{"product_id":"principles-of-managerial-statistics-and-data-science-isbn-9781119486411","title":"Principles of Managerial Statistics and Data Science","description":"\u003cp\u003e\u003cb\u003eIntroduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students  \u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eThrough 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eAssessing if searches during a police stop in San Diego are dependent on driver’s race\u003c\/li\u003e \u003cli\u003eVisualizing the association between fat percentage and moisture percentage in Canadian cheese\u003c\/li\u003e \u003cli\u003eModeling taxi fares in Chicago using data from millions of rides\u003c\/li\u003e \u003cli\u003eAnalyzing mean sales per unit of legal marijuana products in Washington state\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eTopics covered in \u003ci\u003ePrinciples of Managerial Statistics and Data Science \u003c\/i\u003einclude: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: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eIncludes academic material usually covered in introductory Statistics courses, but with a data science twist, and less emphasis in the theory\u003c\/li\u003e \u003cli\u003eRelies on Minitab to present how to perform tasks with a computer\u003c\/li\u003e \u003cli\u003ePresents and motivates use of data that comes from open portals\u003c\/li\u003e \u003cli\u003eFocuses on developing an intuition on how the procedures work\u003c\/li\u003e \u003cli\u003eExposes readers to the potential in Big Data and current failures of its use\u003c\/li\u003e \u003cli\u003eSupplementary 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  \u003c\/li\u003e \u003cli\u003eFeatures an appendix with solutions to some practice problems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003ePrinciples of Managerial Statistics and Data Science \u003c\/i\u003eis a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals.\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgments xvii\u003c\/p\u003e \u003cp\u003eAcronyms xix\u003c\/p\u003e \u003cp\u003eAbout the Companion Site xxi\u003c\/p\u003e \u003cp\u003ePrinciples of Managerial Statistics and Data Science xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Statistics Suck; So Why Do I Need to Learn About It? \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003ePractice Problems 4\u003c\/p\u003e \u003cp\u003e1.2 Data-Based Decision Making: Some Applications 5\u003c\/p\u003e \u003cp\u003e1.3 Statistics Defined 9\u003c\/p\u003e \u003cp\u003e1.4 Use of Technology and the New Buzzwords: Data Science, Data Analytics, and Big Data 11\u003c\/p\u003e \u003cp\u003e1.4.1 A Quick Look at Data Science: Some Definitions 11\u003c\/p\u003e \u003cp\u003eChapter Problems 14\u003c\/p\u003e \u003cp\u003eFurther Reading 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Concepts in Statistics \u003c\/b\u003e\u003cb\u003e15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 15\u003c\/p\u003e \u003cp\u003ePractice Problems 17\u003c\/p\u003e \u003cp\u003e2.2 Type of Data 19\u003c\/p\u003e \u003cp\u003ePractice Problems 20\u003c\/p\u003e \u003cp\u003e2.3 Four Important Notions in Statistics 22\u003c\/p\u003e \u003cp\u003ePractice Problems 24\u003c\/p\u003e \u003cp\u003e2.4 Sampling Methods 25\u003c\/p\u003e \u003cp\u003e2.4.1 Probability Sampling 25\u003c\/p\u003e \u003cp\u003e2.4.2 Nonprobability Sampling 27\u003c\/p\u003e \u003cp\u003ePractice Problems 30\u003c\/p\u003e \u003cp\u003e2.5 Data Management 31\u003c\/p\u003e \u003cp\u003e2.5.1 A Quick Look at Data Science: Data Wrangling Baltimore Housing Variables 34\u003c\/p\u003e \u003cp\u003e2.6 Proposing a Statistical Study 36\u003c\/p\u003e \u003cp\u003eChapter Problems 37\u003c\/p\u003e \u003cp\u003eFurther Reading 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Data Visualization \u003c\/b\u003e\u003cb\u003e41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 41\u003c\/p\u003e \u003cp\u003e3.2 Visualization Methods for Categorical Variables 41\u003c\/p\u003e \u003cp\u003ePractice Problems 46\u003c\/p\u003e \u003cp\u003e3.3 Visualization Methods for Numerical Variables 50\u003c\/p\u003e \u003cp\u003ePractice Problems 56\u003c\/p\u003e \u003cp\u003e3.4 Visualizing Summaries of More than Two Variables Simultaneously 59\u003c\/p\u003e \u003cp\u003e3.4.1 A Quick Look at Data Science: Does Race Affect the Chances of a Driver Being Searched During a Vehicle Stop in San Diego? 66\u003c\/p\u003e \u003cp\u003ePractice Problems 69\u003c\/p\u003e \u003cp\u003e3.5 Novel Data Visualization 75\u003c\/p\u003e \u003cp\u003e3.5.1 A Quick Look at Data Science: Visualizing Association Between Baltimore Housing Variables Over 14 Years 78\u003c\/p\u003e \u003cp\u003eChapter Problems 81\u003c\/p\u003e \u003cp\u003eFurther Reading 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Descriptive Statistics \u003c\/b\u003e\u003cb\u003e97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 97\u003c\/p\u003e \u003cp\u003e4.2 Measures of Centrality 99\u003c\/p\u003e \u003cp\u003ePractice Problems 108\u003c\/p\u003e \u003cp\u003e4.3 Measures of Dispersion 111\u003c\/p\u003e \u003cp\u003ePractice Problems 115\u003c\/p\u003e \u003cp\u003e4.4 Percentiles 116\u003c\/p\u003e \u003cp\u003e4.4.1 Quartiles 117\u003c\/p\u003e \u003cp\u003ePractice Problems 122\u003c\/p\u003e \u003cp\u003e4.5 Measuring the Association Between Two Variables 124\u003c\/p\u003e \u003cp\u003ePractice Problems 128\u003c\/p\u003e \u003cp\u003e4.6 Sample Proportion and Other Numerical Statistics 130\u003c\/p\u003e \u003cp\u003e4.6.1 A Quick Look at Data Science: Murder Rates in Los Angeles 131\u003c\/p\u003e \u003cp\u003e4.7 How to Use Descriptive Statistics 132\u003c\/p\u003e \u003cp\u003eChapter Problems 133\u003c\/p\u003e \u003cp\u003eFurther Reading 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Introduction to Probability \u003c\/b\u003e\u003cb\u003e141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 141\u003c\/p\u003e \u003cp\u003e5.2 Preliminaries 142\u003c\/p\u003e \u003cp\u003ePractice Problems 144\u003c\/p\u003e \u003cp\u003e5.3 The Probability of an Event 145\u003c\/p\u003e \u003cp\u003ePractice Problems 148\u003c\/p\u003e \u003cp\u003e5.4 Rules and Properties of Probabilities 149\u003c\/p\u003e \u003cp\u003ePractice Problems 152\u003c\/p\u003e \u003cp\u003e5.5 Conditional Probability and Independent Events 154\u003c\/p\u003e \u003cp\u003ePractice Problems 159\u003c\/p\u003e \u003cp\u003e5.6 Empirical Probabilities 161\u003c\/p\u003e \u003cp\u003e5.6.1 A Quick Look at Data Science: Missing People Reports in Boston by Day of Week 164\u003c\/p\u003e \u003cp\u003ePractice Problems 165\u003c\/p\u003e \u003cp\u003e5.7 Counting Outcomes 168\u003c\/p\u003e \u003cp\u003ePractice Problems 171\u003c\/p\u003e \u003cp\u003eChapter Problems 171\u003c\/p\u003e \u003cp\u003eFurther Reading 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Discrete Random Variables \u003c\/b\u003e\u003cb\u003e177\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 177\u003c\/p\u003e \u003cp\u003e6.2 General Properties 178\u003c\/p\u003e \u003cp\u003e6.2.1 A Quick Look at Data Science: Number of Stroke Emergency Calls in Manhattan 183\u003c\/p\u003e \u003cp\u003ePractice Problems 184\u003c\/p\u003e \u003cp\u003e6.3 Properties of Expected Value and Variance 186\u003c\/p\u003e \u003cp\u003ePractice Problems 189\u003c\/p\u003e \u003cp\u003e6.4 Bernoulli and Binomial Random Variables 190\u003c\/p\u003e \u003cp\u003ePractice Problems 197\u003c\/p\u003e \u003cp\u003e6.5 Poisson Distribution 198\u003c\/p\u003e \u003cp\u003ePractice Problems 201\u003c\/p\u003e \u003cp\u003e6.6 Optional: Other Useful Probability Distributions 203\u003c\/p\u003e \u003cp\u003eChapter Problems 205\u003c\/p\u003e \u003cp\u003eFurther Reading 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Continuous Random Variables \u003c\/b\u003e\u003cb\u003e209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 209\u003c\/p\u003e \u003cp\u003ePractice Problems 211\u003c\/p\u003e \u003cp\u003e7.2 The Uniform Probability Distribution 211\u003c\/p\u003e \u003cp\u003ePractice Problems 215\u003c\/p\u003e \u003cp\u003e7.3 The Normal Distribution 216\u003c\/p\u003e \u003cp\u003ePractice Problems 225\u003c\/p\u003e \u003cp\u003e7.4 Probabilities for Any Normally Distributed Random Variable 227\u003c\/p\u003e \u003cp\u003e7.4.1 A Quick Look at Data Science: Normal Distribution, A Good Match for University of Puerto Rico SATs? 229\u003c\/p\u003e \u003cp\u003ePractice Problems 231\u003c\/p\u003e \u003cp\u003e7.5 Approximating the Binomial Distribution 234\u003c\/p\u003e \u003cp\u003ePractice Problems 236\u003c\/p\u003e \u003cp\u003e7.6 Exponential Distribution 236\u003c\/p\u003e \u003cp\u003ePractice Problems 238\u003c\/p\u003e \u003cp\u003eChapter Problems 239\u003c\/p\u003e \u003cp\u003eFurther Reading 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Properties of Sample Statistics \u003c\/b\u003e\u003cb\u003e243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 243\u003c\/p\u003e \u003cp\u003e8.2 Expected Value and Standard Deviation of \u003ci\u003ex̄ \u003c\/i\u003e244\u003c\/p\u003e \u003cp\u003ePractice Problems 246\u003c\/p\u003e \u003cp\u003e8.3 Sampling Distribution of \u003ci\u003ex̄ \u003c\/i\u003eWhen Sample Comes From a Normal Distribution 247\u003c\/p\u003e \u003cp\u003ePractice Problems 251\u003c\/p\u003e \u003cp\u003e8.4 Central Limit Theorem 252\u003c\/p\u003e \u003cp\u003e8.4.1 A Quick Look at Data Science: Bacteria at New York City Beaches 257\u003c\/p\u003e \u003cp\u003ePractice Problems 259\u003c\/p\u003e \u003cp\u003e8.5 Other Properties of Estimators 261\u003c\/p\u003e \u003cp\u003eChapter Problems 264\u003c\/p\u003e \u003cp\u003eFurther Reading 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Interval Estimation for One Population Parameter \u003c\/b\u003e\u003cb\u003e269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 269\u003c\/p\u003e \u003cp\u003e9.2 Intuition of a Two-Sided Confidence Interval 270\u003c\/p\u003e \u003cp\u003e9.3 Confidence Interval for the Population Mean: \u003ci\u003e𝜎 \u003c\/i\u003eKnown 271\u003c\/p\u003e \u003cp\u003ePractice Problems 276\u003c\/p\u003e \u003cp\u003e9.4 Determining Sample Size for a Confidence Interval for \u003ci\u003e𝜇 \u003c\/i\u003e278\u003c\/p\u003e \u003cp\u003ePractice Problems 279\u003c\/p\u003e \u003cp\u003e9.5 Confidence Interval for the Population Mean: \u003ci\u003e𝜎 \u003c\/i\u003eUnknown 279\u003c\/p\u003e \u003cp\u003ePractice Problems 284\u003c\/p\u003e \u003cp\u003e9.6 Confidence Interval for \u003ci\u003e𝜋 \u003c\/i\u003e286\u003c\/p\u003e \u003cp\u003ePractice Problems 287\u003c\/p\u003e \u003cp\u003e9.7 Determining Sample Size for \u003ci\u003e𝜋 \u003c\/i\u003eConfidence Interval 288\u003c\/p\u003e \u003cp\u003ePractice Problems 290\u003c\/p\u003e \u003cp\u003e9.8 Optional: Confidence Interval for \u003ci\u003e𝜎 \u003c\/i\u003e290\u003c\/p\u003e \u003cp\u003e9.8.1 A Quick Look at Data Science: A Confidence Interval for the Standard Deviation of Walking Scores in Baltimore 292\u003c\/p\u003e \u003cp\u003eChapter Problems 293\u003c\/p\u003e \u003cp\u003eFurther Reading 296\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Hypothesis Testing for One Population \u003c\/b\u003e\u003cb\u003e297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 297\u003c\/p\u003e \u003cp\u003e10.2 Basics of Hypothesis Testing 299\u003c\/p\u003e \u003cp\u003e10.3 Steps to Perform a Hypothesis Test 304\u003c\/p\u003e \u003cp\u003ePractice Problems 305\u003c\/p\u003e \u003cp\u003e10.4 Inference on the Population Mean: Known Standard Deviation 306\u003c\/p\u003e \u003cp\u003ePractice Problems 318\u003c\/p\u003e \u003cp\u003e10.5 Hypothesis Testing for the Mean (\u003ci\u003e𝜎 \u003c\/i\u003eUnknown) 323\u003c\/p\u003e \u003cp\u003ePractice Problems 327\u003c\/p\u003e \u003cp\u003e10.6 Hypothesis Testing for the Population Proportion 329\u003c\/p\u003e \u003cp\u003e10.6.1 A Quick Look at Data Science: Proportion of New York City High Schools with a Mean SAT Score of 1498 or More 333\u003c\/p\u003e \u003cp\u003ePractice Problems 334\u003c\/p\u003e \u003cp\u003e10.7 Hypothesis Testing for the Population Variance 337\u003c\/p\u003e \u003cp\u003e10.8 More on the \u003ci\u003ep\u003c\/i\u003e-Value and Final Remarks 338\u003c\/p\u003e \u003cp\u003e10.8.1 Misunderstanding the \u003ci\u003ep\u003c\/i\u003e-Value 339\u003c\/p\u003e \u003cp\u003eChapter Problems 343\u003c\/p\u003e \u003cp\u003eFurther Reading 347\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Statistical Inference to Compare Parameters from Two Populations \u003c\/b\u003e\u003cb\u003e349\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 349\u003c\/p\u003e \u003cp\u003e11.2 Inference on Two Population Means 350\u003c\/p\u003e \u003cp\u003e11.3 Inference on Two Population Means – Independent Samples, Variances Known 351\u003c\/p\u003e \u003cp\u003ePractice Problems 357\u003c\/p\u003e \u003cp\u003e11.4 Inference on Two Population Means When Two Independent Samples are Used – Unknown Variances 360\u003c\/p\u003e \u003cp\u003e11.4.1 A Quick Look at Data Science: Suicide Rates Among Asian Men and Women in New York City 364\u003c\/p\u003e \u003cp\u003ePractice Problems 366\u003c\/p\u003e \u003cp\u003e11.5 Inference on Two Means Using Two Dependent Samples 368\u003c\/p\u003e \u003cp\u003ePractice Problems 370\u003c\/p\u003e \u003cp\u003e11.6 Inference on Two Population Proportions 371\u003c\/p\u003e \u003cp\u003ePractice Problems 374\u003c\/p\u003e \u003cp\u003eChapter Problems 375\u003c\/p\u003e \u003cp\u003eReferences 378\u003c\/p\u003e \u003cp\u003eFurther Reading 378\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Analysis of Variance (ANOVA) \u003c\/b\u003e\u003cb\u003e379\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 379\u003c\/p\u003e \u003cp\u003ePractice Problems 382\u003c\/p\u003e \u003cp\u003e12.2 ANOVA for One Factor 383\u003c\/p\u003e \u003cp\u003ePractice Problems 390\u003c\/p\u003e \u003cp\u003e12.3 Multiple Comparisons 391\u003c\/p\u003e \u003cp\u003ePractice Problems 395\u003c\/p\u003e \u003cp\u003e12.4 Diagnostics of ANOVA Assumptions 395\u003c\/p\u003e \u003cp\u003e12.4.1 A Quick Look at Data Science: Emergency Response Time for Cardiac Arrest in New York City 399\u003c\/p\u003e \u003cp\u003ePractice Problems 403\u003c\/p\u003e \u003cp\u003e12.5 ANOVA with Two Factors 404\u003c\/p\u003e \u003cp\u003ePractice Problems 409\u003c\/p\u003e \u003cp\u003e12.6 Extensions to ANOVA 413\u003c\/p\u003e \u003cp\u003eChapter Problems 416\u003c\/p\u003e \u003cp\u003eFurther Reading 419\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Simple Linear Regression \u003c\/b\u003e\u003cb\u003e421\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 421\u003c\/p\u003e \u003cp\u003e13.2 Basics of Simple Linear Regression 423\u003c\/p\u003e \u003cp\u003ePractice Problems 425\u003c\/p\u003e \u003cp\u003e13.3 Fitting the Simple Linear Regression Parameters 426\u003c\/p\u003e \u003cp\u003ePractice Problems 429\u003c\/p\u003e \u003cp\u003e13.4 Inference for Simple Linear Regression 431\u003c\/p\u003e \u003cp\u003ePractice Problems 440\u003c\/p\u003e \u003cp\u003e13.5 Estimating and Predicting the Response Variable 443\u003c\/p\u003e \u003cp\u003ePractice Problems 446\u003c\/p\u003e \u003cp\u003e13.6 A Binary \u003ci\u003eX \u003c\/i\u003e448\u003c\/p\u003e \u003cp\u003ePractice Problems 449\u003c\/p\u003e \u003cp\u003e13.7 Model Diagnostics (Residual Analysis) 450\u003c\/p\u003e \u003cp\u003ePractice Problems 456\u003c\/p\u003e \u003cp\u003e13.8 What Correlation Doesn’t Mean 458\u003c\/p\u003e \u003cp\u003e13.8.1 A Quick Look at Data Science: Can Rate of College Educated People Help Predict the Rate of Narcotic Problems in Baltimore? 461\u003c\/p\u003e \u003cp\u003eChapter Problems 466\u003c\/p\u003e \u003cp\u003eFurther Reading 472\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Multiple Linear Regression \u003c\/b\u003e\u003cb\u003e473\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 473\u003c\/p\u003e \u003cp\u003e14.2 The Multiple Linear Regression Model 474\u003c\/p\u003e \u003cp\u003ePractice Problems 477\u003c\/p\u003e \u003cp\u003e14.3 Inference for Multiple Linear Regression 478\u003c\/p\u003e \u003cp\u003ePractice Problems 483\u003c\/p\u003e \u003cp\u003e14.4 Multicollinearity and Other Modeling Aspects 486\u003c\/p\u003e \u003cp\u003ePractice Problems 490\u003c\/p\u003e \u003cp\u003e14.5 Variability Around the Regression Line: Residuals and Intervals 492\u003c\/p\u003e \u003cp\u003ePractice Problems 494\u003c\/p\u003e \u003cp\u003e14.6 Modifying Predictors 494\u003c\/p\u003e \u003cp\u003ePractice Problems 495\u003c\/p\u003e \u003cp\u003e14.7 General Linear Model 496\u003c\/p\u003e \u003cp\u003ePractice Problems 502\u003c\/p\u003e \u003cp\u003e14.8 Steps to Fit a Multiple Linear Regression Model 505\u003c\/p\u003e \u003cp\u003e14.9 Other Regression Topics 507\u003c\/p\u003e \u003cp\u003e14.9.1 A Quick Look at Data Science: Modeling Taxi Fares in Chicago 510\u003c\/p\u003e \u003cp\u003eChapter Problems 513\u003c\/p\u003e \u003cp\u003eFurther Reading 517\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Inference on Association of Categorical Variables \u003c\/b\u003e\u003cb\u003e519\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 519\u003c\/p\u003e \u003cp\u003e15.2 Association Between Two Categorical Variables 520\u003c\/p\u003e \u003cp\u003e15.2.1 A Quick Look at Data Science: Affordability and Business Environment in Chattanooga 525\u003c\/p\u003e \u003cp\u003ePractice Problems 529\u003c\/p\u003e \u003cp\u003eChapter Problems 532\u003c\/p\u003e \u003cp\u003eFurther Reading 532\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Nonparametric Testing \u003c\/b\u003e\u003cb\u003e533\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 533\u003c\/p\u003e \u003cp\u003e16.2 Sign Tests and Wilcoxon Sign-Rank Tests: One Sample and Matched Pairs Scenarios 533\u003c\/p\u003e \u003cp\u003ePractice Problems 537\u003c\/p\u003e \u003cp\u003e16.3 Wilcoxon Rank-Sum Test: Two Independent Samples 539\u003c\/p\u003e \u003cp\u003e16.3.1 A Quick Look at Data Science: Austin, Texas, as a Place to Live; Do Men Rate It Higher Than Women? 540\u003c\/p\u003e \u003cp\u003ePractice Problems 543\u003c\/p\u003e \u003cp\u003e16.4 Kruskal–Wallis Test: More Than Two Samples 544\u003c\/p\u003e \u003cp\u003ePractice Problems 546\u003c\/p\u003e \u003cp\u003e16.5 Nonparametric Tests Versus Their Parametric Counterparts 547\u003c\/p\u003e \u003cp\u003eChapter Problems 548\u003c\/p\u003e \u003cp\u003eFurther Reading 549\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Forecasting \u003c\/b\u003e\u003cb\u003e551\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 551\u003c\/p\u003e \u003cp\u003e17.2 Time Series Components 552\u003c\/p\u003e \u003cp\u003ePractice Problems 557\u003c\/p\u003e \u003cp\u003e17.3 Simple Forecasting Models 558\u003c\/p\u003e \u003cp\u003ePractice Problems 562\u003c\/p\u003e \u003cp\u003e17.4 Forecasting When Data Has Trend, Seasonality 563\u003c\/p\u003e \u003cp\u003ePractice Problems 569\u003c\/p\u003e \u003cp\u003e17.5 Assessing Forecasts 572\u003c\/p\u003e \u003cp\u003e17.5.1 A Quick Look at Data Science: Forecasting Tourism Jobs in Canada 575\u003c\/p\u003e \u003cp\u003e17.5.2 A Quick Look at Data Science: Forecasting Retail Gross Sales of Marijuana in Denver 577\u003c\/p\u003e \u003cp\u003eChapter Problems 580\u003c\/p\u003e \u003cp\u003eFurther Reading 581\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Math Notation and Symbols \u003c\/b\u003e\u003cb\u003e583\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Summation 583\u003c\/p\u003e \u003cp\u003eA.2 \u003ci\u003ep\u003c\/i\u003eth Power 583\u003c\/p\u003e \u003cp\u003eA.3 Inequalities 584\u003c\/p\u003e \u003cp\u003eA.4 Factorials 584\u003c\/p\u003e \u003cp\u003eA.5 Exponential Function 585\u003c\/p\u003e \u003cp\u003eA.6 Greek and Statistics Symbols 585\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Standard Normal Cumulative Distribution Function \u003c\/b\u003e\u003cb\u003e587\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C \u003ci\u003et \u003c\/i\u003eDistribution Critical Values \u003c\/b\u003e\u003cb\u003e591\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix D Solutions to Odd-Numbered Problems \u003c\/b\u003e\u003cb\u003e593\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIndex 643\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eROBERTO RIVERA, PHD,\u003c\/b\u003e is a Professor, at the College of Business, University of Puerto Rico, Mayagüez. He received his PhD in Statistics from the University of California, Santa Barbara. He founded the Puerto Rico Chapter of the American Statistical Association. Dr. Rivera is also the co-author of \u003ci\u003eApplications of Regression Models in Epidemiology\u003c\/i\u003e (2017).   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eINTRODUCES READERS TO THE PRINCIPLES OF MANAGERIAL STATISTICS AND DATA SCIENCE, WITH AN EMPHASIS ON STATISTICAL LITERACY OF BUSINESS STUDENTS\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThrough 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: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eAssessing if searches during a police stop in San Diego are dependent on driver's race\u003c\/li\u003e \u003cli\u003eVisualizing the association between fat percentage and moisture percentage in Canadian cheese\u003c\/li\u003e \u003cli\u003eModeling taxi fares in Chicago using data from millions of rides\u003c\/li\u003e \u003cli\u003eAnalyzing mean sales per unit of legal marijuana products in Washington state\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eTopics covered in \u003ci\u003ePrinciples of Managerial Statistics and Data Science\u003c\/i\u003e 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: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eIncludes academic material usually covered in introductory Statistics courses, but with a data science twist, and less emphasis in the theory\u003c\/li\u003e \u003cli\u003eRelies on Minitab to present how to perform tasks with a computer\u003c\/li\u003e \u003cli\u003ePresents and motivates use of data that comes from open portals\u003c\/li\u003e \u003cli\u003eFocuses on developing an intuition on how the procedures work\u003c\/li\u003e \u003cli\u003eExposes readers to the potential in Big Data and current failures of its use\u003c\/li\u003e \u003cli\u003eSupplementary 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\u003c\/li\u003e \u003cli\u003eFeatures an appendix with solutions to some practice problems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003ePrinciples of Managerial Statistics and Data Science\u003c\/i\u003e is a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989853192421,"sku":"NP9781119486411","price":133.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119486411.jpg?v=1761785681","url":"https:\/\/k12savings.com\/products\/principles-of-managerial-statistics-and-data-science-isbn-9781119486411","provider":"K12savings","version":"1.0","type":"link"}