#### 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.
Introduction to R for Business Intelligence
Credits
Acknowledgement
www.PacktPub.com
Preface
Free Chapter
Exploratory Data Analysis
Data Mining with Cluster Analysis
Time Series Analysis
Visualizing the Datas Story
Web Dashboards with Shiny
References
R Packages Used in the Book
R Code for Supporting Market Segment Business Case Calculations

## Checking model assumptions

To use linear regression, your data must satisfy the following four core assumptions:

• Linearity

• Independence

• Normality

• Equal variance

It may be helpful to think of these assumptions by their first letters. You can remember that LINE is an important aspect of linear regression. Next, you will learn about each of the assumptions as well as tests that you can perform in R to check whether the data satisfies them.

### Note

Learn more: Checking the assumptions of a statistical model is important. The power and accuracy of any model comes from its adherence to the assumptions. David Robinson (2015) has written a blog called VARIANCE EXPLAINED that describes this topic in an enjoyable way: http://varianceexplained.org/r/kmeans-free-lunch/

### Note

Linearity assumption: The relationship between the predictor and response variables is linear.

In an SLR situation, a quick way to determine linearity is to plot the variables with a scatterplot. Earlier, you saw a strong correlation...