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.

Preface

Who this book is for

What this book covers

To get the most out of this book

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Conventions used

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Part 1 – An Introduction to Machine Learning Functions

Part 1 – An Introduction to Machine Learning Functions

Free Chapter

Chapter 1: Understanding Data Segmentation

Chapter 1: Understanding Data Segmentation

Segmenting data concepts

Grouping data in segments of two and three variables

Further reading

Chapter 2: Applying Linear Regression

Chapter 2: Applying Linear Regression

Understanding the influence of variables in linear regression

Projecting values from predictor variables

Further reading

Chapter 3: What is Time Series?

Chapter 3: What is Time Series?

Technical requirements

Understanding time series data

Designing the time series data model

Doing the forecast

Further reading

Part 2 – Grouping Data to Find Segments and Outliers

Part 2 – Grouping Data to Find Segments and Outliers

Chapter 4: Introduction to Data Grouping

Chapter 4: Introduction to Data Grouping

Technical requirements

Grouping with the K-means machine learning function

Finding groups of multiple variables

Understanding outliers

Further reading

Chapter 5: Finding the Optimal Number of Single Variable Groups

Chapter 5: Finding the Optimal Number of Single Variable Groups

Technical requirements

Finding an optimal number of groups for one variable

Running the K-means function to get the centroids or group average

Finding the groups and centroids of one-variable data with K-means and Excel

Further reading

Chapter 6: Finding the Optimal Number of Multi-Variable Groups

Chapter 6: Finding the Optimal Number of Multi-Variable Groups

Technical requirements

Calculating the optimal number of groups for two and three variables

Determining the groups and average value (centroids) of two and three variables

Using the Elbow and K-means functions with four variables

Questions and answers

Further reading

Chapter 7: Analyzing Outliers for Data Anomalies

Chapter 7: Analyzing Outliers for Data Anomalies

Technical requirements

Representing the data in a 3D chart

K-means data grouping

Pivot analysis of the outliers

Further reading

Part 3 – Simple and Multiple Linear Regression Analysis

Part 3 – Simple and Multiple Linear Regression Analysis

Chapter 8: Finding the Relationship between Variables

Chapter 8: Finding the Relationship between Variables

Technical requirements

Charting the predictive model's regression variables

Calculating the linear model confidence percentage

Statistical significance of the slope

The regression model's value ranges

Further reading

Chapter 9: Building, Training, and Validating a Linear Model

Chapter 9: Building, Training, and Validating a Linear Model

Technical requirements

Calculating the intercept and slope with formulas

Computing coefficient significance – t-statistics and p-value

Getting the residual standard error

Calculating the r-squared

Calculating the f-statistics

Training and testing the model

Doing prediction scenarios with the regression model

Chapter 10: Building, Training, and Validating a Multiple Regression Model

Chapter 10: Building, Training, and Validating a Multiple Regression Model

Technical requirements

Exploring the variables with more influence

Calculating t-statistics and p-values

Calculating residuals standard error and f-statistics

Training and testing the model

Further reading

Part 4 – Predicting Values with Time Series

Part 4 – Predicting Values with Time Series

Chapter 11: Testing Data for Time Series Compliance

Chapter 11: Testing Data for Time Series Compliance

Technical requirements

Visualizing seasonal trends

Researching autocorrelation – past values' influence over present values

Performing the Durbin-Watson autocorrelation test

Questions and answers

Further reading

Chapter 12: Working with Time Series Using the Centered Moving Average and a Trending Component

Chapter 12: Working with Time Series Using the Centered Moving Average and a Trending Component

Technical requirements

Calculating the CMA

Producing the forecast – season and trend line

Further reading

Chapter 13: Training, Validating, and Running the Model

Chapter 13: Training, Validating, and Running the Model

Technical requirements

Training the model

Building and training the forecast model

Testing the forecast model

Doing the forecast

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Calculating residuals standard error and f-statistics

The residuals, or errors, are the unexplained variations of the model. It is the separation of the expected value to the response given by the linear regression.

f-statistics is another test for rejecting the possibility of the slope being equal to zero.

To calculate the residuals, we will write down the equation for a multivariable regression model. Then, we will calculate how well the model fits the expected values. The difference between the expected values (sales revenue) and the results from the regression model is the errors or unexplained variation. Remember that this variation is supposed to be small, with a small standard deviation to have a model useful for prediction.

f-statistics come from the explained and unexplained variations of the expected values and the linear model. We will calculate the f-statistics and then check whether the value is in the region of rejection for accepting the alternate hypothesis...