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Data Forecasting and Segmentation Using Microsoft Excel

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

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781803247731
Length 324 pages
Edition 1st Edition
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Author (1):
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Fernando Roque Fernando Roque
Author Profile Icon Fernando Roque
Fernando Roque
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Table of Contents (19) Chapters Close

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 to choose which variables have a greater influence over the prediction variable, Y. Statistical methods to search for these high influence variables include the coefficient of determination and correlation, which indicates a percentage of the relationship of the variables by measuring their variances, and also whether the relationship is direct or inverse depending on the slope sign. t-statistics, f-statistics, and the p-value determine whether we can reject the null hypothesis that the slope is equal to zero.

These tests help to get the statistical confidence of the variables to generate a prediction model while helping to reject the null hypothesis that the slope is equal to zero.

It is important to evaluate all the statistical tests. Sometimes, f-statistics and the p-value indicate that a variable could affect the model with a slope equal to zero. However, we have to understand all the evaluation methods to include the variables that influence...

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