Principles of Managerial Statistics and Data Science
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Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business studentsThrough 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 wo...
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.
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.
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