- ISBN: 9781319013370 | 1319013376
- Cover: Hardcover
- Copyright: 12/22/2017
See statisics through the eyes of a biologist as Practice of Statistics in the Life Sciences utilizes examples and exercises curated from across the life sciences in order to connect you to the material.
Brigitte Baldi is a graduate of France’s Ecole Normale Supérieure in Paris. In her academic studies, she combined a love of math and quantitative analysis with wide interests in the life sciences. She studied math and biology in a double major and obtained a Masters in molecular biology and biochemistry and a Masters in cognitive sciences. She earned her Ph.D. in neuroscience from the Université Paris VI studying multisensory integration in the brain and used computer simulations to study patterns of brain reorganization after lesion as a post-doctoral fellow at the California Institute of Technology. She then worked as a management consultant advising corporations before returning to academia to teach statistics.
Dr. Baldi is currently a lecturer in the Department of Statistics at the University of California, Irvine. She is actively involved in statistical education. She was a local and later national advisor in the development of the statistics telecourse Statistically Speaking, replacing David Moore’s earlier telecourse Against All Odds. She developed UCI’s first online statistics courses and is interested in ways to integrate new technologies in the classroom to enhance participation and learning. She is currently serving as an elected member to the Executive Committee At Large of the section on Statistical Education of the American Statistical Association.
David S. Moore is Shanti S. Gupta Distinguished Professor of Statistics, Emeritus, at Purdue University and was 1998 president of the American Statistical Association. He received his A.B. from Princeton and his Ph.D. from Cornell, both in mathematics. He has written many research papers in statistical theory and served on the editorial boards of several major journals. Professor Moore is an elected fellow of the American Statistical Association and of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. He has served as program director for statistics and probability at the National Science Foundation. In recent years, Professor Moore has devoted his attention to the teaching of statistics. He was the content developer for the Annenberg/Corporation for Public Broadcasting college-level telecourse Against All Odds: Inside Statistics and for the series of video modules Statistics: Decisions through Data, intended to aid the teaching of statistics in schools. He is the author of influential articles on statistics education and of several leading texts. Professor Moore has served as president of the International Association for Statistical Education and has received the Mathematical Association of America’s national award for distinguished college or university teaching of mathematics.
Part I: Collecting and Exploring Data
Chapter 1 Picturing Distributions with Graphs
Individuals and variables
Identifying categorical and quantitative variables
Categorical variables: pie charts and bar graphs
Quantitative variables: histograms
Interpreting histograms
Quantitative variables: dotplots
Time plots
Discussion: (Mis)adventures in data entry
Chapter 2 Describing Quantitative Distributions with Numbers
Measures of center: median, mean
Measures of spread: percentiles, standard deviation
Graphical displays of numerical summaries
Spotting suspected outliers*
Discussion: Dealing with outliers
Organizing a statistical problem
Chapter 3 Scatterplots and Correlation
Explanatory and response variables
Relationship between two quantitative variables: scatterplots
Adding categorical variables to scatterplots
Measuring linear association: correlation
Chapter 4 Regression
The least-squares regression line
Facts about least-squares regression
Outliers and influential observations
Working with logarithm transformations*
Cautions about correlation and regression
Association does not imply causation
Chapter 5 Two-Way Tables
Marginal distributions
Conditional distributions
Simpsons paradox
Chapter 6 Samples and Observational Studies
Observation versus experiment
Sampling
Sampling designs
Sample surveys
Cohorts and case-control studies
Chapter 7 Designing Experiments
Designing experiments
Randomized comparative experiments
Common experimental designs
Cautions about experimentation
Ethics in experimentation
Discussion: The Tuskegee syphilis study
Chapter 8 Collecting and Exploring Data: Part I Review
Part I Summary
Comprehensive Review Exercises
Large Dataset Exercises
Online Data Sources
EESEE Case Studies
Part II: From Chance to Inference
Chapter 9 Essential Probability Rules
The idea of probability
Probability models
Probability rules
Discrete versus continuous probability models
Random variables
Risk and odds*
Chapter 10 Independence and Conditional Probabilities*
Relationships among several events
Conditional probability
General probability rules
Tree diagrams
Bayess theorem
Discussion: Making sense of conditional probabilities in diagnostic tests
Chapter 11 The Normal Distributions
Normal distributions
The 68-95-99.7 rule
The standard Normal distribution
Finding Normal probabilities
Finding percentiles
Using the standard Normal table*
Normal quantile plots*
Chapter 12 Discrete Probability Distributions*
The binomial setting and binomial distributions
Binomial probabilities
Binomial mean and standard deviation
The Normal approximation to binomial distributions
The Poisson distributions
Poisson probabilities
Chapter 13 Sampling Distributions
Parameters and statistics
Statistical estimation and sampling distributions
The sampling distribution of
The central limit theorem
The sampling distribution of
The law of large numbers*
Chapter 14 Introduction to Inference
Statistical estimation
Margin of error and confidence level
Confidence intervals for the mean
Hypothesis testing
P-value and statistical significance
Tests for a population mean
Tests from confidence intervals
Chapter 15 Inference in Practice
Conditions for inference in practice
How confidence intervals behave
How hypothesis tests behave
Discussion: The scientific approach
Planning studies: selecting an appropriate sample size
Chapter 16 From Chance to Inference: Part II Review
Part II Summary
Comprehensive Review Exercises
Advanced Topics (Optional Material)
Online Data Sources
EESEE Case Studies
Part III: Statistical Inference
Chapter 17 Inference about a Population Mean
Conditions for inference
The t distributions
The one-sample t confidence interval
The one-sample t test
Matched pairs t procedures
Robustness of t procedures
Chapter 18 Comparing Two Means
Comparing two population means
Two-sample t procedures
Robustness again
Avoid the pooled two-sample t procedures*
Avoid inference about standard deviations*
Chapter 19 Inference about a Population Proportion
The sample proportion
Large-sample confidence intervals for a proportion
Accurate confidence intervals for a proportion
Choosing the sample size*
Hypothesis tests for a proportion
Chapter 20 Comparing Two Proportions
Two-sample problems: proportions
The sampling distribution of a difference between proportions
Large-sample confidence intervals for comparing proportions
Accurate confidence intervals for comparing proportions
Hypothesis tests for comparing proportions
Relative risk and odds ratio*
Discussion: Assessing and understanding health risks
Chapter 21 The Chi-Square Test for Goodness of Fit
Hypotheses for goodness of fit
The chi-square test for goodness of fit
Interpreting chi-square results
Conditions for the chi-square test
The chi-square distributions
The chi-square test and the one-sample z test*
Chapter 22 The Chi-Square Test for Two-Way Tables
Two-way tables
The problem of multiple comparisons
Expected counts in two-way tables
The chi-square test
Conditions for the chi-square test
Uses of the chi-square test
Using a table of critical values*
The chi-square test and the two-sample z test*
Chapter 23 Inference for Regression
Conditions for regression inference
Estimating the parameters
Testing the hypothesis of no linear relationship
Testing lack of correlation*
Confidence intervals for the regression slope
Inference about prediction
Checking the conditions for inference
Chapter 24 One-Way Analysis of Variance: Comparing Several Means
Comparing several means
The analysis of variance F test
The idea of analysis of variance
Conditions for ANOVA
F distributions and degrees of freedom
The one-way ANOVA and the pooled two-sample t test*
Details of ANOVA calculations*
Chapter 25 Statistical Inference: Part III Review
Part III Summary
Review Exercises
Supplementary Exercises
EESEE Case Studies
Part IV: Optional Companion Chapters
Chapter 26 More about Analysis of Variance: Follow-up Tests and Two-Way ANOVA
Beyond one-way ANOVA
Follow up analysis: Tukey’s pairwise multiple comparisons
Follow up analysis: contrasts*
Two-way ANOVA: conditions, main effects, and interaction
Inference for two-way ANOVA
Some details of two-way ANOVA*
Chapter 27 Nonparametric Tests
Comparing two samples: the Wilcoxon rank sum test
Matched pairs: the Wilcoxon signed rank test
Comparing several samples: the Kruskal-Wallis test
Chapter 28 Multiple and Logistic Regression
Parallel regression lines
Estimating parameters
Conditions for inference
Inference for multiple regression
Interaction
A case study for multiple regression
Logistic regression
Inference for logistic regression
Notes and Data Sources
Tables
Answers to Selected Exercises
Some Data Sets Recurring Across Chapters
Index
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