Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel and R
John Wiley & Sons Inc (Verlag)
978-1-119-72259-5 (ISBN)
The newest edition contains brand-new code examples for using the popular computer language R to perform the statistical analyses described in the chapters within. You'll learn how to use Excel to generate datasets for R, which can then be used to conduct statistical calculations on your data.
The book also includes a companion website with a new version of BAHR add-in programs for Excel. This new version contains new programs for nonparametric analyses, Student-Newman-Keuls tests, and stratified analyses. Readers will also benefit from coverage of topics like:
Extensive discussions of basic and foundational concepts in statistical methods, including Bayes' Theorem, populations, and samples
A treatment of univariable analysis, covering topics like continuous dependent variables and ordinal dependent variables
An examination of bivariable analysis, including regression analysis and correlation analysis
An analysis of multivariate calculations in statistics and how testing assumptions, like assuming Gaussian distributions or equal variances, affect statistical outcomes
Perfect for health researchers of all kinds, Introduction to Biostatistical Applications in Health Research also belongs on the bookshelves of anyone who wishes to better understand health research literature. Even those without a great deal of mathematical background will benefit greatly from this text.
ROBERT P. HIRSCH, PHD, is on the faculty at the Foundation for Advanced Education in the Sciences as well as a Medical Research Consultant with over thirty years of experience. He received his doctorate in Biology at Kansas State University. He was formerly Professor at the George Washington University - Columbian College of Arts & Science where he helped to develop the Epidemiology and Biostatistics Programs.
Preface to First Edition xiii
Preface to Second Edition xv
About the Companion Website xvii
Part One Basic Concepts 1
1 Thinking About Chance 3
1.1 Properties of Probability 4
1.2 Combinations of Event 8
1.2.1 Intersections 8
1.2.2 Unions 13
1.3 Bayes’ Theorem 16
Chapter Summary 19
Exercises 20
2 Describing Distributions 25
2.1 Types of Data 26
2.2 Describing Distributions Graphically 27
2.2.1 Graphing Discrete Data 27
2.2.2 Graphing Continuous Data 30
2.3 Describing Distributions Mathematically 36
2.3.1 Parameter of Location 37
2.3.2 Parameter of Dispersion 41
2.4 Taking Chance into Account 48
2.4.1 Standard Normal Distribution 49
Chapter Summary 59
Exercises 62
3 Examining Samples 65
3.1 Nature of Samples 66
3.2 Estimation 67
3.2.1 Point Estimates 67
3.2.2 The Sampling Distribution 73
3.2.3 Interval Estimates 78
3.3 Hypothesis Testing 82
3.3.1 Relationship Between Interval Estimation and Hypothesis Testing 89
Chapter Summary 91
Exercises 93
Part Two Univariable Analyses 97
4 Univariable Analysis of A Continuous Dependent Variable 101
4.1 Student’s t-Distribution 103
4.2 Interval Estimation 106
4.3 Hypothesis Testing 109
Chapter Summary 113
Exercises 114
5 Univariable Analysis of An Ordinal Dependent Variable 119
5.1 Nonparametric Methods 120
5.2 Estimation 123
5.3 Wilcoxon Signed-Rank Test 124
5.4 Statistical Power of Nonparametric Tests 128
Chapter Summary 128
Exercises 129
6 Univariable Analysis of A Nominal Dependent Variable 133
6.1 Distribution of Nominal Data 134
6.2 Point Estimates 135
6.2.1 Probabilities 136
6.2.2 Rates 138
6.3 Sampling Distributions 142
6.3.1 Binomial Distribution 143
6.3.2 Poisson Distribution 146
6.4 Interval Estimation 149
6.5 Hypothesis Testing 151
Chapter Summary 155
Exercises 156
Part Three Bivariable Analyses 161
7 Bivariable Analysis of A Continuous Dependent Variable 163
7.1 Continuous Independent Variable 163
7.1.1 Regression Analysis 165
7.1.2 Correlation Analysis 189
7.2 Ordinal Independent Variable 207
7.3 Nominal Independent Variable 207
7.3.1 Estimating the Difference between the Groups 208
7.3.2 Taking Chance into Account 209
Chapter Summary 218
Exercises 221
8 Bivariable Analysis of An Ordinal Dependent Variable 227
8.1 Ordinal Independent Variable 228
8.2 Nominal Independent Variable 236
Chapter Summary 241
Exercises 243
9 Bivariable Analysis of A Nominal Dependent Variable 245
9.1 Continuous Independent Variable 246
9.1.1 Estimation 247
9.1.2 Hypothesis Testing 255
9.2 Nominal Independent Variable 258
9.2.1 Dependent Variable Not Affected by Time: Unpaired Design 259
9.2.2 Hypothesis Testing 266
9.2.3 Dependent Variable Not Affected by Time: Paired Design 277
9.2.4 Dependent Variable Affected by Time 283
Chapter Summary 286
Exercises 288
Part Four Multivariable Analyses 293
10 Multivariable Analysis of A Continuous Dependent Variable 295
10.1 Continuous Independent Variables 296
10.1.1 Multiple Regression Analysis 297
10.1.2 Multiple Correlation Analysis 317
10.2 Nominal Independent Variables 319
10.2.1 Analysis of Variance 320
10.2.2 Posterior Testing 331
10.3 Both Continuous and Nominal Independent Variables 340
10.3.1 Indicator (Dummy) Variables 341
10.3.2 Interaction Variables 343
10.3.3 General Linear Model 348
Chapter Summary 355
Exercises 358
11 Multivariable Analysis of An Ordinal Dependent Variable 367
11.1 Nonparametric Anova 369
11.2 Posterior Testing 375
Chapter Summary 380
Exercises 381
12 Multivariable Analysis of A Nominal Dependent Variable 385
12.1 Continuous and/or Nominal Independent Variables 387
12.1.1 Maximum Likelihood Estimation 387
12.1.2 Logistic Regression Analysis 389
12.1.3 Cox Regression Analysis 399
12.2 Nominal Independent Variables 401
12.2.1 Stratified Analysis 402
12.2.2 Relationship Between Stratified Analysis and Logistic Regression 410
12.2.3 Life Table Analysis 414
Chapter Summary 424
Exercises 427
13 Testing Assumptions 433
13.1 Continuous Dependent Variables 436
13.1.1 Assuming A Gaussian Distribution 437
13.1.2 Transforming Dependent Variables 477
13.1.3 Assuming Equal Variances 485
13.1.4 Assuming Additive Relationships 494
13.1.5 Dealing With Outliers 506
13.2 Nominal Dependent Variables 507
13.2.1 Assuming a Gaussian Distribution 507
13.2.2 Assuming Equal Variances 510
13.2.3 Assuming Additive Relationships 511
13.3 Independent Variables 511
Chapter Summary 513
Exercises 516
Appendix A: Flowcharts 521
Appendix B: Statistical Tables 527
Appendix C: Standard Distributions 597
Appendix D: Excel Primer 601
Appendix E: R Primer 605
Appendix F: Answers To Odd Exercises 609
Index 611
| Erscheinungsdatum | 06.04.2021 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 158 x 234 mm |
| Gewicht | 885 g |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Medizin / Pharmazie ► Medizinische Fachgebiete | |
| ISBN-10 | 1-119-72259-4 / 1119722594 |
| ISBN-13 | 978-1-119-72259-5 / 9781119722595 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
