You're reading from Data Forecasting and Segmentation Using Microsoft Excel Perform data grouping, linear predictions, and time series machine learning statistics without using code

Product type Paperback

Published in May 2022

Publisher Packt

ISBN-13 9781803247731

Length 324 pages

Edition 1st Edition

Languages

C++

Tools

Excel

Concepts

Data Analysis

Author (1):

Fernando Roque

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Table of Contents (19) Chapters

Preface

1. Part 1 – An Introduction to Machine Learning Functions

2. Chapter 1: Understanding Data Segmentation FREE CHAPTER

3. Chapter 2: Applying Linear Regression

4. Chapter 3: What is Time Series?

5. Part 2 – Grouping Data to Find Segments and Outliers

6. Chapter 4: Introduction to Data Grouping

7. Chapter 5: Finding the Optimal Number of Single Variable Groups

8. Chapter 6: Finding the Optimal Number of Multi-Variable Groups

9. Chapter 7: Analyzing Outliers for Data Anomalies

10. Part 3 – Simple and Multiple Linear Regression Analysis

11. Chapter 8: Finding the Relationship between Variables

12. Chapter 9: Building, Training, and Validating a Linear Model

13. Chapter 10: Building, Training, and Validating a Multiple Regression Model

14. Part 4 – Predicting Values with Time Series

15. Chapter 11: Testing Data for Time Series Compliance

16. Chapter 12: Working with Time Series Using the Centered Moving Average and a Trending Component

17. Chapter 13: Training, Validating, and Running the Model

18. Other Books You May Enjoy

Summary

In this chapter, we learned how to build a linear regression formula and, beyond this, how to visualize the distances between expected values and a model. These distances are input for statistical tests to find out whether the model is good enough to predict new values.

The machine learning workflow to use a model for prediction starts by doing a definition of the target information we expect and data validation, using a chart to see the possible relationships between the variables. We use 80% of the known data to train the model and see whether it returns values that make sense to our experience. With the remaining 20% of the data, we test the model and see whether it fits the data that was not part of the training. Finally, we predict new values. We have to apply our judgment to see whether the regression is working or not.

This knowledge is useful to apply statistical tests that reject the null hypothesis that the slope of the linear model is equal to zero. A slope...

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Table of Contents (19) Chapters

Summary

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Table of Contents (19) Chapters

Summary

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