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Intuitive Biostatistics : A Nonmathematical Guide to Statistical Thinking,9780199730063
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Intuitive Biostatistics : A Nonmathematical Guide to Statistical Thinking

by
Edition:
2nd
ISBN13:

9780199730063

ISBN10:
0199730067
Format:
Paperback
Pub. Date:
1/20/2010
Publisher(s):
Oxford University Press, USA
List Price: $73.54
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Customer Reviews

The Meaning of Statistics In Excellent Textbook  April 4, 2011
by


I am very grateful that a professor of mine used this textbook for her class...I had flipped thru other stats textbooks prior to taking her class, and the contents of those other textbooks looked all too similar to the mathematics books that had tortured me for years.
Intuitive Biostatistics is actually enjoyable to read, and it usually teaches at a pace that is reasonable. Many of the examples were quite relevant to my area of study - Nutrition.
Wish more texts were like this one.






Intuitive Biostatistics : A Nonmathematical Guide to Statistical Thinking: 5 out of 5 stars based on 1 user reviews.

Summary

Thoroughly revised and updated, the second edition of Intuitive Biostatistics retains and refines the core perspectives of the previous edition: a focus on how to interpret statistical results rather than on how to analyze data, minimal use of equations, and a detailed review of assumptions and common mistakes. Intuitive Biostatistics, Completely Revised Second Edition, provides a clear introduction to statistics for undergraduate and graduate students and also serves as a statistics refresher for working scientists. NEW TO THIS EDITION: * Chapter 1 shows how our intuitions lead us to misinterpret data, thus explaining the need for statistical rigor. * Chapter 11 explains the lognormal distribution, an essential topic omitted from many other statistics books. * Chapter 21 contrasts testing for equivalence with testing for differences. * Chapters 22, 23, and 40 explore the pervasive problem of multiple comparisons. * Chapters 24 and 25 review testing for normality and outliers. * Chapter 35 shows how statistical hypothesis testing can be understood as comparing the fits of alternative models. * Chapters 37 and 38 provide a brief introduction to multiple, logistic, and proportional hazards regression. * Chapter 46 reviews one example in great depth, reviewing numerous statistical concepts and identifying common mistakes. * Chapter 47 includes 49 multi-part problems, with answers fully discussed in Chapter 48. * New "Q and A" sections throughout the book review key concepts.

Author Biography


Previously held faculty position in the Department of Pharmacology at the University of California San Diego. Currently, founder of GraphPad Software, Inc.

Table of Contents

Preface
Introducing Statistics
Statistics and Probability Are Not Intuitive
Why Statistics Can Be Hard to Learn
From Sample to Population
Confidence Intervals
Confidence Interval of a Proportion
Confidence Interval of Survival Data
Confidence Interval of Counted Data
Continuous Variables
Graphing Continuous Data
Types of Variables
Quantifying Scatter
The Gaussian Distribution
The Lognormal Distribution and Geometric Mean
Confidence Interval of a Mean
The Theory of Confidence Intervals
Error Bars
P Values And Significance
Introducing P Values
Statistical Significance and Hypothesis Testing
Relationship Between Confidence Intervals and Statistical Significance
Interpreting a Result That is Statistically Significant
Interpreting a Result That Is Not Statistically Significant
Statistical Power
Testing For Equivalence or Noninferiority
Challenges In Statistics
Multiple Comparisons Concepts
Multiple Comparison Traps
Gaussian or Not?
Outliers
Statistical Tests
Comparing Observed and Expected Distributions
Comparing Proportions: Prospective and Experimental Studies
Comparing Proportions: Case-Control Studies
Comparing Survival Curves
Comparing Two Means: Unpaired t test
Comparing Two Paired Groups
Correlation
Fitting Models To Data
Simple Linear Regression
Models
Comparing Models
Nonlinear Regression
Multiple, Logistic, and Proportional Hazards Regression
Multiple Regression Traps
The Rest Of Statistics
Analysis of Variance
Multiple Comparison Tests After ANOVA
Nonparametric Methods
Sensitivity Specificity and Receiver-Operator Characteristic Curves
Sample Size
Putting It All Together
Statistical Advice
Choosing a Statistical Test
Capstone Example
Review Problems
Answers to Review Problems
AppendiceS
Statistics with GraphPad
Statistics With Excel
Statistics R
Values of the t Distribution Needed to Compute CIs
A Review of Logarithms
References
Table of Contents provided by Publisher. All Rights Reserved.


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