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 99.8 2.8 3.8 2863
10 10 Female 102.1 3.5 3.8 3574
11 11 Female 100.8 1.7 5.1 1823
12 12 Female 98.5 3.0 5.2 3127
13 13 Female 98.8 2.7 3.0 2810
14 14 Female 100.7 3.5 5.8 3637
15 15 Female 100.5 3.7 0.7 3750
16 16 Female 99.7 3.1 3.0 3243
17 17 Female 99.3 4.0 6.4 4143
18 18 Female 99.7 4.3 3.9 4406
19 19 Female 99.0 4.4 3.3 4472
20 20 Female 99.0 3.4 3.4 3509
21 21 Female 100.9 3.0 4.0 3100
22 22 Female 99.4 3.8 1.9 3851
23 23 Female 99.7 5.8 3.3 5913
24 24 Female 100.5 5.0 5.3 5059
25 25 Female 101.7 4.0 4.3 4127
26 26 Female 101.1 1.6 3.2 1707
27 27 Female 98.5 4.2 2.6 4310
28 28 Female 99.4 2.1 3.8 2156
29 29 Female 98.4 2.1 4.0 2159
30 30 Female 100.7 3.1 2.3 3163
31 31 Female 99.2 5.6 4.7 5730
32 32 Female 99.1 4.8 3.6 4939
33 33 Female 100.2 2.7 4.4 2776
34 34 Female 100.2 3.2 3.4 3323
35 35 Female 100.2 2.2 2.4 2259
36 36 Female 100.3 4.4 3.8 4488
37 37 Female 100.7 2.4 4.0 2485
38 38 Female 100.2 4.1 2.8 4215
39 39 Female 99.8 2.0 1.8 2067
40 40 Female 99.8 6.8 3.3 6930
41 41 Female 100.7 3.3 3.8 3368
42 42 Female 99.9 4.1 4.5 4214
43 43 Female 100.0 4.2 3.1 4339
44 44 Female 99.7 4.1 3.7 4165
45 45 Female 98.9 4.5 4.8 4601
46 46 Female 99.1 3.0 3.5 3125
47 47 Female 98.6 4.8 4.7 4944
48 48 Female 100.4 2.3 3.7 2445
49 49 Female 98.7 2.8 2.2 2914
50 50 Female 100.3 4.0 3.7 4089
51 51 Female 100.4 4.3 3.3 4405
52 52 Female 100.0 2.6 2.3 2724
53 53 Female 100.0 3.8 3.9 3906
54 54 Female 99.8 4.0 3.6 4123
55 55 Male 101.7 3.0 3.9 4634
56 56 Male 100.8 2.2 2.6 3439
57 57 Male 102.2 2.2 4.1 3370
58 58 Male 99.3 3.4 3.4 5256
59 59 Male 99.2 2.2 3.4 3329
60 60 Male 100.5 6.0 2.8 9086
61 61 Male 100.2 2.2 3.1 3409
62 62 Male 99.8 3.2 4.6 4967
63 63 Male 99.0 2.1 4.8 3220
64 64 Male 100.1 2.3 2.9 3527
65 65 Male 100.3 4.3 4.8 6505
66 66 Male 99.4 3.4 4.3 5235
67 67 Male 100.3 2.8 2.0 4322
68 68 Male 101.2 2.6 4.1 3993
69 69 Male 98.6 4.0 3.8 6082
70 70 Male 99.6 3.4 4.7 5250
71 71 Male 101.2 4.6 3.7 6965
72 72 Male 100.2 2.0 2.5 3091
73 73 Male 98.7 4.0 4.0 6026
74 74 Male 101.4 3.0 4.6 4645
75 75 Male 100.4 1.3 3.2 2092
76 76 Male 100.9 3.7 3.3 5592
77 77 Male 100.9 3.2 4.5 4961
78 78 Male 99.7 2.1 3.1 3303
79 79 Male 99.9 2.1 3.6 3239
80 80 Male 100.0 3.9 2.7 5954
81 81 Male 100.8 2.9 3.6 4427
82 82 Male 99.7 2.2 2.6 3369
83 83 Male 102.7 2.5 3.7 3833
84 84 Male 99.0 2.8 4.2 4307
85 85 Male 99.1 2.0 4.4 3155
86 86 Male 98.8 2.7 3.4 4223
87 87 Male 101.0 2.7 4.9 4197
88 88 Male 101.1 5.2 3.5 7939
89 89 Male 99.2 1.8 5.1 2822
90 90 Male 101.2 2.8 2.4 4248
91 91 Male 100.2 3.3 4.3 5062
92 92 Male 98.3 2.8 3.4 4240
93 93 Male 100.0 3.8 3.6 5728
94 94 Male 101.5 2.8 4.0 4323
95 95 Male 100.2 2.8 3.3 4246
96 96 Male 100.4 4.4 4.0 6744
97 97 Male 99.3 3.5 4.4 5353
98 98 Male 101.9 2.6 3.8 4042
99 99 Male 99.0 3.9 3.3 5906
100 100 Male 99.5 3.8 3.2 5826
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 | 99.8 | 2.8 | 3.8 | 2863 |
10 | Female | 102.1 | 3.5 | 3.8 | 3574 |
11 | Female | 100.8 | 1.7 | 5.1 | 1823 |
12 | Female | 98.5 | 3.0 | 5.2 | 3127 |
13 | Female | 98.8 | 2.7 | 3.0 | 2810 |
14 | Female | 100.7 | 3.5 | 5.8 | 3637 |
15 | Female | 100.5 | 3.7 | 0.7 | 3750 |
16 | Female | 99.7 | 3.1 | 3.0 | 3243 |
17 | Female | 99.3 | 4.0 | 6.4 | 4143 |
18 | Female | 99.7 | 4.3 | 3.9 | 4406 |
19 | Female | 99.0 | 4.4 | 3.3 | 4472 |
20 | Female | 99.0 | 3.4 | 3.4 | 3509 |
21 | Female | 100.9 | 3.0 | 4.0 | 3100 |
22 | Female | 99.4 | 3.8 | 1.9 | 3851 |
23 | Female | 99.7 | 5.8 | 3.3 | 5913 |
24 | Female | 100.5 | 5.0 | 5.3 | 5059 |
25 | Female | 101.7 | 4.0 | 4.3 | 4127 |
26 | Female | 101.1 | 1.6 | 3.2 | 1707 |
27 | Female | 98.5 | 4.2 | 2.6 | 4310 |
28 | Female | 99.4 | 2.1 | 3.8 | 2156 |
ID | Gender | A | B | C | Weight | |
---|---|---|---|---|---|---|
29 | 29 | Female | 98.4 | 2.1 | 4.0 | 2159 |
30 | 30 | Female | 100.7 | 3.1 | 2.3 | 3163 |
31 | 31 | Female | 99.2 | 5.6 | 4.7 | 5730 |
32 | 32 | Female | 99.1 | 4.8 | 3.6 | 4939 |
33 | 33 | Female | 100.2 | 2.7 | 4.4 | 2776 |
34 | 34 | Female | 100.2 | 3.2 | 3.4 | 3323 |
35 | 35 | Female | 100.2 | 2.2 | 2.4 | 2259 |
36 | 36 | Female | 100.3 | 4.4 | 3.8 | 4488 |
37 | 37 | Female | 100.7 | 2.4 | 4.0 | 2485 |
38 | 38 | Female | 100.2 | 4.1 | 2.8 | 4215 |
39 | 39 | Female | 99.8 | 2.0 | 1.8 | 2067 |
40 | 40 | Female | 99.8 | 6.8 | 3.3 | 6930 |
41 | 41 | Female | 100.7 | 3.3 | 3.8 | 3368 |
42 | 42 | Female | 99.9 | 4.1 | 4.5 | 4214 |
43 | 43 | Female | 100.0 | 4.2 | 3.1 | 4339 |
44 | 44 | Female | 99.7 | 4.1 | 3.7 | 4165 |
45 | 45 | Female | 98.9 | 4.5 | 4.8 | 4601 |
46 | 46 | Female | 99.1 | 3.0 | 3.5 | 3125 |
47 | 47 | Female | 98.6 | 4.8 | 4.7 | 4944 |
48 | 48 | Female | 100.4 | 2.3 | 3.7 | 2445 |
49 | 49 | Female | 98.7 | 2.8 | 2.2 | 2914 |
50 | 50 | Female | 100.3 | 4.0 | 3.7 | 4089 |
51 | 51 | Female | 100.4 | 4.3 | 3.3 | 4405 |
52 | 52 | Female | 100.0 | 2.6 | 2.3 | 2724 |
53 | 53 | Female | 100.0 | 3.8 | 3.9 | 3906 |
54 | 54 | Female | 99.8 | 4.0 | 3.6 | 4123 |
55 | 55 | Male | 101.7 | 3.0 | 3.9 | 4634 |
56 | 56 | Male | 100.8 | 2.2 | 2.6 | 3439 |
ID | Gender | A | B | C | Weight | |
---|---|---|---|---|---|---|
57 | 57 | Male | 102.2 | 2.2 | 4.1 | 3370 |
58 | 58 | Male | 99.3 | 3.4 | 3.4 | 5256 |
59 | 59 | Male | 99.2 | 2.2 | 3.4 | 3329 |
60 | 60 | Male | 100.5 | 6.0 | 2.8 | 9086 |
61 | 61 | Male | 100.2 | 2.2 | 3.1 | 3409 |
62 | 62 | Male | 99.8 | 3.2 | 4.6 | 4967 |
63 | 63 | Male | 99.0 | 2.1 | 4.8 | 3220 |
64 | 64 | Male | 100.1 | 2.3 | 2.9 | 3527 |
65 | 65 | Male | 100.3 | 4.3 | 4.8 | 6505 |
66 | 66 | Male | 99.4 | 3.4 | 4.3 | 5235 |
67 | 67 | Male | 100.3 | 2.8 | 2.0 | 4322 |
68 | 68 | Male | 101.2 | 2.6 | 4.1 | 3993 |
69 | 69 | Male | 98.6 | 4.0 | 3.8 | 6082 |
70 | 70 | Male | 99.6 | 3.4 | 4.7 | 5250 |
71 | 71 | Male | 101.2 | 4.6 | 3.7 | 6965 |
72 | 72 | Male | 100.2 | 2.0 | 2.5 | 3091 |
73 | 73 | Male | 98.7 | 4.0 | 4.0 | 6026 |
74 | 74 | Male | 101.4 | 3.0 | 4.6 | 4645 |
75 | 75 | Male | 100.4 | 1.3 | 3.2 | 2092 |
76 | 76 | Male | 100.9 | 3.7 | 3.3 | 5592 |
77 | 77 | Male | 100.9 | 3.2 | 4.5 | 4961 |
78 | 78 | Male | 99.7 | 2.1 | 3.1 | 3303 |
79 | 79 | Male | 99.9 | 2.1 | 3.6 | 3239 |
80 | 80 | Male | 100.0 | 3.9 | 2.7 | 5954 |
81 | 81 | Male | 100.8 | 2.9 | 3.6 | 4427 |
82 | 82 | Male | 99.7 | 2.2 | 2.6 | 3369 |
83 | 83 | Male | 102.7 | 2.5 | 3.7 | 3833 |
ID | Gender | A | B | C | Weight | |
---|---|---|---|---|---|---|
84 | 84 | Male | 99.0 | 2.8 | 4.2 | 4307 |
85 | 85 | Male | 99.1 | 2.0 | 4.4 | 3155 |
86 | 86 | Male | 98.8 | 2.7 | 3.4 | 4223 |
87 | 87 | Male | 101.0 | 2.7 | 4.9 | 4197 |
88 | 88 | Male | 101.1 | 5.2 | 3.5 | 7939 |
89 | 89 | Male | 99.2 | 1.8 | 5.1 | 2822 |
90 | 90 | Male | 101.2 | 2.8 | 2.4 | 4248 |
91 | 91 | Male | 100.2 | 3.3 | 4.3 | 5062 |
92 | 92 | Male | 98.3 | 2.8 | 3.4 | 4240 |
93 | 93 | Male | 100.0 | 3.8 | 3.6 | 5728 |
94 | 94 | Male | 101.5 | 2.8 | 4.0 | 4323 |
95 | 95 | Male | 100.2 | 2.8 | 3.3 | 4246 |
96 | 96 | Male | 100.4 | 4.4 | 4.0 | 6744 |
97 | 97 | Male | 99.3 | 3.5 | 4.4 | 5353 |
98 | 98 | Male | 101.9 | 2.6 | 3.8 | 4042 |
99 | 99 | Male | 99.0 | 3.9 | 3.3 | 5906 |
100 | 100 | Male | 99.5 | 3.8 | 3.2 | 5826 |
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 | 2.8 | 2863 |
10 | Female | 3.5 | 3574 |
11 | Female | 1.7 | 1823 |
12 | Female | 3.0 | 3127 |
13 | Female | 2.7 | 2810 |
14 | Female | 3.5 | 3637 |
15 | Female | 3.7 | 3750 |
16 | Female | 3.1 | 3243 |
17 | Female | 4.0 | 4143 |
18 | Female | 4.3 | 4406 |
19 | Female | 4.4 | 4472 |
20 | Female | 3.4 | 3509 |
21 | Female | 3.0 | 3100 |
22 | Female | 3.8 | 3851 |
23 | Female | 5.8 | 5913 |
24 | Female | 5.0 | 5059 |
25 | Female | 4.0 | 4127 |
26 | Female | 1.6 | 1707 |
27 | Female | 4.2 | 4310 |
28 | Female | 2.1 | 2156 |
ID | Gender | B | Weight | |
---|---|---|---|---|
29 | 29 | Female | 2.1 | 2159 |
30 | 30 | Female | 3.1 | 3163 |
31 | 31 | Female | 5.6 | 5730 |
32 | 32 | Female | 4.8 | 4939 |
33 | 33 | Female | 2.7 | 2776 |
34 | 34 | Female | 3.2 | 3323 |
35 | 35 | Female | 2.2 | 2259 |
36 | 36 | Female | 4.4 | 4488 |
37 | 37 | Female | 2.4 | 2485 |
38 | 38 | Female | 4.1 | 4215 |
39 | 39 | Female | 2.0 | 2067 |
40 | 40 | Female | 6.8 | 6930 |
41 | 41 | Female | 3.3 | 3368 |
42 | 42 | Female | 4.1 | 4214 |
43 | 43 | Female | 4.2 | 4339 |
44 | 44 | Female | 4.1 | 4165 |
45 | 45 | Female | 4.5 | 4601 |
46 | 46 | Female | 3.0 | 3125 |
47 | 47 | Female | 4.8 | 4944 |
48 | 48 | Female | 2.3 | 2445 |
49 | 49 | Female | 2.8 | 2914 |
50 | 50 | Female | 4.0 | 4089 |
51 | 51 | Female | 4.3 | 4405 |
52 | 52 | Female | 2.6 | 2724 |
53 | 53 | Female | 3.8 | 3906 |
54 | 54 | Female | 4.0 | 4123 |
55 | 55 | Male | 3.0 | 4634 |
56 | 56 | Male | 2.2 | 3439 |
ID | Gender | B | Weight | |
---|---|---|---|---|
57 | 57 | Male | 2.2 | 3370 |
58 | 58 | Male | 3.4 | 5256 |
59 | 59 | Male | 2.2 | 3329 |
60 | 60 | Male | 6.0 | 9086 |
61 | 61 | Male | 2.2 | 3409 |
62 | 62 | Male | 3.2 | 4967 |
63 | 63 | Male | 2.1 | 3220 |
64 | 64 | Male | 2.3 | 3527 |
65 | 65 | Male | 4.3 | 6505 |
66 | 66 | Male | 3.4 | 5235 |
67 | 67 | Male | 2.8 | 4322 |
68 | 68 | Male | 2.6 | 3993 |
69 | 69 | Male | 4.0 | 6082 |
70 | 70 | Male | 3.4 | 5250 |
71 | 71 | Male | 4.6 | 6965 |
72 | 72 | Male | 2.0 | 3091 |
73 | 73 | Male | 4.0 | 6026 |
74 | 74 | Male | 3.0 | 4645 |
75 | 75 | Male | 1.3 | 2092 |
76 | 76 | Male | 3.7 | 5592 |
77 | 77 | Male | 3.2 | 4961 |
78 | 78 | Male | 2.1 | 3303 |
79 | 79 | Male | 2.1 | 3239 |
80 | 80 | Male | 3.9 | 5954 |
81 | 81 | Male | 2.9 | 4427 |
82 | 82 | Male | 2.2 | 3369 |
83 | 83 | Male | 2.5 | 3833 |
ID | Gender | B | Weight | |
---|---|---|---|---|
84 | 84 | Male | 2.8 | 4307 |
85 | 85 | Male | 2.0 | 3155 |
86 | 86 | Male | 2.7 | 4223 |
87 | 87 | Male | 2.7 | 4197 |
88 | 88 | Male | 5.2 | 7939 |
89 | 89 | Male | 1.8 | 2822 |
90 | 90 | Male | 2.8 | 4248 |
91 | 91 | Male | 3.3 | 5062 |
92 | 92 | Male | 2.8 | 4240 |
93 | 93 | Male | 3.8 | 5728 |
94 | 94 | Male | 2.8 | 4323 |
95 | 95 | Male | 2.8 | 4246 |
96 | 96 | Male | 4.4 | 6744 |
97 | 97 | Male | 3.5 | 5353 |
98 | 98 | Male | 2.6 | 4042 |
99 | 99 | Male | 3.9 | 5906 |
100 | 100 | Male | 3.8 | 5826 |
Gender | A | B | C | Weight | |
---|---|---|---|---|---|
Female:50 | Min. : 70.00 | Min. :1.300 | Min. :0.700 | Min. :1707 | |
Male :50 | 1st Qu.: 99.17 | 1st Qu.:2.600 | 1st Qu.:3.200 | 1st Qu.:3318 | |
NA | Median :100.00 | Median :3.250 | Median :3.700 | Median :4206 | |
NA | Mean : 99.24 | Mean :3.356 | Mean :3.681 | Mean :4279 | |
NA | 3rd Qu.:100.55 | 3rd Qu.:4.000 | 3rd Qu.:4.225 | 3rd Qu.:5015 | |
NA | Max. :110.00 | Max. :6.800 | Max. :6.400 | Max. :9086 |
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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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