【【已更新详细各种人群数据分析】】前所未有，华人demography大调查，完全匿名！-第9页

天子呼来不上床

0操作161 ^#

嫁白人的比嫁abc的多一倍没想到。仔细想想我一个嫁白人的人都不熟。看在我的圈子太种族主义了。

地理2009

0操作162 ^#

主要是把数据清理了一下。把工作范围重新归类，然后个人工资调整到数字。 652 人做了有效的回答，其中 20-39 岁占了87%，这个网站还是很年轻的。30-39 达到61.35%，应该有很多网友是和这个网站一起成长的。

[td=5,1,385]

Age_group

[td=80]

Age_group

[td=76]

Frequency

[td=61]

Percent

[td=83]

Cumulative
Frequency

[td=84]

Cumulative
Percent

[td=80]

20-29

[td=76]

168

[td=61]

25.77

[td=83]

168

[td=84]

25.77

[td=80]

30-39

[td=76]

400

[td=61]

61.35

[td=83]

568

[td=84]

87.12

[td=80]

40-49

[td=76]

80

[td=61]

12.27

[td=83]

648

[td=84]

99.39

[td=80]

50-59

[td=76]

4

[td=61]

0.61

[td=83]

652

[td=84]

100.00

地理2009

0操作163 ^#

大部分美女都嫁给了中国或华裔（87%）。国男/国女达到 82%，其次是白人（12%）。

[td=5,1,406]

Spouse_eth

[td=101]

Spouse_eth

[td=76]

Frequency

[td=61]

Percent

[td=83]

Cumulative
Frequency

[td=84]

Cumulative
Percent

[td=101]

ABC

[td=76]

26

[td=61]

5.09

[td=83]

26

[td=84]

5.09

[td=101]

Korean

[td=76]

2

[td=61]

0.39

[td=83]

28

[td=84]

5.48

[td=101]

Mixed

[td=76]

1

[td=61]

0.20

[td=83]

29

[td=84]

5.68

[td=101]

NativeChinese

[td=76]

419

[td=61]

82.00

[td=83]

448

[td=84]

87.67

[td=101]

Taiwan

[td=76]

1

[td=61]

0.20

[td=83]

449

[td=84]

87.87

[td=101]

White

[td=76]

61

[td=61]

11.94

[td=83]

510

[td=84]

99.80

[td=101]

indonisia

[td=76]

1

[td=61]

0.20

[td=83]

511

[td=84]

100.00

地理2009

0操作164 ^#

有娃， 1娃， 2 娃各占 1/3.

[td=5,1,367]

children

[td=62]

children

[td=76]

Frequency

[td=61]

Percent

[td=83]

Cumulative
Frequency

[td=84]

Cumulative
Percent

[td=62]

0

[td=76]

174

[td=61]

31.75

[td=83]

174

[td=84]

31.75

[td=62]

1

[td=76]

209

[td=61]

38.14

[td=83]

383

[td=84]

69.89

[td=62]

2

[td=76]

165

[td=61]

30.11

[td=83]

548

[td=84]

100.00

地理2009

0操作165 ^#

地理分布：
湾区还是大区，剩下主要集中在东岸与西岸。其他部分人少很多。

[td=5,1,406]

Location

[td=101]

Location

[td=76]

Frequency

[td=61]

Percent

[td=83]

Cumulative
Frequency

[td=84]

Cumulative
Percent

[td=101]

BayArea

[td=76]

122

[td=61]

21.44

[td=83]

122

[td=84]

21.44

[td=101]

Canada

[td=76]

2

[td=61]

0.35

[td=83]

124

[td=84]

21.79

[td=101]

Chicago

[td=76]

27

[td=61]

4.75

[td=83]

151

[td=84]

26.54

[td=101]

China

[td=76]

2

[td=61]

0.35

[td=83]

153

[td=84]

26.89

[td=101]

EastCoast

[td=76]

165

[td=61]

29.00

[td=83]

318

[td=84]

55.89

[td=101]

Florida

[td=76]

9

[td=61]

1.58

[td=83]

327

[td=84]

57.47

[td=101]

LA

[td=76]

3

[td=61]

0.53

[td=83]

330

[td=84]

58.00

[td=101]

MiddleOther

[td=76]

58

[td=61]

10.19

[td=83]

388

[td=84]

68.19

[td=101]

NewYork

[td=76]

50

[td=61]

8.79

[td=83]

438

[td=84]

76.98

[td=101]

Other

[td=76]

9

[td=61]

1.58

[td=83]

447

[td=84]

78.56

[td=101]

OtherCountry

[td=76]

4

[td=61]

0.70

[td=83]

451

[td=84]

79.26

[td=101]

Seatle

[td=76]

18

[td=61]

3.16

[td=83]

469

[td=84]

82.43

[td=101]

Southother

[td=76]

11

[td=61]

1.93

[td=83]

480

[td=84]

84.36

[td=101]

Texas

[td=76]

44

[td=61]

7.73

[td=83]

524

[td=84]

92.09

[td=101]

WestCoast

[td=76]

45

[td=61]

7.91

[td=83]

569

[td=84]

100.00

工作分布：

有生物，制药有关的都放到了pharm 里。所有的scientist, research, post doc 都归到 research，政府工作，教师归到了public sector, 在家的妈妈就是那个beatif, 软件给切掉了，我本来写的是美妈妈们。任何写了码工， engineer 的都归到了IT, 发考题与professor 都在professor 中（出乎我意料的多呢）回答问题中有一位律师，两位医生。和数据有关的包括在了statistian 里面，但是risk analysis 与financial analyst 都包括在了finance 里。

其中IT 还是没有出乎意料的占大头，达到1/4 的人数。 research 达到10%，统计师也有9%，金融与经济加起来有20%，会计师也有8%。出乎意料的学生只有不到3%

[td=71]

occup

[td=76]

Frequency

[td=61]

Percent

[td=83]

Cumulative
Frequency

[td=84]

Cumulative
Percent

[td=71]

Graphic

[td=76]

1

[td=61]

0.38

[td=83]

1

[td=84]

0.38

[td=71]

IT

[td=76]

64

[td=61]

24.24

[td=83]

65

[td=84]

24.62

[td=71]

Lawyer

[td=76]

1

[td=61]

0.38

[td=83]

66

[td=84]

25.00

[td=71]

Pharma

[td=76]

11

[td=61]

4.17

[td=83]

77

[td=84]

29.17

[td=71]

PublicS

[td=76]

15

[td=61]

5.68

[td=83]

92

[td=84]

34.85

[td=71]

account

[td=76]

22

[td=61]

8.33

[td=83]

114

[td=84]

43.18

[td=71]

assista

[td=76]

2

[td=61]

0.76

[td=83]

116

[td=84]

43.94

[td=71]

beautif

[td=76]

13

[td=61]

4.92

[td=83]

129

[td=84]

48.86

[td=71]

businss

[td=76]

16

[td=61]

6.06

[td=83]

145

[td=84]

54.92

[td=71]

designe

[td=76]

1

[td=61]

0.38

[td=83]

146

[td=84]

55.30

[td=71]

doctor

[td=76]

2

[td=61]

0.76

[td=83]

148

[td=84]

56.06

[td=71]

finance

[td=76]

24

[td=61]

9.09

[td=83]

172

[td=84]

65.15

[td=71]

managem

[td=76]

7

[td=61]

2.65

[td=83]

179

[td=84]

67.80

[td=71]

marketi

[td=76]

3

[td=61]

1.14

[td=83]

182

[td=84]

68.94

[td=71]

other

[td=76]

4

[td=61]

1.52

[td=83]

186

[td=84]

70.45

[td=71]

profess

[td=76]

15

[td=61]

5.68

[td=83]

201

[td=84]

76.14

[td=71]

realtor

[td=76]

1

[td=61]

0.38

[td=83]

202

[td=84]

76.52

[td=71]

researc

[td=76]

28

[td=61]

10.61

[td=83]

230

[td=84]

87.12

[td=71]

self_em

[td=76]

4

[td=61]

1.52

[td=83]

234

[td=84]

88.64

[td=71]

statist

[td=76]

23

[td=61]

8.71

[td=83]

257

[td=84]

97.35

[td=71]

student

[td=76]

7

[td=61]

2.65

[td=83]

264

[td=84]

100.00

地理2009

0操作166 ^#

看看来美国时年龄的分布：因为问答中没有对学位的问题，我大概就guess 后把岁数分了一下，《18 岁的是来念高中的， 18 - 22 岁是来念大学的， 22-30 岁基本算作大学以上来的，然后其他是大于30 岁时来美国的。这样分主要是因为来美国念书的美女/帅哥们达到80%, 见下边第一张表：

[td=5,1,395]

reasontoUS2

[td=90]

reasontoUS2

[td=76]

Frequency

[td=61]

Percent

[td=83]

Cumulative
Frequency

[td=84]

Cumulative
Percent

[td=90]

immigration

[td=76]

8

[td=61]

1.32

[td=83]

8

[td=84]

1.32

[td=90]

marriage

[td=76]

104

[td=61]

17.22

[td=83]

112

[td=84]

18.54

[td=90]

other

[td=76]

3

[td=61]

0.50

[td=83]

115

[td=84]

19.04

[td=90]

study

[td=76]

483

[td=61]

79.97

[td=83]

598

[td=84]

99.01

[td=90]

working

[td=76]

6

[td=61]

0.99

[td=83]

604

[td=84]

100.00

那么大家都是什么时候来美国的呢：

[td=85]

initial_year

[td=76]

Frequency

[td=61]

Percent

[td=83]

Cumulative
Frequency

[td=84]

Cumulative
Percent

[td=85]

College

[td=76]

228

[td=61]

34.60

[td=83]

228

[td=84]

34.60

[td=85]

Post_deg

[td=76]

291

[td=61]

44.16

[td=83]

519

[td=84]

78.76

[td=85]

high_school

[td=76]

118

[td=61]

17.91

[td=83]

637

[td=84]

96.66

[td=85]

other

[td=76]

22

[td=61]

3.34

[td=83]

659

[td=84]

100.00

一般以上的人是在22 岁或之前来到美国的。其中18-22 岁的达到34%， 96% 的美女/帅哥们都是在30 岁之前来到美国的。

地理2009

0操作167 ^#

再来看看具体的工资与家庭收入，我把lz 之前的工资做了调整，这样就可以做一些算数了。取了最小的那个值，比如12万 - 20 万，就用了12 来代替。《 5万的是 4.9
大于40 万的是 41 万。个人工资中大部分人都在 5 万到20 万之间， 89%， 8 - 20 万的人有 52%，过半。 12 - 20万有 1/5. （美国7.5万以上算高收入，华人上有将尽 2/3 的个人收入超过这个数字，华人的工资确实高啊。

[td=65]

Personal

[td=76]

Frequency

[td=61]

Percent

[td=83]

Cumulative
Frequency

[td=84]

Cumulative
Percent

[td=65]

4.9

[td=76]

74

[td=61]

14.12

[td=83]

74

[td=84]

14.12

[td=65]

5

[td=76]

142

[td=61]

27.10

[td=83]

216

[td=84]

41.22

[td=65]

8

[td=76]

164

[td=61]

31.30

[td=83]

380

[td=84]

72.52

[td=65]

12

[td=76]

111

[td=61]

21.18

[td=83]

491

[td=84]

93.70

[td=65]

20

[td=76]

29

[td=61]

5.53

[td=83]

520

[td=84]

99.24

[td=65]

41

[td=76]

4

[td=61]

0.76

[td=83]

524

[td=84]

100.00

地理2009

0操作168 ^#

另一个我个人感兴趣的问题是，来的早与收入有关系吗？答案是，没有任何关系。

[td=3,1,236]

Pearson Correlation Coefficients
Prob > |r| under H0: Rho=0
Number of Observations

[td=79]

[td=78]

Initial_age

[td=79]

Personal

[td=79]

Initial_age
Initial_age

[td=78]

1.00000

601

[td=79]

0.03370
0.4572
489

[td=79]

Personal

[td=78]

0.03370
0.4572
489

[td=79]

1.00000

524

地理2009

0操作169 ^#

另一个问题是来的时间与房产有关吗? 最明显是如果》30 岁以上来的，那么明显房产没有其他group 的多。大学与大学以上时间来的房产差不多，过半都是一套房。，高中来的分两级化，没有房产的多，有3套以上的也多。这个可能与来的时间长短有关。

[td=6,1,309]

Table of initial_year by NoRealEs

[td=85]

initial_year

[td=5,1,224]

NoRealEs(NoRealEs)

[td=85]

Frequency
Percent
Row Pct
Col Pct

[td=44]

0

[td=44]

1

[td=44]

2

[td=44]

3+

[td=47]

Total

[td=85]

College

[td=44]

24
10.17
27.91
39.34

[td=44]

39
16.53
45.35
34.82

[td=44]

14
5.93
16.28
38.89

[td=44]

9
3.81
10.47
33.33

[td=47]

86
36.44

[td=85]

Post_deg

[td=44]

25
10.59
22.32
40.98

[td=44]

57
24.15
50.89
50.89

[td=44]

18
7.63
16.07
50.00

[td=44]

12
5.08
10.71
44.44

[td=47]

112
47.46

[td=85]

high_school

[td=44]

10
4.24
32.26
16.39

[td=44]

12
5.08
38.71
10.71

[td=44]

3
1.27
9.68
8.33

[td=44]

6
2.54
19.35
22.22

[td=47]

31
13.14

[td=85]

other

[td=44]

2
0.85
28.57
3.28

[td=44]

4
1.69
57.14
3.57

[td=44]

1
0.42
14.29
2.78

[td=44]

0
0.00
0.00
0.00

[td=47]

7
2.97

[td=85]

Total

[td=44]

61
25.85

[td=44]

112
47.46

[td=44]

36
15.25

[td=44]

27
11.44

[td=47]

236
100.00

[td=6,1,309]

Frequency Missing = 423

地理2009

0操作170 ^#

房产如果按地区划分，哪里人房产多：湾区：一半多的人有一房， 1/5 有两房或多于两房。东部同湾区纽约两房以上分布变化多一些，差不多15% 有三房以上， 10% 有两房。整体看， 75% 的人最少一套房子，其中一套的占50%， 20 % 人有两套或三套。 25% 还没有买房。

地理2009

0操作171 ^#

东岸家庭有2娃数量稍微多与湾区与别的地方。
大部分的地区达到40% 有一个娃。

Giovanna

0操作172 ^#

另一个问题是来的时间与房产有关吗? 最明显是如果》30 岁以上来的，那么明显房产没有其他group 的多。大学与大学以上时间来的房产差不多，过半都是一套房。，高中来的分两级化，没有房产的多，有3套以上的也多。这个可能与来的时间长短有关。

[td=6,1,309]
Table of initial_year by NoRealEs

[td=85]
initial_year

[td=5,1,224]
NoRealEs(NoRealEs)

[td=85]
Frequency
Percent
Row Pct
Col Pct

[td=44]
0

[td=44]
1

[td=44]
2

[td=44]
3+

[td=47]
Total

[td=85]
College

[td=44]
24
10.17
27.91
39.34

[td=44]
39
16.53
45.35
34.82

[td=44]
14
5.93
16.28
38.89

[td=44]
9
3.81
10.47
33.33

[td=47]
86
36.44

[td=85]
Post_deg

[td=44]
25
10.59
22.32
40.98

[td=44]
57
24.15
50.89
50.89

[td=44]
18
7.63
16.07
50.00

[td=44]
12
5.08
10.71
44.44

[td=47]
112
47.46

[td=85]
high_school

[td=44]
10
4.24
32.26
16.39

[td=44]
12
5.08
38.71
10.71

[td=44]
3
1.27
9.68
8.33

[td=44]
6
2.54
19.35
22.22

[td=47]
31
13.14

[td=85]
other

[td=44]
2
0.85
28.57
3.28

[td=44]
4
1.69
57.14
3.57

[td=44]
1
0.42
14.29
2.78

[td=44]
0
0.00
0.00
0.00

[td=47]
7
2.97

[td=85]
Total

[td=44]
61
25.85

[td=44]
112
47.46

[td=44]
36
15.25

[td=44]
27
11.44

[td=47]
236
100.00

[td=6,1,309]
Frequency Missing = 423

地理2009 发表于 1/27/2016 1:34:26 AM [url=http://forums.huaren.us/showtopic.aspx?topicid=1962835&postid=70565583#70565583][/url]

赞你的同时，能做一个收入与年龄的关系吗？
默默期待中：）

dingdang~

0操作173 ^#

有意思，谢谢楼主，刚填了。

地理2009

0操作174 ^#

工资分布：因为取的最低值，所以不是最准的，但是基本可以看到大概趋势， IT 并没有高与别的行业也多，这个可能是各种和IT靠边都规划到其中的，不只是Programer。最后两条是最低，与最高工资。这个可以给大家一个范围 . 哈哈高收入职业 IT ，生化，医生，金融，自己开公司，还有个别发考题。

[td=7,1,417]

Analysis Variable : Personal

[td=62]

occup

[td=34]

N Obs

[td=26]

N

[td=73]

Mean

[td=73]

Std Dev

[td=73]

Minimum

[td=76]

Maximum

[td=62]

Graphic

[td=34]

1

[td=26]

1

[td=73]

4.9000000

[td=73]

.

[td=73]

4.9000000

[td=76]

4.9000000

[td=62]

IT

[td=34]

64

[td=26]

60

[td=73]

9.8666667

[td=73]

5.3913511

[td=73]

5.0000000

[td=76]

41.0000000

[td=62]

Lawyer

[td=34]

1

[td=26]

1

[td=73]

12.0000000

[td=73]

.

[td=73]

12.0000000

[td=76]

12.0000000

[td=62]

Pharma

[td=34]

11

[td=26]

10

[td=73]

9.3800000

[td=73]

4.4243769

[td=73]

4.9000000

[td=76]

20.0000000

[td=62]

PublicS

[td=34]

15

[td=26]

13

[td=73]

7.6692308

[td=73]

4.5497957

[td=73]

4.9000000

[td=76]

20.0000000

[td=62]

account

[td=34]

22

[td=26]

21

[td=73]

6.1761905

[td=73]

1.9232537

[td=73]

4.9000000

[td=76]

12.0000000

[td=62]

assista

[td=34]

2

[td=26]

2

[td=73]

8.4500000

[td=73]

5.0204581

[td=73]

4.9000000

[td=76]

12.0000000

[td=62]

beautif

[td=34]

13

[td=26]

2

[td=73]

6.4500000

[td=73]

2.1920310

[td=73]

4.9000000

[td=76]

8.0000000

[td=62]

businss

[td=34]

16

[td=26]

16

[td=73]

8.8562500

[td=73]

4.2276816

[td=73]

4.9000000

[td=76]

20.0000000

[td=62]

designe

[td=34]

1

[td=26]

1

[td=73]

5.0000000

[td=73]

.

[td=73]

5.0000000

[td=76]

5.0000000

[td=62]

doctor

[td=34]

2

[td=26]

2

[td=73]

20.0000000

[td=73]

0

[td=73]

20.0000000

[td=76]

20.0000000

[td=62]

finance

[td=34]

24

[td=26]

23

[td=73]

12.5173913

[td=73]

7.5536055

[td=73]

4.9000000

[td=76]

41.0000000

[td=62]

managem

[td=34]

7

[td=26]

7

[td=73]

8.4285714

[td=73]

5.3184316

[td=73]

5.0000000

[td=76]

20.0000000

[td=62]

marketi

[td=34]

3

[td=26]

3

[td=73]

7.0000000

[td=73]

1.7320508

[td=73]

5.0000000

[td=76]

8.0000000

[td=62]

other

[td=34]

4

[td=26]

4

[td=73]

7.4500000

[td=73]

3.3669967

[td=73]

4.9000000

[td=76]

12.0000000

[td=62]

profess

[td=34]

15

[td=26]

14

[td=73]

9.2000000

[td=73]

4.3858163

[td=73]

4.9000000

[td=76]

20.0000000

[td=62]

realtor

[td=34]

1

[td=26]

1

[td=73]

4.9000000

[td=73]

.

[td=73]

4.9000000

[td=76]

4.9000000

[td=62]

researc

[td=34]

28

[td=26]

28

[td=73]

6.7321429

[td=73]

2.5461741

[td=73]

4.9000000

[td=76]

12.0000000

[td=62]

self_em

[td=34]

4

[td=26]

3

[td=73]

22.0000000

[td=73]

18.0831413

[td=73]

5.0000000

[td=76]

41.0000000

[td=62]

statist

[td=34]

23

[td=26]

23

[td=73]

7.6521739

[td=73]

2.4607592

[td=73]

5.0000000

[td=76]

12.0000000

[td=62]

student

[td=34]

7

[td=26]

4

[td=73]

4.9000000

[td=73]

0

[td=73]

4.9000000

[td=76]

4.9000000

地理2009

0操作175 ^#

地区平均工资，湾区平均达到接近六位数了。最高工资出现在湾区，东岸，德州。个人认为如果是双职工家庭在湾区收入达到20 以上是正常的，所以也可以说贫困线是 20-25 万。：P 个人收入可以是10 -12.5 万。

[td=7,1,435]

Analysis Variable : Personal

[td=85]

Location

[td=34]

N Obs

[td=29]

N

[td=73]

Mean

[td=66]

Std Dev

[td=72]

Minimum

[td=76]

Maximum

[td=85]

BayArea

[td=34]

122

[td=29]

92

[td=73]

9.7195652

[td=66]

5.3860267

[td=72]

4.9000000

[td=76]

41.0000000

[td=85]

Canada

[td=34]

2

[td=29]

2

[td=73]

8.0000000

[td=66]

0

[td=72]

8.0000000

[td=76]

8.0000000

[td=85]

Chicago

[td=34]

27

[td=29]

25

[td=73]

7.6280000

[td=66]

2.8385031

[td=72]

4.9000000

[td=76]

12.0000000

[td=85]

China

[td=34]

2

[td=29]

2

[td=73]

14.0000000

[td=66]

8.4852814

[td=72]

8.0000000

[td=76]

20.0000000

[td=85]

EastCoast

[td=34]

165

[td=29]

136

[td=73]

8.7345588

[td=66]

4.8666877

[td=72]

4.9000000

[td=76]

41.0000000

[td=85]

Florida

[td=34]

9

[td=29]

7

[td=73]

5.4285714

[td=66]

1.1338934

[td=72]

5.0000000

[td=76]

8.0000000

[td=85]

LA

[td=34]

3

[td=29]

3

[td=73]

7.3000000

[td=66]

4.0706265

[td=72]

4.9000000

[td=76]

12.0000000

[td=85]

MiddleOther

[td=34]

58

[td=29]

47

[td=73]

7.5319149

[td=66]

4.0828436

[td=72]

4.9000000

[td=76]

20.0000000

[td=85]

NewYork

[td=34]

50

[td=29]

41

[td=73]

9.3536585

[td=66]

4.4382484

[td=72]

4.9000000

[td=76]

20.0000000

[td=85]

Other

[td=34]

9

[td=29]

7

[td=73]

5.8285714

[td=66]

1.4840421

[td=72]

4.9000000

[td=76]

8.0000000

[td=85]

OtherCountry

[td=34]

4

[td=29]

3

[td=73]

4.9333333

[td=66]

0.0577350

[td=72]

4.9000000

[td=76]

5.0000000

[td=85]

Seatle

[td=34]

18

[td=29]

13

[td=73]

8.2230769

[td=66]

2.4993845

[td=72]

4.9000000

[td=76]

12.0000000

[td=85]

Southother

[td=34]

11

[td=29]

10

[td=73]

7.2900000

[td=66]

2.8395813

[td=72]

4.9000000

[td=76]

12.0000000

[td=85]

Texas

[td=34]

44

[td=29]

37

[td=73]

8.5621622

[td=66]

6.1610944

[td=72]

4.9000000

[td=76]

41.0000000

[td=85]

WestCoast

[td=34]

45

[td=29]

39

[td=73]

7.0923077

[td=66]

2.6720172

[td=72]

4.9000000

[td=76]

12.0000000

地理2009

0操作176 ^#

工资分布与年龄层： 20-29 岁还是主要集中在 0 - 12 万，其中主要是5到12 万这一段，超过12 万的达到15% ，少于5万占1/4。 32% 在5-8 万中。

30-39 小与5万明显下降，只有10%， 32% 在 8-12 万中，所以随着年龄增加，薪水会张一些。 12万以上达到30%，不到1/3 的人在12 万以上。

40-49 更多人达到 8 万以上， 12万以上达到33%， 1/3 的人达到 12 万以上。 40-49 人数太少，不做比较。目前数据只能做到这个啦。

[td=8,1,441]

Table of Age_group by Personal

[td=160]

Age_group(Age_group)

[td=7,1,280]

Personal

[td=160]

Frequency
Percent
Row Pct
Col Pct

[td=39]

4.9

[td=39]

5

[td=39]

8

[td=39]

12

[td=39]

20

[td=39]

41

[td=47]

Total

[td=160]

20-29

[td=39]

32
6.11
24.43
43.24

[td=39]

43
8.21
32.82
30.28

[td=39]

34
6.49
25.95
20.73

[td=39]

19
3.63
14.50
17.12

[td=39]

2
0.38
1.53
6.90

[td=39]

1
0.19
0.76
25.00

[td=47]

131
25.00

[td=160]

30-39

[td=39]

38
7.25
11.69
51.35

[td=39]

86
16.41
26.46
60.56

[td=39]

105
20.04
32.31
64.02

[td=39]

74
14.12
22.77
66.67

[td=39]

20
3.82
6.15
68.97

[td=39]

2
0.38
0.62
50.00

[td=47]

325
62.02

[td=160]

40-49

[td=39]

4
0.76
6.15
5.41

[td=39]

13
2.48
20.00
9.15

[td=39]

23
4.39
35.38
14.02

[td=39]

17
3.24
26.15
15.32

[td=39]

7
1.34
10.77
24.14

[td=39]

1
0.19
1.54
25.00

[td=47]

65
12.40

[td=160]

50-59

[td=39]

0
0.00
0.00
0.00

[td=39]

0
0.00
0.00
0.00

[td=39]

2
0.38
66.67
1.22

[td=39]

1
0.19
33.33
0.90

[td=39]

0
0.00
0.00
0.00

[td=39]

0
0.00
0.00
0.00

[td=47]

3
0.57

[td=160]

Total

[td=39]

74
14.12

[td=39]

142
27.10

[td=39]

164
31.30

[td=39]

111
21.18

[td=39]

29
5.53

[td=39]

4
0.76

[td=47]

524
100.00

[td=8,1,441]

Frequency Missing = 135

年龄层平均工资：

[td=7,1,430]

Analysis Variable : Personal

[td=80]

Age_group

[td=34]

N Obs

[td=29]

N

[td=73]

Mean

[td=66]

Std Dev

[td=72]

Minimum

[td=76]

Maximum

[td=80]

20-29

[td=34]

168

[td=29]

131

[td=73]

7.2732824

[td=66]

4.2120126

[td=72]

4.9000000

[td=76]

41.0000000

[td=80]

30-39

[td=34]

400

[td=29]

325

[td=73]

8.6960000

[td=66]

4.7218120

[td=72]

4.9000000

[td=76]

41.0000000

[td=80]

40-49

[td=34]

80

[td=29]

65

[td=73]

10.0553846

[td=66]

5.9290606

[td=72]

4.9000000

[td=76]

41.0000000

[td=80]

50-59

[td=34]

4

[td=29]

3

[td=73]

9.3333333

[td=66]

2.3094011

[td=72]

8.0000000

[td=76]

12.0000000

地理2009

0操作177 ^#

最后大家感兴趣的湾区个人工资分布图，　鉴于大部分是美女们，　工资可能会低于湾区马工们的工资（猜的）。　５５％　在８万以上，　３０％　在１２　万以上.　８－２０　万有　４８％。　　

[td=5,1,431]

Personal_annual

[td=126]

Personal_annual

[td=76]

Frequency

[td=61]

Percent

[td=83]

Cumulative
Frequency

[td=84]

Cumulative
Percent

[td=126]

$120000 - $200000

[td=76]

27

[td=61]

23.08

[td=83]

27

[td=84]

23.08

[td=126]

$200000 - $400000

[td=76]

8

[td=61]

6.84

[td=83]

35

[td=84]

29.91

[td=126]

$400000+

[td=76]

1

[td=61]

0.85

[td=83]

36

[td=84]

30.77

[td=126]

$50000 - $80000

[td=76]

18

[td=61]

15.38

[td=83]

54

[td=84]

46.15

[td=126]

$80000 - $120000

[td=76]

30

[td=61]

25.64

[td=83]

84

[td=84]

71.79

[td=126]

49000

[td=76]

8

[td=61]

6.84

[td=83]

92

[td=84]

78.63

[td=126]

NotWorking

[td=76]

25

[td=61]

21.37

[td=83]

117

[td=84]

100.00

plutol

0操作178 ^#

调查中如果再加入一下价值取向的选择题就可以做个回归建模行了结论应该更有意思是个好的社会学调查

xxlyjsw

0操作179 ^#

工资分布：因为取的最低值，所以不是最准的，但是基本可以看到大概趋势， IT 并没有高与别的行业也多，这个可能是各种和IT靠边都规划到其中的，不只是Programer。最后两条是最低，与最高工资。这个可以给大家一个范围 . 哈哈高收入职业 IT ，生化，医生，金融，自己开公司，还有个别发考题。

[td=7,1,417]
Analysis Variable : Personal

[td=62]
occup

[td=34]
N Obs

[td=26]
N

[td=73]
Mean

[td=73]
Std Dev

[td=73]
Minimum

[td=76]
Maximum

[td=62]
Graphic

[td=34]
1

[td=26]
1

[td=73]
4.9000000

[td=73]
.

[td=73]
4.9000000

[td=76]
4.9000000

[td=62]
IT

[td=34]
64

[td=26]
60

[td=73]
9.8666667

[td=73]
5.3913511

[td=73]
5.0000000

[td=76]
41.0000000

[td=62]
Lawyer

[td=34]
1

[td=26]
1

[td=73]
12.0000000

[td=73]
.

[td=73]
12.0000000

[td=76]
12.0000000

[td=62]
Pharma

[td=34]
11

[td=26]
10

[td=73]
9.3800000

[td=73]
4.4243769

[td=73]
4.9000000

[td=76]
20.0000000

[td=62]
PublicS

[td=34]
15

[td=26]
13

[td=73]
7.6692308

[td=73]
4.5497957

[td=73]
4.9000000

[td=76]
20.0000000

[td=62]
account

[td=34]
22

[td=26]
21

[td=73]
6.1761905

[td=73]
1.9232537

[td=73]
4.9000000

[td=76]
12.0000000

[td=62]
assista

[td=34]
2

[td=26]
2

[td=73]
8.4500000

[td=73]
5.0204581

[td=73]
4.9000000

[td=76]
12.0000000

[td=62]
beautif

[td=34]
13

[td=26]
2

[td=73]
6.4500000

[td=73]
2.1920310

[td=73]
4.9000000

[td=76]
8.0000000

[td=62]
businss

[td=34]
16

[td=26]
16

[td=73]
8.8562500

[td=73]
4.2276816

[td=73]
4.9000000

[td=76]
20.0000000

[td=62]
designe

[td=34]
1

[td=26]
1

[td=73]
5.0000000

[td=73]
.

[td=73]
5.0000000

[td=76]
5.0000000

[td=62]
doctor

[td=34]
2

[td=26]
2

[td=73]
20.0000000

[td=73]
0

[td=73]
20.0000000

[td=76]
20.0000000

[td=62]
finance

[td=34]
24

[td=26]
23

[td=73]
12.5173913

[td=73]
7.5536055

[td=73]
4.9000000

[td=76]
41.0000000

[td=62]
managem

[td=34]
7

[td=26]
7

[td=73]
8.4285714

[td=73]
5.3184316

[td=73]
5.0000000

[td=76]
20.0000000

[td=62]
marketi

[td=34]
3

[td=26]
3

[td=73]
7.0000000

[td=73]
1.7320508

[td=73]
5.0000000

[td=76]
8.0000000

[td=62]
other

[td=34]
4

[td=26]
4

[td=73]
7.4500000

[td=73]
3.3669967

[td=73]
4.9000000

[td=76]
12.0000000

[td=62]
profess

[td=34]
15

[td=26]
14

[td=73]
9.2000000

[td=73]
4.3858163

[td=73]
4.9000000

[td=76]
20.0000000

[td=62]
realtor

[td=34]
1

[td=26]
1

[td=73]
4.9000000

[td=73]
.

[td=73]
4.9000000

[td=76]
4.9000000

[td=62]
researc

[td=34]
28

[td=26]
28

[td=73]
6.7321429

[td=73]
2.5461741

[td=73]
4.9000000

[td=76]
12.0000000

[td=62]
self_em

[td=34]
4

[td=26]
3

[td=73]
22.0000000

[td=73]
18.0831413

[td=73]
5.0000000

[td=76]
41.0000000

[td=62]
statist

[td=34]
23

[td=26]
23

[td=73]
7.6521739

[td=73]
2.4607592

[td=73]
5.0000000

[td=76]
12.0000000

[td=62]
student

[td=34]
7

[td=26]
4

[td=73]
4.9000000

[td=73]
0

[td=73]
4.9000000

[td=76]
4.9000000

地理2009 发表于 1/27/2016 1:52:45 AM [url=http://forums.huaren.us/showtopic.aspx?topicid=1962835&postid=70565725#70565725][/url]

楼主终于等到做数据分析的MM了。。。。爱死你了！分析的简直不能更赞

kittycy

0操作180 ^#

发散：消费能力不强的人也喜欢住纽约，或者也能找到途径survive？或者纽约不像湾区，有华人胜任的行业，所以工资分布比较分散？

xxlyjsw 发表于 1/26/2016 9:25:15 PM [url=http://forums.huaren.us/showtopic.aspx?topicid=1962835&postid=70562714#70562714][/url]

纽约已婚有孩子的家庭大都住到东岸其他去了吧。　东岸其他人那么多其实很多就是纽约的就业人口。