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Biostatistics for the Biological and Health Sciences 2nd edition [Kietas viršelis]

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  • Formatas: Hardback, 720 pages, aukštis x plotis x storis: 282x221x30 mm, weight: 1614 g
  • Išleidimo metai: 13-Jun-2017
  • Leidėjas: Pearson
  • ISBN-10: 0134039017
  • ISBN-13: 9780134039015
Kitos knygos pagal šią temą:
Biostatistics for the Biological and Health Sciences 2nd editionDidesnis paveikslėlis
  • Formatas: Hardback, 720 pages, aukštis x plotis x storis: 282x221x30 mm, weight: 1614 g
  • Išleidimo metai: 13-Jun-2017
  • Leidėjas: Pearson
  • ISBN-10: 0134039017
  • ISBN-13: 9780134039015
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780134039015.html
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For courses in Introductory Statistics

 

Real-world applications connect statistical concepts to everyday life.

Biostatistics for the Biological and Health Sciences uses a variety of real-world applications to bring statistical theories and methods to life. Through these examples and a friendly writing style, the 2nd Edition ensures that you understand concepts and develop skills in critical thinking, technology, and communication. The result of collaboration between a biological sciences expert and the author of the #1 statistics book in the country, Biostatistics for the Biological and Health Sciences provides an excellent introduction to statistics for readers interested in the biological, life, medical, and health sciences.

 

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1 Introduction to Statistics
1(39)
1-1 Statistical and Critical Thinking
4(9)
1-2 Types of Data
13(11)
1-3 Collecting Sample Data
24(16)
2 Exploring Data with Tables and Graphs
40(35)
2-1 Frequency Distributions for Organizing and Summarizing Data
42(9)
2-2 Histograms
51(5)
2-3 Graphs That Enlighten and Graphs That Deceive
56(9)
2-4 Scatterplots, Correlation, and Regression
65(10)
3 Describing, Exploring, and Comparing Data
75(43)
3-1 Measures of Center
77(12)
3-2 Measures of Variation
89(13)
3-3 Measures of Relative Standing and Boxplots
102(16)
4 Probability
118(62)
4-1 Basic Concepts of Probability
120(11)
4-2 Addition Rule and Multiplication Rule
131(13)
4-3 Complements, Conditional Probability, and Bayes' Theorem
144(9)
4-4 Risks and Odds
153(9)
4-5 Rates of Mortality, Fertility, and Morbidity
162(5)
4-6 Counting
167(13)
5 Discrete Probability Distributions
180(36)
5-1 Probability Distributions
182(11)
5-2 Binomial Probability Distributions
193(13)
5-3 Poisson Probability Distributions
206(10)
6 Normal Probability Distributions
216(66)
6-1 The Standard Normal Distribution
218(13)
6-2 Real Applications of Normal Distributions
231(10)
6-3 Sampling Distributions and Estimators
241(11)
6-4 The Central Limit Theorem
252(9)
6-5 Assessing Normality
261(8)
6-6 Normal as Approximation to Binomial
269(13)
7 Estimating Parameters and Determining Sample Sizes
282(54)
7-1 Estimating a Population Proportion
284(15)
7-2 Estimating a Population Mean
299(16)
7-3 Estimating a Population Standard Deviation or Variance
315(9)
7-4 Bootstrapping: Using Technology for Estimates
324(12)
8 Hypothesis Testing
336(56)
8-1 Basics of Hypothesis Testing
338(16)
8-2 Testing a Claim About a Proportion
354(12)
8-3 Testing a Claim About a Mean
366(11)
8-4 Testing a Claim About a Standard Deviation or Variance
377(15)
9 Inferences from two Samples
392(50)
9-1 Two Proportions
394(12)
9-2 Two Means: Independent Samples
406(12)
9-3 Two Dependent Samples (Matched Pairs)
418(10)
9-4 Two Variances or Standard Deviations
428(14)
10 Correlation and Regression
442(60)
10-1 Correlation
444(18)
10-2 Regression
462(12)
10-3 Prediction Intervals and Variation
474(7)
10-4 Multiple Regression
481(8)
10-5 Dummy Variables and Logistic Regression
489(13)
11 Goodness-of-Fit and Contingency Tables
502(29)
11-1 Goodness-of-Fit
503(11)
11-2 Contingency Tables
514(17)
12 Analysis of Variance
531(29)
12-1 One-Way ANOVA
533(14)
12-2 Two-Way ANOVA
547(13)
13 Nonparametric Tests
560(43)
13-1 Basics of Nonparametric Tests
562(2)
13-2 Sign Test
564(11)
13-3 Wilcoxon Signed-Ranks Test for Matched Pairs
575(6)
13-4 Wilcoxon Rank-Sum Test for Two Independent Samples
581(5)
13-5 Kruskal-Wallis Test for Three or More Samples
586(6)
13-6 Rank Correlation
592(11)
14 Survival Analysis
603(22)
14-1 Life Tables
604(10)
14-2 Kaplan-Meier Survival Analysis
614(11)
Appendix A Tables 625(13)
Appendix B Data Sets 638(7)
Appendix C Websites and Bibliography of Books 645(1)
Appendix D Answers to Odd-Numbered Section Exercises: (and all Quick Quizzes, all Review Exercises, and all Cumulative Review Exercises) 646(37)
Credits 683(2)
Index 685
Marc Triola, MD, FACP is the Associate Dean for Educational Informatics at NYU School of Medicine, the founding director of the NYU Langone Medical Center Institute for Innovations in Medical Education (IIME), and an Associate Professor of Medicine. Dr. Triolas research experience and expertise focuses on the disruptive effects of the present revolution in education, driven by technological advances, big data, and learning analytics. Dr. Triola has worked to create a learning ecosystem that includes inter-connected computer-based e-learning tools and new ways to effectively integrate growing amounts of electronic data in educational research. 

 

Mario F. Triola is a Professor Emeritus of Mathematics at Dutchess Community College, where he has taught statistics for over 30 years. Marty designed the original Statdisk statistical software, and he has written several manuals and workbooks for technology supporting statistics education. He has been a speaker at many conferences and colleges. Martys consulting work includes the design of casino slot machines, the design of fishing rods, and he has worked with attorneys in determining probabilities in paternity lawsuits, analysing data in medical malpractice lawsuits, identifying salary inequities based on gender, and analysing disputed election results. 

 

Jason Roy, PhD, is Associate Professor of Biostatistics in the Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania. He received his PhD in Biostatistics in 2000 from the University of Michigan.  His statistical research interests are in the areas of causal inference, missing data, and prediction modeling. He is especially interested in the statistical challenges with analysing data from large healthcare databases. He collaborates in many different disease areas, including chronic kidney disease, cardiovascular disease, and liver diseases.