**Statistics for the Behavioral Sciences, 10th Edition**

by Frederick J Gravetter and Larry B. Wallnau

C H A P t E R 1 Introduction to Statistics 1

PREVIEW 2

1.1 Statistics, Science, and Observations 2

1.2 Data Structures, Research Methods, and Statistics 10

1.3 Variables and Measurement 18

1.4 Statistical Notation 25

Summary 29

Focus on Problem Solving 30

Demonstration 1.1 30

Problems 31

CHAP t E R 2 Frequency Distributions 33

PREVIEW 34

2.1 Frequency Distributions and Frequency Distribution Tables 35

2.2 Grouped Frequency Distribution Tables 38

2.3 Frequency Distribution Graphs 42

2.4 Percentiles, Percentile Ranks, and Interpolation 49

2.5 Stem and Leaf Displays 56

Summary 58

Focus on Problem Solving 59

Demonstration 2.1 60

Demonstration 2.2 61

Problems 62

CHA P t E R 3 Central Tendency 67

PREVIEW 68

3.1 Overview 68

3.2 The Mean 70

3.3 The Median 79

3.4 The Mode 83

3.5 Selecting a Measure of Central Tendency 86

3.6 Central Tendency and the Shape of the Distribution 92

Summary 94

Focus on Problem Solving 95

Demonstration 3.1 96

Problems 96

CHAP t ER 4 Variability 99

PREVIEW 100

4.1 Introduction to Variability 101

4.2 Defining Standard Deviation and Variance 103

4.3 Measuring Variance and Standard Deviation for a Population 108

4.4 Measuring Standard Deviation and Variance for a Sample 111

4.5 Sample Variance as an Unbiased Statistic 117

4.6 More about Variance and Standard Deviation 119

Summary 125

Focus on Problem Solving 127

Demonstration 4.1 128

Problems 128

CHA P t E R 5

z-Scores: Location of Scores

and Standardized Distributions 131

PREVIEW 132

5.1 Introduction to z-Scores 133

5.2 z-Scores and Locations in a Distribution 135

5.3 Other Relationships Between z, X, 𝛍, and 𝛔 138

5.4 Using z-Scores to Standardize a Distribution 141

5.5 Other Standardized Distributions Based on z-Scores 145

5.6 Computing z-Scores for Samples 148

5.7 Looking Ahead to Inferential Statistics 150

Summary 153

Focus on Problem Solving 154

Demonstration 5.1 155

Demonstration 5.2 155

Problems 156

CHAP t E R 6 Probability 159

PREVIEW 160

6.1 Introduction to Probability 160

6.2 Probability and the Normal Distribution 165

6.3 Probabilities and Proportions for Scores

from a Normal Distribution 172

6.4 Probability and the Binomial Distribution 179

6.5 Looking Ahead to Inferential Statistics 184

Summary 186

Focus on Problem Solving 187

Demonstration 6.1 188

Demonstration 6.2 188

Problems 189

CHA P t E R 7

Probability and Samples: The Distribution

of Sample Means 193

PREVIEW 194

7.1 Samples, Populations, and the Distribution

of Sample Means 194

7.2 The Distribution of Sample Means for any Population

and any Sample Size 199

7.3 Probability and the Distribution of Sample Means 206

7.4 More about Standard Error 210

7.5 Looking Ahead to Inferential Statistics 215

Summary 219

Focus on Problem Solving 219

Demonstration 7.1 220

Problems 221

CHAP t E R 8 Introduction to Hypothesis Testing 223

PREVIEW 224

8.1 The Logic of Hypothesis Testing 225

8.2 Uncertainty and Errors in Hypothesis Testing 236

8.3 More about Hypothesis Tests 240

8.4 Directional (One-Tailed) Hypothesis Tests 245

8.5 Concerns about Hypothesis Testing: Measuring Effect Size 250

8.6 Statistical Power 254

Summary 260

Focus on Problem Solving 261

Demonstration 8.1 262

Demonstration 8.2 263

Problems 263

CHAP t E R 9 Introduction to the t Statistic 267

PREVIEW 268

9.1 The t Statistic: An Alternative to z 268

9.2 Hypothesis Tests with the t Statistic 274

9.3 Measuring Effect Size for the t Statistic 279

9.4 Directional Hypotheses and One-Tailed Tests 288

Summary 291

Focus on Problem Solving 293

Demonstration 9.1 293

Demonstration 9.2 294

Problems 295

CHAPtER 10 The t Test for Two Independent Samples 299

PREVIEW 300

10.1 Introduction to the Independent-Measures Design 300

10.2 The Null Hypothesis and the Independent-Measures t Statistic 302

10.3 Hypothesis Tests with the Independent-Measures t Statistic 310

10.4 Effect Size and Confidence Intervals for the

Independent-Measures t 316

10.5 The Role of Sample Variance and Sample Size in the

Independent-Measures t Test 322

Summary 325

Focus on Problem Solving 327

Demonstration 10.1 328

Demonstration 10.2 329

Problems 329

CHAPtER 11 The t Test for Two Related Samples 335

PREVIEW 336

11.1 Introduction to Repeated-Measures Designs 336

11.2 The t Statistic for a Repeated-Measures Research Design 339

11.3 Hypothesis Tests for the Repeated-Measures Design 343

11.4 Effect Size and Confidence Intervals for the Repeated-Measures t 347

11.5 Comparing Repeated- and Independent-Measures Designs 352

Summary 355

Focus on Problem Solving 358

Demonstration 11.1 358

Demonstration 11.2 359

Problems 360

CHAPtER 12 Introduction to Analysis of Variance 365

PREVIEW 366

12.1 Introduction (An Overview of Analysis of Variance) 366

12.2 The Logic of Analysis of Variance 372

12.3 ANOVA Notation and Formulas 375

12.4 Examples of Hypothesis Testing and Effect Size with ANOVA 383

12.5 Post Hoc Tests 393

12.6 More about ANOVA 397

Summary 403

Focus on Problem Solving 406

Demonstration 12.1 406

Demonstration 12.2 408

Problems 408

CHAPtER 13 Repeated-Measures Analysis of Variance 413

PREVIEW 414

13.1 Overview of the Repeated-Measures ANOVA 415

13.2 Hypothesis Testing and Effect Size with the

Repeated-Measures ANOVA 420

13.3 More about the Repeated-Measures Design 429

Summary 436

Focus on Problem Solving 438

Demonstration 13.1 439

Demonstration 13.2 440

Problems 441

CHAPtER 14

Two-Factor Analysis of Variance

(Independent Measures) 447

PREVIEW 448

14.1 An Overview of the Two-Factor, Independent-Measures, ANOVA: Main

Effects and Interactions 448

14.2 An Example of the Two-Factor ANOVA and Effect Size 458

14.3 More about the Two-Factor ANOVA 467

Summary 473

Focus on Problem Solving 475

Demonstration 14.1 476

Demonstration 14.2 478

Problems 479

CHAPtER 15 Correlation 485

PREVIEW 486

15.1 Introduction 487

15.2 The Pearson Correlation 489

15.3 Using and Interpreting the Pearson Correlation 495

15.4 Hypothesis Tests with the Pearson Correlation 506

15.5 Alternatives to the Pearson Correlation 510

Summary 520

Focus on Problem Solving 522

Demonstration 15.1 523

Problems 524

CHAPtER 16 Introduction to Regression 529

PREVIEW 530

16.1 Introduction to Linear Equations and Regression 530

16.2 The Standard Error of Estimate and Analysis of Regression:

The Significance of the Regression Equation 538

16.3 Introduction to Multiple Regression with Two Predictor Variables 544

Summary 552

Linear and Multiple Regression 554

Focus on Problem Solving 554

Demonstration 16.1 555

Problems 556

CHAPtER 17

The Chi-Square Statistic: Tests for Goodness

of Fit and Independence 559

PREVIEW 560

17.1 Introduction to Chi-Square: The Test for Goodness of Fit 561

17.2 An Example of the Chi-Square Test for Goodness of Fit 567

17.3 The Chi-Square Test for Independence 573

17.4 Effect Size and Assumptions for the Chi-Square Tests 582

17.5 Special Applications of the Chi-Square Tests 587

Summary 591

Focus on Problem Solving 595

Demonstration 17.1 595

Demonstration 17.2 597

Problems 597

CHAPtER 18 The Binomial Test 603

PREVIEW 604

18.1 Introduction to the Binomial Test 604

18.2 An Example of the Binomial Test 608

18.3 More about the Binomial Test: Relationship with Chi-Square

and the Sign Test 612

Summary 617

Focus on Problem Solving 619

Demonstration 18.1 619

Problems 620

PREVIEW 2

1.1 Statistics, Science, and Observations 2

1.2 Data Structures, Research Methods, and Statistics 10

1.3 Variables and Measurement 18

1.4 Statistical Notation 25

Summary 29

Focus on Problem Solving 30

Demonstration 1.1 30

Problems 31

CHAP t E R 2 Frequency Distributions 33

PREVIEW 34

2.1 Frequency Distributions and Frequency Distribution Tables 35

2.2 Grouped Frequency Distribution Tables 38

2.3 Frequency Distribution Graphs 42

2.4 Percentiles, Percentile Ranks, and Interpolation 49

2.5 Stem and Leaf Displays 56

Summary 58

Focus on Problem Solving 59

Demonstration 2.1 60

Demonstration 2.2 61

Problems 62

CHA P t E R 3 Central Tendency 67

PREVIEW 68

3.1 Overview 68

3.2 The Mean 70

3.3 The Median 79

3.4 The Mode 83

3.5 Selecting a Measure of Central Tendency 86

3.6 Central Tendency and the Shape of the Distribution 92

Summary 94

Focus on Problem Solving 95

Demonstration 3.1 96

Problems 96

CHAP t ER 4 Variability 99

PREVIEW 100

4.1 Introduction to Variability 101

4.2 Defining Standard Deviation and Variance 103

4.3 Measuring Variance and Standard Deviation for a Population 108

4.4 Measuring Standard Deviation and Variance for a Sample 111

4.5 Sample Variance as an Unbiased Statistic 117

4.6 More about Variance and Standard Deviation 119

Summary 125

Focus on Problem Solving 127

Demonstration 4.1 128

Problems 128

CHA P t E R 5

z-Scores: Location of Scores

and Standardized Distributions 131

PREVIEW 132

5.1 Introduction to z-Scores 133

5.2 z-Scores and Locations in a Distribution 135

5.3 Other Relationships Between z, X, 𝛍, and 𝛔 138

5.4 Using z-Scores to Standardize a Distribution 141

5.5 Other Standardized Distributions Based on z-Scores 145

5.6 Computing z-Scores for Samples 148

5.7 Looking Ahead to Inferential Statistics 150

Summary 153

Focus on Problem Solving 154

Demonstration 5.1 155

Demonstration 5.2 155

Problems 156

CHAP t E R 6 Probability 159

PREVIEW 160

6.1 Introduction to Probability 160

6.2 Probability and the Normal Distribution 165

6.3 Probabilities and Proportions for Scores

from a Normal Distribution 172

6.4 Probability and the Binomial Distribution 179

6.5 Looking Ahead to Inferential Statistics 184

Summary 186

Focus on Problem Solving 187

Demonstration 6.1 188

Demonstration 6.2 188

Problems 189

CHA P t E R 7

Probability and Samples: The Distribution

of Sample Means 193

PREVIEW 194

7.1 Samples, Populations, and the Distribution

of Sample Means 194

7.2 The Distribution of Sample Means for any Population

and any Sample Size 199

7.3 Probability and the Distribution of Sample Means 206

7.4 More about Standard Error 210

7.5 Looking Ahead to Inferential Statistics 215

Summary 219

Focus on Problem Solving 219

Demonstration 7.1 220

Problems 221

CHAP t E R 8 Introduction to Hypothesis Testing 223

PREVIEW 224

8.1 The Logic of Hypothesis Testing 225

8.2 Uncertainty and Errors in Hypothesis Testing 236

8.3 More about Hypothesis Tests 240

8.4 Directional (One-Tailed) Hypothesis Tests 245

8.5 Concerns about Hypothesis Testing: Measuring Effect Size 250

8.6 Statistical Power 254

Summary 260

Focus on Problem Solving 261

Demonstration 8.1 262

Demonstration 8.2 263

Problems 263

CHAP t E R 9 Introduction to the t Statistic 267

PREVIEW 268

9.1 The t Statistic: An Alternative to z 268

9.2 Hypothesis Tests with the t Statistic 274

9.3 Measuring Effect Size for the t Statistic 279

9.4 Directional Hypotheses and One-Tailed Tests 288

Summary 291

Focus on Problem Solving 293

Demonstration 9.1 293

Demonstration 9.2 294

Problems 295

CHAPtER 10 The t Test for Two Independent Samples 299

PREVIEW 300

10.1 Introduction to the Independent-Measures Design 300

10.2 The Null Hypothesis and the Independent-Measures t Statistic 302

10.3 Hypothesis Tests with the Independent-Measures t Statistic 310

10.4 Effect Size and Confidence Intervals for the

Independent-Measures t 316

10.5 The Role of Sample Variance and Sample Size in the

Independent-Measures t Test 322

Summary 325

Focus on Problem Solving 327

Demonstration 10.1 328

Demonstration 10.2 329

Problems 329

CHAPtER 11 The t Test for Two Related Samples 335

PREVIEW 336

11.1 Introduction to Repeated-Measures Designs 336

11.2 The t Statistic for a Repeated-Measures Research Design 339

11.3 Hypothesis Tests for the Repeated-Measures Design 343

11.4 Effect Size and Confidence Intervals for the Repeated-Measures t 347

11.5 Comparing Repeated- and Independent-Measures Designs 352

Summary 355

Focus on Problem Solving 358

Demonstration 11.1 358

Demonstration 11.2 359

Problems 360

CHAPtER 12 Introduction to Analysis of Variance 365

PREVIEW 366

12.1 Introduction (An Overview of Analysis of Variance) 366

12.2 The Logic of Analysis of Variance 372

12.3 ANOVA Notation and Formulas 375

12.4 Examples of Hypothesis Testing and Effect Size with ANOVA 383

12.5 Post Hoc Tests 393

12.6 More about ANOVA 397

Summary 403

Focus on Problem Solving 406

Demonstration 12.1 406

Demonstration 12.2 408

Problems 408

CHAPtER 13 Repeated-Measures Analysis of Variance 413

PREVIEW 414

13.1 Overview of the Repeated-Measures ANOVA 415

13.2 Hypothesis Testing and Effect Size with the

Repeated-Measures ANOVA 420

13.3 More about the Repeated-Measures Design 429

Summary 436

Focus on Problem Solving 438

Demonstration 13.1 439

Demonstration 13.2 440

Problems 441

CHAPtER 14

Two-Factor Analysis of Variance

(Independent Measures) 447

PREVIEW 448

14.1 An Overview of the Two-Factor, Independent-Measures, ANOVA: Main

Effects and Interactions 448

14.2 An Example of the Two-Factor ANOVA and Effect Size 458

14.3 More about the Two-Factor ANOVA 467

Summary 473

Focus on Problem Solving 475

Demonstration 14.1 476

Demonstration 14.2 478

Problems 479

CHAPtER 15 Correlation 485

PREVIEW 486

15.1 Introduction 487

15.2 The Pearson Correlation 489

15.3 Using and Interpreting the Pearson Correlation 495

15.4 Hypothesis Tests with the Pearson Correlation 506

15.5 Alternatives to the Pearson Correlation 510

Summary 520

Focus on Problem Solving 522

Demonstration 15.1 523

Problems 524

CHAPtER 16 Introduction to Regression 529

PREVIEW 530

16.1 Introduction to Linear Equations and Regression 530

16.2 The Standard Error of Estimate and Analysis of Regression:

The Significance of the Regression Equation 538

16.3 Introduction to Multiple Regression with Two Predictor Variables 544

Summary 552

Linear and Multiple Regression 554

Focus on Problem Solving 554

Demonstration 16.1 555

Problems 556

CHAPtER 17

The Chi-Square Statistic: Tests for Goodness

of Fit and Independence 559

PREVIEW 560

17.1 Introduction to Chi-Square: The Test for Goodness of Fit 561

17.2 An Example of the Chi-Square Test for Goodness of Fit 567

17.3 The Chi-Square Test for Independence 573

17.4 Effect Size and Assumptions for the Chi-Square Tests 582

17.5 Special Applications of the Chi-Square Tests 587

Summary 591

Focus on Problem Solving 595

Demonstration 17.1 595

Demonstration 17.2 597

Problems 597

CHAPtER 18 The Binomial Test 603

PREVIEW 604

18.1 Introduction to the Binomial Test 604

18.2 An Example of the Binomial Test 608

18.3 More about the Binomial Test: Relationship with Chi-Square

and the Sign Test 612

Summary 617

Focus on Problem Solving 619

Demonstration 18.1 619

Problems 620

APPENDIXES

A Basic Mathematics Review 625

A.1 Symbols and Notation 627

A.2 Proportions: Fractions, Decimals, and Percentages 629

A.3 Negative Numbers 635

A.4 Basic Algebra: Solving Equations 637

A.5 Exponents and Square Roots 640

B Statistical Tables 647

C Solutions for Odd-Numbered Problems in the Text 663

D General Instructions for Using SPSS 683

E Hypothesis Tests for Ordinal Data: Mann-Whitney,

Wilcoxon, Kruskal-Wallis, and Friedman Tests 687

Statistics Organizer: Finding the Right Statistics for Your Data 701

References 717

Name Index 723

Subject Index 725

A Basic Mathematics Review 625

A.1 Symbols and Notation 627

A.2 Proportions: Fractions, Decimals, and Percentages 629

A.3 Negative Numbers 635

A.4 Basic Algebra: Solving Equations 637

A.5 Exponents and Square Roots 640

B Statistical Tables 647

C Solutions for Odd-Numbered Problems in the Text 663

D General Instructions for Using SPSS 683

E Hypothesis Tests for Ordinal Data: Mann-Whitney,

Wilcoxon, Kruskal-Wallis, and Friedman Tests 687

Statistics Organizer: Finding the Right Statistics for Your Data 701

References 717

Name Index 723

Subject Index 725