From f5c480ad8eb043e644b6db1d775b968883db6ac8 Mon Sep 17 00:00:00 2001
From: Josh Mudge <josh@cityofardon.com>
Date: Sat, 2 Mar 2019 17:47:52 -0700
Subject: [PATCH] Add linear regression info.

---
 Codecademy.md | 87 +++++++++++++++++++++++++++++++++++++++++++++++++++
 PHW.md        |  3 ++
 2 files changed, 90 insertions(+)
 create mode 100644 PHW.md

diff --git a/Codecademy.md b/Codecademy.md
index d103034..3cfd4b5 100644
--- a/Codecademy.md
+++ b/Codecademy.md
@@ -18,6 +18,93 @@ https://discuss.codecademy.com/t/what-does-it-mean-that-python-is-an-object-orie
 
   SyntaxError example: `SyntaxError: EOL while scanning string literal`
 
+# Linear Regression
+
+One of the projects in my Python 3 course on Codecademy was to calculate the linear regression of any given line in a set. This is my journey of doing just that, following their instructions. The goal, is to calculate the bounciness of different balls with the least error possible.
+
+Starting with `y = m*x + b`
+
+We can determine the y of a point pretty easily if we have the slope of the line (m) and the intercept (b). Thus, we can write a basic function to calculate the y:
+
+```
+def get_y(m, b, x):
+  y = m*x + b
+  return y
+
+print(get_y(1, 0, 7) == 7)
+print(get_y(5, 10, 3) == 25)
+```
+
+We can then use that to calculate the linear regression of a line:
+
+```
+def calculate_error(m, b, point):
+    x_point = point[0]
+    y_point = point[1]
+
+    y2 = get_y(m, b, x_point)
+
+    y_diff = y_point - y2
+    y_diff = abs(y_diff)
+    return y_diff
+
+```
+
+To get a more accurate result, we need a function that will parse several points at a time:
+
+```
+def calculate_all_error(m, b, points):
+    totalerror = 0
+    for point in points:
+        totalerror += calculate_error(m, b, point)
+
+    return abs(totalerror)
+```
+
+We can test it using their examples:
+
+```
+#every point in this dataset lies upon y=x, so the total error should be zero:
+datapoints = [(1, 1), (3, 3), (5, 5), (-1, -1)]
+print(calculate_all_error(1, 0, datapoints))
+
+#every point in this dataset is 1 unit away from y = x + 1, so the total error should be 4:
+datapoints = [(1, 1), (3, 3), (5, 5), (-1, -1)]
+print(calculate_all_error(1, 1, datapoints))
+
+#every point in this dataset is 1 unit away from y = x - 1, so the total error should be 4:
+datapoints = [(1, 1), (3, 3), (5, 5), (-1, -1)]
+print(calculate_all_error(1, -1, datapoints))
+
+
+#the points in this dataset are 1, 5, 9, and 3 units away from y = -x + 1, respectively, so total error should be
+# 1 + 5 + 9 + 3 = 18
+datapoints = [(1, 1), (3, 3), (5, 5), (-1, -1)]
+print(calculate_all_error(-1, 1, datapoints))
+```
+
+You can save all possible m and b values between -10 and 10 (m values), as well as -20 and 20 (b values) using:
+
+```
+possible_ms = [mv * 0.1 for mv in range(-100, 100)] #your list comprehension here
+possible_bs = [bv * 0.1 for bv in range(-200, 200)] #your list comprehension here
+```
+
+We can find the combination that produces the least error, which is:
+
+```
+    m = 0.3
+    b = 1.7
+    x = 6
+```
+
+The goal was to calculate the bounciness of different balls with the least error possible. With this data, we can calculate how far a given ball would bounce. For example, a 6 cm ball would bounce 3.5 cm. We know this because we can plug in the numbers like this:
+
+```
+get_y(0.3, 1.7, 6)
+```
+
+
 # Math (ex6)
 
 ```
diff --git a/PHW.md b/PHW.md
new file mode 100644
index 0000000..3ab16e6
--- /dev/null
+++ b/PHW.md
@@ -0,0 +1,3 @@
+#
+
+ex19.