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SA-EP5 - Regression Analysis [ ENGLISH ]

SA-EP5 - Regression Analysis [ ENGLISH ]

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 🎙️ Episode Title: Regression Analysis – Predicting the Future with Statistics 🔍 Episode Description: Welcome to another powerful episode of "Pal Talk – Statistics", the podcast where numbers come alive! Today, we’re diving into one of the most essential and widely used techniques in data science, economics, psychology, medicine, and beyond — Regression Analysis. If you’ve ever tried to predict future trends, understand relationships between variables, or model real-world scenarios, then regression is already knocking at your door. In this episode, we break it all down: ✅ What is Regression Analysis? At its core, regression is about examining the relationship between a dependent variable and one or more independent variables. It helps us answer questions like: Does studying more hours lead to higher scores? Does income level influence spending habits? ✅ Types of Regression We explore the most commonly used regression methods: Simple Linear Regression Multiple Linear Regression Logistic Regression (for categorical outcomes) Polynomial Regression Each with real-world examples to clarify when and why to use them. ✅ Key Concepts Made Simple Understand terms like: Intercept and slope R-squared (R²) Residuals and error terms Overfitting and underfitting We’ll explain how these concepts come together to build a strong, predictive model. ✅ How to Perform Regression Analysis From visualizing scatter plots to calculating the best-fit line and interpreting regression coefficients — we guide you through the process step-by-step. ✅ Assumptions of Regression Models Every method has its boundaries. We discuss the major assumptions like linearity, independence, homoscedasticity, and normal distribution of residuals. ✅ Applications in Real Life From forecasting sales and estimating housing prices, to predicting disease risk and analyzing marketing campaigns — regression analysis is used in almost every data-driven field. ✅ Linear vs Logistic Regression Many learners confuse these two — we clarify the difference, focusing on continuous vs categorical outputs. ✅ Tips for Interpreting Results Learn how to go beyond just “getting a model” to actually understanding what the numbers are telling you — and whether the relationship is statistically significant. 👥 Hosts: Speaker 1 (Male): A data analyst with a teaching spirit and deep love for modeling. Speaker 2 (Female): A passionate learner with real-world questions that keep the session relatable. 🎧 Whether you're a student, researcher, business analyst, or someone starting out in machine learning, this episode will equip you with the basics of regression and inspire you to apply it to your own data. 📌 Next on “Pal Talk – Statistics”: Logistic Regression | Residual Analysis | R² and Adjusted R² | Model Validation | and more! 💡 Subscribe, share, and leave a review to help us grow this community of data enthusiasts. 🎓 Pal Talk – Where Data Talks.
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