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Statistics for Data Science

You're reading from   Statistics for Data Science Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788290678
Length 286 pages
Edition 1st Edition
Languages
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Author (1):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
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Toc

Table of Contents (13) Chapters Close

Preface 1. Transitioning from Data Developer to Data Scientist 2. Declaring the Objectives FREE CHAPTER 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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