Overview

Dataset statistics

Number of variables12
Number of observations54
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory108.4 B

Variable types

Text2
Numeric10

Dataset

Description언론사내 주52시간 근무제 도입을 위해 필요한 노력을 나타내는 데이터로써, 인력 충원, 유연근무제, 업무량 조정 등의 데이터를 제공하고 있습니다.
Author한국언론진흥재단
URLhttps://www.data.go.kr/data/15084694/fileData.do

Alerts

법 시행 후 주 52시간 근무제가 잘 지켜지고 있다 is highly overall correlated with 주 52시간 근무제로 인해 신규 인력이 보강되었다 and 2 other fieldsHigh correlation
주 52시간 근무제로 인해 근무 체계가 변화되었다 is highly overall correlated with 주 52시간 근무제로 인해 취재원을 만나는 횟수가 줄었다 and 4 other fieldsHigh correlation
주 52시간 근무제로 인해 신규 인력이 보강되었다 is highly overall correlated with 법 시행 후 주 52시간 근무제가 잘 지켜지고 있다 and 2 other fieldsHigh correlation
주 52시간 근무제로 인해 취재원을 만나는 횟수가 줄었다 is highly overall correlated with 주 52시간 근무제로 인해 근무 체계가 변화되었다 and 4 other fieldsHigh correlation
주 52시간 근무제로 인해 업무량이 줄어들었다 is highly overall correlated with 법 시행 후 주 52시간 근무제가 잘 지켜지고 있다 and 5 other fieldsHigh correlation
주 52시간 근무제로 인해 업무강도가 높아졌다 is highly overall correlated with 주 52시간 근무제로 인해 월 기준 근로수당이 감소하였다High correlation
주 52시간 근무제로 인해 월 기준 근로수당이 감소하였다 is highly overall correlated with 주 52시간 근무제로 인해 근무 체계가 변화되었다 and 4 other fieldsHigh correlation
주 52시간 근무제로 인해 여가시간이 증가하였다 is highly overall correlated with 법 시행 후 주 52시간 근무제가 잘 지켜지고 있다 and 6 other fieldsHigh correlation
주 52시간 근무제로 인해 삶의 질이 향상되는 데 도움이 됐다 is highly overall correlated with 주 52시간 근무제로 인해 근무 체계가 변화되었다 and 4 other fieldsHigh correlation
구분 has unique valuesUnique

Reproduction

Analysis started2024-03-14 19:02:39.704041
Analysis finished2024-03-14 19:03:06.764303
Duration27.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size560.0 B
2024-03-15T04:03:07.588519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.4259259
Min length3

Characters and Unicode

Total characters239
Distinct characters28
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)100.0%

Sample

1st row성별1
2nd row성별2
3rd row연령1
4th row연령2
5th row연령3
ValueCountFrequency (%)
성별1 1
 
1.9%
소속부서16 1
 
1.9%
권역1 1
 
1.9%
소속부서6 1
 
1.9%
소속부서7 1
 
1.9%
소속부서8 1
 
1.9%
소속부서9 1
 
1.9%
소속부서10 1
 
1.9%
소속부서11 1
 
1.9%
소속부서12 1
 
1.9%
Other values (44) 44
81.5%
2024-03-15T04:03:09.158558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29
12.1%
29
12.1%
1 21
 
8.8%
18
 
7.5%
18
 
7.5%
15
 
6.3%
15
 
6.3%
2 9
 
3.8%
7
 
2.9%
4 7
 
2.9%
Other values (18) 71
29.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 174
72.8%
Decimal Number 65
 
27.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
29
16.7%
29
16.7%
18
10.3%
18
10.3%
15
8.6%
15
8.6%
7
 
4.0%
7
 
4.0%
5
 
2.9%
5
 
2.9%
Other values (8) 26
14.9%
Decimal Number
ValueCountFrequency (%)
1 21
32.3%
2 9
13.8%
4 7
 
10.8%
3 7
 
10.8%
5 6
 
9.2%
6 4
 
6.2%
7 4
 
6.2%
8 3
 
4.6%
9 2
 
3.1%
0 2
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 174
72.8%
Common 65
 
27.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
29
16.7%
29
16.7%
18
10.3%
18
10.3%
15
8.6%
15
8.6%
7
 
4.0%
7
 
4.0%
5
 
2.9%
5
 
2.9%
Other values (8) 26
14.9%
Common
ValueCountFrequency (%)
1 21
32.3%
2 9
13.8%
4 7
 
10.8%
3 7
 
10.8%
5 6
 
9.2%
6 4
 
6.2%
7 4
 
6.2%
8 3
 
4.6%
9 2
 
3.1%
0 2
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 174
72.8%
ASCII 65
 
27.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
29
16.7%
29
16.7%
18
10.3%
18
10.3%
15
8.6%
15
8.6%
7
 
4.0%
7
 
4.0%
5
 
2.9%
5
 
2.9%
Other values (8) 26
14.9%
ASCII
ValueCountFrequency (%)
1 21
32.3%
2 9
13.8%
4 7
 
10.8%
3 7
 
10.8%
5 6
 
9.2%
6 4
 
6.2%
7 4
 
6.2%
8 3
 
4.6%
9 2
 
3.1%
0 2
 
3.1%
Distinct52
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size560.0 B
2024-03-15T04:03:10.003249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length5.0740741
Min length2

Characters and Unicode

Total characters274
Distinct characters103
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)92.6%

Sample

1st row남자
2nd row여자
3rd row20대
4th row30-34세
5th row35-39세
ValueCountFrequency (%)
뉴스통신사 2
 
3.3%
이상 2
 
3.3%
기타 2
 
3.3%
인터넷언론사 2
 
3.3%
15-19년 1
 
1.7%
지역전국 1
 
1.7%
취재(보도)일반 1
 
1.7%
남자 1
 
1.7%
국방통일북한 1
 
1.7%
편집(편성)교열부 1
 
1.7%
Other values (46) 46
76.7%
2024-03-15T04:03:11.306830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14
 
5.1%
11
 
4.0%
9
 
3.3%
4 8
 
2.9%
8
 
2.9%
- 8
 
2.9%
0 7
 
2.6%
7
 
2.6%
7
 
2.6%
6
 
2.2%
Other values (93) 189
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 217
79.2%
Decimal Number 37
 
13.5%
Dash Punctuation 8
 
2.9%
Space Separator 6
 
2.2%
Close Punctuation 2
 
0.7%
Open Punctuation 2
 
0.7%
Uppercase Letter 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
14
 
6.5%
11
 
5.1%
9
 
4.1%
8
 
3.7%
7
 
3.2%
7
 
3.2%
6
 
2.8%
5
 
2.3%
5
 
2.3%
5
 
2.3%
Other values (79) 140
64.5%
Decimal Number
ValueCountFrequency (%)
4 8
21.6%
0 7
18.9%
3 5
13.5%
5 5
13.5%
1 5
13.5%
9 4
10.8%
2 2
 
5.4%
6 1
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
I 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 217
79.2%
Common 55
 
20.1%
Latin 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
14
 
6.5%
11
 
5.1%
9
 
4.1%
8
 
3.7%
7
 
3.2%
7
 
3.2%
6
 
2.8%
5
 
2.3%
5
 
2.3%
5
 
2.3%
Other values (79) 140
64.5%
Common
ValueCountFrequency (%)
4 8
14.5%
- 8
14.5%
0 7
12.7%
6
10.9%
3 5
9.1%
5 5
9.1%
1 5
9.1%
9 4
7.3%
) 2
 
3.6%
( 2
 
3.6%
Other values (2) 3
 
5.5%
Latin
ValueCountFrequency (%)
T 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 217
79.2%
ASCII 57
 
20.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
14
 
6.5%
11
 
5.1%
9
 
4.1%
8
 
3.7%
7
 
3.2%
7
 
3.2%
6
 
2.8%
5
 
2.3%
5
 
2.3%
5
 
2.3%
Other values (79) 140
64.5%
ASCII
ValueCountFrequency (%)
4 8
14.0%
- 8
14.0%
0 7
12.3%
6
10.5%
3 5
8.8%
5 5
8.8%
1 5
8.8%
9 4
7.0%
) 2
 
3.5%
( 2
 
3.5%
Other values (4) 5
8.8%

사례수
Real number (ℝ)

Distinct50
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260.2963
Minimum7
Maximum1290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T04:03:11.725662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile25.55
Q175.75
median174.5
Q3355
95-th percentile892.45
Maximum1290
Range1283
Interquartile range (IQR)279.25

Descriptive statistics

Standard deviation275.06648
Coefficient of variation (CV)1.0567438
Kurtosis5.3897125
Mean260.2963
Median Absolute Deviation (MAD)127
Skewness2.1957152
Sum14056
Variance75661.571
MonotonicityNot monotonic
2024-03-15T04:03:12.174194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 2
 
3.7%
51 2
 
3.7%
128 2
 
3.7%
389 2
 
3.7%
1206 1
 
1.9%
129 1
 
1.9%
16 1
 
1.9%
130 1
 
1.9%
55 1
 
1.9%
141 1
 
1.9%
Other values (40) 40
74.1%
ValueCountFrequency (%)
7 1
1.9%
16 1
1.9%
21 1
1.9%
28 2
3.7%
29 1
1.9%
42 1
1.9%
43 1
1.9%
49 1
1.9%
51 2
3.7%
55 1
1.9%
ValueCountFrequency (%)
1290 1
1.9%
1206 1
1.9%
936 1
1.9%
869 1
1.9%
551 1
1.9%
467 1
1.9%
418 1
1.9%
408 1
1.9%
389 2
3.7%
373 1
1.9%
Distinct36
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5966667
Minimum3.16
Maximum3.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T04:03:12.512860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.16
5-th percentile3.352
Q13.48
median3.565
Q33.725
95-th percentile3.8705
Maximum3.96
Range0.8
Interquartile range (IQR)0.245

Descriptive statistics

Standard deviation0.17484225
Coefficient of variation (CV)0.048612302
Kurtosis-0.23049751
Mean3.5966667
Median Absolute Deviation (MAD)0.095
Skewness0.0364826
Sum194.22
Variance0.030569811
MonotonicityNot monotonic
2024-03-15T04:03:12.835782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.62 3
 
5.6%
3.56 3
 
5.6%
3.47 3
 
5.6%
3.48 3
 
5.6%
3.49 2
 
3.7%
3.55 2
 
3.7%
3.85 2
 
3.7%
3.5 2
 
3.7%
3.8 2
 
3.7%
3.43 2
 
3.7%
Other values (26) 30
55.6%
ValueCountFrequency (%)
3.16 1
 
1.9%
3.24 1
 
1.9%
3.3 1
 
1.9%
3.38 1
 
1.9%
3.4 1
 
1.9%
3.41 1
 
1.9%
3.42 1
 
1.9%
3.43 2
3.7%
3.47 3
5.6%
3.48 3
5.6%
ValueCountFrequency (%)
3.96 1
1.9%
3.91 1
1.9%
3.89 1
1.9%
3.86 1
1.9%
3.85 2
3.7%
3.84 1
1.9%
3.82 1
1.9%
3.81 1
1.9%
3.8 2
3.7%
3.75 2
3.7%
Distinct36
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4298148
Minimum2.98
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T04:03:13.347580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.98
5-th percentile3.1265
Q13.29
median3.41
Q33.565
95-th percentile3.7705
Maximum3.94
Range0.96
Interquartile range (IQR)0.275

Descriptive statistics

Standard deviation0.21435723
Coefficient of variation (CV)0.062498193
Kurtosis-0.26239074
Mean3.4298148
Median Absolute Deviation (MAD)0.15
Skewness0.19658643
Sum185.21
Variance0.045949022
MonotonicityNot monotonic
2024-03-15T04:03:13.734313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.18 4
 
7.4%
3.4 4
 
7.4%
3.34 3
 
5.6%
3.45 2
 
3.7%
3.29 2
 
3.7%
3.55 2
 
3.7%
3.41 2
 
3.7%
3.25 2
 
3.7%
3.57 2
 
3.7%
3.52 2
 
3.7%
Other values (26) 29
53.7%
ValueCountFrequency (%)
2.98 1
 
1.9%
3.0 1
 
1.9%
3.12 1
 
1.9%
3.13 1
 
1.9%
3.17 1
 
1.9%
3.18 4
7.4%
3.21 1
 
1.9%
3.23 1
 
1.9%
3.25 2
3.7%
3.29 2
3.7%
ValueCountFrequency (%)
3.94 1
1.9%
3.86 1
1.9%
3.79 1
1.9%
3.76 1
1.9%
3.75 1
1.9%
3.74 2
3.7%
3.66 2
3.7%
3.65 1
1.9%
3.64 1
1.9%
3.58 1
1.9%
Distinct36
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.525
Minimum2.1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T04:03:14.017227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile2.229
Q12.41
median2.52
Q32.615
95-th percentile2.8805
Maximum3
Range0.9
Interquartile range (IQR)0.205

Descriptive statistics

Standard deviation0.18661584
Coefficient of variation (CV)0.073907263
Kurtosis0.38759129
Mean2.525
Median Absolute Deviation (MAD)0.11
Skewness0.28532156
Sum136.35
Variance0.034825472
MonotonicityNot monotonic
2024-03-15T04:03:14.337721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2.43 3
 
5.6%
2.41 3
 
5.6%
2.54 2
 
3.7%
2.52 2
 
3.7%
2.75 2
 
3.7%
2.55 2
 
3.7%
2.46 2
 
3.7%
2.34 2
 
3.7%
2.38 2
 
3.7%
2.57 2
 
3.7%
Other values (26) 32
59.3%
ValueCountFrequency (%)
2.1 1
 
1.9%
2.15 1
 
1.9%
2.19 1
 
1.9%
2.25 1
 
1.9%
2.3 1
 
1.9%
2.34 2
3.7%
2.35 2
3.7%
2.38 2
3.7%
2.39 1
 
1.9%
2.41 3
5.6%
ValueCountFrequency (%)
3.0 1
1.9%
2.93 1
1.9%
2.9 1
1.9%
2.87 1
1.9%
2.75 2
3.7%
2.73 2
3.7%
2.72 2
3.7%
2.69 2
3.7%
2.63 1
1.9%
2.62 1
1.9%
Distinct34
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8074074
Minimum2.37
Maximum3.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T04:03:14.681028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.37
5-th percentile2.4965
Q12.6675
median2.85
Q32.9575
95-th percentile3.034
Maximum3.28
Range0.91
Interquartile range (IQR)0.29

Descriptive statistics

Standard deviation0.18741725
Coefficient of variation (CV)0.066758125
Kurtosis-0.36606692
Mean2.8074074
Median Absolute Deviation (MAD)0.135
Skewness-0.17440193
Sum151.6
Variance0.035125227
MonotonicityNot monotonic
2024-03-15T04:03:14.985610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2.89 4
 
7.4%
2.96 3
 
5.6%
2.69 3
 
5.6%
2.77 3
 
5.6%
3.0 3
 
5.6%
2.91 2
 
3.7%
2.58 2
 
3.7%
2.8 2
 
3.7%
2.59 2
 
3.7%
2.94 2
 
3.7%
Other values (24) 28
51.9%
ValueCountFrequency (%)
2.37 1
1.9%
2.48 1
1.9%
2.49 1
1.9%
2.5 1
1.9%
2.57 2
3.7%
2.58 2
3.7%
2.59 2
3.7%
2.6 1
1.9%
2.61 1
1.9%
2.63 1
1.9%
ValueCountFrequency (%)
3.28 1
 
1.9%
3.11 1
 
1.9%
3.06 1
 
1.9%
3.02 1
 
1.9%
3.0 3
5.6%
2.99 1
 
1.9%
2.98 1
 
1.9%
2.97 2
3.7%
2.96 3
5.6%
2.95 1
 
1.9%
Distinct35
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5412963
Minimum2.23
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T04:03:15.333373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.23
5-th percentile2.2965
Q12.41
median2.51
Q32.6175
95-th percentile2.88
Maximum3
Range0.77
Interquartile range (IQR)0.2075

Descriptive statistics

Standard deviation0.18477979
Coefficient of variation (CV)0.072710841
Kurtosis-0.091283398
Mean2.5412963
Median Absolute Deviation (MAD)0.105
Skewness0.71388578
Sum137.23
Variance0.034143571
MonotonicityNot monotonic
2024-03-15T04:03:15.843882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2.51 6
 
11.1%
2.46 4
 
7.4%
2.59 3
 
5.6%
2.48 3
 
5.6%
2.57 2
 
3.7%
2.5 2
 
3.7%
2.75 2
 
3.7%
2.37 2
 
3.7%
2.82 2
 
3.7%
2.88 2
 
3.7%
Other values (25) 26
48.1%
ValueCountFrequency (%)
2.23 1
1.9%
2.28 1
1.9%
2.29 1
1.9%
2.3 1
1.9%
2.31 1
1.9%
2.32 1
1.9%
2.34 1
1.9%
2.35 1
1.9%
2.37 2
3.7%
2.38 1
1.9%
ValueCountFrequency (%)
3.0 1
1.9%
2.97 1
1.9%
2.88 2
3.7%
2.86 1
1.9%
2.82 2
3.7%
2.79 1
1.9%
2.75 2
3.7%
2.72 1
1.9%
2.7 1
1.9%
2.67 1
1.9%
Distinct31
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.942963
Minimum2.63
Maximum3.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T04:03:16.115769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.63
5-th percentile2.736
Q12.87
median2.95
Q33.02
95-th percentile3.1405
Maximum3.21
Range0.58
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.12908429
Coefficient of variation (CV)0.043862016
Kurtosis0.096719549
Mean2.942963
Median Absolute Deviation (MAD)0.075
Skewness-0.36701629
Sum158.92
Variance0.016662753
MonotonicityNot monotonic
2024-03-15T04:03:16.351272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3.06 4
 
7.4%
2.98 3
 
5.6%
2.76 3
 
5.6%
2.87 3
 
5.6%
2.95 3
 
5.6%
3.02 3
 
5.6%
2.94 3
 
5.6%
2.96 2
 
3.7%
3.0 2
 
3.7%
2.86 2
 
3.7%
Other values (21) 26
48.1%
ValueCountFrequency (%)
2.63 2
3.7%
2.71 1
 
1.9%
2.75 1
 
1.9%
2.76 3
5.6%
2.77 1
 
1.9%
2.81 1
 
1.9%
2.83 1
 
1.9%
2.86 2
3.7%
2.87 3
5.6%
2.88 1
 
1.9%
ValueCountFrequency (%)
3.21 1
 
1.9%
3.18 1
 
1.9%
3.16 1
 
1.9%
3.13 1
 
1.9%
3.1 2
3.7%
3.09 1
 
1.9%
3.07 1
 
1.9%
3.06 4
7.4%
3.02 3
5.6%
3.01 2
3.7%
Distinct39
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.822963
Minimum2.34
Maximum3.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T04:03:16.747276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.34
5-th percentile2.4055
Q12.695
median2.8
Q32.97
95-th percentile3.2105
Maximum3.46
Range1.12
Interquartile range (IQR)0.275

Descriptive statistics

Standard deviation0.24250757
Coefficient of variation (CV)0.085905333
Kurtosis0.10937019
Mean2.822963
Median Absolute Deviation (MAD)0.135
Skewness0.21240592
Sum152.44
Variance0.058809923
MonotonicityNot monotonic
2024-03-15T04:03:17.093166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2.73 3
 
5.6%
2.8 3
 
5.6%
2.79 2
 
3.7%
3.05 2
 
3.7%
2.92 2
 
3.7%
2.76 2
 
3.7%
3.11 2
 
3.7%
2.81 2
 
3.7%
2.34 2
 
3.7%
2.98 2
 
3.7%
Other values (29) 32
59.3%
ValueCountFrequency (%)
2.34 2
3.7%
2.36 1
1.9%
2.43 1
1.9%
2.47 1
1.9%
2.52 1
1.9%
2.53 1
1.9%
2.57 1
1.9%
2.63 1
1.9%
2.64 1
1.9%
2.66 2
3.7%
ValueCountFrequency (%)
3.46 1
1.9%
3.28 1
1.9%
3.23 1
1.9%
3.2 1
1.9%
3.19 1
1.9%
3.14 1
1.9%
3.13 1
1.9%
3.11 2
3.7%
3.09 1
1.9%
3.05 2
3.7%
Distinct31
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0431481
Minimum2.76
Maximum3.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T04:03:17.324865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.76
5-th percentile2.8165
Q12.95
median3.015
Q33.15
95-th percentile3.29
Maximum3.39
Range0.63
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.14650422
Coefficient of variation (CV)0.048142323
Kurtosis-0.54326752
Mean3.0431481
Median Absolute Deviation (MAD)0.11
Skewness0.30735262
Sum164.33
Variance0.021463487
MonotonicityNot monotonic
2024-03-15T04:03:17.645942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2.96 4
 
7.4%
3.29 4
 
7.4%
3.16 3
 
5.6%
2.95 3
 
5.6%
3.03 3
 
5.6%
3.2 3
 
5.6%
3.0 3
 
5.6%
2.98 2
 
3.7%
2.94 2
 
3.7%
2.92 2
 
3.7%
Other values (21) 25
46.3%
ValueCountFrequency (%)
2.76 1
1.9%
2.79 1
1.9%
2.81 1
1.9%
2.82 1
1.9%
2.85 1
1.9%
2.88 2
3.7%
2.89 1
1.9%
2.92 2
3.7%
2.93 1
1.9%
2.94 2
3.7%
ValueCountFrequency (%)
3.39 1
 
1.9%
3.29 4
7.4%
3.27 1
 
1.9%
3.25 1
 
1.9%
3.2 3
5.6%
3.16 3
5.6%
3.15 2
3.7%
3.14 2
3.7%
3.13 2
3.7%
3.12 1
 
1.9%
Distinct34
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0951852
Minimum2.84
Maximum3.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T04:03:17.924267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.84
5-th percentile2.9065
Q12.9825
median3.08
Q33.17
95-th percentile3.3605
Maximum3.39
Range0.55
Interquartile range (IQR)0.1875

Descriptive statistics

Standard deviation0.13237655
Coefficient of variation (CV)0.042768538
Kurtosis-0.20556469
Mean3.0951852
Median Absolute Deviation (MAD)0.095
Skewness0.38772617
Sum167.14
Variance0.01752355
MonotonicityNot monotonic
2024-03-15T04:03:18.192697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3.06 4
 
7.4%
3.05 3
 
5.6%
2.97 3
 
5.6%
3.16 2
 
3.7%
3.38 2
 
3.7%
3.11 2
 
3.7%
3.25 2
 
3.7%
3.26 2
 
3.7%
3.19 2
 
3.7%
2.98 2
 
3.7%
Other values (24) 30
55.6%
ValueCountFrequency (%)
2.84 1
 
1.9%
2.86 1
 
1.9%
2.9 1
 
1.9%
2.91 1
 
1.9%
2.93 1
 
1.9%
2.94 1
 
1.9%
2.95 2
3.7%
2.96 1
 
1.9%
2.97 3
5.6%
2.98 2
3.7%
ValueCountFrequency (%)
3.39 1
1.9%
3.38 2
3.7%
3.35 1
1.9%
3.29 1
1.9%
3.26 2
3.7%
3.25 2
3.7%
3.2 1
1.9%
3.19 2
3.7%
3.18 1
1.9%
3.17 2
3.7%

Interactions

2024-03-15T04:03:03.199889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:40.255280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:42.655459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:44.552549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:46.418914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:48.296313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:50.444974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:53.059210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:55.444944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:58.471786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:03.719478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:40.519496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:42.861041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:44.717038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:46.567927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:48.451697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:50.709143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:53.300924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:55.706632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:58.846872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:04.060907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:40.844638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:43.035713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:45.102409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:46.821132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:48.667421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:50.992653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:53.569466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:55.999133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:59.214968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:04.360352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:41.089881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:43.279723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:45.236706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:46.960262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:48.912274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:51.245520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:53.814942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:56.285656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:59.575998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:04.784665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:41.231742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:43.435665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:45.378433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:47.098953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:49.156901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:51.505295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:54.043864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:56.534603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:00.157681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:05.005620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:41.376359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:43.618807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:45.582568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:47.237981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:49.315250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:51.760010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:54.284946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:56.793500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:00.527652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:05.230161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:41.580205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:43.805317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:45.824768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:47.403816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:49.478898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:52.040850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:54.552638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:57.086698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:01.362522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:05.383275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:41.840930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:43.968075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:45.969837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:47.562000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:49.681708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:52.303643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:54.719867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:57.577054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:01.759354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:05.534008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:42.093496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:44.130399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:46.117427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:47.815852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:49.932801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:52.659815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:54.887098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:57.842006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:02.043363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:05.726201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:42.350941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:44.341366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:46.269546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:48.132387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:50.188294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:52.889498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:55.141533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:02:58.158018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:03:02.515093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T04:03:18.493631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분중분류사례수법 시행 후 주 52시간 근무제가 잘 지켜지고 있다주 52시간 근무제로 인해 근무 체계가 변화되었다주 52시간 근무제로 인해 신규 인력이 보강되었다주 52시간 근무제로 인해 취재원을 만나는 횟수가 줄었다주 52시간 근무제로 인해 업무량이 줄어들었다주 52시간 근무제로 인해 업무강도가 높아졌다주 52시간 근무제로 인해 월 기준 근로수당이 감소하였다주 52시간 근무제로 인해 여가시간이 증가하였다주 52시간 근무제로 인해 삶의 질이 향상되는 데 도움이 됐다
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
중분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사례수1.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
법 시행 후 주 52시간 근무제가 잘 지켜지고 있다1.0001.0000.0001.0000.3920.7520.0000.5560.6990.8290.3550.376
주 52시간 근무제로 인해 근무 체계가 변화되었다1.0001.0000.0000.3921.0000.5150.8440.8120.6370.8490.8130.818
주 52시간 근무제로 인해 신규 인력이 보강되었다1.0001.0000.0000.7520.5151.0000.0000.7580.6570.6450.6220.801
주 52시간 근무제로 인해 취재원을 만나는 횟수가 줄었다1.0001.0000.0000.0000.8440.0001.0000.7840.1960.8350.8070.479
주 52시간 근무제로 인해 업무량이 줄어들었다1.0001.0000.0000.5560.8120.7580.7841.0000.6940.6090.7660.737
주 52시간 근무제로 인해 업무강도가 높아졌다1.0001.0000.0000.6990.6370.6570.1960.6941.0000.7840.0990.564
주 52시간 근무제로 인해 월 기준 근로수당이 감소하였다1.0001.0000.0000.8290.8490.6450.8350.6090.7841.0000.5220.665
주 52시간 근무제로 인해 여가시간이 증가하였다1.0001.0000.0000.3550.8130.6220.8070.7660.0990.5221.0000.891
주 52시간 근무제로 인해 삶의 질이 향상되는 데 도움이 됐다1.0001.0000.0000.3760.8180.8010.4790.7370.5640.6650.8911.000
2024-03-15T04:03:18.817118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사례수법 시행 후 주 52시간 근무제가 잘 지켜지고 있다주 52시간 근무제로 인해 근무 체계가 변화되었다주 52시간 근무제로 인해 신규 인력이 보강되었다주 52시간 근무제로 인해 취재원을 만나는 횟수가 줄었다주 52시간 근무제로 인해 업무량이 줄어들었다주 52시간 근무제로 인해 업무강도가 높아졌다주 52시간 근무제로 인해 월 기준 근로수당이 감소하였다주 52시간 근무제로 인해 여가시간이 증가하였다주 52시간 근무제로 인해 삶의 질이 향상되는 데 도움이 됐다
사례수1.000-0.123-0.403-0.273-0.229-0.323-0.022-0.259-0.407-0.348
법 시행 후 주 52시간 근무제가 잘 지켜지고 있다-0.1231.0000.3510.6020.3600.664-0.2410.0430.5120.358
주 52시간 근무제로 인해 근무 체계가 변화되었다-0.4030.3511.0000.2840.7460.7180.2650.7760.8320.763
주 52시간 근무제로 인해 신규 인력이 보강되었다-0.2730.6020.2841.0000.2150.675-0.326-0.0820.5360.454
주 52시간 근무제로 인해 취재원을 만나는 횟수가 줄었다-0.2290.3600.7460.2151.0000.7280.4830.8110.6090.524
주 52시간 근무제로 인해 업무량이 줄어들었다-0.3230.6640.7180.6750.7281.0000.0260.4520.7960.666
주 52시간 근무제로 인해 업무강도가 높아졌다-0.022-0.2410.265-0.3260.4830.0261.0000.5320.0470.033
주 52시간 근무제로 인해 월 기준 근로수당이 감소하였다-0.2590.0430.776-0.0820.8110.4520.5321.0000.5610.513
주 52시간 근무제로 인해 여가시간이 증가하였다-0.4070.5120.8320.5360.6090.7960.0470.5611.0000.921
주 52시간 근무제로 인해 삶의 질이 향상되는 데 도움이 됐다-0.3480.3580.7630.4540.5240.6660.0330.5130.9211.000

Missing values

2024-03-15T04:03:06.052175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T04:03:06.498979image/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

구분중분류사례수법 시행 후 주 52시간 근무제가 잘 지켜지고 있다주 52시간 근무제로 인해 근무 체계가 변화되었다주 52시간 근무제로 인해 신규 인력이 보강되었다주 52시간 근무제로 인해 취재원을 만나는 횟수가 줄었다주 52시간 근무제로 인해 업무량이 줄어들었다주 52시간 근무제로 인해 업무강도가 높아졌다주 52시간 근무제로 인해 월 기준 근로수당이 감소하였다주 52시간 근무제로 인해 여가시간이 증가하였다주 52시간 근무제로 인해 삶의 질이 향상되는 데 도움이 됐다
0성별1남자12063.623.432.542.842.572.962.793.013.05
1성별2여자5513.483.252.382.62.322.892.762.973.08
2연령120대2163.53.182.472.372.282.762.662.963.07
3연령230-34세3663.413.232.352.572.312.872.712.882.95
4연령335-39세3063.493.362.432.772.42.952.732.953.08
5연령440-44세2593.563.382.452.872.492.952.83.023.1
6연령545-49세2263.623.462.412.982.533.072.82.962.97
7연령650대3243.823.582.732.972.823.012.943.23.17
8연령760대 이상603.853.472.93.02.882.872.823.153.13
9매체유형1신문사8693.483.252.382.712.413.022.742.932.98
구분중분류사례수법 시행 후 주 52시간 근무제가 잘 지켜지고 있다주 52시간 근무제로 인해 근무 체계가 변화되었다주 52시간 근무제로 인해 신규 인력이 보강되었다주 52시간 근무제로 인해 취재원을 만나는 횟수가 줄었다주 52시간 근무제로 인해 업무량이 줄어들었다주 52시간 근무제로 인해 업무강도가 높아졌다주 52시간 근무제로 인해 월 기준 근로수당이 감소하였다주 52시간 근무제로 인해 여가시간이 증가하였다주 52시간 근무제로 인해 삶의 질이 향상되는 데 도움이 됐다
44직위3부장부장대우2273.713.522.632.972.73.022.923.123.09
45직위4차장차장대우3573.563.342.342.912.463.02.782.922.99
46직위5평기자9363.483.292.432.612.372.882.732.953.06
47경력11-4년4183.493.172.542.492.372.772.642.892.96
48경력25-9년3733.543.352.392.662.342.92.692.963.06
49경력310-14년3433.533.42.472.832.512.982.83.053.17
50경력415-19년2153.553.412.412.922.523.022.812.942.95
51경력520년 이상4083.753.552.583.022.723.062.983.143.13
52권역1서울12903.553.42.52.772.52.922.793.033.11
53권역2그 외 지역4673.643.292.462.772.462.972.742.922.94