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E-raamat: Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel (R) and R, Second Edition 2nd Edition [Wiley Online]

  • Formaat: 640 pages
  • Ilmumisaeg: 05-Apr-2021
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119722683
  • ISBN-13: 9781119722687
Teised raamatud teemal:
  • Wiley Online
  • Hind: 144,80 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Formaat: 640 pages
  • Ilmumisaeg: 05-Apr-2021
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119722683
  • ISBN-13: 9781119722687
Teised raamatud teemal:
"Focusing on a basic understanding of the methods and analyses in health research, Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel, 2e provides statistical concepts for interpreting results using Excel. The bookemphasizes the application of methods and presents the most common methodological procedures in health research, which includes multiple regression, ANOVA, ANCOVA, logistic regression, Cox regression, stratified analysis, life table analysis, and nonparametric parallels. Some updates for this new edition: The flowcharts from the first edition will be expanded to include indicators of the assumptions of each procedure. This will be added to facilitate selection of a statistical approach to analyze a particular set of data. The existing twelve chapters describing statistical principals and statistical methods will be maintained. They have been proven to provide students with a clear and useful approach to the subject in use as a textbook and workbook in a graduate statistics course. An additional chapter will be added to the book that discusses the assumptions of statistical procedures. This chapter will describe each assumption, tell how to determine if the assumption is appropriate for a particular set of data, and provide solutions to situations in which the assumptions are not me by the data set. This chapter will provide students and researchers with the information they need to select an appropriate method of analysis and to apply that method to a set of data. The workbook will include a corresponding chapter that will provide students with practice identifying assumptions, testing for their satisfaction, and applying solutions to violation of assumptions. R will also be included to broaden the appeal and audience for the book"--

Focusing on a basic understanding of the methods and analyses in health research, Introduction to Biostatistical Applications in Health Research with Microsoft® Office Excel®, 2e provides statistical concepts for interpreting results using Excel. The book emphasizes the application of methods and presents the most common methodological procedures in health research, which includes multiple regression, ANOVA, ANCOVA, logistic regression, Cox regression, stratified analysis, life table analysis, and nonparametric parallels.

Some updates for this new edition:
The flowcharts from the first edition will be expanded to include indicators of the assumptions of each procedure.  This will be added to facilitate selection of a statistical approach to analyze a particular set of data. The existing twelve chapters describing statistical principals and statistical methods will be maintained. They have been proven to provide students with a clear and useful approach to the subject in use as a textbook and workbook in a graduate statistics course.

An additional chapter will be added to the book that discusses the assumptions of statistical procedures. This chapter will describe each assumption, tell how to determine if the assumption is appropriate for a particular set of data, and provide solutions to situations in which the assumptions are not me by the data set. This chapter will provide students and researchers with the information they need to select an appropriate method of analysis and to apply that method to a set of data.

The workbook will include a corresponding chapter that will provide students with practice identifying assumptions, testing for their satisfaction, and applying solutions to violation of assumptions.

R will also be included to broaden the appeal and audience for the book.
Preface To First Edition xiii
Preface To Second Edition xv
About The Companion Website xvii
PART ONE BASIC CONCEPTS
1(96)
1 Thinking About Chance
3(22)
1.1 Properties of Probability
4(4)
1.2 Combinations of Event
8(8)
1.2.1 Intersections
8(5)
1.2.2 Unions
13(3)
1.3 Bayes' Theorem
16(9)
Chapter Summary
19(1)
Exercises
20(5)
2 Describing Distributions
25(40)
2.1 Types of Data
26(1)
2.2 Describing Distributions Graphically
27(9)
2.2.1 Graphing Discrete Data
27(3)
2.2.2 Graphing Continuous Data
30(6)
2.3 Describing Distributions Mathematically
36(12)
2.3.1 Parameter of Location
37(4)
2.3.2 Parameter of Dispersion
41(7)
2.4 Taking Chance into Account
48(17)
2.4.1 Standard Normal Distribution
49(10)
Chapter Summary
59(3)
Exercises
62(3)
3 Examining Samples
65(32)
3.1 Nature of Samples
66(1)
3.2 Estimation
67(15)
3.2.1 Point Estimates
67(6)
3.2.2 The Sampling Distribution
73(5)
3.2.3 Interval Estimates
78(4)
3.3 Hypothesis Testing
82(15)
3.3.1 Relationship Between Interval Estimation and Hypothesis Testing
89(2)
Chapter Summary
91(2)
Exercises
93(4)
PART TWO UNIVARIABLE ANALYSES
97(64)
4 Univariable Analysis Of A Continuous Dependent Variable
101(18)
4.1 Student's r-Distribution
103(3)
4.2 Interval Estimation
106(3)
4.3 Hypothesis Testing
109(10)
Chapter Summary
113(1)
Exercises
114(5)
5 Univariable Analysis Of An Ordinal Dependent Variable
119(14)
5.1 Nonparametric Methods
120(3)
5.2 Estimation
123(1)
5.3 Wilcoxon Signed-Rank Test
124(4)
5.4 Statistical Power of Nonparametric Tests
128(5)
Chapter Summary
128(1)
Exercises
129(4)
6 Univariable Analysis Of A Nominal Dependent Variable
133(28)
6.1 Distribution of Nominal Data
134(1)
6.2 Point Estimates
135(7)
6.2.1 Probabilities
136(2)
6.2.2 Rates
138(4)
6.3 Sampling Distributions
142(7)
6.3.1 Binomial Distribution
143(3)
6.3.2 Poisson Distribution
146(3)
6.4 Interval Estimation
149(2)
6.5 Hypothesis Testing
151(10)
Chapter Summary
155(1)
Exercises
156(5)
PART THREE BIVARIABLE ANALYSES
161(132)
7 Bivariable Analysis Of A Continuous Dependent Variable
163(64)
7.1 Continuous Independent Variable
163(44)
7.1.1 Regression Analysis
165(24)
7.1.2 Correlation Analysis
189(18)
7.2 Ordinal Independent Variable
207(1)
7.3 Nominal Independent Variable
207(20)
7.3.1 Estimating the Difference between the Groups
208(1)
7.3.2 Taking Chance into Account
209(9)
Chapter Summary
218(3)
Exercises
221(6)
8 Bivariable Analysis Of An Ordinal Dependent Variable
227(18)
8.1 Ordinal Independent Variable
228(8)
8.2 Nominal Independent Variable
236(9)
Chapter Summary
241(2)
Exercises
243(2)
9 Bivariable Analysis Of A Nominal Dependent Variable
245(48)
9.1 Continuous Independent Variable
246(12)
9.1.1 Estimation
247(8)
9.1.2 Hypothesis Testing
255(3)
9.2 Nominal Independent Variable
258(35)
9.2.1 Dependent Variable Not Affected by Time: Unpaired Design
259(7)
9.2.2 Hypothesis Testing
266(11)
9.2.3 Dependent Variable Not Affected by Time: Paired Design
277(6)
9.2.4 Dependent Variable Affected by Time
283(3)
Chapter Summary
286(2)
Exercises
288(5)
PART FOUR MULTIVARIABLE ANALYSES
293(228)
10 Multivariable Analysis Of A Continuous Dependent Variable
295(72)
10.1 Continuous Independent Variables
296(23)
10.1.1 Multiple Regression Analysis
297(20)
10.1.2 Multiple Correlation Analysis
317(2)
10.2 Nominal Independent Variables
319(21)
10.2.1 Analysis of Variance
320(11)
10.2.2 Posterior Testing
331(9)
10.3 Both Continuous and Nominal Independent Variables
340(27)
10.3.1 Indicator (Dummy) Variables
341(2)
10.3.2 Interaction Variables
343(5)
10.3.3 General Linear Model
348(7)
Chapter Summary
355(3)
Exercises
358(9)
11 Multivariable Analysis Of An Ordinal Dependent Variable
367(18)
11.1 Nonparametric Anova
369(6)
11.2 Posterior Testing
375(10)
Chapter Summary
380(1)
Exercises
381(4)
12 Multivariable Analysis Of A Nominal Dependent Variable
385(48)
12.1 Continuous and/or Nominal Independent Variables
387(14)
12.1.1 Maximum Likelihood Estimation
387(2)
12.1.2 Logistic Regression Analysis
389(10)
12.1.3 Cox Regression Analysis
399(2)
12.2 Nominal Independent Variables
401(32)
12.2.1 Stratified Analysis
402(8)
12.2.2 Relationship Between Stratified Analysis and Logistic Regression
410(4)
12.2.3 Life Table Analysis
414(10)
Chapter Summary
424(3)
Exercises
427(6)
13 Testing Assumptions
433(88)
13.1 Continuous Dependent Variables
436(71)
13.1.1 Assuming A Gaussian Distribution
437(40)
13.1.2 Transforming Dependent Variables
477(8)
13.1.3 Assuming Equal Variances
485(9)
13.1.4 Assuming Additive Relationships
494(12)
13.1.5 Dealing With Outliers
506(1)
13.2 Nominal Dependent Variables
507(4)
13.2.1 Assuming a Gaussian Distribution
507(3)
13.2.2 Assuming Equal Variances
510(1)
13.2.3 Assuming Additive Relationships
511(1)
13.3 Independent Variables
511(10)
Chapter Summary
513(3)
Exercises
516(5)
Appendix A Flowcharts 521(6)
Appendix B Statistical Tables 527(70)
Appendix C Standard Distributions 597(4)
Appendix D Excel Primer 601(4)
Appendix E R Primer 605(4)
Appendix F Answers To Odd Exercises 609(2)
Index 611
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.