How can regression analysis help you estimate cost behavior?

Regression Analysis is a specific type of Machine Learning model, used to make predictions. As I am currently learning in my BIDA certification, we use regression for continuous prediction. For discrete predictions, we use classification. Regression can be applied to a wide range of problems where we want to predict a continuous value such as revenue, costs, life expectancy, or film review scores.

By using regression analysis you can get more granular on fixed and variable cost allocations. This can be hugely beneficial to calculate accurate contribution margins on a sku / product level

Simple linear regression aims to fit a straight-line relationship between two variables, X and Y. X is the independent variable and Y is the target variable. After performing the analysis, you can see how Y will move with X, this way, you can predict the cost or amount of Y with the variation of the fixed cost (X).

In my experience, there are three main types of regression: 1-Simple Linear Regression Target (Y) predicted using only one independent variable 2-Multiple Linear Regression Target (Y) predicted using multiple independent variables 3-Non-Linear Regression Target (Y) predicted using multiple independent variables

For more advanced regression analysis, you can use five more models : 1-Log-Log Linear Regression: Log-log regression is used when our data appears non-linear or we have heteroscedastic errors. 2-Polynomial Regression: We use polynomial regression when we have a smooth, non-linear relationship between variables 3-Logistic Regression: Logistic regression is typically used for classification problems 4-Repeated Measure Regression: Repeated measure regression is used when measurements are repeated over time. 5-Segmented Regression: Applies unique regression lines for different regions of X