Create Measurement_of_disperssion.py

pages/Measurement_of_disperssion.py

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+import streamlit as st 
+import numpy as np
+st.subheader("Measure Of Disperssion ",divider=True)
+st.markdown("""Measure Of Disperssion will give spread of our collected data around the central value.It's classifed into two types
+""")
+st.markdown(''':violet[Absolute Measure] \n absolute will give the spread of data in one unit.for example if the given data is in 'cm'
+the output will be in cm''')
+st.markdown(''':violet[Relative Measure] \n  Relative will be free from unit's''')
+st.header("**Absolute Measure**")
+st.subheader("Range",divider=True)
+st.subheader("Quartile Deviation",divider=True)
+st.subheader("Varience",divider=True)
+st.subheader("Standard Deviation",divider=True)
+st.header("**Relative Measure**")
+st.subheader("Coefficent Of Range",divider=True)
+st.subheader("Coefficent Of Quartile Deviation",divider=True)
+st.subheader("Coefficent Of Varience",divider=True)
+st.subheader("Coefficent Of Standard Deviation",divider=True)
+st.markdown(''':orange[**Range**] is one of the measure to find the disperssion.But is not at all mostly used beause it don't focus on the entire data.
+''')
+st.subheader("Absolute Range")
+st.latex(r'''
+    \text{Absolute Range} = \text{Maximum Value} - \text{Minimum Value}
+''')
+st.subheader("Relative Range")
+st.latex(r'''
+    \text{Relative Range} = \frac{\text{Absolute Range}}{\text{Mean}} \times 100
+''')
+st.markdown(''':orange[**Quartile Deviation**] is one of the measure to find the disperssion.In this type the data is divided into 4 equal parts.
+It will mostly focus on the central data.
+''')
+st.subheader("Absolute Quartile Deviation")
+st.latex(r'''
+    QD = \frac{Q3 - Q1}{2}
+''')
+st.subheader("Relative Quartile Deviation")
+st.latex(r'''
+    \text{Relative QD} = \frac{Q3 - Q1}{Q3 + Q1} \times 100
+''')
+st.markdown(''':orange[**Varience**] is one of the measure to find the disperssion.It is one of the best measure to find the disperssion.The only
+drawback is when in Varience is in order to overcome negitive value we square them thus the distance is doubled
+''')
+st.subheader("Absolute Variance")
+st.latex(r'''
+    \text{Var} = \frac{1}{N} \sum_{i=1}^{N} (x_i - \bar{x})^2
+''')
+st.subheader("Relative Variance")
+st.latex(r'''
+    \text{Relative Var} = \frac{\text{Var}}{\bar{x}} \times 100
+''')
+st.markdown(''':orange[**Standard Deviation**] is one of the measure to find the disperssion.It is one of the best measure to find the disperssion.It over comes the 
+disadvantage occured in varience by square rooting it.
+''')
+st.subheader("Absolute Standard Deviation")
+st.latex(r'''
+    \sigma = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (x_i - \bar{x})^2}
+''')
+st.subheader("Relative Standard Deviation")
+st.latex(r'''
+    \text{Relative SD} = \frac{\sigma}{\bar{x}}
+''')
+st.subheader("Distribution",divider=True)
+st.markdown(''':blue[**Distribution**] is a measure will will tell how the shape of data or in which shape the data is spread.It will help in 
+analysis.There are few types of distribution \n *  Normal Distribution \n * Uniform Distribution \n * Binomial Distribution \n * Poisson Distribution
+\n * Exponential Distribution \n * Chi-Square Distribution \n * T-Distribution
+''')