From eb0ee1f0d51d33666376552e610de15f233167f5 Mon Sep 17 00:00:00 2001 From: Christian Kolset Date: Fri, 24 Oct 2025 17:22:07 -0600 Subject: Added signal processing information to data_dump --- tutorials/module_4/4.5 Statistical Analysis II.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) (limited to 'tutorials/module_4/4.5 Statistical Analysis II.md') diff --git a/tutorials/module_4/4.5 Statistical Analysis II.md b/tutorials/module_4/4.5 Statistical Analysis II.md index 3df558b..458bada 100644 --- a/tutorials/module_4/4.5 Statistical Analysis II.md +++ b/tutorials/module_4/4.5 Statistical Analysis II.md @@ -1,7 +1,7 @@ # 4.5 Statistical Analysis II As mentioned in the previous tutorial. Data is what gives us the basis to create models. By now you've probably used excel to create a line of best fit. In this tutorial, we will go deeper into how this works and how we can apply this to create our own models to make our own predictions.ile changes in local repository ​======= - File changes in remote reposito + File changes in remote repository ## Least Square Regression and Line of Best Fit @@ -10,6 +10,7 @@ As mentioned in the previous tutorial. Data is what gives us the basis to create Linear regression is one of the most fundamental techniques in data analysis. It models the relationship between two (or more) variables by fitting a **straight line** that best describes the trend in the data. + ### Linear To find a linear regression line we can apply the @@ -67,6 +68,7 @@ plt.show() Using the + ## Extrapolation basis funct ## Moving average -- cgit v1.2.3