# Introducing Regression Models in SAP Analytics Cloud Smart Predict

Objectives

After completing this lesson, you will be able to:

• Explain regression analysis in Smart Predict

## Use Cases for Regression Models

### Use Augmented Analytics for Payment Forecasting

In the case study, we walk you through a scenario for using regression models in payment forecasting.

#### What sorts of topics can we investigate with a regression model?

• Measuring how an increase in the costs of a product impacts a company’s profits.
• Understanding how sensitive a company’s sales are to changes in advertising spend, promotions, or pricing.
• Analyzing how a change in interest rates affects a stock price.
• Predicting accident and loss claim values for a car insurance company, based on factors such as the attributes of the car, driver information, and demographics.
• Forecasting the future consumption of electricity, based on historical demand, weather forecasts, and pricing.

## Regression Analysis in Smart Predict

#### What is regression analysis?

Regression analysis is a collective name for techniques used for the modeling and analysis of numerical data consisting of values of a target variable and of one or more influencer variables.

The parameters of the regression are estimated and give a "best fit" of the data.

The target variable in the regression equation is modeled as a function of the influencer variables, a constants term, and an error term. The target is a continuous variable.

### Regression Lines

The formula for a simple regression line is represented as an equation: y = a + bx.

Where:

• y is the target.
• a is the intercept (the level of y where x is 0).
• b is the slope of the line.
• x is the influencer variable.

### Multiple Linear Regression

• Multiple linear regression is used to explain the relationship between one continuous target variable and two or more influencer variables.
• The influencer variables can be continuous or categorical.
• Multiple linear regression analysis is the task of fitting a single line through a scatter plot, with multiple dimensions of data points.
• Regression is most often used to:
• Identify the strength of the effect that the influencer variables have on a target variable.
• Forecast effects or impacts of changes - to understand how much the target variable changes when you change the influencer variables. For example, a multiple linear regression can explain how much sales volumes are expected to increase (or decrease) for every one-point increase (or decrease) in workforce hours.
• Predict trends and future values. The multiple linear regression analysis can be used to get point estimates. An example question is: What will the price of gold be six months from now?