Update pages/6_Measurement of Disperssion.py

pages/6_Measurement of Disperssion.py

@@ -93,4 +93,184 @@ st.markdown("""Relative is a method to  find the spread which involves the metho
 <li>Coefficent Of Standard Deviation</li>
 </ul>
 """,unsafe_allow_html=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
+''')
+def absolute_range(list1):
+    max_val = np.max(list1)
+    min_val = np.min(list1)
+    return max_val - min_val
+st.title("Calculate Absolute Range")
+num_input = st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input")
+list1 = []
+value = num_input.split(",")
+for i in value:
+    if i.isdigit():
+        list1.append(int(i))
+if list1:
+    result = absolute_range(list1)
+    st.write("Absolute Range:", result)
+else:
+    st.write("Please enter valid numbers.") 
+def relative_range(list1):
+  max=np.max(list1)
+  min=np.min(list1)
+  return round((max-min)/(max+min)*100,2)
+st.title("Calculate Relative Range")
+num_input1=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input1")
+value=num_input1.split(",")
+list1=[]
+for i in value:
+    if i.isdigit():
+        list1.append(int(i))
+    else:
+        pass
+if list1:
+    result=relative_range(list1)
+    st.write("Relative Range",result)
+else:
+    st.write("Please enter valid numbers.")
+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
+''')
+def abs_quartile_dev(list1):
+    q1=np.percentile(list1,25)
+    q3=np.percentile(list1,75)
+    return round((q3-q1)/2,2)
+st.title("Caluculate Absolute Quartile Deviation")
+num_input2=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input2")
+value=num_input2.split(",")
+list1=[]
+for i in value:
+    if i.isdigit():
+        list1.append(int(i))
+    else:
+        pass
+if list1:
+    result=abs_quartile_dev(list1)
+    st.write("Absolute Quartile Deviation",result)
+else:
+    st.write("Please enter valid numbers.")
+def rel_quartile_dev(list1):
+    q1=np.percentile(list1,25)
+    q3=np.percentile(list1,75)
+    return round(((q3-q1)/(q3+q1))*100,2) 
+st.title("Caluculate Relative Quartile Deviation")
+num_input3=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input3")
+value=num_input3.split(",")
+list1=[]
+for i in value:
+    if i.isdigit():
+        list1.append(int(i))
+    else:
+        pass
+if list1:
+    result=rel_quartile_dev(list1)
+    st.write("Relative Quartile Deviation",result)
+else:
+    st.write("Please enter valid numbers.")
+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
+''')
+def absolute_varience(list1):
+    return np.var(list1)
+st.title("Caluculate Absolute varience")
+num_input4=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input4")
+value=num_input4.split(",")
+list1=[]
+for i in value:
+    if i.isdigit():
+        list1.append(int(i))
+    else:
+        pass
+if list1:
+    result=absolute_varience(list1)
+    st.write("Absolute Varience",result)
+else:
+    st.write("Please enter valid numbers.")
+def relative_varience(list1):
+    mean=round(np.mean(list1),2)
+    std_dev=np.std(list1,ddof=1)
+    return round((std_dev/mean)*100,2)
+st.title("Caluculate Relative Varience")
+num_input5=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input5")
+value=num_input5.split(",")
+list1=[]
+for i in value:
+    if i.isdigit():
+        list1.append(int(i))
+    else:
+        pass
+if list1:
+    result=relative_varience(list1)
+    st.write("Relative Varience",result)
+else:
+    st.write("Please enter valid numbers.")
+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}}
+''')
+def absolute_std(list1):
+    return round(np.std(list1),2)
+st.title("Caluculate Absolute Standard Deviation")
+num_input6=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input6")
+value=num_input6.split(",")
+list1=[]
+for i in value:
+    if i.isdigit():
+        list1.append(int(i))
+    else:
+        pass
+if list1:
+    result=absolute_std(list1)
+    st.write("Absolute Standard Deviation",result)
+else:
+    st.write("Please enter valid numbers.")
+def relative_std(list1):
+    mean=round(np.mean(list1),2)
+    std_dev=np.std(list1,ddof=1)
+    return round((std_dev/mean),2)
+st.title("Caluculate Relative Standard Deviation")
+num_input7=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input7")
+value=num_input7.split(",")
+list1=[]
+for i in value:
+    if i.isdigit():
+        list1.append(int(i))
+    else:
+        pass
+if list1:
+    result=relative_std(list1)
+    st.write("Relative Standard Deviation",result)
+else:
+    st.write("Please enter valid numbers.")