IQR, Lower Fence & Upper Fence Explained | Outlier Detection | Statistics & Probability (Part 13)

Welcome back to *Masked Tutor!* 🥷📊 Notes for Lecture 13- https://drive.google.com/file/d/1Gq6F... In Part 13 of our *Statistics & Probability for Data Science and Data Analytics* series, we explore *Interquartile Range (IQR), Lower Fence, and Upper Fence* with formulas, examples, and outlier detection. These concepts help us understand the spread of data and identify unusually low or high observations using the IQR method. They are important for descriptive statistics, exploratory data analysis (EDA), and data preparation. 📚 *WHAT YOU'LL LEARN IN THIS VIDEO* ✅ What is Interquartile Range (IQR)? ✅ How to calculate IQR ✅ What is the Lower Fence? ✅ What is the Upper Fence? ✅ Lower Fence and Upper Fence formulas ✅ How to identify potential outliers ✅ Step-by-step numerical examples ✅ Understanding quartiles and box plots ✅ Applications in Data Science and Data Analytics 📊 *1. INTERQUARTILE RANGE (IQR)* The Interquartile Range measures the spread of the middle 50% of a dataset. 📌 *Formula:* IQR = Q3 − Q1 Where: • Q1 = First Quartile (25th percentile) • Q3 = Third Quartile (75th percentile) *Example:* Q1 = 10 Q3 = 30 IQR = 30 − 10 = 20 📊 *2. LOWER FENCE* The Lower Fence helps identify unusually low values that may be potential outliers. 📌 *Formula:* Lower Fence = Q1 − 1.5 × IQR Values below the Lower Fence are flagged as potential outliers. 📊 *3. UPPER FENCE* The Upper Fence helps identify unusually high values that may be potential outliers. 📌 *Formula:* Upper Fence = Q3 + 1.5 × IQR Values above the Upper Fence are flagged as potential outliers. 💡 *EXAMPLE* Suppose: Q1 = 10, Q3 = 30, and IQR = 20. Lower Fence = 10 − (1.5 × 20) = −20 Upper Fence = 30 + (1.5 × 20) = 60 Values below −20 or above 60 are potential outliers under the IQR rule. 🎯 *WHO SHOULD WATCH THIS VIDEO?* • Aspiring Data Analysts • Aspiring Data Scientists • Students learning Statistics and Probability • Beginners learning Exploratory Data Analysis (EDA) • Aspiring Machine Learning Engineers • Anyone interested in data analysis and statistical concepts 💡 *WHY ARE IQR AND FENCES IMPORTANT?* • Help summarize the spread of numerical data. • Identify potential outliers in datasets. • Support box plot interpretation. • Help investigate unusual observations during data cleaning. • Provide useful tools for exploratory data analysis. Remember: a potential outlier is not automatically an error. It should be investigated in context before deciding whether to keep or remove it. ━━━━━━━━━━━━━━━━━━━━ 📚 *WATCH THE COMPLETE STATISTICS & PROBABILITY SERIES* ▶️ *Part 1 — Complete Syllabus Discussion*    • Statistics & Probability for Data Science ...   ▶️ *Part 2 — What Is Statistics and Its Uses*    • What Is Statistics? Uses in Data Science &...   ▶️ *Part 3 — Descriptive vs Inferential Statistics*    • Descriptive vs Inferential Statistics Expl...   ▶️ *Part 4 — Introduction to Inferential Statistics*    • Inferential Statistics Explained with Exam...   ▶️ *Part 5 — Types of Sampling*    • Types of Sampling Explained with Examples ...   ▶️ *Part 6 — Types of Data*    • Types of Data Explained | Data Science & D...   ▶️ *Part 7 — Scales of Measurement*    • Scales of Measurement Explained | Data Sci...   ▶️ *Part 8 — Mean and Median*    • Mean and Median Explained with Examples | ...   ▶️ *Part 9 — Mode and Uses of Central Tendency*    • Mode Explained with Examples | Uses of Cen...   ▶️ *Part 10 — Python Implementation of Mean, Median & Mode*    • Python Implementation of Mean, Median & Mo...   ▶️ *Part 11 — Range and Percentage*    • Range and Percentage Explained | Measures ...   ▶️ *Part 12 — Percentiles and Quartiles*    • Percentiles & Quartiles Explained with Exa...   📂 *Complete Statistics & Probability Playlist*    • Statistics & Probability for Data Science ...   ━━━━━━━━━━━━━━━━━━━━ 💬 *QUESTION OF THE DAY* Suppose Q1 = 20 and Q3 = 40. What is the Interquartile Range? A) 10 B) 20 C) 30 D) 60 👇 Comment your answer below! 🔔 Subscribe to *Masked Tutor* for upcoming lectures on Statistics, Probability, Python, Data Science, and Data Analytics. *Masked Tutor — Learn • Analyze • Apply* #Statistics #DataScience #DataAnalytics