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?
