Statistical Analysis - An introduction

Statistics is a science, that helps in interpreting, understanding, presenting a large volume of numerical and categorical data. It is a measure to calculate various parameters and statistic which will represent the data as a whole. Now why is it required, why not to use complete series of numbers collected by either researchers through a survey or terabytes of data gathered by machines?

Answer lies within the question. This data is humongous & it is difficult to analyze the whole data simultaneously. Hence a researcher or data scientist requires best representation of this very large collection of numbers, this is where statistics come into picture & gives an over-all idea about this "data" of  central tendencies by mean, median, mode & spread or distribution by variance or standard deviation.

This useful calculated values are information generated out of the data. How is this information going to benefit a data scientist, & whether it is going to have any "significance" over the statistic of the complete data. Enter the Analysis part of the process. Analysis is using data information & turning them into useful insights which are not visible otherwise. This is generally being supplemented by a data scientist's own knowledge about the subject & hypothesis tests.

Let's take an Example!!

We have a small data of class -V students & this data comprises of

No. of students : 60

Male students : 38

Female students : 22

Student ID	Gender	Height (in cm)	Weight (in kg)	Grade
1	Male	78	33	C
2	Female	78	31	A+
3	Female	103	35	C
4	Female	71	37	C
5	Female	84	28	A
6	Male	104	34	C
7	Male	85	30	B+
8	Male	103	28	A+
9	Male	66	38	C
10	Female	90	40	F
11	Male	108	29	B
12	Female	110	28	A+
13	Female	108	40	A
14	Male	78	36	B+
15	Male	106	30	A
16	Male	91	45	F
17	Male	85	36	A+
18	Male	104	41	F
19	Male	73	42	A+
20	Male	89	32	B
21	Female	74	33	F
22	Male	91	30	A
23	Male	66	43	F
24	Male	104	42	A
25	Male	113	42	B
26	Male	89	42	B+
27	Male	98	37	B+
28	Female	99	34	B+
29	Male	76	42	B
30	Female	71	35	A
31	Male	82	38	B
32	Female	69	38	B
33	Female	85	37	A+
34	Female	69	40	A
35	Female	75	43	C
36	Male	109	28	B
37	Female	73	29	B
38	Male	72	40	B
39	Male	111	40	A+
40	Male	106	33	F
41	Female	115	44	A+
42	Female	68	35	F
43	Female	73	30	A
44	Male	90	37	B
45	Female	66	42	A
46	Male	114	40	B
47	Male	108	28	A
48	Female	107	33	A
49	Male	69	31	A+
50	Male	117	44	B+
51	Male	94	42	F
52	Male	109	35	A
53	Male	92	30	A
54	Male	82	37	A+
55	Female	82	30	B
56	Male	66	36	B
57	Female	105	44	A+
58	Male	88	39	B+
59	Male	83	43	A
60	Male	77	28	C

             

This data is about student's height in cm, weight in kg, & last year's grade.

Now if we say that mean height of female students is 85.2 cm & those of male students is 91.2 cm,  

it means that these mean values are single no. representations of heights for both female & male populations respectively.

While if we compare the height means for both female & male students, we find out that height of female students is lesser than the male students for class-V. Now this is an insight that we could draw looking at the mean values of the data. Let's keep it this way until we reach forthcoming topic of hypothesis testing.

A question may arises why can't we go for one to one comparison of data. Answer is simple & straight, it can be done by creating a cross table for male and female students & then further analyzing the outcomes. This is easier said than done for a large data having millions of observations over numerous variables.

This sets our stage to a further go for a complete series of data analysis & statistical approach. We will next discuss about various components of data.

>>This series of blog is an attempt to debunk the various concepts of  statistical analysis in a simpler way. We hope it is of some help to you guys in getting the things right.