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app.py

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+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+
+# Set page title and layout
+st.set_page_config(page_title='Statistics Basics', layout='wide')
+
+# Set up sidebar
+st.sidebar.title('Parameters')
+mean = st.sidebar.slider('Mean', min_value=-10.0, max_value=10.0, value=0.0, step=0.1)
+std_dev = st.sidebar.slider('Standard Deviation', min_value=0.1, max_value=10.0, value=1.0, step=0.1)
+variance = std_dev ** 2
+variance_slider = st.sidebar.slider('Variance', min_value=0.1, max_value=10.0, value=variance, step=0.1)
+
+# Generate data
+x = np.linspace(-10, 10, 1000)
+y = 1 / (np.sqrt(2 * np.pi * variance_slider)) * np.exp(-0.5 * ((x - mean) ** 2) / variance_slider)
+
+# Calculate statistics
+median = mean
+mode = mean
+
+# Generate random values within the curve boundaries based on kernel density estimate
+num_points = 1000
+samples = np.random.choice(x, size=num_points, p=y / np.sum(y))
+
+# Create DataFrame for plotting
+df = pd.DataFrame({'Values': samples})
+
+# Set seaborn style
+sns.set(style='darkgrid')
+
+# Plot the bell curve and histogram of generated values
+fig, ax = plt.subplots(figsize=(10, 6))
+sns.histplot(df['Values'], kde=True, color='blue', ax=ax)
+sns.kdeplot(df['Values'], color='red', ax=ax)
+ax.plot(x, y, linewidth=2, color='red')
+ax.set_title('Bell Curve with Histogram and KDE')
+ax.set_xlabel('X')
+ax.set_ylabel('Density')
+
+
+# Display statistics
+st.header('Statistics Concepts')
+col1, col2, col3, col4, col5 = st.columns(5)
+col1.subheader('Mean')
+col1.markdown(f"<span style='font-size:24px'>{mean}</span>", unsafe_allow_html=True)
+col2.subheader('Median')
+col2.markdown(f"<span style='font-size:24px'>{median}</span>", unsafe_allow_html=True)
+col3.subheader('Mode')
+col3.markdown(f"<span style='font-size:24px'>{mode}</span>", unsafe_allow_html=True)
+col4.subheader('St.D')
+col4.markdown(f"<span style='font-size:24px'>{std_dev}</span>", unsafe_allow_html=True)
+col5.subheader('Variance')
+col5.markdown(f"<span style='font-size:24px'>{variance_slider}</span>", unsafe_allow_html=True)
+
+
+
+# Display the bell curve, histogram, and KDE plot
+st.header('Bell Curve with Histogram and KDE')
+st.pyplot(fig)