Book 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, Chisquare tests, nonparametric 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
 Cover
 Preface
 Acknowledgments
 Acronyms
 About the Companion Website
 Principles of Managerial Statistics and Data Science
 1 Statistics Suck; So Why Do I Need to Learn About It?
 2 Concepts in Statistics

3 Data Visualization
 3.1 Introduction
 3.2 Visualization Methods for Categorical Variables
 Practice Problems
 3.3 Visualization Methods 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 Uniform Probability 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

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 ( 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 p‐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 Means When Two Independent Samples are Used – Unknown Variances
 Practice Problems
 11.5 Inference on Two Means 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)

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 Regression Model
 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

16 Nonparametric Testing
 16.1 Introduction
 16.2 Sign Tests and Wilcoxon 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
 Appendix A: Math Notation and Symbols
 Appendix B: Standard Normal Cumulative Distribution Function
 Appendix C: t Distribution Critical Values
 Appendix D: Solutions to Odd‐Numbered Problems
 Index
 End User License Agreement
Product information
 Title: Principles of Managerial Statistics and Data Science
 Author(s):
 Release date: February 2020
 Publisher(s): Wiley
 ISBN: 9781119486411
You might also like
book
Data Science for Business and Decision Making
Data Science for Business and Decision Making covers both statistics and operations research while most competing …
book
Data Science for Marketing Analytics
Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated …
book
HandsOn Data Science for Marketing
Optimize your marketing strategies through analytics and machine learning Key Features Understand how data science drives …
book
Statistical Analysis with R For Dummies
Understanding the world of R programming and analysis has never been easier Most guides to R, …