#### Overview of this book

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data
Title Page
Dedication
Contributors
Preface
Variables, Types, and Functions
Julia Workflow
Distributed Computing
Other Books You May Enjoy
Index

## Estimating a linear regression

Linear regression is one of the basic models for predictive modeling. In this recipe, we show you how to implement a fully functional method that allows the estimation of such models. This recipe mainly concentrates on array manipulation, but also shows a typical example of more complex Julia code, combining several standard functionalities.

Make sure you have the DataFrames.jl and CSV.jl packages installed. If they are missing, add them using the following commands:

julia> using Pkg

julia> Pkg.add("CSV")

Linear regression is a model following the

formula. It is known that if you define vector

as the observations of the explained variables and a matrix

of explanatory variables, then the least squares estimator of

is

.

We can see that, in this formula, both explained and explanatory variables must be numeric. In practice, a typical challenge is that raw explanatory variables are often nominal. In such cases...