Principles of Managerial Statistics and Data Science

✍ Scribed by Roberto Rivera

• Publisher: Wiley
• Year: 2020
• Tongue: English
• Leaves: 678
• Edition: 1
• Category: Library

✦ Synopsis

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

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