Definition of Statistics and Role of Statistics
What is statistics?
Where do we use statistics?
Why do we need statistics?
What is statistics?
Where do we use statistics?
Why do we need statistics?
The science of collecting, organizing, and analyzing data, and presenting and interpreting the results derived from that data to gain insights and make informed decisions.
Tabular data (Structured data)
Source: Le Dinh, T., Lee, S. H., Kwon, S. G., & Kwon, K. R. (2022). COVID-19 Chest X-ray Classification and Severity Assessment Using Convolutional and Transformer Neural Networks. Applied Sciences, 12(10), 4861.
Tabular data (Structured data)
Image data
Tabular data (Structured data)
Audio data
Image data
Tabular data (Structured data)
Audio data
Elephant distribution map for Sri Lanka. Image courtesy of Fernando et al (2019). (accessed from https://news.mongabay.com/2019/02/sri-lanka-gets-its-first-data-based-elephant-distribution-map/)
ID Gender A B C Weight
1 1 Male 80.0 3.6 2.5 4000
2 2 Female 90.0 2.5 6.3 5000
3 3 Female 110.0 4.0 4.5 6000
4 4 Female 100.0 4.5 3.2 7000
5 5 Female 91.5 3.0 3.5 7550
6 6 Male 92.0 3.9 3.7 4500
7 7 Male 88.0 4.2 3.8 3375
8 8 Male 70.0 4.6 3.9 5500
9 9 Female 101.2 4.3 4.7 4448
10 10 Female 102.4 3.9 4.2 3962
11 11 Female 100.8 3.3 3.0 3402
12 12 Female 98.9 3.9 3.7 3955
13 13 Female 100.6 3.5 3.3 3564
14 14 Female 99.0 3.6 4.0 3672
15 15 Female 98.6 4.8 4.0 4883
16 16 Female 101.0 4.9 4.0 5009
17 17 Female 98.5 2.8 3.9 2950
18 18 Female 100.2 3.3 3.7 3361
19 19 Female 99.1 4.1 4.4 4184
20 20 Female 99.5 4.7 4.1 4825
21 21 Female 102.0 3.7 4.6 3812
22 22 Female 99.9 2.9 3.0 3041
23 23 Female 102.6 3.0 3.7 3133
24 24 Female 99.6 3.0 3.0 3098
25 25 Female 98.6 1.4 4.0 1474
26 26 Female 100.8 4.1 4.0 4198
27 27 Female 98.7 4.6 3.8 4677
28 28 Female 101.3 3.9 2.5 4015
29 29 Female 100.2 3.6 3.9 3691
30 30 Female 100.9 3.9 5.2 4015
31 31 Female 101.6 3.6 4.9 3711
32 32 Female 102.6 4.7 4.5 4797
33 33 Female 99.4 1.6 1.8 1712
34 34 Female 101.4 3.6 5.0 3731
35 35 Female 98.7 4.4 2.7 4543
36 36 Female 100.9 3.2 3.8 3305
37 37 Female 100.9 2.8 3.7 2891
38 38 Female 98.6 4.2 3.3 4329
39 39 Female 101.7 3.5 1.6 3599
40 40 Female 99.0 4.0 2.6 4056
41 41 Female 100.9 3.3 4.6 3369
42 42 Female 99.7 5.7 3.3 5814
43 43 Female 100.2 1.8 3.8 1940
44 44 Female 99.2 4.1 3.9 4172
45 45 Female 99.0 3.3 2.3 3445
46 46 Female 99.0 5.0 1.6 5118
47 47 Female 99.2 4.0 5.0 4057
48 48 Female 99.0 3.9 3.9 4016
49 49 Female 101.3 3.9 2.3 3958
50 50 Female 98.0 3.0 2.0 3063
51 51 Female 98.8 3.2 3.8 3316
52 52 Female 99.9 2.1 2.8 2219
53 53 Female 100.5 3.4 3.6 3452
54 54 Female 100.1 3.8 3.6 3898
55 55 Male 99.6 3.6 3.0 5493
56 56 Male 100.7 2.7 3.4 4153
57 57 Male 99.1 4.4 1.5 6630
58 58 Male 99.6 2.6 4.4 3971
59 59 Male 100.6 4.4 3.3 6689
60 60 Male 100.6 3.6 6.4 5500
61 61 Male 100.5 3.6 5.5 5532
62 62 Male 101.9 3.1 4.9 4740
63 63 Male 100.1 0.9 2.6 1386
64 64 Male 99.5 2.6 3.2 3983
65 65 Male 99.3 2.6 6.3 3931
66 66 Male 98.8 2.4 4.8 3756
67 67 Male 101.6 1.2 3.8 1933
68 68 Male 98.9 2.6 3.1 3924
69 69 Male 100.7 2.7 4.1 4137
70 70 Male 100.7 3.3 2.8 5110
71 71 Male 101.7 2.8 4.0 4342
72 72 Male 104.4 4.8 4.6 7316
73 73 Male 100.2 6.4 1.3 9765
74 74 Male 99.2 3.5 4.7 5295
75 75 Male 101.1 2.3 3.6 3480
76 76 Male 99.2 3.0 4.1 4584
77 77 Male 98.5 3.8 3.9 5828
78 78 Male 101.3 4.4 3.3 6687
79 79 Male 99.8 3.1 4.6 4752
80 80 Male 100.6 3.3 1.5 5049
81 81 Male 98.3 4.7 4.0 7079
82 82 Male 98.4 3.9 5.1 5973
83 83 Male 98.5 3.5 4.4 5371
84 84 Male 99.5 2.4 2.6 3720
85 85 Male 98.8 6.1 2.4 9305
86 86 Male 99.3 3.5 4.0 5289
87 87 Male 100.2 2.3 4.5 3615
88 88 Male 99.8 0.8 4.5 1332
89 89 Male 101.2 1.7 2.1 2579
90 90 Male 102.3 3.5 1.4 5313
91 91 Male 99.4 3.2 2.3 4860
92 92 Male 99.8 1.9 2.8 2985
93 93 Male 99.5 5.0 2.8 7576
94 94 Male 98.3 3.9 1.5 5911
95 95 Male 99.7 4.1 3.0 6291
96 96 Male 101.1 2.5 3.5 3875
97 97 Male 98.4 4.3 2.2 6487
98 98 Male 102.1 3.4 3.6 5129
99 99 Male 100.5 2.4 3.9 3762
100 100 Male 98.3 3.6 1.8 5483
ID | Gender | A | B | C | Weight |
---|---|---|---|---|---|
1 | Male | 80.0 | 3.6 | 2.5 | 4000 |
2 | Female | 90.0 | 2.5 | 6.3 | 5000 |
3 | Female | 110.0 | 4.0 | 4.5 | 6000 |
4 | Female | 100.0 | 4.5 | 3.2 | 7000 |
5 | Female | 91.5 | 3.0 | 3.5 | 7550 |
6 | Male | 92.0 | 3.9 | 3.7 | 4500 |
7 | Male | 88.0 | 4.2 | 3.8 | 3375 |
8 | Male | 70.0 | 4.6 | 3.9 | 5500 |
9 | Female | 101.2 | 4.3 | 4.7 | 4448 |
10 | Female | 102.4 | 3.9 | 4.2 | 3962 |
11 | Female | 100.8 | 3.3 | 3.0 | 3402 |
12 | Female | 98.9 | 3.9 | 3.7 | 3955 |
13 | Female | 100.6 | 3.5 | 3.3 | 3564 |
14 | Female | 99.0 | 3.6 | 4.0 | 3672 |
15 | Female | 98.6 | 4.8 | 4.0 | 4883 |
16 | Female | 101.0 | 4.9 | 4.0 | 5009 |
17 | Female | 98.5 | 2.8 | 3.9 | 2950 |
18 | Female | 100.2 | 3.3 | 3.7 | 3361 |
19 | Female | 99.1 | 4.1 | 4.4 | 4184 |
20 | Female | 99.5 | 4.7 | 4.1 | 4825 |
21 | Female | 102.0 | 3.7 | 4.6 | 3812 |
22 | Female | 99.9 | 2.9 | 3.0 | 3041 |
23 | Female | 102.6 | 3.0 | 3.7 | 3133 |
24 | Female | 99.6 | 3.0 | 3.0 | 3098 |
25 | Female | 98.6 | 1.4 | 4.0 | 1474 |
26 | Female | 100.8 | 4.1 | 4.0 | 4198 |
27 | Female | 98.7 | 4.6 | 3.8 | 4677 |
28 | Female | 101.3 | 3.9 | 2.5 | 4015 |
ID | Gender | A | B | C | Weight | |
---|---|---|---|---|---|---|
29 | 29 | Female | 100.2 | 3.6 | 3.9 | 3691 |
30 | 30 | Female | 100.9 | 3.9 | 5.2 | 4015 |
31 | 31 | Female | 101.6 | 3.6 | 4.9 | 3711 |
32 | 32 | Female | 102.6 | 4.7 | 4.5 | 4797 |
33 | 33 | Female | 99.4 | 1.6 | 1.8 | 1712 |
34 | 34 | Female | 101.4 | 3.6 | 5.0 | 3731 |
35 | 35 | Female | 98.7 | 4.4 | 2.7 | 4543 |
36 | 36 | Female | 100.9 | 3.2 | 3.8 | 3305 |
37 | 37 | Female | 100.9 | 2.8 | 3.7 | 2891 |
38 | 38 | Female | 98.6 | 4.2 | 3.3 | 4329 |
39 | 39 | Female | 101.7 | 3.5 | 1.6 | 3599 |
40 | 40 | Female | 99.0 | 4.0 | 2.6 | 4056 |
41 | 41 | Female | 100.9 | 3.3 | 4.6 | 3369 |
42 | 42 | Female | 99.7 | 5.7 | 3.3 | 5814 |
43 | 43 | Female | 100.2 | 1.8 | 3.8 | 1940 |
44 | 44 | Female | 99.2 | 4.1 | 3.9 | 4172 |
45 | 45 | Female | 99.0 | 3.3 | 2.3 | 3445 |
46 | 46 | Female | 99.0 | 5.0 | 1.6 | 5118 |
47 | 47 | Female | 99.2 | 4.0 | 5.0 | 4057 |
48 | 48 | Female | 99.0 | 3.9 | 3.9 | 4016 |
49 | 49 | Female | 101.3 | 3.9 | 2.3 | 3958 |
50 | 50 | Female | 98.0 | 3.0 | 2.0 | 3063 |
51 | 51 | Female | 98.8 | 3.2 | 3.8 | 3316 |
52 | 52 | Female | 99.9 | 2.1 | 2.8 | 2219 |
53 | 53 | Female | 100.5 | 3.4 | 3.6 | 3452 |
54 | 54 | Female | 100.1 | 3.8 | 3.6 | 3898 |
55 | 55 | Male | 99.6 | 3.6 | 3.0 | 5493 |
56 | 56 | Male | 100.7 | 2.7 | 3.4 | 4153 |
ID | Gender | A | B | C | Weight | |
---|---|---|---|---|---|---|
57 | 57 | Male | 99.1 | 4.4 | 1.5 | 6630 |
58 | 58 | Male | 99.6 | 2.6 | 4.4 | 3971 |
59 | 59 | Male | 100.6 | 4.4 | 3.3 | 6689 |
60 | 60 | Male | 100.6 | 3.6 | 6.4 | 5500 |
61 | 61 | Male | 100.5 | 3.6 | 5.5 | 5532 |
62 | 62 | Male | 101.9 | 3.1 | 4.9 | 4740 |
63 | 63 | Male | 100.1 | 0.9 | 2.6 | 1386 |
64 | 64 | Male | 99.5 | 2.6 | 3.2 | 3983 |
65 | 65 | Male | 99.3 | 2.6 | 6.3 | 3931 |
66 | 66 | Male | 98.8 | 2.4 | 4.8 | 3756 |
67 | 67 | Male | 101.6 | 1.2 | 3.8 | 1933 |
68 | 68 | Male | 98.9 | 2.6 | 3.1 | 3924 |
69 | 69 | Male | 100.7 | 2.7 | 4.1 | 4137 |
70 | 70 | Male | 100.7 | 3.3 | 2.8 | 5110 |
71 | 71 | Male | 101.7 | 2.8 | 4.0 | 4342 |
72 | 72 | Male | 104.4 | 4.8 | 4.6 | 7316 |
73 | 73 | Male | 100.2 | 6.4 | 1.3 | 9765 |
74 | 74 | Male | 99.2 | 3.5 | 4.7 | 5295 |
75 | 75 | Male | 101.1 | 2.3 | 3.6 | 3480 |
76 | 76 | Male | 99.2 | 3.0 | 4.1 | 4584 |
77 | 77 | Male | 98.5 | 3.8 | 3.9 | 5828 |
78 | 78 | Male | 101.3 | 4.4 | 3.3 | 6687 |
79 | 79 | Male | 99.8 | 3.1 | 4.6 | 4752 |
80 | 80 | Male | 100.6 | 3.3 | 1.5 | 5049 |
81 | 81 | Male | 98.3 | 4.7 | 4.0 | 7079 |
82 | 82 | Male | 98.4 | 3.9 | 5.1 | 5973 |
83 | 83 | Male | 98.5 | 3.5 | 4.4 | 5371 |
ID | Gender | A | B | C | Weight | |
---|---|---|---|---|---|---|
84 | 84 | Male | 99.5 | 2.4 | 2.6 | 3720 |
85 | 85 | Male | 98.8 | 6.1 | 2.4 | 9305 |
86 | 86 | Male | 99.3 | 3.5 | 4.0 | 5289 |
87 | 87 | Male | 100.2 | 2.3 | 4.5 | 3615 |
88 | 88 | Male | 99.8 | 0.8 | 4.5 | 1332 |
89 | 89 | Male | 101.2 | 1.7 | 2.1 | 2579 |
90 | 90 | Male | 102.3 | 3.5 | 1.4 | 5313 |
91 | 91 | Male | 99.4 | 3.2 | 2.3 | 4860 |
92 | 92 | Male | 99.8 | 1.9 | 2.8 | 2985 |
93 | 93 | Male | 99.5 | 5.0 | 2.8 | 7576 |
94 | 94 | Male | 98.3 | 3.9 | 1.5 | 5911 |
95 | 95 | Male | 99.7 | 4.1 | 3.0 | 6291 |
96 | 96 | Male | 101.1 | 2.5 | 3.5 | 3875 |
97 | 97 | Male | 98.4 | 4.3 | 2.2 | 6487 |
98 | 98 | Male | 102.1 | 3.4 | 3.6 | 5129 |
99 | 99 | Male | 100.5 | 2.4 | 3.9 | 3762 |
100 | 100 | Male | 98.3 | 3.6 | 1.8 | 5483 |
ID | Gender | B | Weight |
---|---|---|---|
1 | Male | 3.6 | 4000 |
2 | Female | 2.5 | 5000 |
3 | Female | 4.0 | 6000 |
4 | Female | 4.5 | 7000 |
5 | Female | 3.0 | 7550 |
6 | Male | 3.9 | 4500 |
7 | Male | 4.2 | 3375 |
8 | Male | 4.6 | 5500 |
9 | Female | 4.3 | 4448 |
10 | Female | 3.9 | 3962 |
11 | Female | 3.3 | 3402 |
12 | Female | 3.9 | 3955 |
13 | Female | 3.5 | 3564 |
14 | Female | 3.6 | 3672 |
15 | Female | 4.8 | 4883 |
16 | Female | 4.9 | 5009 |
17 | Female | 2.8 | 2950 |
18 | Female | 3.3 | 3361 |
19 | Female | 4.1 | 4184 |
20 | Female | 4.7 | 4825 |
21 | Female | 3.7 | 3812 |
22 | Female | 2.9 | 3041 |
23 | Female | 3.0 | 3133 |
24 | Female | 3.0 | 3098 |
25 | Female | 1.4 | 1474 |
26 | Female | 4.1 | 4198 |
27 | Female | 4.6 | 4677 |
28 | Female | 3.9 | 4015 |
ID | Gender | B | Weight | |
---|---|---|---|---|
29 | 29 | Female | 3.6 | 3691 |
30 | 30 | Female | 3.9 | 4015 |
31 | 31 | Female | 3.6 | 3711 |
32 | 32 | Female | 4.7 | 4797 |
33 | 33 | Female | 1.6 | 1712 |
34 | 34 | Female | 3.6 | 3731 |
35 | 35 | Female | 4.4 | 4543 |
36 | 36 | Female | 3.2 | 3305 |
37 | 37 | Female | 2.8 | 2891 |
38 | 38 | Female | 4.2 | 4329 |
39 | 39 | Female | 3.5 | 3599 |
40 | 40 | Female | 4.0 | 4056 |
41 | 41 | Female | 3.3 | 3369 |
42 | 42 | Female | 5.7 | 5814 |
43 | 43 | Female | 1.8 | 1940 |
44 | 44 | Female | 4.1 | 4172 |
45 | 45 | Female | 3.3 | 3445 |
46 | 46 | Female | 5.0 | 5118 |
47 | 47 | Female | 4.0 | 4057 |
48 | 48 | Female | 3.9 | 4016 |
49 | 49 | Female | 3.9 | 3958 |
50 | 50 | Female | 3.0 | 3063 |
51 | 51 | Female | 3.2 | 3316 |
52 | 52 | Female | 2.1 | 2219 |
53 | 53 | Female | 3.4 | 3452 |
54 | 54 | Female | 3.8 | 3898 |
55 | 55 | Male | 3.6 | 5493 |
56 | 56 | Male | 2.7 | 4153 |
ID | Gender | B | Weight | |
---|---|---|---|---|
57 | 57 | Male | 4.4 | 6630 |
58 | 58 | Male | 2.6 | 3971 |
59 | 59 | Male | 4.4 | 6689 |
60 | 60 | Male | 3.6 | 5500 |
61 | 61 | Male | 3.6 | 5532 |
62 | 62 | Male | 3.1 | 4740 |
63 | 63 | Male | 0.9 | 1386 |
64 | 64 | Male | 2.6 | 3983 |
65 | 65 | Male | 2.6 | 3931 |
66 | 66 | Male | 2.4 | 3756 |
67 | 67 | Male | 1.2 | 1933 |
68 | 68 | Male | 2.6 | 3924 |
69 | 69 | Male | 2.7 | 4137 |
70 | 70 | Male | 3.3 | 5110 |
71 | 71 | Male | 2.8 | 4342 |
72 | 72 | Male | 4.8 | 7316 |
73 | 73 | Male | 6.4 | 9765 |
74 | 74 | Male | 3.5 | 5295 |
75 | 75 | Male | 2.3 | 3480 |
76 | 76 | Male | 3.0 | 4584 |
77 | 77 | Male | 3.8 | 5828 |
78 | 78 | Male | 4.4 | 6687 |
79 | 79 | Male | 3.1 | 4752 |
80 | 80 | Male | 3.3 | 5049 |
81 | 81 | Male | 4.7 | 7079 |
82 | 82 | Male | 3.9 | 5973 |
83 | 83 | Male | 3.5 | 5371 |
ID | Gender | B | Weight | |
---|---|---|---|---|
84 | 84 | Male | 2.4 | 3720 |
85 | 85 | Male | 6.1 | 9305 |
86 | 86 | Male | 3.5 | 5289 |
87 | 87 | Male | 2.3 | 3615 |
88 | 88 | Male | 0.8 | 1332 |
89 | 89 | Male | 1.7 | 2579 |
90 | 90 | Male | 3.5 | 5313 |
91 | 91 | Male | 3.2 | 4860 |
92 | 92 | Male | 1.9 | 2985 |
93 | 93 | Male | 5.0 | 7576 |
94 | 94 | Male | 3.9 | 5911 |
95 | 95 | Male | 4.1 | 6291 |
96 | 96 | Male | 2.5 | 3875 |
97 | 97 | Male | 4.3 | 6487 |
98 | 98 | Male | 3.4 | 5129 |
99 | 99 | Male | 2.4 | 3762 |
100 | 100 | Male | 3.6 | 5483 |
Gender | A | B | C | Weight | |
---|---|---|---|---|---|
Female:50 | Min. : 70.00 | Min. :0.800 | Min. :1.300 | Min. :1332 | |
Male :50 | 1st Qu.: 98.97 | 1st Qu.:2.875 | 1st Qu.:2.800 | 1st Qu.:3590 | |
NA | Median : 99.80 | Median :3.550 | Median :3.700 | Median :4097 | |
NA | Mean : 99.27 | Mean :3.480 | Mean :3.576 | Mean :4447 | |
NA | 3rd Qu.:100.90 | 3rd Qu.:4.100 | 3rd Qu.:4.125 | 3rd Qu.:5290 | |
NA | Max. :110.00 | Max. :6.400 | Max. :6.400 | Max. :9765 |
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Example:
When we say “informed decisions,” it refers to choices made after carefully considering relevant information or data, rather than guessing or relying on assumptions.
What is statistics?
Where do we use statistics?
Why do we need statistics?
What is statistics?
Where do we use statistics?
Why do we need statistics?
How often do you find yourself relying on statistics in your daily routines or decision-making processes?
Could you provide a few examples of how you utilize statistical information?
10:00
Terminology | Field |
---|---|
Epidemiology | The study and analysis of the patterns, causes and effects of health and disease conditions |
Astrostatistics | Applies statistical analysis to the understanding of astronomical data |
Biostatistics | Studies biological phenomena |
Demography | Statistical study of all populations |
Social statistics | Study human behavior in a social environment |
Chemometrics | Science of extracting information from chemical systems by data-driven means |
Terminology | Description |
---|---|
Actuarial statistics | Discipline that deals with assessing the risks in insurance and finance field. |
Forensic statistics | Studies DNA testing results |
Spatial statistics | Analysis of spatial data |
Econometrics | Uses economic theory, mathematics, and statistical inference to quantify economic phenomena. |
Jurimetrics | Application of probability and statistics to law. |
Psychometrics | Applies statistical methods to psychological measurements |
What is statistics?
Where do we use statistics?
Why do we need statistics?
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