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Student Solutions Manual for Biostatistics for the Biological and Health Sciences 2nd edition [Minkštas viršelis]

  • Formatas: Paperback / softback, 144 pages, aukštis x plotis x storis: 280x215x10 mm, weight: 388 g
  • Išleidimo metai: 01-Jun-2017
  • Leidėjas: Pearson
  • ISBN-10: 0134039092
  • ISBN-13: 9780134039091
Kitos knygos pagal šią temą:
Student Solutions Manual for Biostatistics for the Biological and Health  Sciences 2nd edition
  • Formatas: Paperback / softback, 144 pages, aukštis x plotis x storis: 280x215x10 mm, weight: 388 g
  • Išleidimo metai: 01-Jun-2017
  • Leidėjas: Pearson
  • ISBN-10: 0134039092
  • ISBN-13: 9780134039091
Kitos knygos pagal šią temą:

Biostatistics for the Biological and Health Sciences uses a variety of real-world applications to bring statistical theories and methods to life for the biological, life, medical, and health sciences. This title ensures that you understand concepts and develop skills in critical thinking, technology, and communication.



For courses in Biostatistics.

This package includes MyLab 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 students understand concepts and develop skills in critical thinking, technology, and communication. The result of collaboration between two biological sciences experts 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 students studying the biological, life, medical, and health sciences.

 

Personalize learning with MyLab Statistics

MyLab™ Statistics is an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts.

1.      Introduction to Statistics
2.      Exploring Data with Tables and Graphs
3.      Describing, Exploring, and Comparing Data
4.      Probability
5.      Discrete Probability Distributions
6.      Normal Probability Distributions
7.      Estimating Parameters and Determining Sample Sizes
8.      Hypothesis Testing
9.      Inferences from Two Samples
10.   Correlation and Regression
11.   Goodness-of-Fit and Contingency Tables
12.   Analysis of Variance
13.   Nonparametric Tests
14.   Survival Analysis
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.