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authorChristian Kolset <christian.kolset@gmail.com>2025-04-24 16:25:31 -0600
committerChristian Kolset <christian.kolset@gmail.com>2025-04-24 16:25:31 -0600
commit652f88728eb91bae1c4f30b63d1fbe60788ea938 (patch)
tree65cfe591da183b969885e8c557b1ac5810727ec8 /tutorials/module_4
parent42fca6122f4baf847ec2794b172abbc6a2193407 (diff)
Added jupyter notebook converter script. Converts markdown (.md) tutorials to jupyter notebook (.ipynb).
Diffstat (limited to 'tutorials/module_4')
-rw-r--r--tutorials/module_4/linear_regression.md18
-rw-r--r--tutorials/module_4/notebook_4/plotting.ipynb17
-rw-r--r--tutorials/module_4/plotting.md3
3 files changed, 38 insertions, 0 deletions
diff --git a/tutorials/module_4/linear_regression.md b/tutorials/module_4/linear_regression.md
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+++ b/tutorials/module_4/linear_regression.md
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+# Linear Regression
+
+
+
+## Statical tools
+Numpy comes with some useful statistical tools that we can use to analyze our data.
+
+### Mean
+The mean is the average of a set of numbers. It is calculated by summing all the numbers and dividing by the count of numbers.
+
+```python
+import numpy as np
+
+mean = np.mean([1, 2, 3, 4, 5])
+median = np.median([1, 2, 3, 4, 5])
+std = np.std([1, 2, 3, 4, 5])
+variance = np.var([1, 2, 3, 4, 5])
+```
diff --git a/tutorials/module_4/notebook_4/plotting.ipynb b/tutorials/module_4/notebook_4/plotting.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plotting\n",
+ "\n",
+ "## matlibplot"
+ ],
+ "id": "c1faa4f4-55cb-4e59-a732-bada47ed2a19"
+ }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {}
+}
diff --git a/tutorials/module_4/plotting.md b/tutorials/module_4/plotting.md
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index 0000000..b309e2f
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+++ b/tutorials/module_4/plotting.md
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+# Plotting
+
+## matlibplot