What is Regression? Let’s break it down!

Regression is a statistical method used to understand the relationship between a dependent variable (outcome) and one or more independent variables (predictors). It helps us predict values, identify trends, and draw conclusions about how variables interact in a dataset.

📘 Key Concepts of Regression:

1. Dependent Variable (Y):
   The outcome or target you’re trying to predict or explain.

2. Independent Variable(s) (X):
   The factor(s) you believe influence the outcome.

3. Regression Line:
   A mathematical equation that best fits the data, usually in the form:

   Y=β0+β1X+εY = β_0 + β_1X + εY=β0​+β1​X+ε

   Where:

   • β0β_0β0​ is the intercept,

   • β1β_1β1​ is the slope (how much Y changes with X),

   • $\epsilon$ is the error term.

📊 Common Types of Regression:

(Note: The reel already mentions the 5 types — you can link to them or let others elaborate.)

• Simple Linear Regression – one independent variable

• Multiple Linear Regression – multiple predictors

• Logistic Regression – for binary outcomes

• Polynomial Regression – for nonlinear relationships

• Ridge/Lasso Regression – for regularization and avoiding overfitting

🎯 Why is Regression Important?

• Forecasting future trends (e.g., sales, disease rates)

• Understanding impact of variables (e.g., smoking on health)

• Modeling real-world phenomena in business, healthcare, engineering, etc.

🧪 Use Cases in Research & Medicine:

• Predicting patient outcomes based on risk factors

• Evaluating treatment effectiveness

• Analyzing survey data

• Publishing regression-based findings in high-impact journals

🧵 Discussion Prompts:

💬 Have you used regression in your own research?
💬 What challenges have you faced with assumptions like normality, homoscedasticity, or multicollinearity?
💬 Which type of regression do you find most useful, and why?