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Statistics for Data Science

You're reading from Statistics for Data Science

Product type Book

Published in Nov 2017

Publisher Packt

ISBN-13 9781788290678

Pages 286 pages

Edition 1st Edition

Languages

Concepts

Table of Contents (19) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Transitioning from Data Developer to Data Scientist

2. Declaring the Objectives

3. A Developer's Approach to Data Cleaning

4. Data Mining and the Database Developer

5. Statistical Analysis for the Database Developer

6. Database Progression to Database Regression

7. Regularization for Database Improvement

8. Database Development and Assessment

9. Databases and Neural Networks

10. Boosting your Database

11. Database Classification using Support Vector Machines

12. Database Structures and Machine Learning

A working example

Let's now get back to our real-world example of project profitability!

We know that our consulting service organizations project results data describes the results of all its project work over time. There are 100 projects (or observations) in our data and consists of two variables hours billed and profit. The first variable is self-explanatory: it's the total number of hours billed to the client for that project. The second is a US dollar amount that equates to the revenues collected from the client after subtracting all expenses (for the project).

We know that each project has both expenses and revenue, and some projects are profitable while others are not. In addition, even projects that are profitable vary greatly in their level of profitability. Again, the firm is interested in identifying which variables (if any) are candidates for predicting how profitable a project will be.

Let's get started with our statistical analysis!

Establishing the data profile

Before attempting...

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