Linear Regression
Silvia C. IBM AI Programs and Data Science Certified
Data Analyst | Skilled in AI Automation & Data Science with Python | Transforming Complex Data into Strategic Insights | Driving Business Growth through Advanced Analytics and Visualization
Summary
This podcast explains simple linear regression, a statistical technique used to predict a continuous dependent variable (e.g., CO2 emission) based on one independent variable (e.g., engine size). The sources break down the process, starting with defining the relationship between the variables using a "best fit line," then exploring how to find the equation of this line using the method of least squares. They introduce the concepts of intercept, slope, and residual error, and explain how to minimize the error by adjusting the line's parameters. Finally, the sources emphasize the simplicity and interpretability of linear regression, but also mention its limitations, such as its sensitivity to outliers and the assumption of a linear relationship between variables.