1 files changed, 12 insertions, 2 deletions
diff --git a/tutorials/module_4/4.4 Statistical Analysis.md b/tutorials/module_4/4.4 Statistical Analysis.md
index bf3a8bd..09ac1fb 100644
--- a/tutorials/module_4/4.4 Statistical Analysis.md
+++ b/tutorials/module_4/4.4 Statistical Analysis.md
@@ -6,8 +6,12 @@
 - Correlation and regression
 - Uncertainty, error bars, confidence intervals
 ---
+## Engineering Models
+
+- Curve fitting
+- 
 ## Statistical tools
-Numpy comes with some useful statistical tools that we can use to analyze our data. We can use these tools when working with data, it’s important to understand the **central tendency** and **spread** of your dataset. NumPy provides several built-in functions to quickly compute common statistical metrics such as **mean**, **median**, **standard deviation**, and **variance**. These are fundamental tools for analyzing measurement consistency, uncertainty, and identifying trends in data.
+Both Numpy and Pandas come with some useful statistical tools that we can use to analyze our data. We can use these tools when working with data, it’s important to understand the **central tendency** and **spread** of your dataset. NumPy provides several built-in functions to quickly compute common statistical metrics such as **mean**, **median**, **standard deviation**, and **variance**. These are fundamental tools for analyzing measurement consistency, uncertainty, and identifying trends in data.
 ```python
 import numpy as np
 
@@ -17,4 +21,10 @@ std = np.std([1, 2, 3, 4, 5])
 variance = np.var([1, 2, 3, 4, 5])
 ```
 
-As seen in the previous lecture, pandas also includes several built-in statistical tools that make it easy to analyze entire datasets directly from a DataFrame. Instead of applying individual NumPy functions to each column, you can use methods such as `.mean()`, `.std()`, `.var()`, and especially `.describe()` to generate quick summaries of your data. These tools are convenient when working with experimental or simulation data that contain multiple variables, allowing you to assess trends, variability, and potential outliers all at once.
-\ No newline at end of file
+Pandas also includes several built-in statistical tools that make it easy to analyze entire datasets directly from a DataFrame. When working with pandas we can use methods such as `.mean()`, `.std()`, `.var()`, and especially `.describe()` to generate quick summaries of your data. These tools are convenient when working with experimental or simulation data that contain multiple variables, allowing you to assess trends, variability, and potential outliers all at once.
+
+## Statistical Distribution
+
+
+
+## Problem: Spectroscopy