Overview

Dataset statistics

Number of variables15
Number of observations95
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.4 KiB
Average record size in memory133.4 B

Variable types

Numeric11
Categorical4

Dataset

Description해외방송시장조사 보고서 중 OTT관련 이용행태 및 만족도 등에 대한 데이터 중 해외국가별(영국, 호주, 러시아 등 5개국) 연령, 소득, 인종, 지역, 학력 성별 유료 OTT 서비스 비가입 이유에 대한 통계데이터
URLhttps://www.data.go.kr/data/15102283/fileData.do

Alerts

조사연도 has constant value ""Constant
분류 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 분류High correlation
연번 is highly overall correlated with 분류High correlation
연번 has unique valuesUnique
이용요금이비싸서(비율) has 10 (10.5%) zerosZeros
특별히볼만한재미있고좋은콘텐츠가없어서(비율) has 36 (37.9%) zerosZeros
지상파TV나유료방송으로도충분해서(비율) has 11 (11.6%) zerosZeros
온라인동영상서비스(OTT)가만족스럽지않아서(비율) has 51 (53.7%) zerosZeros
가입방법을잘몰라서(비율) has 35 (36.8%) zerosZeros
이용방법을잘몰라서(비율) has 25 (26.3%) zerosZeros
콘텐츠품질이떨어져서(비율) has 73 (76.8%) zerosZeros
자녀들에게부정적인영향을미칠것같아서(비율) has 64 (67.4%) zerosZeros
외국서비스를이용하는것을좋아하지않아서(비율) has 38 (40.0%) zerosZeros

Reproduction

Analysis started2023-12-12 15:44:55.997835
Analysis finished2023-12-12 15:45:12.672880
Duration16.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48
Minimum1
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T00:45:12.808420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.7
Q124.5
median48
Q371.5
95-th percentile90.3
Maximum95
Range94
Interquartile range (IQR)47

Descriptive statistics

Standard deviation27.568098
Coefficient of variation (CV)0.57433536
Kurtosis-1.2
Mean48
Median Absolute Deviation (MAD)24
Skewness0
Sum4560
Variance760
MonotonicityStrictly increasing
2023-12-13T00:45:13.034529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
2 1
 
1.1%
71 1
 
1.1%
70 1
 
1.1%
69 1
 
1.1%
68 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
Other values (85) 85
89.5%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
95 1
1.1%
94 1
1.1%
93 1
1.1%
92 1
1.1%
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%

조사연도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
2020
95 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 95
100.0%

Length

2023-12-13T00:45:13.229241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:45:13.342104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 95
100.0%

국가
Categorical

Distinct5
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size892.0 B
영국
19 
호주
19 
러시아
19 
브라질
19 
UAE
19 

Length

Max length3
Median length3
Mean length2.6
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영국
2nd row호주
3rd row러시아
4th row브라질
5th rowUAE

Common Values

ValueCountFrequency (%)
영국 19
20.0%
호주 19
20.0%
러시아 19
20.0%
브라질 19
20.0%
UAE 19
20.0%

Length

2023-12-13T00:45:13.481382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:45:13.655364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영국 19
20.0%
호주 19
20.0%
러시아 19
20.0%
브라질 19
20.0%
uae 19
20.0%

분류
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size892.0 B
직업별
35 
연령별
25 
성별
10 
학력별
10 
소득별
10 

Length

Max length3
Median length3
Mean length2.8947368
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
직업별 35
36.8%
연령별 25
26.3%
성별 10
 
10.5%
학력별 10
 
10.5%
소득별 10
 
10.5%
국가별 5
 
5.3%

Length

2023-12-13T00:45:13.839104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:45:13.993553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
직업별 35
36.8%
연령별 25
26.3%
성별 10
 
10.5%
학력별 10
 
10.5%
소득별 10
 
10.5%
국가별 5
 
5.3%

구분
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size892.0 B
전체
 
5
남성
 
5
여성
 
5
10대
 
5
20대
 
5
Other values (14)
70 

Length

Max length7
Median length6
Mean length4.0526316
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 5
 
5.3%
남성 5
 
5.3%
여성 5
 
5.3%
10대 5
 
5.3%
20대 5
 
5.3%
30대 5
 
5.3%
40대 5
 
5.3%
50대 이상 5
 
5.3%
고졸 이하 5
 
5.3%
대졸 이상 5
 
5.3%
Other values (9) 45
47.4%

Length

2023-12-13T00:45:14.135812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이상 15
 
12.0%
이하 10
 
8.0%
평균 10
 
8.0%
10대 5
 
4.0%
20대 5
 
4.0%
5
 
4.0%
무직/구직 5
 
4.0%
은퇴자 5
 
4.0%
가사노동자 5
 
4.0%
학생 5
 
4.0%
Other values (11) 55
44.0%

사례수(명)
Real number (ℝ)

Distinct41
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.926316
Minimum1
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T00:45:14.254032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median15
Q329
95-th percentile59.3
Maximum118
Range117
Interquartile range (IQR)22

Descriptive statistics

Standard deviation19.727528
Coefficient of variation (CV)0.94271387
Kurtosis5.5187691
Mean20.926316
Median Absolute Deviation (MAD)10
Skewness1.9700665
Sum1988
Variance389.17536
MonotonicityNot monotonic
2023-12-13T00:45:14.449806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
6 6
 
6.3%
4 5
 
5.3%
10 5
 
5.3%
19 4
 
4.2%
5 4
 
4.2%
9 4
 
4.2%
8 4
 
4.2%
2 4
 
4.2%
13 3
 
3.2%
14 3
 
3.2%
Other values (31) 53
55.8%
ValueCountFrequency (%)
1 2
 
2.1%
2 4
4.2%
3 2
 
2.1%
4 5
5.3%
5 4
4.2%
6 6
6.3%
7 2
 
2.1%
8 4
4.2%
9 4
4.2%
10 5
5.3%
ValueCountFrequency (%)
118 1
1.1%
66 1
1.1%
65 1
1.1%
64 1
1.1%
60 1
1.1%
59 1
1.1%
57 2
2.1%
56 1
1.1%
55 1
1.1%
54 1
1.1%

이용요금이비싸서(비율)
Real number (ℝ)

ZEROS 

Distinct52
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.102105
Minimum0
Maximum50
Zeros10
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T00:45:14.637630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.4
median22.9
Q333.3
95-th percentile42.34
Maximum50
Range50
Interquartile range (IQR)18.9

Descriptive statistics

Standard deviation13.171915
Coefficient of variation (CV)0.57016083
Kurtosis-0.67357245
Mean23.102105
Median Absolute Deviation (MAD)9.6
Skewness-0.03372446
Sum2194.7
Variance173.49936
MonotonicityNot monotonic
2023-12-13T00:45:14.840717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.5%
25.0 5
 
5.3%
40.0 5
 
5.3%
33.3 4
 
4.2%
22.2 4
 
4.2%
10.0 3
 
3.2%
16.7 3
 
3.2%
37.5 3
 
3.2%
50.0 3
 
3.2%
11.1 2
 
2.1%
Other values (42) 53
55.8%
ValueCountFrequency (%)
0.0 10
10.5%
5.6 1
 
1.1%
6.3 1
 
1.1%
7.7 1
 
1.1%
10.0 3
 
3.2%
10.5 1
 
1.1%
11.1 2
 
2.1%
11.8 1
 
1.1%
13.3 2
 
2.1%
14.3 2
 
2.1%
ValueCountFrequency (%)
50.0 3
3.2%
46.2 1
 
1.1%
42.9 1
 
1.1%
42.1 1
 
1.1%
41.2 1
 
1.1%
40.0 5
5.3%
38.7 2
 
2.1%
38.1 1
 
1.1%
37.5 3
3.2%
37.1 1
 
1.1%
Distinct38
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9021053
Minimum0
Maximum33.3
Zeros36
Zeros (%)37.9%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T00:45:15.006008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.7
Q310
95-th percentile16.97
Maximum33.3
Range33.3
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.5779286
Coefficient of variation (CV)1.1145055
Kurtosis2.8328374
Mean5.9021053
Median Absolute Deviation (MAD)4.7
Skewness1.4300782
Sum560.7
Variance43.269144
MonotonicityNot monotonic
2023-12-13T00:45:15.184286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.0 36
37.9%
11.1 5
 
5.3%
12.5 4
 
4.2%
6.3 4
 
4.2%
10.0 3
 
3.2%
5.7 3
 
3.2%
14.3 3
 
3.2%
25.0 2
 
2.1%
3.5 2
 
2.1%
7.7 2
 
2.1%
Other values (28) 31
32.6%
ValueCountFrequency (%)
0.0 36
37.9%
1.5 1
 
1.1%
1.9 1
 
1.1%
3.3 2
 
2.1%
3.4 1
 
1.1%
3.5 2
 
2.1%
3.6 1
 
1.1%
3.8 1
 
1.1%
4.0 1
 
1.1%
4.4 1
 
1.1%
ValueCountFrequency (%)
33.3 1
 
1.1%
25.0 2
 
2.1%
20.0 1
 
1.1%
17.6 1
 
1.1%
16.7 1
 
1.1%
14.3 3
3.2%
13.3 1
 
1.1%
12.5 4
4.2%
11.8 1
 
1.1%
11.1 5
5.3%
Distinct52
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.646316
Minimum0
Maximum100
Zeros11
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T00:45:15.382789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.55
median25
Q333.95
95-th percentile54.08
Maximum100
Range100
Interquartile range (IQR)17.4

Descriptive statistics

Standard deviation19.570542
Coefficient of variation (CV)0.73445585
Kurtosis4.5949512
Mean26.646316
Median Absolute Deviation (MAD)8.6
Skewness1.6555222
Sum2531.4
Variance383.00613
MonotonicityNot monotonic
2023-12-13T00:45:15.562743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 11
 
11.6%
20.0 8
 
8.4%
25.0 7
 
7.4%
50.0 5
 
5.3%
33.3 3
 
3.2%
16.7 3
 
3.2%
14.3 3
 
3.2%
22.2 3
 
3.2%
100.0 3
 
3.2%
18.8 2
 
2.1%
Other values (42) 47
49.5%
ValueCountFrequency (%)
0.0 11
11.6%
7.1 1
 
1.1%
9.5 1
 
1.1%
10.0 1
 
1.1%
11.1 1
 
1.1%
12.5 2
 
2.1%
13.3 2
 
2.1%
14.3 3
 
3.2%
15.8 1
 
1.1%
16.4 1
 
1.1%
ValueCountFrequency (%)
100.0 3
3.2%
66.7 1
 
1.1%
63.6 1
 
1.1%
50.0 5
5.3%
45.0 1
 
1.1%
44.4 1
 
1.1%
42.9 1
 
1.1%
41.0 1
 
1.1%
40.7 1
 
1.1%
40.6 1
 
1.1%
Distinct31
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6505263
Minimum0
Maximum25
Zeros51
Zeros (%)53.7%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T00:45:15.768754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.3
95-th percentile16.7
Maximum25
Range25
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation5.9625939
Coefficient of variation (CV)1.6333519
Kurtosis4.272211
Mean3.6505263
Median Absolute Deviation (MAD)0
Skewness2.1227868
Sum346.8
Variance35.552526
MonotonicityNot monotonic
2023-12-13T00:45:15.957679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 51
53.7%
6.3 3
 
3.2%
25.0 3
 
3.2%
16.7 3
 
3.2%
5.3 3
 
3.2%
3.1 3
 
3.2%
10.0 2
 
2.1%
3.2 2
 
2.1%
3.3 2
 
2.1%
3.6 2
 
2.1%
Other values (21) 21
22.1%
ValueCountFrequency (%)
0.0 51
53.7%
1.5 1
 
1.1%
1.7 1
 
1.1%
1.8 1
 
1.1%
1.9 1
 
1.1%
2.2 1
 
1.1%
2.5 1
 
1.1%
2.8 1
 
1.1%
2.9 1
 
1.1%
3.1 3
 
3.2%
ValueCountFrequency (%)
25.0 3
3.2%
20.0 1
 
1.1%
16.7 3
3.2%
14.3 1
 
1.1%
12.5 1
 
1.1%
11.8 1
 
1.1%
10.5 1
 
1.1%
10.0 2
2.1%
9.5 1
 
1.1%
7.7 1
 
1.1%

가입방법을잘몰라서(비율)
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5284211
Minimum0
Maximum33.3
Zeros35
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T00:45:16.101520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.6
Q313
95-th percentile20.93
Maximum33.3
Range33.3
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.2104197
Coefficient of variation (CV)1.0905899
Kurtosis0.72243038
Mean7.5284211
Median Absolute Deviation (MAD)5.6
Skewness1.0666932
Sum715.2
Variance67.410992
MonotonicityNot monotonic
2023-12-13T00:45:16.294441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 35
36.8%
20.0 5
 
5.3%
14.3 5
 
5.3%
16.7 5
 
5.3%
7.7 3
 
3.2%
11.1 3
 
3.2%
6.3 3
 
3.2%
10.0 2
 
2.1%
5.6 2
 
2.1%
5.1 2
 
2.1%
Other values (27) 30
31.6%
ValueCountFrequency (%)
0.0 35
36.8%
1.5 1
 
1.1%
2.2 1
 
1.1%
2.6 1
 
1.1%
3.1 1
 
1.1%
3.2 1
 
1.1%
3.3 1
 
1.1%
3.5 1
 
1.1%
3.7 1
 
1.1%
4.7 1
 
1.1%
ValueCountFrequency (%)
33.3 2
 
2.1%
28.6 1
 
1.1%
25.0 1
 
1.1%
23.1 1
 
1.1%
20.0 5
5.3%
16.7 5
5.3%
15.8 1
 
1.1%
15.4 1
 
1.1%
15.0 1
 
1.1%
14.3 5
5.3%

이용방법을잘몰라서(비율)
Real number (ℝ)

ZEROS 

Distinct43
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4347368
Minimum0
Maximum40
Zeros25
Zeros (%)26.3%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T00:45:16.500734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.6
Q312.5
95-th percentile25
Maximum40
Range40
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation8.1967659
Coefficient of variation (CV)0.9717868
Kurtosis2.6202724
Mean8.4347368
Median Absolute Deviation (MAD)4.9
Skewness1.4232808
Sum801.3
Variance67.186972
MonotonicityNot monotonic
2023-12-13T00:45:16.692375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.0 25
26.3%
7.7 5
 
5.3%
12.5 5
 
5.3%
9.1 3
 
3.2%
14.3 3
 
3.2%
20.0 3
 
3.2%
10.0 3
 
3.2%
7.1 3
 
3.2%
11.1 3
 
3.2%
13.3 2
 
2.1%
Other values (33) 40
42.1%
ValueCountFrequency (%)
0.0 25
26.3%
2.9 1
 
1.1%
3.3 2
 
2.1%
3.5 1
 
1.1%
3.7 2
 
2.1%
3.8 1
 
1.1%
4.0 1
 
1.1%
4.6 1
 
1.1%
4.8 1
 
1.1%
5.3 2
 
2.1%
ValueCountFrequency (%)
40.0 1
 
1.1%
33.3 2
2.1%
27.3 1
 
1.1%
25.0 2
2.1%
22.2 1
 
1.1%
20.0 3
3.2%
16.7 2
2.1%
16.0 1
 
1.1%
14.3 3
3.2%
13.9 1
 
1.1%
Distinct17
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6284211
Minimum0
Maximum16.7
Zeros73
Zeros (%)76.8%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T00:45:16.883770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.31
Maximum16.7
Range16.7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.742586
Coefficient of variation (CV)2.2982913
Kurtosis4.6891488
Mean1.6284211
Median Absolute Deviation (MAD)0
Skewness2.3927287
Sum154.7
Variance14.00695
MonotonicityNot monotonic
2023-12-13T00:45:17.098388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.0 73
76.8%
12.5 3
 
3.2%
1.7 2
 
2.1%
3.1 2
 
2.1%
10.0 2
 
2.1%
11.1 2
 
2.1%
11.8 1
 
1.1%
5.6 1
 
1.1%
2.6 1
 
1.1%
1.8 1
 
1.1%
Other values (7) 7
 
7.4%
ValueCountFrequency (%)
0.0 73
76.8%
0.8 1
 
1.1%
1.6 1
 
1.1%
1.7 2
 
2.1%
1.8 1
 
1.1%
2.6 1
 
1.1%
3.1 2
 
2.1%
3.6 1
 
1.1%
5.5 1
 
1.1%
5.6 1
 
1.1%
ValueCountFrequency (%)
16.7 1
 
1.1%
12.5 3
3.2%
11.8 1
 
1.1%
11.1 2
2.1%
10.0 2
2.1%
9.1 1
 
1.1%
6.3 1
 
1.1%
5.6 1
 
1.1%
5.5 1
 
1.1%
3.6 1
 
1.1%
Distinct22
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3221053
Minimum0
Maximum25
Zeros64
Zeros (%)67.4%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T00:45:17.280094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.95
95-th percentile11.29
Maximum25
Range25
Interquartile range (IQR)2.95

Descriptive statistics

Standard deviation4.8803379
Coefficient of variation (CV)2.1016867
Kurtosis10.442842
Mean2.3221053
Median Absolute Deviation (MAD)0
Skewness3.0684885
Sum220.6
Variance23.817698
MonotonicityNot monotonic
2023-12-13T00:45:17.457016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 64
67.4%
2.9 3
 
3.2%
5.6 3
 
3.2%
6.3 3
 
3.2%
3.6 2
 
2.1%
25.0 2
 
2.1%
6.7 2
 
2.1%
14.3 2
 
2.1%
10.0 1
 
1.1%
5.3 1
 
1.1%
Other values (12) 12
 
12.6%
ValueCountFrequency (%)
0.0 64
67.4%
1.6 1
 
1.1%
1.7 1
 
1.1%
1.8 1
 
1.1%
1.9 1
 
1.1%
2.9 3
 
3.2%
3.0 1
 
1.1%
3.5 1
 
1.1%
3.6 2
 
2.1%
3.8 1
 
1.1%
ValueCountFrequency (%)
25.0 2
2.1%
20.0 1
 
1.1%
14.3 2
2.1%
10.0 1
 
1.1%
9.1 1
 
1.1%
6.7 2
2.1%
6.3 3
3.2%
5.9 1
 
1.1%
5.6 3
3.2%
5.3 1
 
1.1%
Distinct41
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6547368
Minimum0
Maximum50
Zeros38
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-13T00:45:17.661188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.8
Q310.6
95-th percentile22.2
Maximum50
Range50
Interquartile range (IQR)10.6

Descriptive statistics

Standard deviation8.324148
Coefficient of variation (CV)1.2508606
Kurtosis7.1541152
Mean6.6547368
Median Absolute Deviation (MAD)4.8
Skewness2.1156922
Sum632.2
Variance69.29144
MonotonicityNot monotonic
2023-12-13T00:45:17.841738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 38
40.0%
12.5 4
 
4.2%
16.7 3
 
3.2%
6.3 3
 
3.2%
11.1 2
 
2.1%
7.0 2
 
2.1%
22.2 2
 
2.1%
25.0 2
 
2.1%
10.0 2
 
2.1%
15.4 2
 
2.1%
Other values (31) 35
36.8%
ValueCountFrequency (%)
0.0 38
40.0%
1.7 1
 
1.1%
2.8 1
 
1.1%
3.1 1
 
1.1%
3.6 2
 
2.1%
3.7 1
 
1.1%
3.8 1
 
1.1%
4.0 2
 
2.1%
4.8 1
 
1.1%
5.0 1
 
1.1%
ValueCountFrequency (%)
50.0 1
 
1.1%
30.0 1
 
1.1%
25.0 2
2.1%
22.2 2
2.1%
20.0 1
 
1.1%
16.7 3
3.2%
15.8 1
 
1.1%
15.4 2
2.1%
14.3 1
 
1.1%
13.3 1
 
1.1%

Interactions

2023-12-13T00:45:10.643090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:56.836782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:58.151575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:59.564576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:01.035679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:02.467981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:04.210310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:05.553567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:06.698666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:07.951787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:09.293203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:10.750332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:56.952468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:58.263134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:59.695434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:01.163575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:02.595587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:04.311939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:05.654385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:06.786065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:08.051354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:09.418058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:11.207048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:57.074483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:58.374576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:59.854857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:01.296395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:02.729482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:04.431963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:05.766870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:06.884779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:08.167359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:09.547480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:11.317374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:57.198079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:58.501422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:59.973819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:01.397284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:02.863530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:04.547160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:05.889428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:06.987194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:08.277838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:09.719654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:11.449579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:57.329863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:58.630072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:00.087292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:01.545374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:03.015536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:04.686867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:05.987121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:07.093161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:08.383603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:09.848580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:11.556992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:57.456278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:58.761779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:00.198174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:01.662666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:03.127302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:04.833456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:06.096828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:07.177697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:08.497666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:09.963182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:11.664133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:57.584281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:58.896770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:00.301954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:01.786278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:03.262546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:04.948637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:06.190259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:07.291519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:08.606772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:10.065008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:11.772715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:57.707812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:59.027308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:00.436091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:01.933760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:03.747287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:05.064527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:06.287276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:07.442977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:08.736792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:10.172686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:11.893530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:57.820076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:59.172778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:00.595620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:02.081493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:03.887626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:05.181707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:06.406076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:07.581256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:08.871554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:10.298471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:11.990074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:57.939251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:59.289947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:00.744046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:02.207689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:04.004409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:05.316720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:06.517933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:07.701793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:08.989891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:10.411988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:12.083794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:58.055799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:59.431390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:00.887263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:02.340377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:04.106355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:05.445890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:06.606719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:07.834942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:09.147856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:45:10.526222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:45:18.319327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번국가분류구분사례수(명)이용요금이비싸서(비율)특별히볼만한재미있고좋은콘텐츠가없어서(비율)지상파TV나유료방송으로도충분해서(비율)온라인동영상서비스(OTT)가만족스럽지않아서(비율)가입방법을잘몰라서(비율)이용방법을잘몰라서(비율)콘텐츠품질이떨어져서(비율)자녀들에게부정적인영향을미칠것같아서(비율)외국서비스를이용하는것을좋아하지않아서(비율)
연번1.0000.7910.9290.8460.4650.3100.0830.0000.0000.3650.1940.0000.0000.196
국가0.7911.0000.0000.0000.1110.4750.3190.2960.1920.6980.0000.3550.2990.410
분류0.9290.0001.0001.0000.6010.0000.2210.0000.1790.1820.0990.1090.0000.000
구분0.8460.0001.0001.0000.6540.2330.4450.3220.4840.4400.3990.0000.4220.161
사례수(명)0.4650.1110.6010.6541.0000.3250.3130.3100.4590.2700.2670.0000.2500.261
이용요금이비싸서(비율)0.3100.4750.0000.2330.3251.0000.3280.5220.0000.4580.4370.2570.0000.300
특별히볼만한재미있고좋은콘텐츠가없어서(비율)0.0830.3190.2210.4450.3130.3281.0000.2380.4150.5900.0660.0000.3080.000
지상파TV나유료방송으로도충분해서(비율)0.0000.2960.0000.3220.3100.5220.2381.0000.3680.2230.3430.0000.1570.170
온라인동영상서비스(OTT)가만족스럽지않아서(비율)0.0000.1920.1790.4840.4590.0000.4150.3681.0000.0000.6120.0000.7840.000
가입방법을잘몰라서(비율)0.3650.6980.1820.4400.2700.4580.5900.2230.0001.0000.0000.4390.0000.454
이용방법을잘몰라서(비율)0.1940.0000.0990.3990.2670.4370.0660.3430.6120.0001.0000.0000.1710.665
콘텐츠품질이떨어져서(비율)0.0000.3550.1090.0000.0000.2570.0000.0000.0000.4390.0001.0000.3100.000
자녀들에게부정적인영향을미칠것같아서(비율)0.0000.2990.0000.4220.2500.0000.3080.1570.7840.0000.1710.3101.0000.000
외국서비스를이용하는것을좋아하지않아서(비율)0.1960.4100.0000.1610.2610.3000.0000.1700.0000.4540.6650.0000.0001.000
2023-12-13T00:45:18.514683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가분류구분
국가1.0000.0000.000
분류0.0001.0000.924
구분0.0000.9241.000
2023-12-13T00:45:18.648984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사례수(명)이용요금이비싸서(비율)특별히볼만한재미있고좋은콘텐츠가없어서(비율)지상파TV나유료방송으로도충분해서(비율)온라인동영상서비스(OTT)가만족스럽지않아서(비율)가입방법을잘몰라서(비율)이용방법을잘몰라서(비율)콘텐츠품질이떨어져서(비율)자녀들에게부정적인영향을미칠것같아서(비율)외국서비스를이용하는것을좋아하지않아서(비율)국가분류구분
연번1.000-0.386-0.079-0.1060.034-0.1220.101-0.160-0.030-0.168-0.0270.4330.8010.492
사례수(명)-0.3861.0000.2160.2320.1110.1690.1830.1780.2010.2110.2370.0650.4100.333
이용요금이비싸서(비율)-0.0790.2161.0000.053-0.1820.061-0.2450.138-0.138-0.122-0.1430.1960.0720.137
특별히볼만한재미있고좋은콘텐츠가없어서(비율)-0.1060.2320.0531.000-0.2850.0390.1490.0360.2260.1250.0920.1830.1050.174
지상파TV나유료방송으로도충분해서(비율)0.0340.111-0.182-0.2851.000-0.315-0.273-0.381-0.073-0.151-0.3110.1670.0550.077
온라인동영상서비스(OTT)가만족스럽지않아서(비율)-0.1220.1690.0610.039-0.3151.0000.0550.175-0.0100.4020.0440.1100.1230.196
가입방법을잘몰라서(비율)0.1010.183-0.2450.149-0.2730.0551.0000.0180.048-0.0570.0980.3510.0880.165
이용방법을잘몰라서(비율)-0.1600.1780.1380.036-0.3810.1750.0181.0000.0730.0110.1330.0470.0580.078
콘텐츠품질이떨어져서(비율)-0.0300.201-0.1380.226-0.073-0.0100.0480.0731.0000.0960.1390.2210.0530.000
자녀들에게부정적인영향을미칠것같아서(비율)-0.1680.211-0.1220.125-0.1510.402-0.0570.0110.0961.0000.1750.1920.0000.177
외국서비스를이용하는것을좋아하지않아서(비율)-0.0270.237-0.1430.092-0.3110.0440.0980.1330.1390.1751.0000.2510.0310.000
국가0.4330.0650.1960.1830.1670.1100.3510.0470.2210.1920.2511.0000.0000.000
분류0.8010.4100.0720.1050.0550.1230.0880.0580.0530.0000.0310.0001.0000.924
구분0.4920.3330.1370.1740.0770.1960.1650.0780.0000.1770.0000.0000.9241.000

Missing values

2023-12-13T00:45:12.234573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:45:12.530675image/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

연번조사연도국가분류구분사례수(명)이용요금이비싸서(비율)특별히볼만한재미있고좋은콘텐츠가없어서(비율)지상파TV나유료방송으로도충분해서(비율)온라인동영상서비스(OTT)가만족스럽지않아서(비율)가입방법을잘몰라서(비율)이용방법을잘몰라서(비율)콘텐츠품질이떨어져서(비율)자녀들에게부정적인영향을미칠것같아서(비율)외국서비스를이용하는것을좋아하지않아서(비율)
012020영국국가별전체6630.36.128.81.51.59.10.03.06.1
122020호주국가별전체11824.63.429.72.55.17.60.81.77.6
232020러시아국가별전체5928.88.532.21.75.16.81.70.01.7
342020브라질국가별전체5625.03.619.65.414.310.70.01.83.6
452020UAE국가별전체5514.59.116.43.612.79.15.53.612.7
562020영국성별남성2138.19.514.30.00.09.50.00.04.8
672020영국성별여성4526.74.435.62.22.28.90.04.46.7
782020호주성별남성6426.64.725.03.14.710.91.61.66.3
892020호주성별여성5422.21.935.21.95.63.70.01.99.3
9102020러시아성별남성2722.211.140.70.03.73.70.00.03.7
연번조사연도국가분류구분사례수(명)이용요금이비싸서(비율)특별히볼만한재미있고좋은콘텐츠가없어서(비율)지상파TV나유료방송으로도충분해서(비율)온라인동영상서비스(OTT)가만족스럽지않아서(비율)가입방법을잘몰라서(비율)이용방법을잘몰라서(비율)콘텐츠품질이떨어져서(비율)자녀들에게부정적인영향을미칠것같아서(비율)외국서비스를이용하는것을좋아하지않아서(비율)
85862020영국소득별평균 이하2842.97.125.03.60.07.10.03.610.7
86872020영국소득별평균 이상825.012.525.00.00.012.512.50.012.5
87882020호주소득별평균 이하5726.33.526.35.37.03.51.80.07.0
88892020호주소득별평균 이상2015.05.045.00.010.010.00.00.05.0
89902020러시아소득별평균 이하3930.85.141.00.02.67.72.60.00.0
90912020러시아소득별평균 이상837.50.025.012.512.50.00.00.012.5
91922020브라질소득별평균 이하1926.310.521.110.515.80.00.05.30.0
92932020브라질소득별평균 이상137.70.030.80.023.17.70.00.015.4
93942020UAE소득별평균 이하1910.510.515.85.310.510.50.00.015.8
94952020UAE소득별평균 이상1822.211.111.10.016.711.15.65.616.7