Regression analysis is a statistical method that helps us understand the relationship between a dependent variable and one or more independent variables. There are three main types of regression:
1. Linear Regression
Linear regression is the most basic type of regression. It assumes a linear relationship between the dependent and independent variables. This means that the relationship can be represented by a straight line.
- Example: Predicting house prices based on the size of the house.
- Practical Insight: Linear regression is widely used in various fields like finance, marketing, and healthcare for predicting outcomes and understanding relationships.
2. Logistic Regression
Logistic regression is used to predict the probability of a categorical dependent variable, typically with two outcomes (binary). It uses a sigmoid function to transform the linear combination of independent variables into a probability between 0 and 1.
- Example: Predicting whether a customer will click on an ad based on their demographics and browsing history.
- Practical Insight: Logistic regression is commonly used in machine learning for classification tasks, such as spam detection and credit risk assessment.
3. Polynomial Regression
Polynomial regression is a type of regression that uses a polynomial function to model the relationship between the dependent and independent variables. This allows for more complex relationships than a simple linear model.
- Example: Predicting the growth of a plant based on the amount of sunlight it receives.
- Practical Insight: Polynomial regression is useful when the relationship between variables is non-linear, and a linear model would not accurately represent the data.
These are the three main types of regression, each with its own applications and strengths. Choosing the right type of regression depends on the nature of the data and the research question.
