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

Answers

Here are the answers to the preceding questions:

The first input value is the coefficient of determination. Its formula is the explained variation divided by the total variation. The second input value is the sign of the slope. If it is positive, the relationship is direct. If not, the relationship is inverse.
t-statistics tell us whether the null hypothesis that the slope is equal to zero can be rejected. The slope with a non-zero value means a relationship between the variables. This is the alternative hypothesis.
The model just gives trends, not exact results. The scenarios give us an idea of the range of values that the model predicts. It helps to analyze whether the results make sense or not, based on our experience.
The unexplained variation or errors of the linear model is the distance of the model from the expected values. These distances must be short to have an effective predictor model. If it is not, the worst-case scenario is a high standard...

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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

Answers

Authors (1)

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Table of Contents (19) Chapters

Answers

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