Update pages/Measurement_of_disperssion.py

pages/Measurement_of_disperssion.py

@@ -120,6 +120,40 @@ 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("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=relative_range(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("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_range(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.
 ''')
@@ -131,6 +165,40 @@ 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("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=relative_range(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("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_range(list1)
+        st.write("Relative Standard Deviation",result)
+else:
+    st.write("Please enter valid numbers.")
 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