Principles of Managerial Statistics and Data Science

Description

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. Contents Preface Acknowledgments Acronyms About the CompanionWebsite Principles ofManagerial Statistics and Data Science 1 Statistics Suck; SoWhy Do I Need to Learn About It? 1.1 Introduction Practice Problems 1.2 Data-Based Decision Making: Some Applications 1.3 Statistics Defined 1.4 Use of Technology and the New Buzzwords: Data Science, Data Analytics, and Big Data Chapter Problems Further Reading 2 Concepts in Statistics 2.1 Introduction Practice Problems 2.2 Type of Data Practice Problems 2.3 Four Important Notions in Statistics Practice Problems 2.4 SamplingMethods Practice Problems 2.5 Data Management 2.6 Proposing a Statistical Study Chapter Problems Further Reading 3 Data Visualization 3.1 Introduction 3.2 VisualizationMethods for Categorical Variables Practice Problems 3.3 VisualizationMethods for Numerical Variables Practice Problems 3.4 Visualizing Summaries of More than Two Variables Simultaneously Practice Problems 3.5 Novel Data Visualization Chapter Problems Further Reading 4 Descriptive Statistics 4.1 Introduction 4.2 Measures of Centrality Practice Problems 4.3 Measures of Dispersion Practice Problems 4.4 Percentiles Practice Problems 4.5 Measuring the Association Between Two Variables Practice Problems 4.6 Sample Proportion and Other Numerical Statistics 4.7 How to Use Descriptive Statistics Chapter Problems Further Reading 5 Introduction to Probability 5.1 Introduction 5.2 Preliminaries Practice Problems 5.3 The Probability of an Event Practice Problems 5.4 Rules and Properties of Probabilities Practice Problems 5.5 Conditional Probability and Independent Events Practice Problems 5.6 Empirical Probabilities Practice Problems 5.7 Counting Outcomes Practice Problems Chapter Problems Further Reading 6 Discrete Random Variables 6.1 Introduction 6.2 General Properties Practice Problems 6.3 Properties of Expected Value and Variance Practice Problems 6.4 Bernoulli and Binomial Random Variables Practice Problems 6.5 Poisson Distribution Practice Problems 6.6 Optional: Other Useful Probability Distributions Chapter Problems Further Reading 7 Continuous Random Variables 7.1 Introduction Practice Problems 7.2 The UniformProbability Distribution Practice Problems 7.3 The Normal Distribution Practice Problems 7.4 Probabilities for Any Normally Distributed Random Variable Practice Problems 7.5 Approximating the Binomial Distribution Practice Problems 7.6 Exponential Distribution Practice Problems Chapter Problems Further Reading 8 Properties of Sample Statistics 8.1 Introduction 8.2 Expected Value and Standard Deviation of Practice Problems 8.3 Sampling Distribution of When Sample Comes Froma Normal Distribution Practice Problems 8.4 Central Limit Theorem Practice Problems 8.5 Other Properties of Estimators Chapter Problems Further Reading 9 Interval Estimation for One Population Parameter 9.1 Introduction 9.2 Intuition of a Two-Sided Confidence Interval 9.3 Confidence Interval for the Population Mean: Known Practice Problems 9.4 Determining Sample Size for a Confidence Interval for Practice Problems 9.5 Confidence Interval for the Population Mean: Unknown Practice Problems 9.6 Confidence Interval for Practice Problems 9.7 Determining Sample Size for Confidence Interval Practice Problems 9.8 Optional: Confidence Interval for Chapter Problems Further Reading 10 Hypothesis Testing for One Population 10.1 Introduction 10.2 Basics of Hypothesis Testing 10.3 Steps to Perform a Hypothesis Test Practice Problems 10.4 Inference on the Population Mean: Known Standard Deviation Practice Problems 10.5 Hypothesis Testing for the Mean (𝝈 Unknown) Practice Problems 10.6 Hypothesis Testing for the Population Proportion Practice Problems 10.7 Hypothesis Testing for the Population Variance 10.8 More on the Value and Final Remarks Chapter Problems Further Reading 11 Statistical Inference to Compare Parameters from Two Populations 11.1 Introduction 11.2 Inference on Two Population Means 11.3 Inference on Two Population Means – Independent Samples, Variances Known Practice Problems 11.4 Inference on Two Population MeansWhen Two Independent Samples are Used – Unknown Variances Practice Problems 11.5 Inference on TwoMeans Using Two Dependent Samples Practice Problems 11.6 Inference on Two Population Proportions Practice Problems Chapter Problems References Further Reading 12 Analysis of Variance (ANOVA) 12.1 Introduction Practice Problems 12.2 ANOVA for One Factor Practice Problems 12.3 Multiple Comparisons Practice Problems 12.4 Diagnostics of ANOVA Assumptions Practice Problems 12.5 ANOVA with Two Factors Practice Problems 12.6 Extensions to ANOVA Chapter Problems Further Reading 13 Simple Linear Regression 13.1 Introduction 13.2 Basics of Simple Linear Regression Practice Problems 13.3 Fitting the Simple Linear Regression Parameters Practice Problems 13.4 Inference for Simple Linear Regression Practice Problems 13.5 Estimating and Predicting the Response Variable Practice Problems 13.6 A Binary Practice Problems 13.7 Model Diagnostics (Residual Analysis) Practice Problems 13.8 What Correlation Doesn’t Mean Chapter Problems Further Reading 14 Multiple Linear Regression 14.1 Introduction 14.2 The Multiple Linear RegressionModel Practice Problems 14.3 Inference for Multiple Linear Regression Practice Problems 14.4 Multicollinearity and Other Modeling Aspects Practice Problems 14.5 Variability Around the Regression Line: Residuals and Intervals Practice Problems 14.6 Modifying Predictors Practice Problems 14.7 General Linear Model Practice Problems 14.8 Steps to Fit a Multiple Linear Regression Model 14.9 Other Regression Topics Chapter Problems Further Reading 15 Inference on Association of Categorical Variables 15.1 Introduction 15.2 Association Between Two Categorical Variables Practice Problems Chapter Problems Further Reading 16 Nonparametric Testing 16.1 Introduction 16.2 Sign Tests andWilcoxon Sign-Rank Tests: One Sample and Matched Pairs Scenarios Practice Problems 16.3 Wilcoxon Rank-Sum Test: Two Independent Samples Practice Problems 16.4 Kruskal–Wallis Test: More Than Two Samples Practice Problems 16.5 Nonparametric Tests Versus Their Parametric Counterparts Chapter Problems Further Reading 17 Forecasting 17.1 Introduction 17.2 Time Series Components Practice Problems 17.3 Simple Forecasting Models Practice Problems 17.4 Forecasting When Data Has Trend, Seasonality Practice Problems 17.5 Assessing Forecasts Chapter Problems Further Reading Appendix A Math Notation and Symbols A.1 Summation A.2 pth Power A.3 Inequalities A.4 Factorials A.5 Exponential Function A.6 Greek and Statistics Symbols Appendix B Standard Normal Cumulative Distribution Function Appendix C t Distribution Critical Values Appendix D Solutions to Odd-Numbered Problems Index