#### 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
Part 1 – An Introduction to Machine Learning Functions
Free Chapter
Chapter 1: Understanding Data Segmentation
Chapter 2: Applying Linear Regression
Chapter 3: What is Time Series?
Part 2 – Grouping Data to Find Segments and Outliers
Chapter 4: Introduction to Data Grouping
Chapter 5: Finding the Optimal Number of Single Variable Groups
Chapter 6: Finding the Optimal Number of Multi-Variable Groups
Chapter 7: Analyzing Outliers for Data Anomalies
Part 3 – Simple and Multiple Linear Regression Analysis
Chapter 8: Finding the Relationship between Variables
Chapter 9: Building, Training, and Validating a Linear Model
Chapter 10: Building, Training, and Validating a Multiple Regression Model
Part 4 – Predicting Values with Time Series
Chapter 11: Testing Data for Time Series Compliance
Chapter 12: Working with Time Series Using the Centered Moving Average and a Trending Component
Chapter 13: Training, Validating, and Running the Model
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# 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...`