Richard J. Rossi

Applied Biostatistics for the Health Sciences (eBook, ePUB)

• Format: ePub

Produktbeschreibung

APPLIED BIOSTATISTICS FOR THE HEALTH SCIENCES In this newly revised edition of Applied Biostatistics for the Health Sciences, accomplished statistician Dr. Richard Rossi delivers a robust and easy-to-understand exploration of statistics in the context of applied health science and biostatistics. The book covers sample design, logistic regression, experimental design, survival analysis, basic statistical computation, and many more topics with a strong focus on the correct use and interpretation of statistics. The author also explains how to assess the quality of observed data, how to collect quality data, and the use of confidence intervals in conjunction with hypothesis and significance tests. * A thorough introduction to biostatistics, including explanations of fundamental concepts like populations, samples, statistics, biomedical studies, and data set examples * A comprehensive exploration of population descriptions, including qualitative and quantitative variables, multivariate data, measures of dispersion, and probability * Practical discussions of random sampling, summarizing random samples, and the measurement of the reliability of statistics * In-depth examinations of confidence intervals, statistical hypothesis testing, simple and multiple linear regression, and experimental design Perfect for health science and biostatistics students and professors at the upper undergraduate and graduate levels, Applied Biostatistics for the Health Sciences is also a must-read reference for practitioners and professionals in the fields of pharmacy, biochemistry, nursing, health care informatics, and the applied health sciences.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 688
• Erscheinungstermin: 27. April 2022
• Englisch
• ISBN-13: 9781119722700
• Artikelnr.: 63927020

Autorenporträt

Richard J. Rossi, PhD, is Director of the Data Science Program, former Director of the Statistics Program, and former Head of Mathematical Sciences at Montana Technical University, USA. He is author of Theorems, Corollaries, Lemmas, and Methods of Proof and Mathematical Statistics: An Introduction to Likelihood Based Inference, both published by Wiley. His research focuses on nonparametric density estimation, finite mixture models, and computational statistics.

Inhaltsangabe

Preface xi

Chapter 1 Introduction To Biostatistics 1

1.1 What is Biostatistics? 1

1.2 Populations, Samples, and Statistics 2

1.2.1 The Basic Biostatistical Terminology 3

1.2.2 Biomedical Studies 5

1.2.3 Observational Studies Versus Experiments 7

1.3 Clinical Trials 9

1.3.1 Safety and Ethical Considerations in a Clinical Trial 9

1.3.2 Types of Clinical Trials 10

1.3.3 The Phases of a Clinical Trial 10

1.4 Data Set Descriptions 12

1.4.1 Birth Weight Data Set 12

1.4.2 Body Fat Data Set 12

1.4.3 Coronary Heart Disease Data Set 13

1.4.4 Prostate Cancer Study Data Set 13

1.4.5 Intensive Care Unit Data Set 14

1.4.6 Mammography Experience Study Data Set 14

1.4.7 Benign Breast Disease Study 14

1.4.8 Exerbike Data Sets 15

Glossary 17

Exercises 19

Chapter 2 Describing Populations 24

2.1 Populations and Variables 24

2.1.1 Qualitative Variables 25

2.1.2 Quantitative Variables 26

2.1.3 Multivariate Data 28

2.2 Population Distributions and Parameters 29

2.2.1 Distributions 30

2.2.2 Describing a Population with Parameters 34

2.2.3 Proportions and Percentiles 35

2.2.4 Parameters Measuring Centrality 37

2.2.5 Measures of Dispersion 40

2.2.6 The Coefficient of Variation 43

2.2.7 Parameters for Bivariate Populations 45

2.3 Probability 48

2.3.1 Basic Probability Rules 50

2.3.2 Conditional Probability 52

2.3.3 Independence 54

2.3.4 The Relative Risk and the Odds Ratio 56

2.4 Probability Models 59

2.4.1 The Binomial Probability Model 59

2.4.2 The Normal Probability Model 62

2.4.3 Z Scores 69

Glossary 69

Exercises 71

Chapter 3 Random Sampling 83

3.1 Obtaining Representative Data 83

3.1.1 The Sampling Plan 85

3.1.2 Probability Samples 85

3.2 Commonly Used Sampling Plans 87

3.2.1 Simple Random Sampling 87

3.2.2 Stratified Random Sampling 91

3.2.3 Cluster Sampling 92

3.2.4 Systematic Sampling 94

3.3 Determining the Sample Size 95

3.3.1 The Sample Size for Simple and Systematic Random Samples 96

3.3.2 The Sample Size for a Stratified Random Sample 99

Glossary 105

Exercises 107

Chapter 4 Summarizing Random Samples 115

4.1 Samples and Inferential Statistics 115

4.2 Inferential Graphical Statistics 116

4.2.1 Bar and Pie Charts 116

4.2.2 Boxplots 120

4.2.3 Histograms 126

4.2.4 Normal Probability Plots 132

4.3 Numerical Statistics for Univariate Data Sets 134

4.3.1 Estimating Population Proportions 135

4.3.2 Estimating Population Percentiles 142

4.3.3 Estimating the Mean, Median, and Mode 143

4.3.4 Estimating the Variance and Standard Deviation 149

4.3.5 Linear Transformations 153

4.3.6 The Plug-in Rule for Estimation 156

4.4 Statistics for Multivariate Data Sets 158

4.4.1 Graphical Statistics for Bivariate Data Sets 158

4.4.2 Numerical Summaries for Bivariate Data Sets 160

4.4.3 Fitting Lines to Scatterplots 166

Glossary 167

Exercises 170

Chapter 5 Measuring The Reliability of Statistics 186

5.1 Sampling Distributions 186

5.1.1 Unbiased Estimators 188

5.1.2 Measuring the Accuracy of an Estimator 189<