A naukri.com initiative
Medium
1M
237
Image Credit: Medium
Linear Regression and the LINE Test: Understanding Assumptions for Valid Models
- Linear regression is commonly used to predict patterns in data by fitting a line to observed data points
- The LINE test is a mnemonic used for checking if a linear regression model satisfies key assumptions
- L (Linearity), I (Independence), N (Normality), and E (Equal Variance) are the four main assumptions of regression
- For a reliable model, all LINE assumptions should be met before drawing conclusions
- Linear regression is applied in many use cases like predicting sales trends, forecasting demand, or estimating the impact of marketing expenditure on sales
- In a real-world scenario, we can use linear regression to analyze the effect of a marketing budget on sales
- We visually inspect the provided data and fit a linear regression model using the Statsmodels library
- The F-test is used to test the overall significance of the model, while the R-squared value measures how well the independent variable explains the variability in the dependent variable
- By visualizing the best-fit line, we can interpret the results and understand the impact of the independent variable on the dependent variable
- To ensure reliable results, we verify that the LINE assumptions hold true by checking for linearity, independence, normality, and equal variance
Read Full Article
14 Likes
For uninterrupted reading, download the app