Batch 61&63: Regression analysis in R

📊 Welcome to Batch 61 & 63!
In this combined tutorial, we’ll dive deep into Regression Analysis in R, covering everything from simple linear regression to multiple regression models. Whether you’re a student, researcher, or data professional, understanding how to build and interpret regression models is essential for data-driven decision-making. By the end of this video, you’ll be able to structure your data properly, run regression analyses in R, and interpret your results confidently.

What You’ll Learn:
1️⃣ Simple Linear Regression

Modeling a relationship between one independent variable and a dependent variable.
Understanding regression coefficients, R-squared, and residuals.
2️⃣ Multiple Linear Regression

Adding more predictors to your regression model.
Checking assumptions (linearity, multicollinearity, homoscedasticity).
3️⃣ Model Diagnostics

Plotting residuals vs. fitted values.
Using diagnostic tools like plot(lm_model) to assess model fit.
4️⃣ Data Preparation

Converting variables to the right type (factors vs. numeric).
Dealing with missing values or outliers.
5️⃣ Interpretation and Reporting

Making sense of p-values, confidence intervals, and effect sizes.
Reporting results in a clear, concise manner.