Have you ever thought about how companies predict sales or how analysts forecast stock prices? One of the most common tools behind these predictions is regression analysis. It helps us understand how one factor affects another, and it is widely used in data science and analytics.
What is Regression Analysis?
Regression is a method that studies the relationship between a dependent variable (the thing you want to predict) and independent variables (the factors affecting it).
For example:
Predicting house prices based on area and location
Estimating crop yields based on rainfall and fertiliser use
Forecasting sales using past performance and ad spend
It creates an equation that can be used to make predictions and understand trends.
Common Types of Regression Analysis
Simple Linear Regression: Shows how one factor affects another.
Multiple Linear Regression: Uses many factors together to predict outcomes.
Polynomial Regression: Works when the data follows a curved pattern.
Ridge and Lasso Regression: Used when there are too many related variables.
Logistic Regression: Used when the result is yes/no (like pass or fail).
Support Vector Regression: Works well for complex or noisy data.
Why You Should Learn It
Regression is used in marketing, business, finance, agriculture, sports, and even healthcare. It helps in turning raw data into useful insights. For students entering the field of analytics or data science, learning regression is a great starting point.
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