An Introduction to Statistical Methods and Data Analysis

, by Ott, R. Lyman; Longnecker, Micheal T.

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ISBN: 9780534251222 | 0534251226
Cover: Hardcover
Copyright: 12/20/2000

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Statistics is a thought process. In this comprehensive introduction to statistical methods and data analysis, the process is presented utilizing a four-step approach: 1) gathering data, 2) summarizing data, 3) analyzing data, and 4) communicating the results of data analyses.

PART 1 Introduction

(17)

What Is Statistics?

(15)

Introduction

(4)

Why Study Statistics?

(1)

Some Current Applications of Statistics

(4)

What Do Statisticians Do?

(2)

Quality and Process Improvement

(2)

A Note to the Student

(1)

Summary

(3)

Supplementary Exercises

(3)

PART 2 Collecting Data

(22)

Using Surveys and Scientific Studies to Gather Data

(21)

Introduction

(1)

Surveys

(8)

Scientific Studies

(7)

Observational Studies

(1)

Data Management: Preparing Data for Summarization and Analysis

(3)

Summary

(1)

PART 3 Summarizing Data

(82)

Data Description

(81)

Introduction

(1)

Calculators, Computers, and Software Systems

(2)

Describing Data on a Single Variable: Graphical Methods

(26)

Describing Data on a Single Variable: Measures of Central Tendency

(12)

Describing Data on a Single Variable: Measures of Variability

(15)

The Boxplot

(5)

Summarizing Data from More Than One Variable

101

(8)

Summary

109

(12)

Key Formulas

110

(1)

Supplementary Exercises

110

(11)

PART 4 Tools and Concepts

121

(70)

Probability and Probability Distributions

122

(69)

How Probability Can Be Used in Making Inferences

122

(3)

Finding the Probability of an Event

125

(3)

Basic Event Relations and Probability Laws

128

(3)

Conditional Probability and Independence

131

(5)

Bayes' Formula

136

(5)

Variables: Discrete and Continuous

141

(1)

Probability Distributions for Discrete Random Variables

142

(2)

A Useful Discrete Random Variable: The Binomial

144

(10)

Probability Distributions for Continuous Random Variables

154

(3)

A Useful Continuous Random Variable: The Normal Distribution

157

(9)

Random Sampling

166

(5)

Sampling Distributions

171

(11)

Normal Approximation to the Binomial

182

(3)

Minitab Instructions

185

(1)

Summary

186

(5)

Key Formulas

187

(1)

Supplementary Exercises

187

(4)

PART 5 Analyzing Data: Central Values, Variances, and Proportions

191

(338)

Inferences about Population Central Values

192

(71)

Introduction and Case Study

192

(4)

Estimation of μ

196

(8)

Choosing the Sample Size for Estimating μ

204

(3)

A Statistical Test for μ

207

(12)

Choosing the Sample Size for μ

219

(5)

The Level of Significance of a Statistical Test

224

(4)

Inferences about μ for a Normal Population, σ Unknown

228

(15)

Inferences about the Median

243

(7)

Summary

250

(13)

Key Formulas

251

(2)

Supplementary Exercises

253

(10)

Inferences Comparing Two Population Central Values

263

(78)

Introduction and Case Study

263

(4)

Inferences about μ1 - μ2: Independent Samples

267

(20)

A Nonparametric Alternative: The Wilcoxon Rank Sum Test

287

(12)

Inferences about μ1 - μ2: Paired Data

299

(9)

A Nonparametric Alternative: The Wilcoxon Signed-Rank Test

308

(6)

Choosing Sample Sizes for Inferences about μ1 - μ2

314

(2)

Summary

316

(25)

Key Formulas

317

(2)

Supplementary Exercises

319

(22)

Inferences about Population Variances

341

(38)

Introduction and Case Study

341

(3)

Estimation and Tests for a Population Variance

344

(11)

Estimation and Tests for Comparing Two Population Variances

355

(10)

Tests for Comparing t > 2 Population Variances

365

(8)

Summary

373

(6)

Key Formulas

373

(1)

Supplementary Exercises

374

(5)

Inferences about More Than Two Population Central Values

379

(48)

Introduction and Case Study

379

(5)

A Statistical Test about More Than Two Population Means: An Analysis of Variance

384

(10)

The Model for Observations in a Completely Randomized Design

394

(2)

Checking on the AOV Conditions

396

(7)

An Alternative Analysis: Transformations of the Data

403

(7)

A Nonparametric Alternative: The Kruskal-Wallis Test

410

(4)

Summary

414

(13)

Key Formulas

415

(1)

Supplementary Exercises

416

(11)

Multiple Comparisons

427

(42)

Introduction and Case Study

427

(4)

Linear Contrasts

431

(7)

Which Error Rate Is Controlled?

438

(2)

Fisher's Least Significant Difference

440

(4)

Tukey's W Procedure

444

(3)

Student-Newman-Keuls Procedure

447

(3)

Dunnett's Procedure: Comparison of Treatments to a Control

450

(2)

Scheffe's S Method

452

(6)

Summary

458

(11)

Key Formulas

459

(1)

Supplementary Exercises

459

(10)

Categorical Data

469

(60)

Introduction and Case Study

469

(2)

Inferences about a Population Proportion π

471

(11)

Inferences about the Difference between Two Population Proportions, π1 - π2

482

(6)

Inferences about Several Proportions: Chi-Square Goodness-of-Fit Test

488

(9)

The Poisson Distribution

497

(4)

Contingency Tables: Tests for Independence and Homogeneity

501

(9)

Measuring Strength of Relation

510

(6)

Odds and Odds Ratios

516

(4)

Summary

520

(9)

Key Formulas

520

(1)

Supplementary Exercises

521

(8)

PART 6 Analyzing Data: Regression Methods and Model Building

529

(300)

Linear Regression and Correlation

531

(86)

Introduction and Case Study

531

(9)

Estimating Model Parameters

540

(17)

Inferences about Regression Parameters

557

(10)

Predicting New Y Values Using Regression

567

(9)

Examining Lack of Fit in Linear Regression

576

(6)

The Inverse Regression Problem (Calibration)

582

(8)

Correlation

590

(10)

Summary

600

(17)

Key Formulas

602

(1)

Supplementary Exercises

603

(14)

Multiple Regression and the General Linear Model

617

(88)

Introduction and Case Study

617

(8)

The General Linear Model

625

(2)

Estimating Multiple Regression Coefficients

627

(19)

Inferences in Multiple Regression

646

(11)

Testing a Subset of Regression Coefficients

657

(9)

Forecasting Using Multiple Regression

666

(4)

Comparing the Slopes of Several Regression Lines

670

(5)

Logistic Regression

675

(8)

Some Multiple Regression Theory (Optional)

683

(4)

Summary

687

(18)

Key Formulas

688

(1)

Supplementary Exercises

689

(16)

More on Multiple Regression

705

(124)

Introduction and Case Study

705

(2)

Selecting the Variables (Step 1)

707

(20)

Formulating the Model (Step 2)

727

(31)

Checking Model Assumptions (Step 3)

758

(24)

Summary

782

(47)

Key Formulas

783

(1)

Supplementary Exercises

783

(46)

PART 7 Design of Experiments and Analysis of Variance

829

(261)

Design Concepts for Experiments and Studies

830

(23)

Introduction

830

(1)

Types of Studies

831

(1)

Designed Experiments: Terminology

832

(4)

Controlling Experimental Error

836

(4)

Randomization of Treatments to Experimental Units

840

(5)

Determining the Number of Replications

845

(4)

Summary

849

(4)

Supplementary Exercises

849

(4)

Analysis of Variance for Standard Designs

853

(90)

Introduction and Case Study

853

(2)

Completely Randomized Design with Single Factor

855

(4)

Randomized Complete Block Design

859

(20)

Latin Square Design

879

(12)

Factorial Treatment Structure in a Completely Randomized Design

891

(23)

Factorial Treatment Structure in a Randomized Complete Block Design

914

(2)

Estimation of Treatment Differences and Comparisons of Treatment Means

916

(6)

Summary

922

(21)

Key Formulas

923

(1)

Supplementary Exercises

924

(19)

The Analysis of Covariance

943

(32)

Introduction and Case Study

943

(3)

A Completely Randomized Design with One Covariate

946

(13)

The Extrapolation Problem

959

(3)

Multiple Covariates and More Complicated Designs

962

(8)

Summary

970

(5)

Supplementary Exercises

971

(4)

Analysis of Variance for Some Fixed-, Random-, and Mixed-Effects Models

975

(50)

Introduction and Case Study

975

(3)

A One-Factor Experiment with Treatment Effects Random: A Random-Effects Model

978

(5)

Extensions of Random-Effects Models

983

(9)

Mixed-Effects Models

992

(8)

Rules for Obtaining Expected Mean Squares

1000

(10)

Nested Sampling and the Split-Plot Design

1010

(9)

Summary

1019

(6)

Supplementary Exercises

1020

(5)

Repeated Measures and Crossover Designs

1025

(26)

Introduction and Case Study

1025

(4)

Single-Factor Experiments with Repeated Measures

1029

(2)

Two-Factor Experiments with Repeated Measures on One of the Factors

1031

(9)

Crossover Designs

1040

(4)

Summary

1044

(7)

Supplementary Exercises

1045

(6)

Analysis of Variance for Some Unbalanced Designs

1051

(26)

Introduction and Case Study

1051

(2)

A Randomized Block Design with One or More Missing Observations

1053

(6)

A Latin Square Design with Missing Data

1059

(4)

Balanced Incomplete Block (BIB) Designs

1063

(9)

Summary

1072

(5)

Key Formulas

1072

(2)

Supplementary Exercises

1074

(3)

Communicating and Documenting the Results of Analyses

1077

(13)

Introduction

1077

(1)

The Difficulty of Good Communication

1078

(1)

Communication Hurdles: Graphical Distortions

1079

(3)

Communication Hurdles: Biased Samples

1082

(1)

Communication Hurdles: Sample Size

1083

(1)

Preparing Data for Statistical Analysis

1084

(3)

Guidelines for a Statistical Analysis and Report

1087

(1)

Documentation and Storage of Results

1088

(1)

Summary

1089

(1)

Supplementary Exercise

1089

(1)

Appendix: Statistical Tables

1090

(40)

References

1130

(3)

Index

1133

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An Introduction to Statistical Methods and Data Analysis

Summary

Table of Contents

Supplemental Materials