Overview

Dataset statistics

Number of variables13
Number of observations1806
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory202.9 KiB
Average record size in memory115.1 B

Variable types

Categorical2
Numeric10
Text1

Dataset

Description방위사업청에서 운영되고 있는 사이버교육과정 평가 결과(년도, 과정명, 번호, 평가문제, 응시율 등으로 구성)를 제공합니다.
Author방위사업청
URLhttps://www.data.go.kr/data/15038473/fileData.do

Alerts

1차 응시율 is highly overall correlated with 년도 and 1 other fieldsHigh correlation
2차 응시율 is highly overall correlated with 3차 응시율 and 3 other fieldsHigh correlation
3차 응시율 is highly overall correlated with 2차 응시율 and 3 other fieldsHigh correlation
4차 응시율 is highly overall correlated with 2차 응시율 and 2 other fieldsHigh correlation
5차 응시율 is highly overall correlated with 6차 응시율 and 4 other fieldsHigh correlation
6차 응시율 is highly overall correlated with 4차 응시율 and 5 other fieldsHigh correlation
7차 응시율 is highly overall correlated with 5차 응시율 and 5 other fieldsHigh correlation
8차 응시율 is highly overall correlated with 3차 응시율 and 5 other fieldsHigh correlation
9차 응시율 is highly overall correlated with 3차 응시율 and 6 other fieldsHigh correlation
년도 is highly overall correlated with 1차 응시율 and 3 other fieldsHigh correlation
과정명 is highly overall correlated with 1차 응시율 and 8 other fieldsHigh correlation
1차 응시율 has 219 (12.1%) zerosZeros
2차 응시율 has 219 (12.1%) zerosZeros
3차 응시율 has 292 (16.2%) zerosZeros
4차 응시율 has 356 (19.7%) zerosZeros
5차 응시율 has 554 (30.7%) zerosZeros
6차 응시율 has 554 (30.7%) zerosZeros
7차 응시율 has 900 (49.8%) zerosZeros
8차 응시율 has 900 (49.8%) zerosZeros
9차 응시율 has 900 (49.8%) zerosZeros

Reproduction

Analysis started2024-03-14 10:35:47.054438
Analysis finished2024-03-14 10:36:13.221395
Duration26.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
2020
830 
2019
757 
2021
 
73
2022
 
73
2023
 
73

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2020 830
46.0%
2019 757
41.9%
2021 73
 
4.0%
2022 73
 
4.0%
2023 73
 
4.0%

Length

2024-03-14T19:36:13.417989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:36:13.753453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 830
46.0%
2019 757
41.9%
2021 73
 
4.0%
2022 73
 
4.0%
2023 73
 
4.0%

과정명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
역사에서 배우는 공직자의 길
300 
독립운동가를 통해 본 나라사랑과 국가
272 
공직가치
188 
청탁금지법의 이해
178 
청탹금지법의 이해
174 
Other values (7)
694 

Length

Max length20
Median length14
Mean length11.848283
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공직가치
2nd row공직가치
3rd row공직가치
4th row공직가치
5th row공직가치

Common Values

ValueCountFrequency (%)
역사에서 배우는 공직자의 길 300
16.6%
독립운동가를 통해 본 나라사랑과 국가 272
15.1%
공직가치 188
10.4%
청탁금지법의 이해 178
9.9%
청탹금지법의 이해 174
9.6%
사이버청렴교육 128
7.1%
세종대왕 리더십 120
 
6.6%
사례로 배우는 부패영향평가 100
 
5.5%
세상을 바꾸는 힘, 공익신고 100
 
5.5%
이순신 장군의 청렴리더십 98
 
5.4%
Other values (2) 148
8.2%

Length

2024-03-14T19:36:14.171206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
배우는 400
 
7.8%
이해 352
 
6.9%
역사에서 300
 
5.9%
공직자의 300
 
5.9%
300
 
5.9%
독립운동가를 272
 
5.3%
통해 272
 
5.3%
272
 
5.3%
나라사랑과 272
 
5.3%
국가 272
 
5.3%
Other values (19) 2098
41.1%

번호
Real number (ℝ)

Distinct150
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.491694
Minimum1
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2024-03-14T19:36:14.572211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q117
median36
Q357
95-th percentile121
Maximum150
Range149
Interquartile range (IQR)40

Descriptive statistics

Standard deviation34.68768
Coefficient of variation (CV)0.7975702
Kurtosis0.58314489
Mean43.491694
Median Absolute Deviation (MAD)20
Skewness1.1068928
Sum78546
Variance1203.2351
MonotonicityNot monotonic
2024-03-14T19:36:15.009912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 28
 
1.6%
9 28
 
1.6%
2 28
 
1.6%
14 28
 
1.6%
13 28
 
1.6%
12 28
 
1.6%
11 28
 
1.6%
10 28
 
1.6%
15 28
 
1.6%
8 28
 
1.6%
Other values (140) 1526
84.5%
ValueCountFrequency (%)
1 28
1.6%
2 28
1.6%
3 28
1.6%
4 28
1.6%
5 28
1.6%
6 28
1.6%
7 28
1.6%
8 28
1.6%
9 28
1.6%
10 28
1.6%
ValueCountFrequency (%)
150 2
0.1%
149 2
0.1%
148 2
0.1%
147 2
0.1%
146 2
0.1%
145 2
0.1%
144 2
0.1%
143 2
0.1%
142 2
0.1%
141 2
0.1%
Distinct766
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
2024-03-14T19:36:16.303401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length256
Median length108
Mean length44.707641
Min length9

Characters and Unicode

Total characters80742
Distinct characters760
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.6%

Sample

1st row‘깨어진 창 이론’과 관련된 내용이 아닌 것은?
2nd row권위주의적 성격의 일관된 행동형태가 아닌 것은?
3rd row스탠리밀그램 실험을 통해 알게 된 파괴적복종의 원인이 아닌 것은?
4th row스탠리 밀그램의 실험을 통해서 알 수 있게 된 것은?
5th row공무원노조의 대표적인 불법행위 유형이 아닌 것은?
ValueCountFrequency (%)
것은 756
 
3.9%
374
 
2.0%
대한 270
 
1.4%
다음 228
 
1.2%
아닌 204
 
1.1%
않은 198
 
1.0%
무엇입니까 170
 
0.9%
152
 
0.8%
o 144
 
0.8%
옳지 132
 
0.7%
Other values (4398) 16523
86.3%
2024-03-14T19:36:18.083749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17352
 
21.5%
1714
 
2.1%
1526
 
1.9%
1526
 
1.9%
1304
 
1.6%
1286
 
1.6%
1286
 
1.6%
? 1155
 
1.4%
1118
 
1.4%
1020
 
1.3%
Other values (750) 51455
63.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 58414
72.3%
Space Separator 17352
 
21.5%
Other Punctuation 2428
 
3.0%
Decimal Number 702
 
0.9%
Uppercase Letter 402
 
0.5%
Lowercase Letter 332
 
0.4%
Open Punctuation 330
 
0.4%
Close Punctuation 328
 
0.4%
Dash Punctuation 186
 
0.2%
Final Punctuation 134
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1714
 
2.9%
1526
 
2.6%
1526
 
2.6%
1304
 
2.2%
1286
 
2.2%
1286
 
2.2%
1118
 
1.9%
1020
 
1.7%
968
 
1.7%
936
 
1.6%
Other values (679) 45730
78.3%
Lowercase Letter
ValueCountFrequency (%)
i 40
12.0%
e 34
10.2%
s 30
9.0%
l 30
9.0%
a 28
 
8.4%
n 26
 
7.8%
r 20
 
6.0%
w 20
 
6.0%
h 18
 
5.4%
t 16
 
4.8%
Other values (11) 70
21.1%
Uppercase Letter
ValueCountFrequency (%)
O 150
37.3%
X 142
35.3%
C 18
 
4.5%
I 14
 
3.5%
P 14
 
3.5%
D 10
 
2.5%
E 10
 
2.5%
H 8
 
2.0%
R 6
 
1.5%
T 6
 
1.5%
Other values (9) 24
 
6.0%
Decimal Number
ValueCountFrequency (%)
1 176
25.1%
0 144
20.5%
3 76
10.8%
9 72
10.3%
2 56
 
8.0%
8 44
 
6.3%
5 42
 
6.0%
4 36
 
5.1%
6 34
 
4.8%
7 22
 
3.1%
Other Punctuation
ValueCountFrequency (%)
? 1155
47.6%
. 818
33.7%
, 389
 
16.0%
· 28
 
1.2%
" 16
 
0.7%
: 12
 
0.5%
' 10
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 304
92.1%
[ 18
 
5.5%
4
 
1.2%
4
 
1.2%
Close Punctuation
ValueCountFrequency (%)
) 302
92.1%
] 18
 
5.5%
4
 
1.2%
4
 
1.2%
Final Punctuation
ValueCountFrequency (%)
124
92.5%
10
 
7.5%
Initial Punctuation
ValueCountFrequency (%)
124
92.5%
10
 
7.5%
Space Separator
ValueCountFrequency (%)
17352
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 58318
72.2%
Common 21594
 
26.7%
Latin 734
 
0.9%
Han 96
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1714
 
2.9%
1526
 
2.6%
1526
 
2.6%
1304
 
2.2%
1286
 
2.2%
1286
 
2.2%
1118
 
1.9%
1020
 
1.7%
968
 
1.7%
936
 
1.6%
Other values (633) 45634
78.3%
Han
ValueCountFrequency (%)
6
 
6.2%
2
 
2.1%
2
 
2.1%
2
 
2.1%
使 2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
Other values (36) 72
75.0%
Latin
ValueCountFrequency (%)
O 150
20.4%
X 142
19.3%
i 40
 
5.4%
e 34
 
4.6%
s 30
 
4.1%
l 30
 
4.1%
a 28
 
3.8%
n 26
 
3.5%
r 20
 
2.7%
w 20
 
2.7%
Other values (30) 214
29.2%
Common
ValueCountFrequency (%)
17352
80.4%
? 1155
 
5.3%
. 818
 
3.8%
, 389
 
1.8%
( 304
 
1.4%
) 302
 
1.4%
- 186
 
0.9%
1 176
 
0.8%
0 144
 
0.7%
124
 
0.6%
Other values (21) 644
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 58308
72.2%
ASCII 22016
 
27.3%
Punctuation 268
 
0.3%
CJK 94
 
0.1%
None 44
 
0.1%
Compat Jamo 10
 
< 0.1%
CJK Compat Ideographs 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17352
78.8%
? 1155
 
5.2%
. 818
 
3.7%
, 389
 
1.8%
( 304
 
1.4%
) 302
 
1.4%
- 186
 
0.8%
1 176
 
0.8%
O 150
 
0.7%
0 144
 
0.7%
Other values (52) 1040
 
4.7%
Hangul
ValueCountFrequency (%)
1714
 
2.9%
1526
 
2.6%
1526
 
2.6%
1304
 
2.2%
1286
 
2.2%
1286
 
2.2%
1118
 
1.9%
1020
 
1.7%
968
 
1.7%
936
 
1.6%
Other values (628) 45624
78.2%
Punctuation
ValueCountFrequency (%)
124
46.3%
124
46.3%
10
 
3.7%
10
 
3.7%
None
ValueCountFrequency (%)
· 28
63.6%
4
 
9.1%
4
 
9.1%
4
 
9.1%
4
 
9.1%
CJK
ValueCountFrequency (%)
6
 
6.4%
2
 
2.1%
2
 
2.1%
2
 
2.1%
使 2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
Other values (35) 70
74.5%
Compat Jamo
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%
CJK Compat Ideographs
ValueCountFrequency (%)
2
100.0%

1차 응시율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.366939
Minimum0
Maximum88.465964
Zeros219
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2024-03-14T19:36:18.472517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q168.8
median72.4
Q376.1
95-th percentile77.8
Maximum88.465964
Range88.465964
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation24.259914
Coefficient of variation (CV)0.37690022
Kurtosis3.0792977
Mean64.366939
Median Absolute Deviation (MAD)3.6
Skewness-2.1886545
Sum116246.69
Variance588.54342
MonotonicityNot monotonic
2024-03-14T19:36:18.846924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 219
 
12.1%
70.1 150
 
8.3%
77.8 150
 
8.3%
68.8 136
 
7.5%
72.4 136
 
7.5%
69.8 94
 
5.2%
73.8 94
 
5.2%
77.1 64
 
3.5%
76.7 64
 
3.5%
76.8 60
 
3.3%
Other values (13) 639
35.4%
ValueCountFrequency (%)
0.0 219
12.1%
66.7 50
 
2.8%
67.6 60
 
3.3%
68.8 136
7.5%
69.8 94
5.2%
70.1 150
8.3%
70.2 49
 
2.7%
71.0 44
 
2.4%
71.1 49
 
2.7%
71.4 50
 
2.8%
ValueCountFrequency (%)
88.46596357 15
 
0.8%
86.16901408 58
 
3.2%
77.8 150
8.3%
77.1 64
3.5%
76.8 60
 
3.3%
76.7 64
3.5%
76.1 44
 
2.4%
74.4 60
 
3.3%
74.2 50
 
2.8%
73.8 94
5.2%

2차 응시율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.317386
Minimum0
Maximum76.5
Zeros219
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2024-03-14T19:36:19.205526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q161.7
median67.9
Q372.7
95-th percentile76.5
Maximum76.5
Range76.5
Interquartile range (IQR)11

Descriptive statistics

Standard deviation23.020801
Coefficient of variation (CV)0.38166112
Kurtosis2.7846908
Mean60.317386
Median Absolute Deviation (MAD)6
Skewness-2.0948966
Sum108933.2
Variance529.95729
MonotonicityNot monotonic
2024-03-14T19:36:19.604880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 219
12.1%
66.7 184
 
10.2%
73.9 150
 
8.3%
61.7 150
 
8.3%
69.4 136
 
7.5%
71.1 136
 
7.5%
75.0 100
 
5.5%
74.4 94
 
5.2%
76.5 94
 
5.2%
71.9 64
 
3.5%
Other values (10) 479
26.5%
ValueCountFrequency (%)
0.0 219
12.1%
52.0 15
 
0.8%
56.4 58
 
3.2%
58.3 44
 
2.4%
61.2 49
 
2.7%
61.7 150
8.3%
64.3 60
 
3.3%
65.7 60
 
3.3%
66.7 184
10.2%
67.6 50
 
2.8%
ValueCountFrequency (%)
76.5 94
5.2%
75.0 100
5.5%
74.4 94
5.2%
73.9 150
8.3%
72.7 50
 
2.8%
71.9 64
3.5%
71.6 49
 
2.7%
71.1 136
7.5%
69.4 136
7.5%
67.9 44
 
2.4%

3차 응시율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.18887
Minimum0
Maximum95.5
Zeros292
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2024-03-14T19:36:19.992597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170.2
median77.7
Q379.7
95-th percentile86.2
Maximum95.5
Range95.5
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation29.17487
Coefficient of variation (CV)0.44754373
Kurtosis1.1227901
Mean65.18887
Median Absolute Deviation (MAD)4.7
Skewness-1.6885657
Sum117731.1
Variance851.17307
MonotonicityNot monotonic
2024-03-14T19:36:20.374197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 292
16.2%
79.7 150
 
8.3%
77.7 150
 
8.3%
70.2 136
 
7.5%
78.4 136
 
7.5%
85.2 94
 
5.2%
78.6 94
 
5.2%
86.2 64
 
3.5%
82.4 64
 
3.5%
72.0 60
 
3.3%
Other values (11) 566
31.3%
ValueCountFrequency (%)
0.0 292
16.2%
66.7 60
 
3.3%
68.1 44
 
2.4%
70.2 136
7.5%
70.5 49
 
2.7%
72.0 60
 
3.3%
73.7 50
 
2.8%
74.3 60
 
3.3%
75.3 60
 
3.3%
75.8 44
 
2.4%
ValueCountFrequency (%)
95.5 50
 
2.8%
86.2 64
3.5%
85.2 94
5.2%
83.9 50
 
2.8%
82.4 64
3.5%
81.1 50
 
2.8%
80.2 49
 
2.7%
79.7 150
8.3%
78.6 94
5.2%
78.4 136
7.5%

4차 응시율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.178738
Minimum0
Maximum94.3
Zeros356
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2024-03-14T19:36:20.731783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q169.3
median73
Q377.2
95-th percentile94.3
Maximum94.3
Range94.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation30.962872
Coefficient of variation (CV)0.50610512
Kurtosis0.14045484
Mean61.178738
Median Absolute Deviation (MAD)4.2
Skewness-1.3619997
Sum110488.8
Variance958.69946
MonotonicityNot monotonic
2024-03-14T19:36:21.095608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 356
19.7%
77.2 196
10.9%
69.3 150
 
8.3%
81.5 150
 
8.3%
70.8 136
 
7.5%
80.6 94
 
5.2%
94.3 94
 
5.2%
73.0 64
 
3.5%
73.8 60
 
3.3%
74.1 60
 
3.3%
Other values (9) 446
24.7%
ValueCountFrequency (%)
0.0 356
19.7%
64.8 50
 
2.8%
69.3 150
8.3%
69.7 50
 
2.8%
70.3 50
 
2.8%
70.8 136
 
7.5%
72.1 60
 
3.3%
72.3 49
 
2.7%
73.0 64
 
3.5%
73.8 60
 
3.3%
ValueCountFrequency (%)
94.3 94
5.2%
88.9 50
 
2.8%
81.5 150
8.3%
80.9 49
 
2.7%
80.6 94
5.2%
77.2 196
10.9%
76.1 44
 
2.4%
74.1 60
 
3.3%
74.0 44
 
2.4%
73.8 60
 
3.3%

5차 응시율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.786545
Minimum0
Maximum83.3
Zeros554
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2024-03-14T19:36:21.446459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median67.8
Q373.4
95-th percentile82.8
Maximum83.3
Range83.3
Interquartile range (IQR)73.4

Descriptive statistics

Standard deviation33.472738
Coefficient of variation (CV)0.67232497
Kurtosis-1.3054242
Mean49.786545
Median Absolute Deviation (MAD)6.3
Skewness-0.76987675
Sum89914.5
Variance1120.4242
MonotonicityNot monotonic
2024-03-14T19:36:21.798097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 554
30.7%
67.8 210
 
11.6%
65.9 150
 
8.3%
73.4 136
 
7.5%
69.1 136
 
7.5%
79.5 94
 
5.2%
71.5 64
 
3.5%
64.6 60
 
3.3%
82.0 60
 
3.3%
83.3 50
 
2.8%
Other values (6) 292
16.2%
ValueCountFrequency (%)
0.0 554
30.7%
64.6 60
 
3.3%
65.9 150
 
8.3%
66.7 44
 
2.4%
67.4 50
 
2.8%
67.8 210
 
11.6%
69.1 136
 
7.5%
70.5 49
 
2.7%
71.5 64
 
3.5%
73.4 136
 
7.5%
ValueCountFrequency (%)
83.3 50
 
2.8%
82.8 49
 
2.7%
82.0 60
3.3%
79.5 94
5.2%
77.6 50
 
2.8%
74.1 50
 
2.8%
73.4 136
7.5%
71.5 64
3.5%
70.5 49
 
2.7%
69.1 136
7.5%

6차 응시율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.053378
Minimum0
Maximum90.9
Zeros554
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2024-03-14T19:36:22.145312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median70.8
Q373.3
95-th percentile79.9
Maximum90.9
Range90.9
Interquartile range (IQR)73.3

Descriptive statistics

Standard deviation34.210191
Coefficient of variation (CV)0.67008673
Kurtosis-1.2978887
Mean51.053378
Median Absolute Deviation (MAD)4.2
Skewness-0.7902465
Sum92202.4
Variance1170.3371
MonotonicityNot monotonic
2024-03-14T19:36:22.500953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.0 554
30.7%
72.2 150
 
8.3%
68.2 150
 
8.3%
73.0 136
 
7.5%
77.1 136
 
7.5%
75.0 94
 
5.2%
79.9 64
 
3.5%
70.8 60
 
3.3%
73.3 60
 
3.3%
72.0 60
 
3.3%
Other values (7) 342
18.9%
ValueCountFrequency (%)
0.0 554
30.7%
66.9 50
 
2.8%
68.2 150
 
8.3%
70.1 49
 
2.7%
70.4 50
 
2.8%
70.8 60
 
3.3%
71.5 49
 
2.7%
72.0 60
 
3.3%
72.2 150
 
8.3%
73.0 136
 
7.5%
ValueCountFrequency (%)
90.9 50
 
2.8%
79.9 64
3.5%
78.2 44
 
2.4%
77.1 136
7.5%
76.2 50
 
2.8%
75.0 94
5.2%
73.3 60
 
3.3%
73.0 136
7.5%
72.2 150
8.3%
72.0 60
 
3.3%

7차 응시율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.747176
Minimum0
Maximum86.2
Zeros900
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2024-03-14T19:36:22.846299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median67.3
Q375.4
95-th percentile81.1
Maximum86.2
Range86.2
Interquartile range (IQR)75.4

Descriptive statistics

Standard deviation37.783873
Coefficient of variation (CV)1.0009722
Kurtosis-1.9702814
Mean37.747176
Median Absolute Deviation (MAD)18.9
Skewness0.017295708
Sum68171.4
Variance1427.6211
MonotonicityNot monotonic
2024-03-14T19:36:23.186655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 900
49.8%
75.4 150
 
8.3%
67.3 136
 
7.5%
81.1 94
 
5.2%
77.9 64
 
3.5%
74.4 60
 
3.3%
75.0 60
 
3.3%
77.8 50
 
2.8%
78.4 50
 
2.8%
69.4 50
 
2.8%
Other values (4) 192
 
10.6%
ValueCountFrequency (%)
0.0 900
49.8%
67.3 136
 
7.5%
69.4 50
 
2.8%
72.7 49
 
2.7%
74.4 60
 
3.3%
74.5 49
 
2.7%
75.0 60
 
3.3%
75.4 150
 
8.3%
77.0 50
 
2.8%
77.8 50
 
2.8%
ValueCountFrequency (%)
86.2 44
 
2.4%
81.1 94
5.2%
78.4 50
 
2.8%
77.9 64
3.5%
77.8 50
 
2.8%
77.0 50
 
2.8%
75.4 150
8.3%
75.0 60
 
3.3%
74.5 49
 
2.7%
74.4 60
 
3.3%

8차 응시율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.390421
Minimum0
Maximum83.3
Zeros900
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2024-03-14T19:36:23.524925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median57.9
Q372.5
95-th percentile83.1
Maximum83.3
Range83.3
Interquartile range (IQR)72.5

Descriptive statistics

Standard deviation36.516459
Coefficient of variation (CV)1.0034635
Kurtosis-1.9523928
Mean36.390421
Median Absolute Deviation (MAD)25.4
Skewness0.031397768
Sum65721.1
Variance1333.4518
MonotonicityNot monotonic
2024-03-14T19:36:23.863245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 900
49.8%
72.9 150
 
8.3%
69.6 136
 
7.5%
75.0 94
 
5.2%
83.1 64
 
3.5%
64.5 60
 
3.3%
72.5 60
 
3.3%
83.3 50
 
2.8%
72.3 50
 
2.8%
57.9 50
 
2.8%
Other values (4) 192
 
10.6%
ValueCountFrequency (%)
0.0 900
49.8%
57.9 50
 
2.8%
64.5 60
 
3.3%
69.6 136
 
7.5%
70.7 49
 
2.7%
71.7 44
 
2.4%
72.0 49
 
2.7%
72.3 50
 
2.8%
72.5 60
 
3.3%
72.9 150
 
8.3%
ValueCountFrequency (%)
83.3 50
 
2.8%
83.1 64
3.5%
78.2 50
 
2.8%
75.0 94
5.2%
72.9 150
8.3%
72.5 60
 
3.3%
72.3 50
 
2.8%
72.0 49
 
2.7%
71.7 44
 
2.4%
70.7 49
 
2.7%

9차 응시율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.260908
Minimum0
Maximum84.6
Zeros900
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2024-03-14T19:36:24.197973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median52.7
Q369
95-th percentile75
Maximum84.6
Range84.6
Interquartile range (IQR)69

Descriptive statistics

Standard deviation34.615318
Coefficient of variation (CV)1.0103444
Kurtosis-1.9084585
Mean34.260908
Median Absolute Deviation (MAD)31.9
Skewness0.068392606
Sum61875.2
Variance1198.2202
MonotonicityNot monotonic
2024-03-14T19:36:24.540869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 900
49.8%
72.2 150
 
8.3%
69.0 136
 
7.5%
73.5 94
 
5.2%
75.0 64
 
3.5%
73.3 60
 
3.3%
64.3 60
 
3.3%
84.6 50
 
2.8%
66.7 50
 
2.8%
52.7 50
 
2.8%
Other values (4) 192
 
10.6%
ValueCountFrequency (%)
0.0 900
49.8%
52.7 50
 
2.8%
53.4 49
 
2.7%
54.7 50
 
2.8%
63.8 49
 
2.7%
64.3 60
 
3.3%
66.7 50
 
2.8%
68.6 44
 
2.4%
69.0 136
 
7.5%
72.2 150
 
8.3%
ValueCountFrequency (%)
84.6 50
 
2.8%
75.0 64
3.5%
73.5 94
5.2%
73.3 60
 
3.3%
72.2 150
8.3%
69.0 136
7.5%
68.6 44
 
2.4%
66.7 50
 
2.8%
64.3 60
 
3.3%
63.8 49
 
2.7%

Interactions

2024-03-14T19:36:09.801661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:48.250436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:50.701230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:52.302222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:55.007804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:57.554026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:00.055473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:02.081977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:04.597254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:07.106604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:10.065544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:48.530214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:50.866657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:52.482472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:55.280232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:57.828453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:00.216811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:02.348092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:04.860238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:07.370679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:10.312866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:48.786201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:51.012368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:52.671817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:55.529637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:58.071770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:00.361821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:02.591294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:05.104991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:07.619679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:10.586203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:49.067239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:51.184290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:52.981357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:55.803487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:58.343312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:00.531695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:02.874291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:05.374617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:07.893843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:10.838112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:49.333555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:51.339065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:53.246755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:56.058709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:58.595100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:00.684818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:03.123491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:05.625936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:08.149513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:11.081694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:49.587888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:51.538557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:53.507136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:56.309622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:58.840540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:00.840006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:03.371646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:05.872305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:08.394357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:11.326142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:49.844040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:51.715955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:53.765545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:56.559333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:59.084008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:01.093491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:03.615456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:06.117445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:08.640100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:11.576623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:50.098423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:51.860077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:54.230154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:56.806412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:59.330176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:01.340147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:03.859104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:06.367001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:08.883045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:11.820951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:50.367546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:52.006050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:54.488752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:57.056628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:59.573515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:01.585731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:04.105471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:06.611466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:09.128447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:12.068088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:50.543955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:52.155256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:54.746379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:57.304033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:35:59.818834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:01.834154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:04.348853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:06.862578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:36:09.559051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T19:36:24.781836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도과정명번호1차 응시율2차 응시율3차 응시율4차 응시율5차 응시율6차 응시율7차 응시율8차 응시율9차 응시율
년도1.0000.7010.2130.8030.8810.8290.8330.5010.5140.5840.8250.865
과정명0.7011.0000.5350.8870.8710.8930.8430.9330.9740.9110.8310.814
번호0.2130.5351.0000.2770.4280.4400.3790.3460.3060.2640.2350.344
1차 응시율0.8030.8870.2771.0000.6320.6940.6710.8440.7800.7270.4170.411
2차 응시율0.8810.8710.4280.6321.0000.8580.8330.6050.4830.3940.7110.668
3차 응시율0.8290.8930.4400.6940.8581.0000.8690.5930.6360.5330.8220.814
4차 응시율0.8330.8430.3790.6710.8330.8691.0000.5830.6710.5440.8110.885
5차 응시율0.5010.9330.3460.8440.6050.5930.5831.0000.9410.8670.5630.592
6차 응시율0.5140.9740.3060.7800.4830.6360.6710.9411.0000.9060.5670.779
7차 응시율0.5840.9110.2640.7270.3940.5330.5440.8670.9061.0000.7580.731
8차 응시율0.8250.8310.2350.4170.7110.8220.8110.5630.5670.7581.0000.928
9차 응시율0.8650.8140.3440.4110.6680.8140.8850.5920.7790.7310.9281.000
2024-03-14T19:36:25.096586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도과정명
년도1.0000.483
과정명0.4831.000
2024-03-14T19:36:25.546709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호1차 응시율2차 응시율3차 응시율4차 응시율5차 응시율6차 응시율7차 응시율8차 응시율9차 응시율년도과정명
번호1.0000.0500.1890.1410.1820.0970.161-0.0320.0030.0520.0900.258
1차 응시율0.0501.0000.2390.1490.2170.1660.2430.1210.0470.0300.7630.599
2차 응시율0.1890.2391.0000.5590.6770.4750.4830.2490.2600.2470.5340.721
3차 응시율0.1410.1490.5591.0000.3390.4160.2410.4990.5820.5090.4580.763
4차 응시율0.1820.2170.6770.3391.0000.4180.5140.0670.0510.0680.4640.672
5차 응시율0.0970.1660.4750.4160.4181.0000.8150.6700.7050.6480.4290.679
6차 응시율0.1610.2430.4830.2410.5140.8151.0000.5400.5050.5450.4420.789
7차 응시율-0.0320.1210.2490.4990.0670.6700.5401.0000.9610.9160.5100.637
8차 응시율0.0030.0470.2600.5820.0510.7050.5050.9611.0000.9320.4530.653
9차 응시율0.0520.0300.2470.5090.0680.6480.5450.9160.9321.0000.5090.626
년도0.0900.7630.5340.4580.4640.4290.4420.5100.4530.5091.0000.483
과정명0.2580.5990.7210.7630.6720.6790.7890.6370.6530.6260.4831.000

Missing values

2024-03-14T19:36:12.434590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:36:12.995251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

년도과정명번호평가문제1차 응시율2차 응시율3차 응시율4차 응시율5차 응시율6차 응시율7차 응시율8차 응시율9차 응시율
02019공직가치1‘깨어진 창 이론’과 관련된 내용이 아닌 것은?73.874.478.680.679.575.081.175.073.5
12019공직가치2권위주의적 성격의 일관된 행동형태가 아닌 것은?73.874.478.680.679.575.081.175.073.5
22019공직가치3스탠리밀그램 실험을 통해 알게 된 파괴적복종의 원인이 아닌 것은?73.874.478.680.679.575.081.175.073.5
32019공직가치4스탠리 밀그램의 실험을 통해서 알 수 있게 된 것은?73.874.478.680.679.575.081.175.073.5
42019공직가치5공무원노조의 대표적인 불법행위 유형이 아닌 것은?73.874.478.680.679.575.081.175.073.5
52019공직가치6다음 사항 중에 사실과 다르게 기술하고 있는 문항은?73.874.478.680.679.575.081.175.073.5
62019공직가치72008년도 한국법제연구원의 국민대상 설문에서 ‘우리 사회에서 가장 시급히 없애야할 범죄가 무엇이냐’는 질문에 가장 크게 응답한 항목은 무엇입니까?73.874.478.680.679.575.081.175.073.5
72019공직가치82008년도 한국법제연구원의 조사결과에 의할 때, 우리나라 사람들이 법을 지키지 않는 가장 큰 이유는 무엇입니까?73.874.478.680.679.575.081.175.073.5
82019공직가치9국가경쟁력위원회의 보고서에 따르면, 다음과 같은 이유 때문에 우리나라가 법질서 준수수준이 낮다고 합니다. 그에 해당되지 않는 것은?73.874.478.680.679.575.081.175.073.5
92019공직가치10‘부정부패한 공직자에게 오줌발을 먹여주자’는 다소 격한 구호의 캠페인을 시작했지만 그리 효과를 거두고 있지 못한 나라는 어느 나라입니까?73.874.478.680.679.575.081.175.073.5
년도과정명번호평가문제1차 응시율2차 응시율3차 응시율4차 응시율5차 응시율6차 응시율7차 응시율8차 응시율9차 응시율
17962023청탹금지법의 이해49아너코드에 관한 설명 중 틀린 것은 무엇입니까?0.00.00.00.00.00.00.00.00.0
17972023청탹금지법의 이해50청탁금지법이 필요한 이유가 아닌 것은 무엇입니까?0.00.00.00.00.00.00.00.00.0
17982023청탹금지법의 이해51청탁금지법에서 금지하는 내용 중 틀린 것은 무엇입니까?0.00.00.00.00.00.00.00.00.0
17992023청탹금지법의 이해52청탁금지법을 준수하는 공무원의 모습 중 틀린 것은 무엇입니까?0.00.00.00.00.00.00.00.00.0
18002023청탹금지법의 이해53청탁금지법 제정 의의가 아닌 것은 무엇입니까?0.00.00.00.00.00.00.00.00.0
18012023청탹금지법의 이해54공직자 등의 친족이 제공하는 금품은 자유롭게 주고 받을 수 있습니다. 여기서 말하는 친족의 범위가 아닌 것은 무엇입니까?0.00.00.00.00.00.00.00.00.0
18022023청탹금지법의 이해55금품 수수 시 처리 절차 중 틀린 것은 무엇입니까?0.00.00.00.00.00.00.00.00.0
18032023청탹금지법의 이해56기관의 기강을 바로 세우기 위한 노력 중 틀린 것은 무엇입니까?0.00.00.00.00.00.00.00.00.0
18042023청탹금지법의 이해57청탁금지법에 대한 내용 중 틀린 것은 무엇입니까?0.00.00.00.00.00.00.00.00.0
18052023청탹금지법의 이해58청탁금지법 제정이 가지고 올 효과로 적절치 않은 것은 무엇입니까?0.00.00.00.00.00.00.00.00.0