Practice of Statistics in the Life Sciences, Digital Update, 4th Edition Brigitte Baldi, David Moore

Section I: Gathering and Analyzing Information

Chapter 1 Representing Distributions with Visuals

Individuals and variables

Identifying different types of variables

Categorical variables: visual representations

Quantitative variables: graphical displays

Analyzing graphical displays

Quantitative variables: dot plots

Timelines

Discussion: Data entry challenges

Chapter 2 Summarizing Quantitative Distributions with Statistics

Measures of center: median, average

Measures of spread: percentiles, deviation

Visual representations of numerical data

Detecting potential outliers*

Managing outliers

Structuring a statistical problem

Chapter 3 Relationships and Correlations

Explanatory vs. response variables

Correlation between two numerical variables: visual representation

Incorporating categorical data into visual representations

Calculating linear association: correlation coefficient

Chapter 4 Linear Regression

Calculating the regression line

Understanding regression data

Outliers and influential points

Utilizing logarithmic transformations*

Cautions regarding correlation and regression

Association does not imply causation

Chapter 5 Analysis of Two-Way Tables

Overall distributions

Conditional distributions

Interesting phenomena in statistics

Chapter 6 Sampling and Observational Studies

Observational vs. experimental studies

Sampling methods

Types of sampling methodologies

Survey techniques

Cohort studies and case-control studies

Chapter 7 Experimental Design

Planning experiments

Comparative experiments using randomization

Common experimental setups

Considerations for carrying out experiments

Ethical considerations in research

Discussion: Ethical issues in the Tuskegee syphilis study

Chapter 8 Gathering and Analyzing Information: Section I Review

Section I Overview

Comprehensive Review Exercises

Extensive Dataset Exercises

Online Data Resources

EESEE Case Studies

Section II: Probability and Inference

Chapter 9 Fundamental Probability Concepts

Understanding probability theory

Probability models and calculations

Basic principles of probability

Difference between discrete and continuous probability models

Random variables

Risk assessment and odds*

Chapter 10 Relationships and Conditional Probabilities*

Interrelations among multiple events

Conditional probability scenarios

Fundamental probability principles

Visual aids like tree diagrams

Bayesian theorem application

Discussion: Interpreting diagnostic test results with conditional probabilities

Chapter 11 The Gaussian Distributions

Normal distribution properties

68-95-99.7 rule insights

Standard normal distribution

Determining probabilities with normal distribution

Calculating percentiles

Utilizing normal distribution tables*

Normal quantile plots*

Chapter 12 Discrete Probability Distributions*

Binomial setup and associated distributions

Calculating binomial probabilities

Mean and deviation of binomial distributions

Usage of normal approximation in binomial scenarios

Poisson distributions

Poisson probabilities

Chapter 13 Assessment of Sampling Distributions

Parameters and statistics relationship

Statistical estimations and distribution patterns

Central limit theorem application in sampling distributions

Law of large numbers in sampling distribution*

Chapter 14 Introduction to Inferential Statistics

Statistical estimation approaches

Confidence level estimation and margin of error

Mean confidence intervals

Hypothesis testing, P-values, and statistical significance

Population mean testing methods

Deriving tests from confidence intervals

Chapter 15 Application of Inference Methods

Criteria for implementing inference techniques

Behavior patterns of confidence intervals

Behavior patterns of hypothesis testing

Discussion: Scientific method discourse

Research planning considerations: sample size determination

Chapter 16 From Probability to Inference: Section II Review

Section II Summary

Comprehensive Review Exercises

Advanced Topics (Optional Content)

Online Data Resources

EESEE Case Studies

Section III: Statistical Inference

Chapter 17 Inference about a Population Mean

Conditions for inference

T-distribution characteristics

Single-sample t-test confidence interval

Single-sample t-test procedures

Matched pairs t-test methods

T-test robustness assessment

Chapter 18 Comparison of Two Means

Comparing means of two populations

Two-sample t-test methodologies

Robustness evaluation

Avoiding pooled two-sample t-test procedures*

Avoiding standard deviation inferences*

Chapter 19 Inference about a Population Proportion

Sample proportion calculation

Large-sample proportion confidence intervals

Accurate proportion confidence intervals

Determining sample sizes*

Proportion hypothesis testing

Chapter 20 Comparison of Two Proportions

Comparing proportions in two-sample scenarios

Difference between proportions sampling distribution

Large-sample proportion comparison confidence intervals

Accurate proportion comparison confidence intervals

Proportion comparison hypothesis testing

Relative risk and odds ratio*

Discussion: Assessment and interpretation of health risks

Chapter 21 Chi-Square Test for Goodness of Fit

Goodness of fit hypotheses

Chi-square test application for goodness of fit

Interpreting chi-square test outcomes

Chi-square test conditions

Chi-square distribution implications

Chi-square test and single-sample z-test*

Chapter 22 Chi-Square Test for Analysis of Two-Way Tables

Utilizing two-way tables

Challenges of multiple comparisons

Expected values within two-way tables

Chi-square test approaches

Chi-square test conditions

Applications of chi-square tests

Utilizing critical value tables*

Chi-square test and two-sample z-test*

Chapter 23 Inference for Regression

Regression inference conditions

Parameter estimations

Testing linear relationship hypotheses

Testing lack of correlation*

Regression slope confidence intervals

Predictive inference considerations

Inference conditions validation

Chapter 24 One-Way Analysis of Variance: Multiple Mean Comparisons

Comparing multiple means

F-test analysis of variance assessment

Analysis of variance concepts

ANOVA conditions, F-distributions, and degrees of freedom

One-way ANOVA and pooled two-sample t-test*

ANOVA calculation specifics*

Chapter 25 Statistical Inference: Section III Review

Section III Summary

Review Exercises

Supplementary Exercises

EESEE Case Studies

Section IV: Adjunct Companion Sections

Chapter 26 Further Analysis of Variance: Post-Hoc Tests and Two-Way ANOVA

Advanced ANOVA frameworks

Post-hoc analysis methods: pairwise multiple comparisons with Tukey’s

Post-hoc analysis: contrasts*

Two-way ANOVA: conditions, principal effects, and interactions

Inference for two-way ANOVA

Specifics of two-way ANOVA*

Chapter 27 Nonparametric Tests

Comparing two samples: Wilcoxon rank sum test

Matched pairs comparisons: Wilcoxon signed rank test

Comparing multiple samples: Kruskal-Wallis test

Chapter 28 Multiple and Logistic Regression

Simultaneous regression models

Parameter estimations

Inference conditions validation

Multiple regression inference

Interaction effects

Multiple regression case study

Logistic regression models

Inference for logistic regression

Notes and Data Resources

Tables

Solutions to Selected Exercises

Recurring Data Sets Across Chapters

Index