Скачать или смотреть Analytics And Visualization With R || CMA 6 | DAY 9 || Parameter Estimation With R

In this lecture, we dive deep into parameter estimation—a key process in fitting mathematical or statistical models to real-world data. If you've ever built a model and wondered how to make it actually match your data, this is where it starts to get practical!

👨‍🏫 We cover:

What parameter estimation is and why it matters in real-world modeling

The difference between statistical (functional) models and mathematical (mechanistic) models

Key estimation techniques: Least Squares, Maximum Likelihood Estimation (MLE), and Bayesian inference

An introduction to curve fitting using linear and nonlinear models

How to import your own data into R and perform parameter estimation

Hands-on steps to load, visualize, and fit a line to your dataset in RStudio

💡 Whether you're working on malaria modeling, TMA responses, or just trying to calibrate a simple linear model, this session offers both the theory and the practice to get you started.

🛠️ Tools used:

R programming language

Functions like lm(), nls(), and read.csv()

📁 Data files used in this video are shared on our class platform. Please check the group chat or contact your facilitator if you need access.

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