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+# OLS Ordinary Least Squares
+
+The OLS general model $\hat{y}$ is defined by: 
+
+$$ \hat{y} = \theta_0+\theta_1 x_1 $$
+
+Applying the partial derivatives with rescpect $\theta_0$ and equaliting to zero:
+
+$$\frac{\partial SSR}{\partial \theta_0}=0 $$
+
+here SSR is defined as:
+
+$$ \sum_{i=1}^n (y^i - \hat{y}^i)^2 $$
+
+Resulting in:
+
+$$ \theta_0 = \frac{\sum_{i=1}^n y^i}{n} - \frac{\theta_1 \sum_{i=1}^n x^i}{n}$$
+
+or 
+
+$$ \theta_0 = \bar{y} -\theta_1 \bar{x} $$
+
+In a similar way, the partial derivative of SSR with respect of $\theta_1$ will result in: 
+
+$$\theta_1 = \frac{\sum_{i=1}^n x^i(y^i-\bar{y}) }{\sum_{i=1}^n x^i(x^i-\bar{x})}$$
+
+# Implementing OLS in Python
+
+
+```python
+import numpy as np
+x = np.linspace(0,4,20)
+theta0 = 3.9654
+theta1 = 2.5456
+y = theta0+theta1*x
+y
+```
+
+
+
+
+    array([ 3.9654    ,  4.50131579,  5.03723158,  5.57314737,  6.10906316,
+            6.64497895,  7.18089474,  7.71681053,  8.25272632,  8.78864211,
+            9.32455789,  9.86047368, 10.39638947, 10.93230526, 11.46822105,
+           12.00413684, 12.54005263, 13.07596842, 13.61188421, 14.1478    ])
+
+
+
+
+```python
+import matplotlib.pyplot as plt 
+plt.plot(x,y, '.k')
+plt.show()
+```
+
+
+    
+![png](main_files/main_5_0.png)
+    
+
+
+
+```python
+x = 4*np.random.rand(50, 1)
+y = theta0 + theta1*x+0.5*np.random.randn(50, 1)
+plt.plot(x,y, '*k')
+plt.show()
+```
+
+
+    
+![png](main_files/main_6_0.png)
+    
+
+
+## Implementing with `for` 
+$$\theta_1 = \frac{\sum_{i=1}^n x^i(y^i-\bar{y}) }{\sum_{i=1}^n x^i(x^i-\bar{x})}$$
+
+
+```python
+# for implementation for computing theta1:
+xAve = x.mean()
+yAve = y.mean()
+num = 0
+den = 0
+for i in range(len(x)):
+    num = num + x[i]*(y[i]-yAve)
+    den = den + x[i]*(x[i]-xAve)
+theta1Hat = num/den
+print(theta1Hat)
+```
+
+    [2.4717291]
+
+
+
+```python
+# for implementation for theta0:
+# $$ \theta_0 = \bar{y} -\theta_1 \bar{x} $$
+theta0Hat = yAve - theta1Hat*xAve
+print(theta0Hat)
+#real values are
+#theta0 = 3.9654
+#theta1 = 2.5456
+```
+
+    [4.18459936]
+
+
+
+```python
+total = 0
+for i in range(len(x)):
+    total = total + x[i]
+total/len(x)
+```
+
+
+
+
+    array([2.27654582])
+
+
+
+## Implementing OLS by numpy methods
+
+
+```python
+# For theta1:
+# $$\theta_1 = \frac{\sum_{i=1}^n x^i(y^i-\bar{y}) }{\sum_{i=1}^n x^i(x^i-\bar{x})}$$
+num2 = np.sum(x*(y-y.mean()))
+den2 = np.sum(x*(x-x.mean()))
+theta1Hat2 = num2/den2
+print(theta1Hat2)
+
+# Efficacy --> time
+
+```
+
+    2.4717291029649546
+
+
+
+```python
+theta0Hat2 = yAve-theta1Hat2*xAve
+theta0Hat2
+```
+
+
+
+
+    4.184599360470533
+
+
+
+# Comparing Model and Data
+
+
+```python
+xNew = np.linspace(0,4,20)
+yHat = theta0Hat + theta1Hat*xNew
+plt.plot(xNew, yHat, '-*r', label="$\hat{y}$")
+plt.plot(x,y,'.k', label="data")
+plt.legend()
+plt.show()
+```
+
+
+    
+![png](main_files/main_15_0.png)
+    
+
+
+# Functions for data and OLS
+
+
+```python
+def DataGen(xn: float,n: int, disp,theta0=3.9654,theta1=2.5456):
+    x = xn*np.random.rand(n, 1)
+    #theta0 = 3.9654
+    #theta1 = 2.5456
+    y = theta0+theta1*x+disp*np.random.randn(n,1)
+    return x,y
+```
+
+
+```python
+x,y = DataGen(9, 100, 1, 0,1)
+```
+
+
+```python
+plt.plot(x,y,'.k')
+plt.show()
+```
+
+
+    
+![png](main_files/main_19_0.png)
+    
+
+
+
+```python
+def MyOLS(x,y):
+    # for implementation for computing theta1:
+    xAve = x.mean()
+    yAve = y.mean()
+    num = 0
+    den = 0
+    for i in range(len(x)):
+        num = num + x[i]*(y[i]-yAve)
+        den = den + x[i]*(x[i]-xAve)
+    theta1Hat = num/den
+    theta0Hat = yAve - theta1Hat*xAve
+    return theta0Hat, theta1Hat
+```
+
+
+```python
+the0, the1 = MyOLS(x,y)
+the1
+```
+
+
+
+
+    array([1.12539439])
+
+
+
+# TODO - Students
+- [ ] Efficacy --> time: For method Vs. Numpy
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