Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students    Through a statistical perspective, this book introduces readers to the topic of data science, including Big Data, data analytics, and data wrangling. Chapters include multiple examples showing the application of the theoretical aspects presented. It features practice problems designed to ensure that readers understand the concepts and can apply them using real data. Over 100 open data sets used for examples and problems come from regions throughout the world, allowing the instructor to adapt the application to local data with which students can identify. Applications with these data sets include: Assessing if searches during a police stop in San Diego are dependent on driver’s race Visualizing the association between fat percentage and moisture percentage in Canadian cheese Modeling taxi fares in Chicago using data from millions of rides Analyzing mean sales per unit of legal marijuana products in Washington state Topics covered in Principles of Managerial Statistics and Data Science include:data visualization; descriptive measures; probability; probability distributions; mathematical expectation; confidence intervals; and hypothesis testing. Analysis of variance; simple linear regression; and multiple linear regression are also included. In addition, the book offers contingency tables, Chi-square tests, non-parametric methods, and time series methods. The textbook:  Includes academic material usually covered in introductory Statistics courses, but with a data science twist, and less emphasis in the theory Relies on Minitab to present how to perform tasks with a computer Presents and motivates use of data that comes from open portals Focuses on developing an intuition on how the procedures work Exposes readers to the potential in Big Data and current failures of its use Supplementary material includes: a companion website that houses PowerPoint slides; an Instructor's Manual with tips, a syllabus model, and project ideas; R code to reproduce examples and case studies; and information about the open portal data   Features an appendix with solutions to some practice problems Principles of Managerial Statistics and Data Science is a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals.

Table of Contents:
Preface xv Acknowledgments xvii Acronyms xix About the Companion Site xxi Principles of Managerial Statistics and Data Science xxiii 1 Statistics Suck; So Why Do I Need to Learn About It? 1 1.1 Introduction 1 Practice Problems 4 1.2 Data-Based Decision Making: Some Applications 5 1.3 Statistics Defined 9 1.4 Use of Technology and the New Buzzwords: Data Science, Data Analytics, and Big Data 11 1.4.1 A Quick Look at Data Science: Some Definitions 11 Chapter Problems 14 Further Reading 14 2 Concepts in Statistics 15 2.1 Introduction 15 Practice Problems 17 2.2 Type of Data 19 Practice Problems 20 2.3 Four Important Notions in Statistics 22 Practice Problems 24 2.4 Sampling Methods 25 2.4.1 Probability Sampling 25 2.4.2 Nonprobability Sampling 27 Practice Problems 30 2.5 Data Management 31 2.5.1 A Quick Look at Data Science: Data Wrangling Baltimore Housing Variables 34 2.6 Proposing a Statistical Study 36 Chapter Problems 37 Further Reading 39 3 Data Visualization 41 3.1 Introduction 41 3.2 Visualization Methods for Categorical Variables 41 Practice Problems 46 3.3 Visualization Methods for Numerical Variables 50 Practice Problems 56 3.4 Visualizing Summaries of More than Two Variables Simultaneously 59 3.4.1 A Quick Look at Data Science: Does Race Affect the Chances of a Driver Being Searched During a Vehicle Stop in San Diego? 66 Practice Problems 69 3.5 Novel Data Visualization 75 3.5.1 A Quick Look at Data Science: Visualizing Association Between Baltimore Housing Variables Over 14 Years 78 Chapter Problems 81 Further Reading 96 4 Descriptive Statistics 97 4.1 Introduction 97 4.2 Measures of Centrality 99 Practice Problems 108 4.3 Measures of Dispersion 111 Practice Problems 115 4.4 Percentiles 116 4.4.1 Quartiles 117 Practice Problems 122 4.5 Measuring the Association Between Two Variables 124 Practice Problems 128 4.6 Sample Proportion and Other Numerical Statistics 130 4.6.1 A Quick Look at Data Science: Murder Rates in Los Angeles 131 4.7 How to Use Descriptive Statistics 132 Chapter Problems 133 Further Reading 139 5 Introduction to Probability 141 5.1 Introduction 141 5.2 Preliminaries 142 Practice Problems 144 5.3 The Probability of an Event 145 Practice Problems 148 5.4 Rules and Properties of Probabilities 149 Practice Problems 152 5.5 Conditional Probability and Independent Events 154 Practice Problems 159 5.6 Empirical Probabilities 161 5.6.1 A Quick Look at Data Science: Missing People Reports in Boston by Day of Week 164 Practice Problems 165 5.7 Counting Outcomes 168 Practice Problems 171 Chapter Problems 171 Further Reading 175 6 Discrete Random Variables 177 6.1 Introduction 177 6.2 General Properties 178 6.2.1 A Quick Look at Data Science: Number of Stroke Emergency Calls in Manhattan 183 Practice Problems 184 6.3 Properties of Expected Value and Variance 186 Practice Problems 189 6.4 Bernoulli and Binomial Random Variables 190 Practice Problems 197 6.5 Poisson Distribution 198 Practice Problems 201 6.6 Optional: Other Useful Probability Distributions 203 Chapter Problems 205 Further Reading 208 7 Continuous Random Variables 209 7.1 Introduction 209 Practice Problems 211 7.2 The Uniform Probability Distribution 211 Practice Problems 215 7.3 The Normal Distribution 216 Practice Problems 225 7.4 Probabilities for Any Normally Distributed Random Variable 227 7.4.1 A Quick Look at Data Science: Normal Distribution, A Good Match for University of Puerto Rico SATs? 229 Practice Problems 231 7.5 Approximating the Binomial Distribution 234 Practice Problems 236 7.6 Exponential Distribution 236 Practice Problems 238 Chapter Problems 239 Further Reading 242 8 Properties of Sample Statistics 243 8.1 Introduction 243 8.2 Expected Value and Standard Deviation of x̄ 244 Practice Problems 246 8.3 Sampling Distribution of x̄ When Sample Comes From a Normal Distribution 247 Practice Problems 251 8.4 Central Limit Theorem 252 8.4.1 A Quick Look at Data Science: Bacteria at New York City Beaches 257 Practice Problems 259 8.5 Other Properties of Estimators 261 Chapter Problems 264 Further Reading 267 9 Interval Estimation for One Population Parameter 269 9.1 Introduction 269 9.2 Intuition of a Two-Sided Confidence Interval 270 9.3 Confidence Interval for the Population Mean: 𝜎 Known 271 Practice Problems 276 9.4 Determining Sample Size for a Confidence Interval for 𝜇 278 Practice Problems 279 9.5 Confidence Interval for the Population Mean: 𝜎 Unknown 279 Practice Problems 284 9.6 Confidence Interval for 𝜋 286 Practice Problems 287 9.7 Determining Sample Size for 𝜋 Confidence Interval 288 Practice Problems 290 9.8 Optional: Confidence Interval for 𝜎 290 9.8.1 A Quick Look at Data Science: A Confidence Interval for the Standard Deviation of Walking Scores in Baltimore 292 Chapter Problems 293 Further Reading 296 10 Hypothesis Testing for One Population 297 10.1 Introduction 297 10.2 Basics of Hypothesis Testing 299 10.3 Steps to Perform a Hypothesis Test 304 Practice Problems 305 10.4 Inference on the Population Mean: Known Standard Deviation 306 Practice Problems 318 10.5 Hypothesis Testing for the Mean (𝜎 Unknown) 323 Practice Problems 327 10.6 Hypothesis Testing for the Population Proportion 329 10.6.1 A Quick Look at Data Science: Proportion of New York City High Schools with a Mean SAT Score of 1498 or More 333 Practice Problems 334 10.7 Hypothesis Testing for the Population Variance 337 10.8 More on the p-Value and Final Remarks 338 10.8.1 Misunderstanding the p-Value 339 Chapter Problems 343 Further Reading 347 11 Statistical Inference to Compare Parameters from Two Populations 349 11.1 Introduction 349 11.2 Inference on Two Population Means 350 11.3 Inference on Two Population Means – Independent Samples, Variances Known 351 Practice Problems 357 11.4 Inference on Two Population Means When Two Independent Samples are Used – Unknown Variances 360 11.4.1 A Quick Look at Data Science: Suicide Rates Among Asian Men and Women in New York City 364 Practice Problems 366 11.5 Inference on Two Means Using Two Dependent Samples 368 Practice Problems 370 11.6 Inference on Two Population Proportions 371 Practice Problems 374 Chapter Problems 375 References 378 Further Reading 378 12 Analysis of Variance (ANOVA) 379 12.1 Introduction 379 Practice Problems 382 12.2 ANOVA for One Factor 383 Practice Problems 390 12.3 Multiple Comparisons 391 Practice Problems 395 12.4 Diagnostics of ANOVA Assumptions 395 12.4.1 A Quick Look at Data Science: Emergency Response Time for Cardiac Arrest in New York City 399 Practice Problems 403 12.5 ANOVA with Two Factors 404 Practice Problems 409 12.6 Extensions to ANOVA 413 Chapter Problems 416 Further Reading 419 13 Simple Linear Regression 421 13.1 Introduction 421 13.2 Basics of Simple Linear Regression 423 Practice Problems 425 13.3 Fitting the Simple Linear Regression Parameters 426 Practice Problems 429 13.4 Inference for Simple Linear Regression 431 Practice Problems 440 13.5 Estimating and Predicting the Response Variable 443 Practice Problems 446 13.6 A Binary X 448 Practice Problems 449 13.7 Model Diagnostics (Residual Analysis) 450 Practice Problems 456 13.8 What Correlation Doesn’t Mean 458 13.8.1 A Quick Look at Data Science: Can Rate of College Educated People Help Predict the Rate of Narcotic Problems in Baltimore? 461 Chapter Problems 466 Further Reading 472 14 Multiple Linear Regression 473 14.1 Introduction 473 14.2 The Multiple Linear Regression Model 474 Practice Problems 477 14.3 Inference for Multiple Linear Regression 478 Practice Problems 483 14.4 Multicollinearity and Other Modeling Aspects 486 Practice Problems 490 14.5 Variability Around the Regression Line: Residuals and Intervals 492 Practice Problems 494 14.6 Modifying Predictors 494 Practice Problems 495 14.7 General Linear Model 496 Practice Problems 502 14.8 Steps to Fit a Multiple Linear Regression Model 505 14.9 Other Regression Topics 507 14.9.1 A Quick Look at Data Science: Modeling Taxi Fares in Chicago 510 Chapter Problems 513 Further Reading 517 15 Inference on Association of Categorical Variables 519 15.1 Introduction 519 15.2 Association Between Two Categorical Variables 520 15.2.1 A Quick Look at Data Science: Affordability and Business Environment in Chattanooga 525 Practice Problems 529 Chapter Problems 532 Further Reading 532 16 Nonparametric Testing 533 16.1 Introduction 533 16.2 Sign Tests and Wilcoxon Sign-Rank Tests: One Sample and Matched Pairs Scenarios 533 Practice Problems 537 16.3 Wilcoxon Rank-Sum Test: Two Independent Samples 539 16.3.1 A Quick Look at Data Science: Austin, Texas, as a Place to Live; Do Men Rate It Higher Than Women? 540 Practice Problems 543 16.4 Kruskal–Wallis Test: More Than Two Samples 544 Practice Problems 546 16.5 Nonparametric Tests Versus Their Parametric Counterparts 547 Chapter Problems 548 Further Reading 549 17 Forecasting 551 17.1 Introduction 551 17.2 Time Series Components 552 Practice Problems 557 17.3 Simple Forecasting Models 558 Practice Problems 562 17.4 Forecasting When Data Has Trend, Seasonality 563 Practice Problems 569 17.5 Assessing Forecasts 572 17.5.1 A Quick Look at Data Science: Forecasting Tourism Jobs in Canada 575 17.5.2 A Quick Look at Data Science: Forecasting Retail Gross Sales of Marijuana in Denver 577 Chapter Problems 580 Further Reading 581 Appendix A Math Notation and Symbols 583 A.1 Summation 583 A.2 pth Power 583 A.3 Inequalities 584 A.4 Factorials 584 A.5 Exponential Function 585 A.6 Greek and Statistics Symbols 585 Appendix B Standard Normal Cumulative Distribution Function 587 Appendix C t Distribution Critical Values 591 Appendix D Solutions to Odd-Numbered Problems 593 Index 643