Book Image

Data Forecasting and Segmentation Using Microsoft Excel

By : Fernando Roque
Book Image

Data Forecasting and Segmentation Using Microsoft Excel

By: Fernando Roque

Overview of this book

Data Forecasting and Segmentation Using Microsoft Excel guides you through basic statistics to test whether your data can be used to perform regression predictions and time series forecasts. The exercises covered in this book use real-life data from Kaggle, such as demand for seasonal air tickets and credit card fraud detection. You’ll learn how to apply the grouping K-means algorithm, which helps you find segments of your data that are impossible to see with other analyses, such as business intelligence (BI) and pivot analysis. By analyzing groups returned by K-means, you’ll be able to detect outliers that could indicate possible fraud or a bad function in network packets. By the end of this Microsoft Excel book, you’ll be able to use the classification algorithm to group data with different variables. You’ll also be able to train linear and time series models to perform predictions and forecasts based on past data.
Table of Contents (19 chapters)
1
Part 1 – An Introduction to Machine Learning Functions
5
Part 2 – Grouping Data to Find Segments and Outliers
10
Part 3 – Simple and Multiple Linear Regression Analysis
14
Part 4 – Predicting Values with Time Series

Performing the Durbin-Watson autocorrelation test

The Durbin-Watson autocorrelation test determines whether past data influences the present so that we can make predictions with it. This test returns a value interpretation, depending on its range. The function returns a value between 0 and 4. The interpretation is outlined here:

  • The value is 2 – there is no autocorrelation.
  • The value is between 0 and 2 – it has positive autocorrelation, meaning that the relationship is growing over time. This is common in time-series data.
  • The value is between 2 and 4 – it has negative autocorrelation. The values are decreasing over time.

The test returns the following results:

  • Accept the null hypothesis and that the data has no autocorrelation.
  • Reject the null hypothesis and accept the alternative hypothesis that the data has autocorrelation.

We are going to perform the Durbin-Watson test step by step and analyze its value. The formula...