Rent 9780073215754 | Applied Statistics in Business and Economics with St CD-ROM

Summary Table of Contents Author Biography

This new text offers an Excel focused approach to using statistics in business. All statistical concepts are illustrated with applied examples immediately upon introduction. Modern computing tools and applications are introduced, and the text maintains a strong focus on presenting statistical concepts as applied in business --as opposed to providing programming methods used to find a mathematical solution. Interpretation is heavily emphasized, enabling students to take full advantage of Excel to develop and drive problem-solving skills.

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Overview of Statistics

(20)

What is Statistics?

(1)

Why Study Statistics?

(1)

Communication

(1)

Computer Skills

(1)

Information Management

(1)

Technical Literacy

(1)

Career Advancement

(1)

Quality Improvement

(1)

Uses of Statistics

(1)

Auditing

(1)

Marketing

(1)

Health Care

(1)

Quality Control

(1)

Purchasing

(1)

Medicine

(1)

Forecasting

(1)

Product Warranty

(1)

Statistical Challenges

(3)

Working with Imperfect Data

(1)

Dealing with Practical Constraints

(1)

Upholding Ethical Standards

(1)

Using Consultants

(2)

Writing and Presenting Reports

(5)

Rules for ``Power'' Writing

(1)

Writing Style

(1)

Spelling and Grammar

(1)

Organizing a Technical Report

(1)

Writing an Executive Summary

(1)

Tables and Graphs

(1)

Rules for Presenting Oral Reports

(1)

The Three Ps

(2)

Statistical Pitfalls

(2)

Pitfall 1: Making Conclusions about a Large Population from a Small Sample

(1)

Pitfall 2: Making Conclusions from Nonrandom Samples

(1)

Pitfall 3: Attaching Importance to Rare Observations from Large Samples

(1)

Pitfall 4: Using Poor Survey Methods

(1)

Pitfall 5: Assuming a Causal Link Based Only on Observed Association

(1)

Pitfall 6: Making Generalizations about Individuals from Observations about Groups

(1)

Pitfall 7: Unconscious Bias

(1)

Pitfall 8: Attaching Practical Importance to Every Statistically Significant Study Result

(1)

Statistics: An Evolving Field

(6)

Chapter Summary

(6)

Data Collection

(36)

Definitions

(3)

Subjects, Variables, and Data Sets

(1)

Data Types

(2)

Level of Measurement

(4)

Nominal Measurement

(1)

Ordinal Measurement

(1)

Interval Measurement

(1)

Ratio Measurement

(1)

Changing Data by Recoding

(1)

Time-Series versus Cross-Sectional Data

(1)

Time-Series Data

(1)

Cross-Sectional Data

(1)

Sampling Concepts

(2)

Sample or Census?

(1)

Parameters and Statistics

(1)

Target Population

(1)

Finite or Infinite?

(1)

Sampling Methods

(9)

Simple Random Sample

(2)

Random Number Tables

(1)

Setting Up a Rule

(1)

With or Without Replacement?

(1)

Computer Methods

(1)

Row/Column Data Arrays

(1)

Randomizing a List

(1)

Systematic Sample

(2)

Stratified Sample

(1)

Applications of Stratified Sampling

(1)

Cluster Sample

(1)

Judgment Sample

(1)

Convenience Sample

(1)

Sample Size

(1)

Data Sources

(1)

Survey Research

(15)

Survey Types

(1)

Response Rates

(1)

Getting Advice

(1)

Questionnaire Design

(1)

Question Wording

(2)

Coding and Data Screening

(1)

Sources of Error

(1)

Data File Format

(2)

Chapter Summary

(9)

Describing Data Visually

(54)

Visual Description

(2)

Measurement

(1)

Sorting

(1)

Dot Plots

(4)

Small Sample: Home Prices

(2)

Comparing Groups

(2)

Frequency Distributions and Histograms

(7)

Bins and Bin Limits

(1)

Constructing a Frequency Distribution

(1)

Histograms

(1)

Excel Histograms

(2)

MegaStat Histograms

(1)

MINITAB Histograms

(1)

Modal Class

(1)

Shape

(3)

Line Charts

(4)

Simple Line Charts

(1)

Grid Lines

(1)

Log Scales

(2)

Tips for Effective Line Charts

(1)

Bar Charts

(4)

Plain Bar Charts

(1)

3-D and Novelty Bar Charts

(1)

Pareto Charts

(1)

Stacked Bar Chart

(1)

Bar Charts for Time-Series Data

(1)

Tips for Effective Bar Charts

(1)

Scatter Plots

(6)

Policy Making

(1)

Degree of Association

(2)

Making a Scatter Plot in Excel

(2)

Tables

(1)

Tips for Effective Tables

(1)

Pie Charts

(3)

An Oft-Abused Chart

(1)

Pie Chart Options

(3)

Effective Excel Charts

(4)

Chart Wizard

(1)

Embellished Charts

(3)

Maps and Pictograms

(1)

Spatial Variation and GIS

(1)

Pictograms

(1)

Deceptive Graphs

(17)

Error 1: Nonzero Origin

(1)

Error 2: Elastic Graph Proportions

(1)

Error 3: Dramatic Title

(1)

Error 4: Distracting Pictures

(1)

Error 5: Authority Figures

(1)

Error 6: 3-D and Rotated Graphs

(1)

Error 7: Missing Axis Demarcations

(1)

Error 8: Missing Measurement Units or Definitions

(1)

Error 9: Vague Source

(1)

Error 10: Complex Graphs

(1)

Error 11: Gratuitous Effects

(1)

Error 12: Estimated Data

(1)

Error 13: Area Trick

(1)

Final Advice

(1)

Further Challenges

(1)

Chapter Summary

(13)

Descriptive Statistics

112

(56)

Numerical Description

113

(6)

Preliminary Analysis

114

(1)

Sorting

114

(1)

Visual Displays

114

(2)

Descriptive Statistics in Excel

116

(1)

Descriptive Statistics in MegaStat

117

(2)

Central Tendency

119

(11)

Mean

119

(1)

Characteristics of the Mean

119

(1)

Median

120

(1)

Characteristics of the Median

121

(1)

Mode

121

(2)

Skewness

123

(4)

Geometric Mean

127

(1)

Growth Rates

127

(1)

Midrange

128

(1)

Trimmed Mean

128

(2)

Dispersion

130

(6)

Range

131

(1)

Variance

131

(1)

Standard Deviation

131

(1)

Calculating a Standard Deviation

132

(1)

Characteristics of the Standard Deviation

133

(1)

Coefficient of Variation

133

(1)

Mean Absolute Deviation

133

(2)

Central Tendency versus Dispersion: Manufacturing

135

(1)

Central Tendency and Dispersion: Job Performance

135

(1)

Standardized Data

136

(5)

Chebyshev's Theorem

136

(1)

The Empirical Rule

137

(1)

Unusual Observations

137

(1)

Defining a Standardized Variable

138

(1)

Outliers

139

(1)

Estimating Sigma

139

(2)

Percentiles and Quartiles

141

(4)

Percentiles

141

(1)

Quartiles

141

(1)

Method of Medians

142

(1)

Formula Method

143

(1)

Excel Quartiles

143

(1)

Dispersion Using Quartiles

144

(1)

Midhinge

144

(1)

Midspread (Interquartile Range)

145

(1)

Coefficient of Quartile Variation

145

(1)

Box Plots

145

(2)

Fences and Unusual Data Values

146

(1)

Grouped Data

147

(2)

Nature and Grouped Data

147

(1)

Mean and Standard Deviation

148

(1)

Accuracy Issues

148

(1)

Properties of Grouped Estimates

149

(1)

Skewness and Kurtosis

149

(19)

Skewness

149

(1)

Kurtosis

150

(2)

Chapter Summary

152

(16)

Probability

168

(40)

Random Experiments

169

(2)

Sample Space

169

(1)

Events

170

(1)

Probability

171

(3)

Definitions

171

(1)

What Is ``Probability''?

171

(1)

Empirical Approach

172

(1)

Law of Large Numbers

172

(1)

Practical Issues for Actuaries

172

(1)

Classical Approach

173

(1)

Subjective Approach

174

(1)

Rules of Probability

174

(6)

Complement of an Event

174

(1)

Odds of an Event

175

(1)

Union of Two Events

175

(1)

Intersection of Two Events

175

(1)

General Law of Addition

176

(1)

Mutually Exclusive Events

177

(1)

Special Law of Addition

177

(1)

Collectively Exhaustive Sets

177

(1)

Forced Dichotomy

177

(1)

Conditional Probability

178

(2)

Independent Events

180

(3)

Dependent Events

180

(1)

Actuaries Again

181

(1)

Multiplication Law for Independent Events

181

(1)

The Five Nines Rule

181

(1)

How Much Redundancy Is Needed?

182

(1)

Applications of Redundancy

182

(1)

Contingency Tables

183

(7)

What Is a Contingency Table?

183

(1)

Marginal Probabilities

184

(1)

Joint Probabilities

184

(1)

Conditional Probabilities

185

(1)

Independence

185

(1)

Relative Frequencies

186

(1)

How Do We Get a Contingency Table?

187

(3)

Tree Diagrams

190

(1)

What Is a Tree?

190

(1)

Bayes's Theorem (Optional)

191

(5)

How Bayes's Theorem Works

191

(1)

General Form of Bayes's Theorem

192

(4)

Counting Rules (Optional)

196

(12)

Fundamental Rule of Counting

196

(1)

Factorials

197

(1)

Permutations

198

(1)

Combinations

198

(2)

Chapter Summary

200

(8)

Discrete Distributions

208

(44)

Probability Models

209

(1)

Discrete Distributions

209

(6)

Random Variables

209

(1)

Probability Distributions

210

(1)

Expected Value

211

(1)

Application: Life Insurance

212

(1)

Application: Raffle Tickets

212

(1)

Actuarial Fairness

213

(1)

Variance and Standard Deviation

213

(1)

What Is a PDF or CDF?

214

(1)

Uniform Distribution

215

(4)

Characteristics of the Uniform Distribution

215

(2)

Application: Pumping Gas

217

(1)

Uniform Random Integers

218

(1)

Application: Copier Codes

218

(1)

Uniform Model in Learning Stats

219

(1)

Bernoulli Distribution

219

(1)

Bernoulli Experiments

219

(1)

Binomial Distribution

220

(8)

Characteristics of the Binomial Distribution

220

(1)

Binomial Shape

221

(1)

Application: Uninsured Patients

222

(1)

Using the Binomial Formula

222

(2)

Using Tables: Appendix A

224

(1)

Compound Events

224

(1)

Binomial Probabilities: Excel

225

(1)

Binomial Probabilities: MegaStat

225

(1)

Binomial Probabilities: Visual Statistics

225

(1)

Binomial Probabilities: LearningStats

225

(1)

Binomial Random Data

225

(1)

Recognizing Binomial Applications

225

(3)

Poisson Distribution

228

(7)

Poisson Processes

228

(1)

Characteristics of the Poisson Distribution

229

(1)

Using the Poisson Formula

230

(2)

Compound Events

232

(1)

Poisson Probabilities: Tables (Appendix B)

232

(1)

Poisson Probabilities: Excel

232

(1)

Poisson Probabilities: Visual Statistics

232

(1)

Recognizing Poisson Applications

233

(1)

Poisson Approximation to Binomial (Optional)

234

(1)

Hypergeometric Distribution

235

(5)

Characteristics of the Hypergeometric Distribution

235

(1)

Using the Hypergeometric Formula

236

(1)

Hypergeometric Probabilities: Excel

237

(1)

Hypergeometric Probabilities: Visual Statistics

237

(1)

Hypergeometric Probabilities: LearningStats

238

(1)

Recognizing Hypergeometric Applications

238

(1)

Binomial Application to the Hypergeometric (Optional)

239

(1)

Geometric Distribution (Optional)

240

(2)

Characteristics of the Geometric Distribution

240

(1)

Using LearningStats

241

(1)

Transformations of Random Variables (Optional)

242

(10)

Linear Transformation

242

(1)

Application: Exam Scores

242

(1)

Application: Total Cost

242

(1)

Sums of Random Variables

243

(1)

Application: Gasoline Expenses

243

(1)

Application: Project Scheduling

243

(1)

Chapter Summary

244

(8)

Continuous Distributions

252

(40)

Continuous Variables

253

(1)

Events as Intervals

253

(1)

Describing a Continuous Distribution

253

(2)

PDFs and CDFs

253

(1)

Probabilities as Areas

254

(1)

Expected Value and Variance

255

(1)

Oh My, Calculus?

255

(1)

Uniform Continuous Distribution

255

(3)

Characteristics of the Uniform Distribution

255

(2)

Special Case: Unit Rectangular

257

(1)

Uses of the Uniform Model

258

(1)

Normal Distribution

258

(3)

Characteristics of the Normal Distribution

258

(2)

What Is Normal?

260

(1)

Standard Normal Distribution

261

(12)

Characteristics of the Standard Normal

261

(1)

Normal Areas from Appendix C-1

262

(1)

Basis for the Empirical Rule

263

(1)

Normal Areas from Appendix C-2

264

(1)

Finding z for a Given Area

265

(2)

Finding Normal Areas with Excel

267

(1)

Finding Areas by Using Standardized Variables

267

(2)

Inverse Normal

269

(1)

Using Excel Without Standardizing

270

(1)

Normal Random Data (Optional)

271

(2)

Normal Approximation to the Binomial (Optional)

273

(3)

When Is Approximation Needed?

273

(3)

Normal Approximation to the Poisson (Optional)

276

(1)

When Is Approximation Needed?

276

(1)

Exponential Distribution

277

(5)

Characteristics of the Exponential Distribution

277

(2)

Inverse Exponential

279

(1)

Mean Time Between Events

280

(1)

Using Excel

281

(1)

Triangular Distribution (Optional)

282

(10)

Characteristics of the Triangular Distribution

282

(2)

Special Case: Symmetric Triangular

284

(1)

Uses of the Triangular

284

(1)

Chapter Summary

284

(8)

Sampling Distributions and Estimation

292

(54)

Sampling Variation

293

(2)

Estimators and Sampling Distributions

295

(3)

Some Terminology

295

(1)

Sampling Distributions

295

(1)

Bias

295

(2)

Efficiency

297

(1)

Consistency

297

(1)

Sample Mean and the Central Limit Theorem

298

(8)

Central Limit Theorem for a Mean

299

(1)

Symmetric Population: Uniform Distribution

299

(1)

Skewed Population: Waiting Time

300

(2)

Range of Sample Means

302

(1)

Illustration: GMAT Scores

303

(1)

Sample Size and Standard Error

304

(1)

Illustration: All Possible Samples from a Uniform Population

304

(2)

Confidence Interval for a Mean (μ) with Known σ

306

(3)

What Is a Confidence Interval?

306

(2)

Choosing a Confidence Interval

308

(1)

Is σ Ever Known?

308

(1)

Confidence Interval for a Mean (μ) with Unknown σ

309

(8)

Student's t Distribution

309

(1)

Degrees of Freedom

310

(1)

Comparison of z and t

310

(3)

Confidence Interval Width

313

(1)

A ``Good'' Sample?

313

(1)

More Analysis Needed

314

(1)

Messy Data?

314

(1)

Using Appendix D

315

(1)

Using Excel

315

(1)

Using MegaStat

316

(1)

Using MINITAB

316

(1)

Confidence Interval for a Proportion (π)

317

(9)

Illustration: Internet Hotel Reservations

318

(1)

Applying the CLT

318

(2)

When Is It Safe to Assume Normality?

320

(1)

Standard Error of the Proportion

320

(1)

Confidence Interval for π

321

(1)

Narrowing the Interval?

322

(1)

Using Excel and MegaStat

323

(1)

Small Samples: MINITAB

323

(1)

Polls and Margin of Error

324

(1)

Rule of Three

324

(1)

Very Quick Rule

325

(1)

Advice on Proportions

325

(1)

Sample Size Determination for a Mean

326

(3)

A Myth

326

(1)

Sample Size to Estimate μ

326

(1)

How to Estimate σ

327

(1)

Using LearningStats

328

(1)

Using MegaStat

328

(1)

Caution 1: Units of Measure

328

(1)

Caution 2: Using z

328

(1)

Caution 3: Larger n Is Better

328

(1)

Sample Size Determination for a Proportion

329

(2)

Alternatives

330

(1)

Practical Advice

330

(1)

Using LearningStats

331

(1)

Caution 1: Units of Measure

331

(1)

Caution 2: Finite Population

331

(1)

Confidence Interval for the Difference of Two Means, μ1 -- μ2 (Optional)

331

(3)

Should Sample Sizes Be Equal?

333

(1)

Confidence Interval for the Difference of Two Proportions, π1 -- π2 (Optional)

334

(1)

Confidence Interval for a Population Variance, σ2 (Optional)

335

(11)

Chi-Square Distribution

335

(1)

Confidence Interval for σ

336

(1)

Using LearningStats

336

(1)

Caution: Assumption of Normality

336

(1)

Chapter Summary

337

(9)

One-Sample Hypothesis Tests

346

(48)

Logic of Hypothesis Testing

347

(10)

Process of Science

347

(1)

Who Tests Hypotheses?

348

(1)

Good News

348

(1)

Hypothesis Formulation

348

(1)

Can Hypotheses Be Proved?

348

(1)

Role of Evidence

349

(1)

Types of Error

349

(1)

Statistical Hypothesis Testing

350

(1)

One-Sided Tests

351

(1)

When to Use a One-Sided Test

352

(1)

Decision Rule

352

(1)

Type I Error

352

(2)

Type II Error

354

(1)

Power of a Test

354

(1)

Relationship Between α and β

355

(1)

Consequences of Type II Error

355

(1)

Choice of α

355

(1)

Statistical Significance versus Practical Importance

355

(2)

Testing a Proportion

357

(10)

Critical Value

358

(1)

p-Value Method

359

(1)

Interpreting a p-Value

360

(1)

Two-Tailed Test

360

(1)

Calculating a p-Value for a Two-Tailed Test

361

(1)

Effect of α

362

(1)

Using the p-Value

363

(1)

Effect of a Larger Sample

364

(2)

Small Samples and Non-Normality (Optional)

366

(1)

Testing a Mean: Known Population Variance

367

(5)

Test Statistic

367

(1)

One-Tailed Test

368

(1)

p-Value Method

369

(1)

Two-Tailed Test

369

(1)

Using the p-Value

370

(1)

Analogy to Confidence Intervals

371

(1)

Significance versus Importance

371

(1)

Testing a Mean: Unknown Population Variance

372

(5)

Using Student's t

372

(1)

Sensitivity to α

373

(1)

Using the p-Value

373

(1)

Significance versus Importance

374

(1)

Normality Assumption

374

(1)

Confidence Interval versus Hypothesis Test

374

(1)

Using MegaStat

375

(1)

Large Samples

375

(2)

Power Curves and OC Curves (Optional)

377

(7)

Power Curve for a Mean: An Example

377

(1)

Calculating Power

378

(2)

Effect of Sample Size

380

(1)

Relationship of the Power and OC Curves

380

(1)

Power Curve for Tests of a Proportion

381

(2)

Using LearningStats

383

(1)

Using Visual Statistics

383

(1)

Tests for One Variance (Optional)

384

(10)

Using MegaStat

385

(1)

When to Use Tests for One Variance

386

(1)

Chapter Summary

387

(7)

Two-Sample Hypothesis Tests

394

(44)

Two-Sample Tests

395

(2)

What Is a Two Sample Test?

395

(1)

Basis of Two-Sample Tests

396

(1)

Test Procedure

396

(1)

Comparing Two Proportions

397

(9)

Testing for Zero Difference: π1 = π2

397

(1)

Sample Proportions

397

(1)

Pooled Proportion

397

(1)

Test Statistic

397

(2)

Using the p-Value

399

(1)

Checking Normality

399

(1)

Small Samples

400

(1)

Must Sample Sizes Be Equal?

400

(1)

Using Software for Calculations

400

(1)

Analogy to Confidence Intervals

400

(1)

Separate Confidence Intervals

401

(2)

Testing for Nonzero Difference (Optional)

403

(1)

Test Statistic

403

(1)

Using the p-Value

404

(2)

Comparing Two Means: Independent Samples

406

(8)

Format of Hypotheses

406

(1)

Test Statistic

406

(1)

Case 1: Known Variances

407

(1)

Case 2: Unknown Variances, Assumed Equal

407

(1)

Case 3: Unknown Variances, Assumed Unequal

407

(4)

Which Assumption is Best?

411

(1)

Must Sample Sizes Be Equal?

411

(1)

Large Samples

411

(1)

Caution: Three Issues

411

(3)

Comparing Two Means: Paired Samples

414

(6)

Paired Data

414

(1)

Paired t Test

414

(2)

Excel's Paired Difference Test

416

(1)

Analogy to Confidence Interval

416

(1)

Why Not Treat Paired Data As Independent Samples?

417

(3)

Comparing Two Variances

420

(18)

Format of Hypotheses

420

(1)

The F Test

420

(1)

Critical Values

420

(1)

Illustration: Collision Damage

421

(1)

Comparison of Means

422

(1)

Comparison of Variances: Two-Tailed Test

422

(2)

Comparison of Variances: One-Tailed Test

424

(1)

Excels F Test

425

(1)

Assumptions of the F Test

425

(1)

Significance versus Importance

426

(1)

Chapter Summary

427

(11)

Analysis of Variance

438

(50)

Overview of ANOVA

439

(3)

The Goal: Explaining Variation

439

(1)

Illustration: Manufacturing Defect Rates

440

(1)

Illustration: Hospital Length of Stay

441

(1)

Illustration: Automobile Painting

441

(1)

ANOVA Calculations

441

(1)

ANOVA Assumptions

442

(1)

One-Factor ANOVA (Completely Randomized Model)

442

(8)

Data Format

442

(1)

Hypotheses to Be Tested

443

(1)

One-Factor ANOVA as a Linear Model

443

(1)

Group Means

443

(1)

Partitioned Sum of Squares

443

(2)

Test Statistic

445

(1)

Decision Rule

445

(3)

Using MINITAB

448

(2)

Multiple Comparisons

450

(2)

Tukey's Test

450

(1)

Using MegaStat

451

(1)

Tests for Homogeneity of Variances (Optional)

452

(4)

ANOVA Assumptions

452

(1)

Hartley's Fmax Test

452

(2)

Levene's Test

454

(2)

Two-Factor ANOVA without Replication (Randomized Block Model)

456

(8)

Data Format

456

(1)

Two-Factor ANOVA Model

457

(1)

Hypotheses to Be Tested

457

(1)

Randomized Block Model

457

(1)

Format of Calculation of Nonreplicated Two-Factor ANOVA

458

(2)

Using MegaStat

460

(1)

Multiple Comparisons

461

(1)

Limitations of Two-Factor ANOVA without Replication

461

(3)

Two-Factor ANOVA with Replication (Full Factorial Model)

464

(9)

What Does Replication Accomplish?

464

(1)

Format of Hypotheses

464

(1)

Format of Data

465

(1)

Sources of Variation

465

(3)

Using MegaStat

468

(1)

Interaction Effect

468

(2)

Tukey Tests of Pairs of Means

470

(1)

Significance versus Importance

470

(3)

General Linear Model (Optional)

473

(3)

Higher-Order ANOVA Models

473

(1)

What Is GLM?

474

(2)

Experimental Design: An Overview (Optional)

476

(12)

What Is Experimental Design?

476

(1)

2k Models

476

(1)

Fractional Factorial Designs

476

(1)

Nested or Hierarchical Design

477

(1)

Random Effects Models

477

(1)

Chapter Summary

477

(11)

Bivariate Regression

488

(70)

Visual Displays and Correlation Analysis

489

(11)

Visual Displays

489

(1)

Correlation Coefficient

490

(1)

Tests for Significance

490

(4)

Quick Rule for Significance

494

(1)

Role of Sample Size

494

(1)

Using Excel

494

(2)

Regression: The Next Step?

496

(3)

Autocorrelation

499

(1)

Bivariate Regression

500

(2)

What Is Bivariate Regression?

500

(1)

Model Form

500

(1)

Interpreting a Fitted Regression

501

(1)

Prediction Using Regression

501

(1)

Regression Terminology

502

(3)

Models and Parameters

502

(1)

Estimating a Regression Line by Eye

502

(1)

Fitting a Regression on a Scatter Plot in Excel

502

(1)

Illustration: Piper Cheyenne Fuel Consumption

503

(2)

Ordinary Least Squares Formulas

505

(6)

Slope and Intercept

505

(1)

Illustration: Exam Scores and Study Time

506

(1)

Assessing Fit

507

(1)

Coefficient of Determination

508

(3)

Tests for Significance

511

(6)

Standard Error of Regression

511

(1)

Confidence Intervals for Slope and Intercept

511

(1)

Hypothesis Tests

512

(1)

Test for Zero Slope: Exam Scores

512

(1)

Using Excel: Exam Scores

513

(1)

Using MegaStat: Exam Scores

513

(1)

Using MINITAB: Exam Scores

514

(2)

Using MegaStat: U.S. Income and Taxes

516

(1)

MegaStat's Confidence Intervals: U.S. Income and Taxes

516

(1)

Test for Zero Slope: Tax Data

516

(1)

Analysis of Variance: Overall Fit

517

(5)

Decomposition of Variance

517

(1)

F Statistic for Overall Fit

518

(4)

Confidence and Prediction Intervals for Y

522

(2)

How to Construct an Interval Estimate for Y

522

(1)

Two Illustrations: Exam Scores and Taxes

522

(2)

Quick Rules for Confidence and Prediction Intervals

524

(1)

Violations of Assumptions

524

(7)

Three Important Assumptions

524

(1)

Non-Normal Errors

524

(1)

Histogram of Residuals

524

(1)

Normal Probability Plot

525

(1)

What to Do About Non-Normality?

525

(1)

Heteroscedastic Errors (Nonconstant Variance)

526

(1)

Tests for Heteroscedasticity

526

(1)

What to Do about Heteroscedasticity?

527

(1)

Autocorrelated Errors

527

(1)

Runs Test for Autocorrelation

528

(1)

Durbin-Watson Test

528

(1)

What to Do about Autocorrelation?

529

(2)

Unusual Observations

531

(5)

Standardized Residuals: Excel

531

(1)

Studentized Residuals: MINITAB

531

(1)

Studentized Residuals: MegaStat

532

(1)

Leverage and Influence

532

(2)

Studentized Deleted Residuals

534

(2)

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