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)

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An Introduction to Statistical Methods and Data Analysis

Summary

Table of Contents

Supplemental Materials