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

Calculating t-statistics and p-values

In this section, we will apply our knowledge of how to ascertain whether the variables have a strong relationship to predict new values with a regression model. The complexity, in this case, is that we are not working with just one predictive variable. Now, we are working with two or more variables in a multiple linear regression model.

The statistical probes to determine the significance of the relationship are as follows:

Determination coefficient
Correlation coefficient
t-statistics
p-value

To find the coefficients, we have to review the distances between the average of the expected values and the linear model.

Figure 10.8 – 3D chart distances; linear model to expected results

Figure 10.8 is the 3D chart of the variations we saw in the previous chapter. The errors or unexplained variations (sum of squares errors (SSEs)) are the distance between the expected values and the linear...

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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

Calculating t-statistics and p-values

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Table of Contents (19) Chapters

Calculating t-statistics and p-values

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