Add scatterplot

app.py

@@ -2,16 +2,25 @@ import streamlit as st
 import numpy as np
 import matplotlib.pyplot as plt
 
-number_of_observations = st.slider('Number of observations', min_value=50, max_value=150)
+number_of_observations = st.slider('Number of observations', min_value=50, max_value=150, value=50)
+noise_standard_deviation = st.slider('Standard deviation of the noise', min_value = 0.0, max_value=0.5, value=0.25)
+
+np.random.seed(2)
+
 X = np.column_stack((np.ones(number_of_observations), 
-                     np.random.random(number_of_observations))     
-                                    
+                     np.random.random(number_of_observations)))      
+
+w = np.array([3.0, -20.0, 32.0])  # coefficients                                    
 
-fig, ax = plt.subplots()
+X = np.column_stack((X, X[:,1] ** 2))   # add x**2 column
+additional_noise = 8 * np.random.binomial(1, 0.03, size = number_of_observations)
+y = np.dot(X, w) + noise_standard_deviation * np.random.randn(number_of_observations) \
+        + additional_noise	
+
+fig, ax = plt.subplots(dpi=320)
 ax.set_xlim((0,1))
 ax.set_ylim((-5,20))
-ax.scatter(X[:,1], y, c='r', edgecolors='#fda172')
-#line_thickness = 2
-#line, = ax.plot([], [], lw=line_thickness)
+ax.scatter(X[:,1], y, c='r', edgecolors='black')
+
 st.pyplot(fig)
 st.write(X[:5, :])