Applied Statistics and Multivariate Data Analysis for Business and Economics, 1st ed. 2019
A Modern Approach Using SPSS, Stata, and Excel

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Language: Anglais

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This textbook will familiarize students in economics and business, as well as practitioners, with the basic principles, techniques, and applications of applied statistics, statistical testing, and multivariate data analysis. Drawing on practical examples from the business world, it demonstrates the methods of univariate, bivariate, and multivariate statistical analysis. The textbook covers a range of topics, from data collection and scaling to the presentation and simple univariate analysis of quantitative data, while also providing advanced analytical procedures for assessing multivariate relationships. Accordingly, it addresses all topics typically covered in university courses on statistics and advanced applied data analysis. In addition, it does not limit itself to presenting applied methods, but also discusses the related use of Excel, SPSS, and Stata.
Contents

Preface  2

List of Figures  11

List of Tables  18

1     Statistics and Empirical Research   19

1.1     Do Statistics Lie?  19

1.2     Different Types of Statistics  21

1.3     The Generation of Knowledge Through Statistics  24

1.4     The Phases of Empirical Research   26

1.4.1     From Exploration to Theory  26

1.4.2     From Theories to Models  27

1.4.3     From Models to Business Intelligence  31

References  33

2     From Disarray to Dataset  34

2.1     Data Collection   34

2.2     Level of Measurement  35

2.3     Scaling and Coding   39

2.4     Missing Values  41

2.5     Outliers and Obviously Incorrect Values  43

2.6     Chapter Exercises  43

2.7     Exercise Solutions  44

References  45

3     Univariate Data Analysis  46

3.1     First Steps in Data Analysis  46

3.2     Measures of Central Tendency  54

3.2.1     Mode or Modal Value  54

3.2.2     Mean   55

3.2.3     Geometric Mean   60

3.2.4     Harmonic Mean   61

3.2.5     The Median   64

3.2.6     Quartile and Percentile  67

3.3     The Boxplot: A First Look at Distributions  68

3.4     Dispersion Parameters  72

3.4.1     Standard Deviation and Variance  73

3.4.2     The Coefficient of Variation   75

3.5     Skewness and Kurtosis  76

3.6     Robustness of Parameters  79

3.7     Measures of Concentration   80

3.8     Using the Computer to Calculate Univariate Parameters  83

3.8.1     Calculating Univariate Parameters with SPSS  83

3.8.2     Calculating Univariate Parameters with Stata  84

3.8.3     Calculating Univariate Parameters with Excel  85

3.9     Chapter Exercises  86

3.10     Exercise Solutions  89

References  93

4     Bivariate Association   94

4.1     Bivariate Scale Combinations  94

4.2     Association Between Two Nominal Variables  95

4.2.1     Contingency Tables  95

4.2.2     Chi-Square Calculations  97

4.2.3     The Phi Coefficient  102

4.2.4     The Contingency Coefficient  105

4.2.5     Cramer's V   107

4.2.6     Nominal Associations with SPSS  107

4.2.7     Nominal Associations with Stata  112

4.2.8     Nominal Associations with Excel  112

4.3     Association Between Two Metric Variables  114

4.3.1     The Scatterplot  114

4.3.2     The Bravais-Pearson Correlation Coefficient  117

4.4     Relationships Between Ordinal Variables  121

4.4.1     Spearman’s Rank Correlation Coefficient (Spearman’s rho)  123

4.4.2     Kendall’s Tau (t)  128

4.5     Measuring the Association Between Two Variables with Different Scales  135

4.5.1     Measuring the Association Between Nominal and Metric Variables  135

4.5.2     Measuring the Association Between Nominal and Ordinal Variables  138

4.5.3     Association between Ordinal and Metric variables  139

4.6     Calculating Correlation with a Computer  141

4.6.1     Calculating Correlation with SPSS  141

4.6.2     Calculating Correlation with Stata  142

4.6.3     Calculating Correlation with Excel  143

4.7     Spurious Correlations  146

4.7.1     Partial Correlation   148

4.7.2     Partial Correlations with SPSS  149

4.7.3     Partial Correlations with Stata  150

4.7.4     Partial Correlation with Excel  151

4.8     Chapter Exercises  152

4.9     Exercise Solutions  158

References  164

5     Classical Measurement Theory   165

5.1     Sources of Sampling Errors  166

5.2     Sources of Nonsampling Errors  169

References  172

6     Calculating Probability   173

6.1     Key Terms for Calculating Probability  173

6.2     Probability Definitions  176

6.3     Foundations of Probability Calculus  180

6.3.1     Probability Tree  180

6.3.2     Combinatorics  181

6.3.3     The Inclusion–Exclusion Principle for Disjoint Events  187

6.3.4     Inclusion–Exclusion Principle for Nondisjoint Events  188

6.3.5     Conditional Probability  189

6.3.6     Independent Events and Law of Multiplication   190

6.3.7     Law of Total Probability  191

6.3.8     Bayes’ Theorem   192

6.3.9     Postscript: The Monty Hall Problem   193

6.4     Chapter Exercises  197

6.5     Exercise Solutions  200

References  209

7     Random Variables and Probability Distributions  210

7.1     Discrete Distributions  212

7.1.1     Binomial Distribution   212

7.1.1.1     Calculating Binomial Distributions using Excel 215

7.1.1.2     Calculating Binomial Distributions using Stata  216

7.1.2     Hypergeometric Distribution   217

7.1.2.1     Calculating Hypergeometric Distributions using Excel 220

7.1.2.2     Calculating the Hypergeometric Distribution using Stata  221

7.1.3     The Poisson Distribution   222

7.1.3.1     Calculating the Poisson Distribution using Excel 224

7.1.3.2     Calculating the Poisson Distribution using Stata  225

7.2     Continuous Distributions  226

7.2.1     The Continuous Uniform Distribution   228

7.2.2     The Normal Distribution   231

7.2.2.1     Calculating the Normal Distribution using Excel 241

7.2.2.2     Calculating the Normal Distribution using Stata  242

7.3     Important Distributions for Testing   243

7.3.1     The Chi-Squared Distribution   243

7.3.1.1     Calculating the Chi-Squared Distribution using Excel 245

7.3.1.2     Calculating the Chi-Squared Distribution using Stata  246

7.3.2     The t-Distribution   247

7.3.2.1     Calculating the t-Distribution using Excel 249

7.3.2.2     Calculating the t-Distribution using Stata  250

7.3.3     The F-distribution   251

7.3.3.1     Calculating the F-Distribution using Excel 252

7.3.3.2     Calculating the F-Distribution using Stata  253

7.4     Chapter Exercises  254

7.5     Exercise Solutions  258

References  268

8     Parameter Estimation   269

8.1     Point estimation   269

8.2     Interval estimation   277

8.2.1     The confidence interval for the mean of a population (m)  277

8.2.2     Planning the sample size for mean estimation   284

8.2.3     Confidence intervals for proportions  287

8.2.4     Planning sample sizes for proportions  290

8.2.5     The confidence interval for variances  291

8.2.6     Calculating confidence intervals with the computer  292

8.2.6.1     Calculating confidence intervals with Excel 292

8.2.6.2     Calculating confidence intervals with SPSS  296

8.2.6.3     Calculating confidence intervals with Stata  297

8.3     Chapter Exercises  301

8.4     Exercise Solutions  303

References  306

9     Hypothesis Testing   307

9.1     Fundamentals of Hypothesis Testing   307

9.2     One-Sample Tests  312

9.2.1     One-sample Z-test (when s is known)  312

9.2.2     One-sample t-test (when s is not known)  316

9.2.3     Probability value (p-value)  319

9.2.4     One-sample t-test with SPSS, Stata, and Excel  319

9.3     Tests for two dependent samples  323

9.3.1     The t-test for dependent samples  323

9.3.1.1     The paired t-test with SPSS  328

9.3.1.2     The paired t-test with Stata  329

9.3.1.3     The paired t-test with Excel 331

9.3.2     The Wilcoxon signed-rank test  332

9.3.2.1     The Wilcoxon signed-rank test with SPSS  337

9.3.2.2     The Wilcoxon signed-rank test with Stata  338

9.3.2.3     The Wilcoxon signed-rank test with Excel 339

9.4     Tests for two independent samples  340

9.4.1     The t-test of two independent samples  340

9.4.1.1     The t-test for two independent samples with SPSS  343

9.4.1.2     The t-test for two independent samples with Stata  345

9.4.1.3     The t-test for two independent samples with Excel 346

9.4.2     The Mann-Whitney U test (Wilcoxon rank-sum test)  348

9.4.2.1     The Mann-Whitney U test with SPSS  352

9.4.2.2     The Mann-Whitney U test with Stata  353

9.5     Tests for k independent samples  354

9.5.1     Analysis of Variance (ANOVA)  354

9.5.1.1     One-way Analysis of Variance (ANOVA)  355

9.5.1.2     Two-way Analysis of Variance (ANOVA)  360

9.5.1.3     Analysis of covariance (ANCOVA)  364

9.5.1.4     ANOVA/ANCOVA with SPSS  367

9.5.1.5     ANOVA/ANCOVA with Stata  368

9.5.1.6     ANOVA with Excel 369

9.5.2     Kruskal-Wallis test (H test)  371

9.5.2.1     Kruskal-Wallis H Test with SPSS  376

9.5.2.2     Kruskal-Wallis H Test with Stata  378

9.6     Other Tests  379

9.6.1     Chi-square test of independence  379

9.6.1.1     Chi-square test of independence with SPSS  381

9.6.1.2     Chi-Square Test of Independence with Stata  384

9.6.1.3     Chi-Square Test of Independence with Excel 384

9.6.2     Tests for normal distribution   386

9.6.2.1     Testing for normal distribution with SPSS  388

9.6.2.2     Testing for normal distribution with Stata  389

9.7     Chapter Exercises  389

9.8     Exercise Solutions  400

References  415

10     Regression Analysis  418

10.1     First Steps in Regression Analysis  418

10.2     Coefficients of Bivariate Regression   420

10.3     Multivariate Regression Coefficients  425

10.4     The Goodness of Fit of Regression Lines  426

10.5     Regression Calculations with the Computer  429

10.5.1     Regression Calculations with Excel  429

10.5.2     Regression Calculations with SPSS and Stata  430

10.6     Goodness of Fit of Multivariate Regressions  432

10.7     Regression with an Independent Dummy Variable  433

10.8     Leverage Effects of Data Points  435

10.9     Nonlinear Regressions  437

10.10     Approaches to Regression Diagnostics  441

10.11     Chapter Exercises  447

10.12     Exercise Solutions  454

References  457

11     Time Series and Indices  458

11.1     Price Indices  459

11.2     Quantity Indices  466

11.3     Value Indices (Sales Indices)  468

11.4     Deflating Time Series by Price Indices  469

11.5     Shifting Bases and Chaining Indices  470

11.6     Chapter Exercises  472

11.7     Exercise Solutions  473

References  475

12     Cluster Analysis  476

12.1     Hierarchical Cluster Analysis  477

12.2     K-Means Cluster Analysis  493

12.3     Cluster Analysis with SPSS and Stata  495

12.4     Chapter Exercises  499

12.5     Exercise Solutions  502

References  503

13     Factor Analysis  505

13.1     Factor Analysis: Foundations, Methods, Interpretations  505

13.2     Factor Analysis with SPSS and Stata  514

13.3     Chapter Exercises  517

13.4     Exercise Solutions  518

References  519

List of Formulas  520

Appendix   532

Appendix 1: The Standard Normal Distribution   533

Appendix 2: The Chi-Squared Distribution   534

Appendix 3: The Student’s t-Distribution   536

Appendix 4: Critical Values for the Wilcoxon Signed-Rank Test  537

Index   538

Thomas Cleff is a Professor of Quantitative Methods for Business and Economics at Pforzheim University, Germany, and a Research Associate at the Centre for European Economic Research (ZEW), Mannheim, Germany. Since joining Pforzheim University in 2000, Cleff has spearheaded the development of an international dual-degree program, was appointed Vice Dean in 2012 and Dean of the Business School in 2014. He is an expert on international accreditation and partnership programmes between institutions of higher education. In 2013, he became a board member at the Network of International Business and Economic Schools (NIBES), and a member of the international advisory board at the South American Education Quality Accreditation Agency (EQUAA); in 2016 he joined the AACSB Initial Accreditation Committee. He has served as a Visiting Professor at several universities, including ESCEM Tours-Poitiers, France; Simon Fraser University, Vancouver, Canada; TEC de Monterrey, Mexico; and Gadjah Mada University, Indonesia.

 Cleff holds degrees in Economics and Management from Pantheon-Sorbonne University, France and the University of Wuppertal, Germany. He was a Senior Researcher at the Centre for European Economic Research (ZEW) in Mannheim from 1997 to 2000, where he led a variety of international research projects in the fields of innovation and strategic management. His main research interests are in international marketing, white-collar crime, brand research, innovation, and industry studies. 


Offers a broad overview of the most important statistical and multivariate methods

Uses Excel, SPSS, and Stata, showing readers how to perform even complex data analyses independently

Includes a wealth of numerical examples and exercises