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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

Calculating the optimal number of groups for two and three variables

In this section, we will explore the groups of products and sales results for two and three variables. In the previous chapter, we analyzed the groups using the single variable of revenue. Now, we will perform a multivariable grouping calculation by performing the following activities:

  • Finding the optimal number of segments for two variables:
    • Revenue
    • Quantity
  • Using the elbow function to get the number of groups for three variables:
    • Revenue
    • Quantity
    • Month of sale

Computing the optimal number of segments for two and three variables is more complex than the simple example of the last chapter. We will do a chart of the variables before we use the Elbow function to get an idea of the data dispersion. For the three variables, we will use the R function included in this book to execute a 3D chart.

Finding the optimal number of segments for two variables – revenue and quantity

An analysis involving...

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