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

Calculating the f-statistics

The f-statistics are another test to see whether the slope is equal to zero. The null hypothesis is that the slope is zero. The f-statistics test whether we can reject this. To calculate it, we have to define the mean squared error first.

The regression for the mean squared error is the explained variation regression (SSE) divided by the regression degrees of freedom minus 1. In this example, we have the regression for two variables. The degree of freedom is 1:

Regression Mean Squared Error = SSR / Regression Degrees of Freedom
Regression Mean Squared Error = 610.277 / 1 = 610.277

The residual mean squared error is the unexplained variation residual sum of squares (SSE) divided by the degrees of freedom of the residuals. The residual degrees of freedom are the number of records of the data source minus 2. In this case, we have 23 records. The degrees of freedom are 21:

Residual Mean Squared Error = Unexplained Variation Residual Sum of Squares...