Your Regression Could Be Wrong (Even If It Looks Perfect) | Data Analysis Mistakes Explained

You ran your regression, your p-values look great, R² is high… but your supervisor says the model is wrong. Why does this happen?

In this video, I break down one of the most common mistakes in research and data analysis — choosing the wrong regression model.

You will learn:

Why linear regression does not always work
When to use linear plateau models in agriculture and soil science
What censored data is and when to use Tobit regression
Difference between continuous data and count data
When to use Poisson vs Negative Binomial regression
What zero-inflated data means and how to handle it
Logistic, multinomial, and ordered regression explained simply
Introduction to survival analysis and Cox regression
When to use multivariate probit models

This video is especially useful for:

PhD students
Early-career researchers
Anyone working with real-world data in agriculture, economics, or social science

👉 Remember:
Good analysis is not about running regression. It is about choosing the right model for your data.

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Видео Your Regression Could Be Wrong (Even If It Looks Perfect) | Data Analysis Mistakes Explained канала Nayem Hasan