#### 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

## Using a simple linear regression

You built a linear regression model by simply using the `lm()` function on your data. You also used the LINE approach to make sure that your model satisfied the assumptions of linear regression. Building SLRs is often straightforward, but it is very important that you know how to interpret the output.

### Interpreting model output

There is a lot of information available within a linear regression model. You can see an expanded output by using the `summary()` function:

```summary(model1)
```

The following is the output:

```Call:
lm(formula = revenues ~ marketing_total, data = adverts)
Residuals:
Min      1Q  Median      3Q     Max
-8.6197 -1.8963 -0.0006  2.1705  9.3689
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)     32.006696   0.635590   50.36   <2e-16 ***
marketing_total  0.051929   0.002437   21.31   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.054 on 170 degrees...```