Assumptions Of Linear Regression | What To Do If The Assumptions Do Not Hold? | Part 1

🔥 The video talks about the assumptions of the linear regression. There are four main assumptions that should hold for the statistical properties to be valid. Additionally, we discuss what to do if the assumptions do not hold? What if the real-world datasets are too complex for the assumptions to hold? Splitting the dataset into train and test datasets and using them for model evaluation can be a good way to validate the performance and reliability of the linear regression model, however, you should be very careful before relying on the statistical properties, if there are violations. Stay tuned to learn about the way you can check the assumptions, as well as about how to effectively apply the train-test split approach. 🔍 Key points covered: 0:00 - Introduction. 0:18 - 1. Linearity 0:35 - 2. Independence 0:52 - 3. Homoscedasticity 1:05 - 4. Normality 1:18 - What if the assumptions do not hold? 1:32 - Resolve the violation. 1:44 - Train-Test Split Approach. 2:15 - Subscribe to us! 🔔 Don't forget to like, subscribe, and hit the bell icon to stay updated with our latest videos! 🤖 Note that we use synthetic generations, such as AI-generated images and voices, to enhance the appeal and engagement of our content. 🌐 If you have any questions or topics you want us to cover, leave a comment below. Additionally, share with your thoughts about the content, how do you think we can make them better? Thanks for watching!