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

Summary

In this chapter, we learned to choose which variables have a greater influence over the prediction variable, Y. Statistical methods to search for these high influence variables include the coefficient of determination and correlation, which indicates a percentage of the relationship of the variables by measuring their variances, and also whether the relationship is direct or inverse depending on the slope sign. t-statistics, f-statistics, and the p-value determine whether we can reject the null hypothesis that the slope is equal to zero.

These tests help to get the statistical confidence of the variables to generate a prediction model while helping to reject the null hypothesis that the slope is equal to zero.

It is important to evaluate all the statistical tests. Sometimes, f-statistics and the p-value indicate that a variable could affect the model with a slope equal to zero. However, we have to understand all the evaluation methods to include the variables that influence...