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# Practice of Statistics in the Life Sciences, Digital Update

## Fourth EditionBrigitte Baldi; David S. Moore

©2022ISBN:9781319416850

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*The Practice of Statistics in the Life Sciences *effectively teaches essential statistical concepts and fosters an understanding for how the principles apply to analysis of data across life science fields.

Learn essential statistics through the eyes of a biologist as *Practice of Statistics in the Life Sciences *provides you with examples and exercises pooled from across the life sciences. Emphasizing statistical thinking, real data, and what statisticians actually do, this book opens up statistics practice specifically for you. Resources and quizzing online in

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Learn More## 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

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