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

Introducing multiple linear regression

It is time to introduce you to the topic of MLR. In SLR, you will use a single predictor variable. Most business problems deal with outputs dependent on more than two input variables. MLR is the technique used for situations having two or more predictor variables.

In the Using a simple linear regression section, you looked at revenue as a function of the marketing budget. You learned a great deal about the relationship by regressing the revenues variable on the marketing_total variable.

The total amount spent on marketing is the sum of google_adwords, facebook, and twitter marketing expenditures. Using MLR, you can examine the relationship among revenue and some or all of these component budgets.

You will formulate an MLR similar to an SLR, but with more predictor terms. You can think of this formulation as Y regressed on X1 and X2 and so forth. The most common relationship is an additive relationship, and the + operator is used to include multiple variables...