这篇博客详细介绍了前端作业FE-HW1的内容，包括两个问题。Problem 1涉及统计分析、直方图绘制、行业统计以及变量转换。Problem 2涵盖了新变量生成、分组统计、缺失值检查、离群值处理和箱形图绘制。每个问题下又分为多个子任务，展示了数据处理的不同方面。
FE-HW1
文章目录
Problem 1
sysuse nlsw88.dta, clear //调入第一问的数据
1-(1)
1-(1) 统计age grade wage hours ttl_exp tenure的平均值、标准差、中位数、最小值和最大值并输出为Excel 表格可以接受的格式
logout, save("$Out\Table01") excel replace: ///
tabstat age grade wage hours ttl_exp tenure, ///
stat(mean sd p50 min max) format(%7.2f) column(statistic)
** variable | mean sd p50 min max
**-------------+--------------------------------------------------
** age | 39.15 3.06 39.00 34.00 46.00
** grade | 13.10 2.52 12.00 0.00 18.00
** wage | 7.77 5.76 6.27 1.00 40.75
** hours | 37.22 10.51 40.00 1.00 80.00
** ttl_exp | 12.53 4.61 13.13 0.12 28.88
** tenure | 5.98 5.51 3.83 0.00 25.92
**----------------------------------------------------------------
**
1-(2)
1-(2) 产生新变量
gen age2 = age^2 //生成新变量age2等于age的平方
gen ln_wage = ln(wage) //生成新变量ln_wage等于wage的自然对数
egen wage_mean = mean(wage) //生成新变量wage_mean来表示wage的均值
gen dum = (wage>wage_mean) //生产新逻辑变量dum
1-(3)
1-(3) 绘制 ttl_exp 变量的直方图和密度函数图
histogram ttl_exp //绘制 ttl_exp 变量的直方图
graph export His_ttl.png
kdensity ttl_exp //绘制 ttl_exp 变量的密度函数图
graph export Kendi_ttl.png
由图形观察可知，ttl_exp的样本总体是一个右侧拖一个尾巴是正偏态分布
1-(4)
1-(4) 对industry做如下统计
1-(4)-(1)
1-(4)-(1) 每个行业的观察值个数
logout, save("$Out\1-(4)-1") excel replace: ///
tab industry
* tab industry
*
* industry | Freq. Percent Cum.
*------------------------+-----------------------------------
* Ag/Forestry/Fisheries | 17 0.76 0.76
* Mining | 4 0.18 0.94
* Construction | 29 1.30 2.24
* Manufacturing | 367 16.44 18.68
* Transport/Comm/Utility | 90 4.03 22.72
* Wholesale/Retail Trade | 333 14.92 37.63
*Finance/Ins/Real Estate | 192 8.60 46.24
* Business/Repair Svc | 86 3.85 50.09
* Personal Services | 97 4.35 54.44
* Entertainment/Rec Svc | 17 0.76 55.20
* Professional Services | 824 36.92 92.11
* Public Administration | 176 7.89 100.00
*------------------------+-----------------------------------
* Total | 2,232 100.00
1-(4)-(2)
1-(4)-(2) 各个行业妇女的平均工资(wage)、平均工作时数(hours)和平均年龄(age)
logout, save("$Out\1-(4)-2") excel replace: ///
bysort industry: tabstat wage hours age, ///
stat(mean) //分组统计
*-> industry = Ag/Forestry/Fisheries *-> industry = Mining
* stats | wage hours age * stats | wage hours age
*---------+------------------------------ *---------+---------------------------
* mean | 5.621121 34.47059 39.94118 * mean | 15.34959 40 37.25
*---------------------------------------- *-------------------------------------
*-> industry = Construction *-> industry = Manufacturing
* stats | wage hours age * stats | wage hours age
*---------+------------------------------ *---------+---------------------------
* mean | 7.564934 35.65517 38.62069 * mean | 7.501578 40.89373 38.9891
*---------------------------------------- *-------------------------------------
*-> industry = Transport/Comm/Utility *-> industry = Wholesale/Retail Trade
* stats | wage hours age * stats | wage hours age
*---------+------------------------------ *---------+---------------------------
* mean | 11.44335 39.85556 39.27778 * mean | 6.125897 35.24699 39.28829
*---------------------------------------- *-------------------------------------
*-> industry = Finance/Ins/Real Estate *-> industry = Business/Repair Svc
* stats | wage hours age * stats | wage hours age
*---------+------------------------------ *---------+---------------------------
* mean | 9.843174 38.51563 38.82813 * mean | 7.51579 33.15116 38.73256
*---------------------------------------- *-------------------------------------
*-> industry = Personal Services *-> industry = Entertainment/Rec Svc
* stats | wage hours age * stats | wage hours age
*---------+------------------------------ *---------+---------------------------
* mean | 7.871186 36.71655 39.23908 * mean | 6.724409 34.35294 40.11765
*---------------------------------------- *-------------------------------------
*-> industry = Professional Services *-> industry = Public Administration
* stats | wage hours age * stats | wage hours age
*---------+------------------------------ *---------+---------------------------
* mean | 7.871186 36.71655 39.23908 * mean | 9.148407 38.54545 39.15909
*---------------------------------------- *-------------------------------------
*-> industry = .
* stats | wage hours age
*---------+------------------------------
* mean | 5.13411 35 40.28571
*----------------------------------------
1-(4)-(3)
1-(4)-(3) 统计不同行业中白种人、黑种人和其他人种的比例
logout, save("$Out\1-(4)-3") excel replace: ///
tab industry race, col //分组统计频率
* | race
* industry | white black other | Total
*----------------------+---------------------------------+----------
*Ag/Forestry/Fisheries | 13 4 0 | 17
* | 0.80 0.69 0.00 | 0.76
*----------------------+---------------------------------+----------
* Mining | 4 0 0 | 4
* | 0.25 0.00 0.00 | 0.18
*----------------------+---------------------------------+----------
* Construction | 24 4 1 | 29
* | 1.48 0.69 3.85 | 1.30
*----------------------+---------------------------------+----------
* Manufacturing | 229 134 4 | 367
* | 14.07 23.14 15.38 | 16.44
*----------------------+---------------------------------+----------
*Transport/Comm/Utilit | 62 27 1 | 90
* | 3.81 4.66 3.85 | 4.03
*----------------------+---------------------------------+----------
*Wholesale/Retail Trad | 267 66 0 | 333
* | 16.41 11.40 0.00 | 14.92
*----------------------+---------------------------------+----------
*Finance/Ins/Real Esta | 165 25 2 | 192
* | 10.14 4.32 7.69 | 8.60
*----------------------+---------------------------------+----------
* Business/Repair Svc | 64 20 2 | 86
* | 3.93 3.45 7.69 | 3.85
*----------------------+---------------------------------+----------
* Personal Services | 51 45 1 | 97
* | 3.13 7.77 3.85 | 4.35
*----------------------+---------------------------------+----------
*Entertainment/Rec Svc | 14 3 0 | 17
* | 0.86 0.52 0.00 | 0.76
*----------------------+---------------------------------+----------
*Professional Services | 616 197 11 | 824
* | 37.86 34.02 42.31 | 36.92
*----------------------+---------------------------------+----------
*Public Administration | 118 54 4 | 176
* | 7.25 9.33 15.38 | 7.89
*----------------------+---------------------------------+----------
* Total | 1,627 579 26 | 2,232
* | 100.00 100.00 100.00 | 100.00
1-(5)
1-(5) 为race变量做标签
label define race 1 "白种人" 2 "黑种人" 3 "其它"
label value race race //将值签race赋给变量race
1-(6)
1-(6) 续别变量转类别变量
1-(6)-(1)
1-(6)-(1) 按规则产生新变量G_age
gen G_age = 1
replace G_age = 2 if(37<age)
replace G_age = 3 if(42<age)
1-(6)-(2)
1-(6)-(2) 为G_age变量添加数字-文字对应表
label define G_age 1 "37岁以下" 2 "38岁到42岁之间" 3 "43岁以上"
label value G_age G_age //将值签G_age赋给变量G_age
Problem 2
webuse "nhanes2f.dta", clear //调入第二问的数据
2-(1)
2-(1)根据要求生成新变量av_height
bysort race : egen av_height = mean(height)
2-(2)
2-(2)根据要求生成新变量sd_height
egen sd_height = std(height)
2-(3)
2-(3)先依次求出按各个变量分组的组态别数，可能的组合个数为三者之积
bysort sex : count //按sex分类计数
*---------------------------------------------------------------
*-> sex = Male
*4,909
*---------------------------------------------------------------
*-> sex = Female
*5,428
//则按sex分类有2个组态别
bysort race : count //按race分类计数
*---------------------------------------------------------------
*-> race = White
* 9,051
*---------------------------------------------------------------
*-> race = Black
* 1,086
*---------------------------------------------------------------
*-> race = Other
* 200
//按race分类有3个组态别
bysort region : count //按region分类计数
*-> region = NE
*2,086
*---------------------------------------------------------------
*-> region = MW
*2,773
*---------------------------------------------------------------
*-> region = S
*2,853
*---------------------------------------------------------------
*-> region = W
*2,625
//按region分类有4个组态别
* 所以共有2*3*4=24个组态别，接下来输出每个组态别的个数
logout, save("$Out\2-(3)") excel replace: ///
bysort sex race region : count
* bysort sex race region : count
*---------------------------------------------------------------
*-> sex = Male, race = White, region = NE
*957
*---------------------------------------------------------------
*-> sex = Male, race = White, region = MW
*1,170
*---------------------------------------------------------------
*-> sex = Male, race = White, region = S
*1,076
*---------------------------------------------------------------
*-> sex = Male, race = White, region = W
*1,103
*---------------------------------------------------------------
*-> sex = Male, race = Black, region = NE
*51
*---------------------------------------------------------------
*-> sex = Male, race = Black, region = MW
*133
*---------------------------------------------------------------
*-> sex = Male, race = Black, region = S
*247
*---------------------------------------------------------------
*-> sex = Male, race = Black, region = W
*69
*---------------------------------------------------------------
*-> sex = Male, race = Other, region = NE
*5
*---------------------------------------------------------------
*-> sex = Male, race = Other, region = MW
*7
*---------------------------------------------------------------
*-> sex = Male, race = Other, region = S
*9
*---------------------------------------------------------------
*-> sex = Male, race = Other, region = W
*82
*---------------------------------------------------------------
*-> sex = Female, race = White, region = NE
*1,012
*---------------------------------------------------------------
*-> sex = Female, race = White, region = MW
*1,291
*---------------------------------------------------------------
*-> sex = Female, race = White, region = S
*1,208
*---------------------------------------------------------------
*-> sex = Female, race = White, region = W
*1,234
*---------------------------------------------------------------
*-> sex = Female, race = Black, region = NE
*55
*---------------------------------------------------------------
*-> sex = Female, race = Black, region = MW
*162
*---------------------------------------------------------------
*-> sex = Female, race = Black, region = S
*301
*---------------------------------------------------------------
*-> sex = Female, race = Black, region = W
*68
*---------------------------------------------------------------
*-> sex = Female, race = Other, region = NE
*6
*---------------------------------------------------------------
*-> sex = Female, race = Other, region = MW
*10
*---------------------------------------------------------------
*-> sex = Female, race = Other, region = S
*12
*---------------------------------------------------------------
*-> sex = Female, race = Other, region = W
*69
2-(4)
2-(4)首先查看各个变量缺漏值的个数
misstable sum tcresult tgresult hdresult corpuscl health sizplace
* misstable sum tcresult tgresult hdresult corpuscl health sizplace
* Obs<.
* +------------------------------
* | | Unique
* Variable | Obs=. Obs>. Obs<. | values Min Max
*-------------+--------------------------------+------------------------------
* tgresult | 5,293 5,044 | 423 16 2238
* hdresult | 1,629 8,708 | 108 15 187
* corpuscl | 89 10,248 | 411 58.3 125.9
* health | 2 10,335 | 5 1 5
*-----------------------------------------------------------------------------
//其中tcresult因为没有缺漏值没有没有显示在Variable中
//接下来删除缺漏值
dropmiss tgresult hdresult corpuscl,obs force any
//删除之后检查删除效果
misstable sum tgresult hdresult corpuscl
* misstable sum tgresult hdresult corpuscl
*(variables nonmissing or string)
2-(5)
2-(5)首先求出height的第25百分位和第75百分位，再计算四分位间距，最后得到上下界
tabstat height , stat(p25 p75)
* tabstat height , stat(p25 p75)
*
* variable | p25 p75
*-------------+--------------------
* height | 160.699 175.098
*----------------------------------
*第25百分位为160.699，第75百分位为175.098，所以四分位间距为14.399
*上界为Q3+1.5*IQR=196.697,下界为Q1-1.5*IQR=139.101
2-(6)
2-(6)绘制height和weight的箱形图
graph box height
graph box weight
观察图形可知，身高有巨人和侏儒这样的离群值，而体重只有超重的离群值而没有超轻的离群值。
2-(7)
2-(7) 生成一个新变量height_w，对height进行缩尾处理
winsor2 height, suffix(_w) cuts(1 99)
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