Practice of Statistics in the Life Sciences, Digital Update
Fourth Edition ©2022 Brigitte Baldi; David S. Moore Formats: Digital & Print
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Authors

Brigitte Baldi
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 postdoctoral 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
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 AB from Princeton and his PhD 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 collegelevel 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.
Table of Contents
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 leastsquares regression line
Facts about leastsquares regression
Outliers and influential observations
Working with logarithm transformations*
Cautions about correlation and regression
Association does not imply causation
Chapter 5 TwoWay Tables
Marginal distributions
Conditional distributions
Simpsons paradox
Chapter 6 Samples and Observational Studies
Observation versus experiment
Sampling
Sampling designs
Sample surveys
Cohorts and casecontrol 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 689599.7 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 Pvalue 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 onesample t confidence interval
The onesample t test
Matched pairs t procedures
Robustness of t procedures
Chapter 18 Comparing Two Means
Comparing two population means
Twosample t procedures
Robustness again
Avoid the pooled twosample t procedures*
Avoid inference about standard deviations*
Chapter 19 Inference about a Population Proportion
The sample proportion
Largesample 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
Twosample problems: proportions
The sampling distribution of a difference between proportions
Largesample 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 ChiSquare Test for Goodness of Fit
Hypotheses for goodness of fit
The chisquare test for goodness of fit
Interpreting chisquare results
Conditions for the chisquare test
The chisquare distributions
The chisquare test and the onesample z test*
Chapter 22 The ChiSquare Test for TwoWay Tables
Twoway tables
The problem of multiple comparisons
Expected counts in twoway tables
The chisquare test
Conditions for the chisquare test
Uses of the chisquare test
Using a table of critical values*
The chisquare test and the twosample 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 OneWay Analysis of Variance: Comparing Several Means
Comparing several means
The analysis of variance F test
The idea of analysis of variance
Conditions for ANOVA Fdistributions and degrees of freedom
The oneway ANOVA and the pooled twosample 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: Followup Tests and TwoWay ANOVA
Beyond oneway ANOVA
Follow up analysis: Tukey’s pairwise multiple comparisons
Follow up analysis: contrasts*
Twoway ANOVA: conditions, main effects, and interaction
Inference for twoway ANOVA
Some details of twoway ANOVA*
Chapter 27 Nonparametric Tests
Comparing two samples: the Wilcoxon rank sum test
Matched pairs: the Wilcoxon signed rank test
Comparing several samples: the KruskalWallis 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
Product Updates
New to the Digital Update
 Additional bookspecific homework questions including multiple choice and free response, each with hints, answerspecific feedback, and a fully worked solution
 Instructor Activity Guides provide instructors with easytoimplement active learning guides and resources to facilitate active learning and engagement.
 Learning Objectives tagged to all assessments within Achieve
 R markdown files
 Updated ebook to meet the most current content and accessibility standards to serve all students
 Updated data sets
 RStudio Video Technology Manuals
 EESEE case studies in a new, accessible and easytouse interface.
Authors

Brigitte Baldi
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 postdoctoral 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
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 AB from Princeton and his PhD 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 collegelevel 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.
Table of Contents
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 leastsquares regression line
Facts about leastsquares regression
Outliers and influential observations
Working with logarithm transformations*
Cautions about correlation and regression
Association does not imply causation
Chapter 5 TwoWay Tables
Marginal distributions
Conditional distributions
Simpsons paradox
Chapter 6 Samples and Observational Studies
Observation versus experiment
Sampling
Sampling designs
Sample surveys
Cohorts and casecontrol 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 689599.7 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 Pvalue 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 onesample t confidence interval
The onesample t test
Matched pairs t procedures
Robustness of t procedures
Chapter 18 Comparing Two Means
Comparing two population means
Twosample t procedures
Robustness again
Avoid the pooled twosample t procedures*
Avoid inference about standard deviations*
Chapter 19 Inference about a Population Proportion
The sample proportion
Largesample 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
Twosample problems: proportions
The sampling distribution of a difference between proportions
Largesample 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 ChiSquare Test for Goodness of Fit
Hypotheses for goodness of fit
The chisquare test for goodness of fit
Interpreting chisquare results
Conditions for the chisquare test
The chisquare distributions
The chisquare test and the onesample z test*
Chapter 22 The ChiSquare Test for TwoWay Tables
Twoway tables
The problem of multiple comparisons
Expected counts in twoway tables
The chisquare test
Conditions for the chisquare test
Uses of the chisquare test
Using a table of critical values*
The chisquare test and the twosample 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 OneWay Analysis of Variance: Comparing Several Means
Comparing several means
The analysis of variance F test
The idea of analysis of variance
Conditions for ANOVA Fdistributions and degrees of freedom
The oneway ANOVA and the pooled twosample 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: Followup Tests and TwoWay ANOVA
Beyond oneway ANOVA
Follow up analysis: Tukey’s pairwise multiple comparisons
Follow up analysis: contrasts*
Twoway ANOVA: conditions, main effects, and interaction
Inference for twoway ANOVA
Some details of twoway ANOVA*
Chapter 27 Nonparametric Tests
Comparing two samples: the Wilcoxon rank sum test
Matched pairs: the Wilcoxon signed rank test
Comparing several samples: the KruskalWallis 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
Product Updates
New to the Digital Update
 Additional bookspecific homework questions including multiple choice and free response, each with hints, answerspecific feedback, and a fully worked solution
 Instructor Activity Guides provide instructors with easytoimplement active learning guides and resources to facilitate active learning and engagement.
 Learning Objectives tagged to all assessments within Achieve
 R markdown files
 Updated ebook to meet the most current content and accessibility standards to serve all students
 Updated data sets
 RStudio Video Technology Manuals
 EESEE case studies in a new, accessible and easytouse interface.
Apply Essential Statistical Skills Across the Life Sciences
Now available with Macmillan’s groundbreaking online learning platform Achieve, The Practice of Statistics in the Life Sciences gives biology students an introduction to statistical practice all their own. It covers essential statistical topics with examples and exercises drawn from across the life sciences, including the fields of nursing, public health, and allied health.
Achieve for The Practice of Statistics in the Life Sciences integrates outcomebased learning objectives and a wealth of examples with assessment in an easytouse interface. Students are provided with rich digital resources that solidify conceptual understanding, as well as homework problems with hints, answerspecific feedback, and a fully worked solution, designed to teach as they assess.
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Brigitte Baldi; David S. Moore  Fourth Edition  ©2022  ISBN:9781319499464
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Practice of Statistics in the Life Sciences, Digital Update
Now available with Macmillan’s groundbreaking online learning platform Achieve, The Practice of Statistics in the Life Sciences gives biology students an introduction to statistical practice all their own. It covers essential statistical topics with examples and exercises drawn from across the life sciences, including the fields of nursing, public health, and allied health.
Achieve for The Practice of Statistics in the Life Sciences integrates outcomebased learning objectives and a wealth of examples with assessment in an easytouse interface. Students are provided with rich digital resources that solidify conceptual understanding, as well as homework problems with hints, answerspecific feedback, and a fully worked solution, designed to teach as they assess.