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What are Assumptions of Linear Regression? Easy Explanation for Data Science Interviews

In this video, we’ll go over the four assumptions of linear regression. Interviewers frequently ask questions about linear regression, so we’ll dive into both the assumptions themselves, as well as how to diagnose violations of the assumptions. I’ll also offer insight into which assumptions are critical versus which are less important. 🟢Get all my free data science interview resources https://www.emmading.com/resources 🟡 Product Case Interview Cheatsheet https://www.emmading.com/product-case... 🟠 Statistics Interview Cheatsheet https://www.emmading.com/statistics-i... 🟣 Behavioral Interview Cheatsheet https://www.emmading.com/behavioral-i... 🔵 Data Science Resume Checklist https://www.emmading.com/data-science... ✅ We work with Experienced Data Scientists to help them land their next dream jobs. Apply now: https://www.emmading.com/coaching // Comment Got any questions? Something to add? Write a comment below to chat. // Let's connect on LinkedIn:   / emmading001   ==================== Contents of this video: ==================== 00:00 Introduction 00:43 The Assumptions 01:38 Things To Note 02:04 Assumption #1 02:50 How To Diagnose 03:16 Residual Plots 03:48 Assumption #2 04:17 How To Diagnose 04:52 Assumption #3 05:19 Violations of Normality 05:57 Q-Q Plots 07:12 Summary of Q-Q Plots 07:24 Assumption #4 07:55 How To Diagnose 08:15 Residual Plots 08:53 To Summarize