Book Image

Introduction to R for Business Intelligence

By : Jay Gendron
Book Image

Introduction to R for Business Intelligence

By: Jay Gendron

Overview of this book

Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards. After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.
Table of Contents (19 chapters)
Introduction to R for Business Intelligence
About the Author
About the Reviewers
R Packages Used in the Book
R Code for Supporting Market Segment Business Case Calculations


In this chapter, you examined a business scenario using SLR with one predictor variable and one response variable. You began by understanding the basic components of a simple linear regression including model formulation, inspection, interpretation, and diagnostics. You then learned how to determine whether the data met all the necessary assumptions for liner models, as well as how to use the model to predict outcomes and quantify your confidence in the results.

In situations where the data violated any assumptions, you learned how to transform the data using a structured approach. You also learned how to identify anomalous data known as outliers and determine whether they were influential points. Finally, you began to learn about models that have more than one predictor variable and required multiple linear regression.

In the next chapter, you will learn a different type of modeling called cluster analysis. It is useful for data that has very different characteristics and cannot be...