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| author | Christian Kolset <christian.kolset@gmail.com> | 2025-03-31 17:26:14 -0600 |
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| committer | Christian Kolset <christian.kolset@gmail.com> | 2025-03-31 17:27:21 -0600 |
| commit | 9cea323dee47040c9935119dbc84694c911ac9ac (patch) | |
| tree | f5658fd15038ef2162d882067bd2b0b99f110b00 /tutorials/linear_regression.md | |
| parent | 2abe25f95ff9ac0500f01bf385e991c9a6dae019 (diff) | |
Added tutorial: Linear regression. And added Notebook: Basics of python
with link in TOC.
Diffstat (limited to 'tutorials/linear_regression.md')
| -rw-r--r-- | tutorials/linear_regression.md | 18 |
1 files changed, 18 insertions, 0 deletions
diff --git a/tutorials/linear_regression.md b/tutorials/linear_regression.md new file mode 100644 index 0000000..b21415b --- /dev/null +++ b/tutorials/linear_regression.md @@ -0,0 +1,18 @@ +# 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]) +``` |
