Linear Regression Using Non-Linear Data: Understanding Ordinary Least Squares (OLS)
Linear regression is a powerful statistical method for modeling the relationship between a dependent variable (target) and one or more independent variables (features). However, its name might be misleading, as it can be applied to model non-linear relationships between variables as well. In this blog post, we’ll explore how to use linear regression to analyze non-linear data and discuss the steps to perform Ordinary Least Squares (OLS) regression on such data.
Understanding Non-Linear Data:
Non-linear data refers to data where the relationship between the independent and dependent variables cannot be accurately described by a linear equation (e.g., y = mx + b). Instead, these relationships are often curvilinear, exponential, logarithmic, or polynomial in nature. Linear regression can still be useful in these cases by making the data more interpretable or capturing trends within the data.
Steps to Complete OLS Using Non-Linear Data:
