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

Training and testing the model

Now that we have done statistical tests to see whether there is a significant relationship between the predictor variable (horsepower) and the result variable (miles per gallon), we are ready to test the model with known values and see how well it fits. The tests we did are as follows:

The coefficient of determination
The coefficient of correlation
t-statistics
The p-value
f-statistics

We will provide a summary of them in the following paragraphs.

The coefficient of determination is the level of confidence to determine the Y value based on the X value. It is the percentage of trust in the relationship between horsepower and miles-per-gallon performance. For this example, it has a value of 0.7287, meaning that 72% of the predictor values (horsepower) determine the result of miles per gallon.

The coefficient of correlation measures the strength of the relationship between the X predictor variable (horsepower) and the...

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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

Table of Contents (19) Chapters

Training and testing the model

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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

Table of Contents (19) Chapters

Training and testing the model

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