# Introducing regression models in SAP Analytics Cloud Smart Predict

After completing this lesson, you will be able to: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 interaction below, we will walk you through a case study 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 stock price is affected by a change in interest rates
- 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, so as to 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 will change 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 manpower hours.
- Predict trends and future values. The multiple linear regression analysis can be used to get point estimates. An example question may be "what will the price of gold be 6 month from now?"

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