Introducing regression models in SAP Analytics Cloud Smart Predict

Objectives
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
A regression chart with target value on the y axis, influencer variable on the x axis and the formula y=a+bx

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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