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

Getting the residual standard error

This term is used to calculate the t-statistics. The distance between the expected values is represented by the dots in Figure 9.15, and the straight line of the model is measured by the unexplained variation values. This best-case scenario for these unexplained variation values is to have a small residual standard deviation or a close distance between the expected values and the model:

Figure 9.15 – Unexplained variation or SS errors

The unexplained variation distances have several points. The residual standard error says how scattered these points are. The ideal scenario is that we have a small average of unexplained variation and also a small standard deviation of unexplained variation. This means that the linear model fits the expected values of horsepower and miles per gallon.

Use the following distance measures between the model and the expected values to do a statistical analysis of the confidence of the...