Statistical analysis helps explore data relationship and develop high-end models to frame better decisions. It’s an intricate process of collecting and evaluating data to define the nature of data that has to be analyzed.
Below, we dig into the basics of statistical application in R and Python using the measure of central tendency.
As body methods for the study of numerical data, if some rows or columns are too long, in such cases, it becomes necessary to summarize data in an easily manageable form. The purpose is to serve by classifying the data in the form of frequency distribution and various graphs. When data relate to a variable, the process of summarization can be taken a step further by using certain descriptive measures. The dim is to focus on certain features that are central frequency and description.
In a set of data, they have a tendency, notwithstanding their variability, to cluster-around a central value and the tendency of the quantitative statistical observations is called central tendency.
The three measures of the central tendency are commonly used is:-
The description of these 3 estimators start below:-
Mean is the average of central tendency and is the most commonly used measures.
The concept of mean is divided into three parts:-
Mainly the mean refers to an arithmetic mean.
The arithmetic mean of a set of observations is defined to be their sum, divided by the number of observations.
For n numbers of observation (x_{1},x_{2},… ,x_{n} )
For frequency distribution where have frequencies. (i=1,2,3…)
Let’s, calculate the mean of Age, Height & Weight from the given data.
Name | Sex | Age | Height | Weight |
Ritesh | M | 24 | 6.9 | 112.5 |
Heena | F | 23 | 5.65 | 84 |
Kritika | F | 23 | 6.53 | 98 |
Anuradha | F | 24 | 6.28 | 102.5 |
Gaurav | M | 24 | 6.35 | 102.5 |
Prakash | M | 22 | 5.73 | 83 |
Aarti | F | 22 | 5.98 | 84.5 |
Meena | F | 25 | 6.25 | 112.5 |
Utkarsh | M | 23 | 6.25 | 84 |
Chirag | M | 22 | 5.9 | 99.5 |
Neha | F | 21 | 5.13 | 50.5 |
Smrita | F | 24 | 6.43 | 90 |
Therefore,
Age (Mean) = 23.08333333, Height (Mean) = 6.12, weight(Mean) = 85.625
The weighted mean is denoted that the mean with frequency.
Month | Price Per Ton | Tons Purchased |
January | Rs. 52.49 | 26 |
February | Rs. 62.23 | 34 |
March | Rs. 87.26 | 40 |
April | Rs. 45.25 | 54 |
May | Rs. 78.56 | 13 |
June | Rs. 69.25 | 45 |
Month | Price (Rs) Per Ton (x) | Tons Purchased (f) | fx=y (Main Data) |
January | 52.49 | 26 | 1364.74 |
February | 62.23 | 34 | 2115.82 |
March | 87.26 | 40 | 3490.4 |
April | 45.25 | 54 | 2443.5 |
May | 78.56 | 13 | 1021.28 |
June | 69.25 | 45 | 3116.25 |
Total | 395.04 | N=212 | 13551.99 |
The price is denoted as x (52.49, 62.23, 87.26, 45.25, 78.56, 69.25 [in Rs.])=395.04
The amount of purchased (frequency) is denoted by f (26, 34, 40, 54, 13, 45) = 212 (N)
Then multiply the x and f and we get the total amount which is denoted by y, fx(y) = 13551.99
To calculate the weighted mean from R & Python we get the same result = 63.9244811.
Want to know more about the nature of data? Keen to perform high-end statistical analysis using Python and R? Follow DexLab Analytics, an excellent Python training center in Gurgaon, India. Our team of consultants will help you learn the basics of R and Python in the easiest manner possible.
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