contingency Table10.6 Applicability of Methods10.7 Confidence Interval for Difference Between Probabilities10.8 Paired Data and 2 × 2 Tables10.9 Relative Risk and the Odds Ratio10.10 Summary of Chi-Square TestUnit III Highlights and StudyUNIT IV: MODELING RELATIONSHIPS Comparing the Means of Many Independent Samples 11.1 Introduction11.2 The Basic One-Way Analysis of Variance11.3 The Analysis of Variance Model11.4 The Global F Test11.5 Applicability of Methods11.6 One-Way Randomized Blocks Design11.7 Two-Way ANOVA11.8 Linear Combinations of Means11.9 Multiple Comparisons11.10 PerspectiveLinear Regression and Correlation 12.1 Introduction12.2 The Correlation Coefficient12.3 The Fitted Regression Line12.4 Parametric Interpretation of Regression: The Linear Model12.5 Statistical Inference Concerning β112.6 Guidelines for Interpreting Regression and Correlation12.7 Precision in Prediction12.8 Perspective12.9 Summary of FormulasUnit IV Highlights and StudyA Summary of Inference Methods 13.1 Introduction13.2 Data Analysis ExamplesChapter Appendices Chapter Notes Statistical Tables Answers to Selected Exercises