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# Practice of Statistics in the Life Sciences, Digital Update

## Fourth Edition| ©2022 Brigitte Baldi; David S. Moore

Now available with Macmillan’s ground-breaking 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 acr...

Now available with Macmillan’s ground-breaking 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 outcome-based learning objectives and a wealth of examples with assessment in an easy-to-use interface. Students are provided with rich digital resources that solidify conceptual understanding, as well as homework problems with hints, answer-specific feedback, and a fully worked solution, designed to teach as they assess.

Access all your course tools in one place!

ISBN:9781319416850

Take notes, add highlights, and download our mobile-friendly e-books.

ISBN:9781319416867

Save money with our loose, 3-hole punched pages.

ISBN:9781319244422

Read and study old-school with our bound texts.

ISBN:9781319492311

This package includes Achieve and Loose-Leaf.

**GO DIGITAL WITH ACHIEVE**

**Apply Essential Statistical Skills Across the Life Sciences**

Now available with Macmillan’s ground-breaking 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 outcome-based learning objectives and a wealth of examples with assessment in an easy-to-use interface. Students are provided with rich digital resources that solidify conceptual understanding, as well as homework problems with hints, answer-specific feedback, and a fully worked solution, designed to teach as they assess.

Features

**Achieve Online Homework**

**Over 3,000 homework questions**of varying difficulty, Bloom’s level, and question type. Every homework question includes a hint, answer-specific feedback, and a fully worked solution.

Question types in Achieve include:

○ Multiple choice

○ Ranking

○ Sorting

○ Numeric entry

○ Multi-part questions

○ Questions with algorithmically regenerating values

○ Embedded data sets**LearningCurve adaptive quizzing**puts the concept of "testing to learn" into action, motivating students to engage with the text's content to identify areas of proficiency. Easy-to-use reporting tools help teachers pinpoint areas to focus on in class.- An accessible,
**interactive e-book**allows students to highlight and take notes, print select pages, and have the text read aloud to them. - Over 140
**StatTutors**--multimedia tutorials that explore important concepts and procedures in a presentation that combines video, audio, and interactive features--are assignable, gradeable, and organized by chapter. , are visual interactives that allow students to manipulate data and variables in calculations and see the results graphically. Applets also contain assessment questions to test students’ comprehension.__UPDATED__Applet Activities,__now powered by Desmos__**Instructor Activity Guides**present a range of active learning activities and assignments designed to engage students in in-person or online classes.**Videos**provide additional exposure to key concepts and examples. Videos are narrated and close-captioned. Video types include:

○ Whiteboard-style problem-solving videos

○ StatTutor video lessons

○ Animated lectures and documentary-style videos that illustrate real world scenarios involving statistics(Electronic Encyclopedia of Statistical Examples and Exercises) Case Studies, developed by the Ohio State University Statistics Department, teach students to apply their statistical skills by exploring actual case studies using real data.__UPDATED__EESEE**Video Technology Manuals**are brief instructional videos that provide basic introductions for working with CrunchIt!, Excel, SPSS, TI-83/84 calculators, JMP, Minitab, R, RCmdr

**Statistical software options**

**CrunchIt!,**Macmillan’s proprietary online statistical software powered by R, handles every computation and graphing function an introductory statistics student needs. CrunchIt! is preloaded with data sets, and it allows editing and importing additional data.- Students also receive access to
**JMP**Student Edition (developed by SAS). With the student edition of JMP, students handle large data, visualizations, and analysis for which the professional version is renowned. Additionally, text-specific data sets are included for download. - For other statistical software, Achieve includes data sets, including those for:

○ Excel

○ Minitab

○ R&RC

○ SPSS

○ TI Calculators

○ Mac-text & PC-text

○ CSV file export

New to This Edition

**New to the Digital Update**Achieve

- Additional book-specific homework questions including multiple choice and free response, each with hints, answer-specific feedback, and a fully worked solution
**Instructor Activity Guides**provide instructors with easy-to-implement active learning guides and resources to facilitate active learning and engagement.**Learning Objectives**tagged to all assessments within Achieve**R markdown files**- Updated e-book 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 easy-to-use interface.

**
Practice of Statistics in the Life Sciences, Digital Update**

Fourth Edition| ©2022

Brigitte Baldi; David S. Moore

# Digital Options

## Achieve

Achieve is a comprehensive set of interconnected teaching and assessment tools that incorporate the most effective elements from Macmillan Learning's market leading solutions in a single, easy-to-use platform.

## E-book

Read online (or offline) with all the highlighting and notetaking tools you need to be successful in this course.

**Practice of Statistics in the Life Sciences, Digital Update**

Fourth Edition| 2022

Brigitte Baldi; David S. Moore

## 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 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

Simpson's 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

Bayes's 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

**Practice of Statistics in the Life Sciences, Digital Update**

Fourth Edition| 2022

Brigitte Baldi; David S. Moore

## 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 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

**Practice of Statistics in the Life Sciences, Digital Update**

Fourth Edition| 2022

Brigitte Baldi; David S. Moore