Linear_Regression_Presentation.pptxhfakhk

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1. Linear Regression An Introduction to the Fundamental Regression Technique

2. Introduction to Linear Regression Linear Regression is a statistical method used for predicting a continuous outcome based on one or more input variables. It is widely used for predictive analysis and modeling relationships between variables.

3. Types of Linear Regression 1. Simple Linear Regression: - Predicts the outcome using one independent variable. 2. Multiple Linear Regression: - Uses two or more independent variables to predict the outcome.

4. Mathematical Equation Simple Linear Regression: y = mx + c + ϵ Multiple Linear Regression: y = b0 + b1x1 + b2x2 + ... + bnxn + ϵ Where: - y: Predicted value - x: Independent variable - m, b1, b2...: Coefficients - c, b0: Intercept - ϵ: Error term

5. How Linear Regression Works 1. Fits the best line that minimizes the difference between actual and predicted values. 2. Uses Mean Squared Error (MSE) as a loss function. 3. Optimized using techniques like Gradient Descent or Ordinary Least Squares (OLS).

6. Assumptions of Linear Regression 1. Linearity: Relationship between variables is linear. 2. Homoscedasticity: Constant variance of errors. 3. No Multicollinearity: Independent variables should not be highly correlated. 4. Normality of Errors: Errors should be normally distributed.

7. Applications of Linear Regression • Predicting house prices • Stock market forecasting • Sales forecasting • Risk management in finance

8. Conclusion Linear Regression is a simple yet powerful tool for predictive analysis. It is essential for understanding relationships between variables and making data-driven decisions.