Fundamentals of Biostatistics 8th Edition Rosner Solutions Manual

Product details:

• ISBN-10 ‏ : ‎ 130526892X
• ISBN-13 ‏ : ‎ 978-1305268920
• Author: Bernard Rosner

FUNDAMENTALS OF BIOSTATISTICS leads you through the methods, techniques, and computations of statistics necessary for success in the medical field. Every new concept is developed systematically through completely worked out examples from current medical research problems.

Table contents:

1. Chapter 1: General Overview
2. Chapter 2: Descriptive Statistics
3. 2.1 Introduction
4. 2.2 Measures of Location
5. 2.3 Some Properties of the Arithmetic Mean
6. 2.4 Measures of Spread
7. 2.5 Some Properties of the Variance and Standard Deviation
8. 2.6 The Coefficient of Variation
9. 2.7 Grouped Data
10. 2.8 Graphic Methods
11. 2.9 Case Study 1: Effects of Lead Exposureon Neurological and Psychological Function in Children
12. 2.10 Case Study 2: Effects of Tobacco Use on Bone-Mineral Density in Middle-Aged Women
13. 2.11 Obtaining Descriptive Statistics on the Computer
14. 2.12 Summary
15. Problems
16. Chapter 3: Probability
17. 3.1 Introduction
18. 3.2 Definition of Probability
19. 3.3 Some Useful Probabilistic Notation
20. 3.4 The Multiplication Law of Probability
21. 3.5 The Addition Law of Probability
22. 3.6 Conditional Probability
23. 3.7 Bayes’ Rule and Screening Tests
24. 3.8 Bayesian Inference
25. 3.9 ROC Curves
26. 3.10 Prevalence and Incidence
27. 3.11 Summary
28. Problems
29. Chapter 4: Discrete Probability Distributions
30. 4.1 Introduction
31. 4.2 Random Variables
32. 4.3 The Probability-Mass Function for a Discrete Random Variable
33. 4.4 The Expected Value of a Discrete Random Variable
34. 4.5 The Variance of a Discrete Random Variable
35. 4.6 The Cumulative-Distribution Function of a Discrete Random Variable
36. 4.7 Permutations and Combinations
37. 4.8 The Binomial Distribution
38. 4.9 Expected Value and Variance of the Binomial Distribution
39. 4.10 The Poisson Distribution
40. 4.11 Computation of Poisson Probabilities
41. 4.12 Expected Value and Variance of the Poisson Distribution
42. 4.13 Poisson Approximation to the Binomial Distribution
43. 4.14 Summary
44. Problems
45. Chapter 5: Continuous Probability Distributions
46. 5.1 Introduction
47. 5.2 General Concepts
48. 5.3 The Normal Distribution
49. 5.4 Properties of the Standard Normal Distribution
50. 5.5 Conversion from an N (μ,σ 2) Distribution to an N(0,1) Distribution
51. 5.6 Linear Combinations of Random Variables
52. 5.7 Normal Approximation to the Binomial Distribution
53. 5.8 Normal Approximation to the Poisson Distribution
54. 5.9 Summary
55. Problems
56. Chapter 6: Estimation
57. 6.1 Introduction
58. 6.2 The Relationship Between Population and Sample
59. 6.3 Random-Number Tables
60. 6.4 Randomized Clinical Trials
61. 6.5 Estimation of the Mean of a Distribution
62. 6.6 Case Study: Effects of Tobacco Use on Bone-Mineral Density (BMD) in Middle-Aged Women
63. 6.7 Estimation of the Variance of a Distribution
64. 6.8 Estimation for the Binomial Distribution
65. 6.9 Estimation for the Poisson Distribution
66. 6.10 One-Sided Confidence Intervals
67. 6.11 The Bootstrap
68. 6.12 Summary
69. Problems
70. Chapter 7: Hypothesis Testing: One-Sample Inference
71. 7.1 Introduction
72. 7.2 General Concepts
73. 7.3 One-Sample Test for the Mean of a Normal Distribution: One-Sided Alternatives
74. 7.4 One-Sample Test for the Mean of a Normal Distribution: Two-Sided Alternatives
75. 7.5 The Relationship Between Hypothes is Testing and Confidence Intervals
76. 7.6 The Power of a Test
77. 7.7 Sample-Size Determination
78. 7.8 One Sample X Square Test for the Variance of a Normal Distribution
79. 7.9 One-Sample Inference forr the Binomial Distribution
80. 7.10 One-Sample Inference for the Poisson Distribution
81. 7.11 Case Study: Effects of Tobacco Use on Bone-Mineral Density in Middle-Aged Women
82. 7.12 Derivation of Selected Formulas
83. 7.13 Summary
84. Problems
85. Chapter 8: Hypothesis Testing: Two-Sample Inference
86. 8.1 Introduction
87. 8.2 The Paired t Test
88. 8.3 Interval Estimation for the Comparison of Means from Two Paired Samples
89. 8.4 Two-Sample t Test for Independent Samples with Equal Variances
90. 8.5 Interval Estimation for the Comparison of Means from Two Independent Samples (Equal Variance Cas
91. 8.6 Testing for the Equality of Two Variances
92. 8.7 Two-Sample t Test for Independent Samples with Unequal Variances
93. 8.8 Case Study: Effects of Lead Exposure on Neurologic and Psychological Function in Children
94. 8.9 Estimation of Sample Size and Power for Comparing Two Means
95. 8.10 The Treatment of Outliers
96. 8.11 Derivation of Equation 8.13 (p. 290)
97. 8.12 Summary
98. Problems
99. Chapter 9: Nonparametric Methods
100. 9.1 Introduction
101. 9.2 The Sign Test
102. 9.3 The Wilcoxon Signed-Rank Test
103. 9.4 The Wilcoxon Rank-Sum Test
104. 9.5 Case Study: Effects of Lead Exposure on Neurological and Psychological Function in Children
105. 9.6 Permutation Tests
106. 9.7 Summary
107. Problems
108. Chapter 10: Hypothesis Testing: Categorical Data
109. 10.1 Introduction
110. 10.2 Two-Sample Test for Binomial Proportions
111. 10.3 Fisher’s Exact Test
112. 10.4 Two-Sample Test for Binomial Proportions for Matched-Pair Data (McNemar’s Test)
113. 10.5 Estimation of Sample Size and Power for Comparing Two Binomial Proportions
114. 10.6 R x C Contingency Tables
115. 10.7 Chi-Square Goodness-of-Fit Test
116. 10.8 The Kappa Statistic
117. 10.9 Derivation of Selected Formulas
118. 10.10 Summary
119. Problems
120. Chapter 11: Regression and Correlation Methods
121. 11.1 Introduction
122. 11.2 General Concepts
123. 11.3 Fitting Regression Lines – The Method of Least Squares
124. 11.4 Inferences About Parameters from Regression Lines
125. 11.5 Interval Estimation for Linear Regression
126. 11.6 Assessing the Goodness of Fit of Regression Lines
127. 11.7 The Correlation Coefficient
128. 11.8 Statistical Inference for Correlation Coefficients
129. 11.9 Multiple Regression
130. 11.10 Case Study: Effects of Lead Exposure on Neurologic and Psychological Function in Children
131. 11.11 Partial and Multiple Correlation
132. 11.12 Rank Correlation
133. 11.13 Interval Estimation for Rank-Correlation Coefficients
134. 11.14 Derivation of Equation 11.26 (page 499)
135. 11.15 Summary
136. Problems
137. Chapter 12: Multisample Inference
138. 12.1 Introduction to the One-Way Analysis of Variance
139. 12.2 One-Way ANOVA – Fixed-Effects Model
140. 12.3 Hypothesis Testing in One-Way ANOVA – Fixed-Effects Model
141. 12.4 Comparisons of Specific Groups in One-Way ANOVA
142. 12.5 Case Study: Effects of Lead Exposure on Neurologic and Psychological Function in Children
143. 12.6 Two-Way ANOVA
144. 12.7 The Kruskal-Wallis Test
145. 12.8 One-Way ANOVA – The Random-Effects Model
146. 12.9 The Intraclass Correlation Coefficient
147. 12.10 Mixed Models
148. 12.11 Derivation of Equation 12.30 (p.605)
149. 12.12 Summary
150. Problems
151. Chapter 13: Design and Analysis Techniques for Epidemiologic Studies
152. 13.1 Introduction
153. 13.2 Study Design
154. 13.3 Measures of Effect for Categorical Data
155. 13.4 Attributable Risk
156. 13.5 Confounding and Standardization
157. 13.6 Methods of Inference for Stratified Categorical Data – The Mantel-Haenszel Test
158. 13.7 Multiple Logistic Regression
159. 13.8 Extensions to Logistic Regression
160. 13.9 Sample Size Estimation for Logistic Regression
161. 13.10 Meta-Analysis
162. 13.11 Equivalence Studies
163. 13.12 The Cross-Over Design
164. 13.13 Clustered Binary Data
165. 13.14 Longitudinal Data Analysis
166. 13.15 Measurement-Error Methods
167. 13.16 Missing Data
168. 13.17 Derivation of Selected Formulas
169. 13.18 Summary
170. Problems
171. Chapter 14: Hypothesis Testing: Person-Time Data
172. 14.1 Measure of Effect for Person-Time Data
173. 14.2 One-Sample Inference for Incidence-Rate Data
174. 14.3 Two-Sample Inference for Incidence-Rate Data
175. 14.4 Power and Sample-Size Estimation for Person-Time Data
176. 14.5 Inference for Stratified Person-Time Data
177. 14.6 Power and Sample-Size Estimation for Stratified Person-Time Data
178. 14.7 Testing for Trend: Incidence-Rate Data
179. 14.8 Introduction to Survival Analysis
180. 14.9 Estimation of Survival Curves: The Kaplan-Meier Estimator
181. 14.10 The Log-Rank Test
182. 14.11 The Proportional-Hazards Model
183. 14.12 Power and Sample-Size Estimation under the Proportional-Hazards Model
184. 14.13 Parametric Survival Analysis
185. 14.14 Parametric Regression Models for Survival Data
186. 14.15 Derivation of Selected Formulas
187. 14.16 Summary
188. Problems
189. Appendix|Tables
190. Answers to Selected Problems
191. Flowchart: Methods of Statistical Inference
192. Index of Statistical Software
193. Index
194. Index of Data Sets

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