#### 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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# Doing prediction scenarios with the regression model

As we saw earlier, the regression model is not an exact answer for our prediction's needs because it depends on the level of the relationship between the causal variables and the Y result/effect variable.

Taking this into account, a more realistic answer is to present three scenarios of the prediction model. The scenarios depend on the confidence level of the slope. We have the upper and lower scenarios, depending on the value assigned to the slope. These scenarios exhibit the different value range for miles-per-gallon performance, depending on the variation of horsepower from 0 to 150:

• Linear model scenario: Miles-per-gallon performance goes from 34 to 14
• Upper scenario: Performance of miles per gallon has a range between 30 and 17
• Lower scenario: Performance of miles per gallon goes from 30 to 0

The formula for the confidence level of the slope is as follows:

From previous...