Update app.py

app.py

@@ -1,3 +1,78 @@
 import streamlit as st
 import numpy as np
-import pandas as pd
+import pandas as pd
+
+st.title("**Introduction to Statistics**")
+st.subheader("What is statistics ?")
+st.write("statistics is a huge field where in this field we are going to deal with data like ( how to perform collecting data,interpreting data, analyzing data,structuring data).")
+
+st.subheader("**Types of Statistics**")
+st.write("There are two types of statistcs.")
+st.write("1. Descriptive Statistic")
+st.write("2. Inferential Statistic")
+
+st.subheader("**1. Descriptive Statistics**")
+st.write("Descriptive statistics involves describing,summarizing and organizing the data so it can be easily understood.example- classroom of maths students (20).marks of the first sem (84,86,78,72,75,65,80,81,45,87,67,54).whar is the average marks of the students in the class?")
+
+st.subheader("**Population**")
+st.write("**Population** - Population data is a whole and complete set.The measurable characteristic of the population, like the mean or standard deviation, is known as the parameter.")
+
+st.subheader("**Sample**")
+st.write("**Sample** - The sample is a subset of the population that is derived using sampling.The measurable characteristic of the sample is called a statistic.")
+
+st.subheader("**2. Inferential Statistcs**")
+st.write("Inferential statistics where is we used the data that we have measured to form conclusions.example- Are the marks of the students of this classroom simlar to the age of the maths classroom in the college? ")
+
+st.subheader("**Types of Descriptive statistics**")
+st.write("There are three types of Descriptive statistics. 1)Distribution 2) Measures of central tendency 3) Measures of Dispersion")
+
+st.subheader("**Measures of Central Tendency**")
+st.write("Measures of central tendency estimate the center, or average, of a data set. The mean, median and mode are 3 ways of finding the average.")
+
+st.subheader("**Mean**")
+st.write("The mean, or M, is the most commonly used method for finding the average.To find the mean, simply add up all response values and divide the sum by the total number of responses. The total number of responses or observations is called N.")
+
+st.header("**There are three types of Mean")
+st.write("**Arithmetic Mean** - It is the sum of the sampled values divided by thenumber of items in the sample.")
+st.write("**Geometric Mean** - It is an average that is useful for sets of positivenumbers that are interpreted according to their product.")
+st.write("**Harmonic Mean** - The harmonic mean is a numerical average calculated by dividing the number of observations by the reciprocal of each number.")
+
+st.subheader("**Median**")
+st.write("The median is the value that’s exactly in the middle of a data set.To find the median, order each response value from the smallest to the biggest. Then, the median is the number in the middle. If there are two numbers in the middle, find their mean.")
+
+st.subheader("**Mode**")
+st.write("The mode is the simply the most popular or most frequent response value. A data set can have no mode, one mode, or more than one mode.To find the mode, order your data set from lowest to highest and find the response that occurs most frequently.")
+
+st.subheader("**Measures Of Dispersion**")
+st.write("Measures of variability give you a sense of how spread out the response values are. The range, standard deviation and variance each reflect different aspects of spread.")
+
+st.header("**There are two types of dispersion**")
+st.subheader("Absolute Measure of dispersion")
+st.subheader("Relative Measure of dispersion")
+
+st.write("**Absolute** - he measures of dispersion that are measured and expressed in the units of data themselves are called Absolute Measure of Dispersion. For example – Meters, Dollars, Kg, etc.")
+
+st.subheader("absolute measures of dispersion are: ")
+st.write("**Range** - It is defined as the difference between the largest and the smallest value in the distribution.")
+
+st.write("**Mean Deviation** - It is the arithmetic mean of the difference between the values and their mean.")
+
+st.write("**Standard Deviation** - It is the square root of the arithmetic average of the square of the deviations measured from the mean.")
+
+st.write("**Variance** - It is defined as the average of the square deviation from the mean of the given data set.")
+
+st.write("**Quartile Deviation** - It is defined as half of the difference between the third quartile and the first quartile in a given data set.")
+
+st.write("**Interquartile Range** - The difference between upper(Q3 ) and lower(Q1) quartile is called Interterquartile Range. Its formula is given as Q3 – Q1.")
+
+
+st.write("**Relative** - We use relative measures of dispersion to measure the two quantities that have different units to get a better idea about the scattering of the data.")
+
+st.subheader("Relative measures of dispersion:")
+st.write("**Coefficient of Range** - It is defined as the ratio of the difference between the highest and lowest value in a data set to the sum of the highest and lowest value.")
+
+st.write("**Coefficient of Variation** - It is defined as the ratio of the standard deviation to the mean of the data set. We use percentages to express the coefficient of variation.")
+
+st.write("**Coefficient of Mean Deviation**- It is defined as the ratio of the mean deviation to the value of the central point of the data set.")
+
+st.write("**Coefficient of Quartile Deviation** - It is defined as the ratio of the difference between the third quartile and the first quartile to the sum of the third and first quartiles.")