Introduction to Stata–Handout3:Panel Data
Hayley Fisher
1December2010
Key reference:Cameron and Trivedi(2009),chapter8.
1Data used in this lecture
This lecture uses data from the?rst four waves of the British Household Panel Survey(BHPS).I will make the relevant source?les temporarily available on my website but cannot host them there permanently.You can get the full set of?les from the ESDS(https://www.doczj.com/doc/6014997910.html,/?ndingData/bhps.asp).If you want to learn more about the BHPS and how to use it in Stata,I recommend the BHPS introductory courses provided by the UK Longitudinal Studies Centre(ULSC)at the University of Essex–details and course materials are available at https://www.doczj.com/doc/6014997910.html,/survey/bhps/courses.These notes have been loosely based on parts of their course.
2The British Household Panel Survey
The BHPS began in1991and has interviewed its initial sample,and additional household members,every year since then.5,500households were selected initially,with additional samples of Scotland,Wales and Northern Ireland added since.Currently17waves of the survey are available.For a full description of the survey see Taylor,Brice,Buck and Prentice-Lane(2009).
Longitudinal datasets such as the BHPS are rarely provided in a format that is straightforward to read into Stata and start working with.The BHPS is available in a number of formats including Stata,but as a series of?les containing di?erent variables,split by year and di?erent parts of the survey to give manageable ?le sizes.I am using the‘individual response’and‘household response’?les from the?rst four waves of the survey.A substantial part of this lecture will be devoted to putting together a panel from these datasets.
2.1Assembling a cross section using individual and household data
Start your do-?le to assemble your dataset by de?ning a macro for the folder in which the original data?les are stored.This makes it easy to alter the folder referenced if necessary in future.Here I use a global macro: global dir BHPS
To recall a global macro we pre?x it with a$sign–so here$dir.
We could simply read in the entire?rst?le in question(aindresp),but this is a large dataset with many variables.Instead,we can load in just speci?c variables.We need to look at the codebook accompanying the dataset to choose the variables(alternatively look at the online codebooks at https://www.doczj.com/doc/6014997910.html,/ survey/bhps/documentation/volume-b-codebooks).We read in the speci?c variables by typing:
use ahid apno asex aage pid amastat ahgspn aqfachi afiyr afiyrl using$dir/aindresp Note that all variables except pid have the pre?x a.This is a convention in the BHPS data?les–all ?les and variables associated with wave1have the pre?x a,for wave2it is b and so on.Let’s describe the data to see what has been loaded here.
1
.describe
Contains data from BHPS/aindresp.dta
obs:10,264
vars:10
size:348,976(99.9%of memory free)
----------------------------------------------------------------------------
storage display value
variable name type format label variable label
----------------------------------------------------------------------------
ahid long%12.0g household identification number
apno byte%8.0g person number
asex byte%8.0g asex sex
pid long%12.0g cross-wave person identifier
amastat byte%8.0g amastat marital status
ahgspn byte%8.0g ahgspn pno of spouse/partner
aage byte%8.0g aage age at date of interview
aqfachi byte%8.0g aqfachi highest academic qualification
afiyrl double%10.0g afiyrl annual labour income(1.9.90-1.9.91)
afiyr double%10.0g afiyr annual income(1.9.90-1.9.91)
----------------------------------------------------------------------------
Sorted by:
Three variables here are vital for the construction of our panel dataset.ahid is a household identi?cation number which we will use to match data from the household?le,and apno is a person identi?cation number within a given household.This can be used in combination with,for example,ahgspan to match couples together.pid is a cross-wave person identi?er–it has no a pre?x since it matches the same variable in all waves–this connects people over time.We also have data on individuals’sex,age,academic quali?cations, labour income and total income.
We are going to merge in data from the household?le,so we need to sort the individual data by the household identi?cation number and save it.
.sort ahid
.save aind,replace
Then we load data from the household response?le–hhresp.dta.
use ahid atenure ahhsize ankids afihhyr using$dir/ahhresp
.describe
Contains data from BHPS/ahhresp.dta
obs:5,511
vars:5
size:104,709(99.9%of memory free)
--------------------------------------------------------------------------------------storage display value
variable name type format label variable label
--------------------------------------------------------------------------------------ahid long%12.0g household identification number
ahhsize byte%8.0g ahhsize number of persons in household
ankids byte%8.0g ankids number of children in household
atenure byte%8.0g atenure housing tenure
afihhyr double%10.0g afihhyr annual household income(1.9.90-1.9.91)
--------------------------------------------------------------------------------------Sorted by:
2
This gives household ID numbers and data on household size,number of children,how housing is owned and total household income.We see there are5,511observations(as opposed to10,264from the individual dataset).After sorting by household ID we can merge the two datasets together:
.merge ahid using aind
variable ahid does not uniquely identify observations in aind.dta
.tabulate_merge
_merge|Freq.Percent Cum.
------------+-----------------------------------
1|60.060.06
3|10,26499.94100.00
------------+-----------------------------------
Total|10,270100.00
.keep if_merge==3
(6observations deleted)
.drop_merge
We see that there are6observations for which there is no individual data,just household data.I drop these.
Another useful command for describing data is codebook.This produces a codebook based on what is in Stata.I have extracted the codebook from my log?le and posted it on my website for reference.
Having saved the dataset,we can do some analysis with this cross section.First,recode the missing values.Page A3-14of Taylor et al.(2009)outlines the way missing values are handled in these data?les–any negative values are in fact missing.We can recode these easily together using mvdecode:
.mvdecode_all,mv(-9/-1)
atenure:17missing values generated
ahgspn:1missing value generated
aqfachi:371missing values generated
afiyrl:352missing values generated
afiyr:352missing values generated
Here_all can be replaced with a list of variables.Having created the necessary variables,simple cross section regressions can be performed.Here I show that the xi pre?x can be used to create interaction terms as well as a series of category dummies.
.generate married=amastat==1
.generate age2=aage^2
.generate lwages=log(afiyrl)
(3937missing values generated)
.xi:regress lwages aage age2i.asex*i.married ankids i.aqfachi,vce(robust)
i.asex_Iasex_1-2(naturally coded;_Iasex_1omitted)
i.married_Imarried_0-1(naturally coded;_Imarried_0omitted)
i.asex*i.marr~d_IaseXmar_#_#(coded as above)
i.aqfachi_Iaqfachi_1-7(naturally coded;_Iaqfachi_1omitted)
Linear regression Number of obs=6318
F(12,6305)=246.80
Prob>F=0.0000
R-squared=0.3383
Root MSE=.92103
------------------------------------------------------------------------------
|Robust
lwages|Coef.Std.Err.t P>|t|[95%Conf.Interval]
3
-------------+----------------------------------------------------------------aage|.1932962.007231326.730.000.1791205.2074719
age2|-.0023262.0000903-25.750.000-.0025033-.0021491 _Iasex_2|-.3133317.0411562-7.610.000-.3940119-.2326515
_Imarried_1|.4771106.039076512.210.000.4005075.5537138
_IaseXmar_~1|-.7551111.0507519-14.880.000-.854602-.6556201 ankids|-.2082833.0148482-14.030.000-.2373909-.1791756
_Iaqfachi_2|-.1241103.0967663-1.280.200-.3138051.0655845
_Iaqfachi_3|-.1621818.0964851-1.680.093-.3513254.0269619
_Iaqfachi_4|-.3929866.0912033-4.310.000-.5717762-.2141971
_Iaqfachi_5|-.5157779.0899938-5.730.000-.6921963-.3393595
_Iaqfachi_6|-.5263538.1001379-5.260.000-.7226582-.3300494
_Iaqfachi_7|-.7906337.0903131-8.750.000-.967678-.6135893 _cons| 5.916767.167703235.280.000 5.588011 6.245522
------------------------------------------------------------------------------
Here_Iasex_2gives the e?ect of being female,_Imarried_1the e?ect of being married for men,and _IaseXmar_~1the di?erence in the e?ect of being married on wages between women and men.
2.2Assembling a two wave panel
Panel data can be in two formats–long or wide.Wide data stores each variable separately for each wave, so only has one observation for each individual:
PID inc1inc2inc3inc4
2600660700750
3250280200210
4150190250300
Long data stores all observations of a variable,for example income,in the same variable,and has a wave variable and multiple observations for each individual:
PID wave inc
12210
13220
14250
21600
22660
23700
24750
To use the panel data features in Stata you need to have your data in long format.If your data is in wide format you can recon?gure it using the reshape command.
To put together a two wave panel in long format we need to extract the same data for wave2–with pre?x b.This is done as above:
.use bhid bpno bsex bage pid bmastat bhgspn bqfachi bfiyr bfiyrl using$dir/bindresp
.sort bhid
.save bind,replace
file bind.dta saved
.use bhid btenure bhhsize bnkids bfihhyr using$dir/bhhresp
.sort bhid
4
.merge bhid using bind
variable bhid does not uniquely identify observations in bind.dta
.keep if_merge==3
(2observations deleted)
.drop_merge
.save wave2,replace
file wave2.dta saved
There are two other steps to take to both the wave1and wave2datasets constructed here–to add a wave variable(just using generate,and to remove the pre?x which is done using the command renpfix.
.generate wave=2
.renpfix b
To combine the two?les we use the append command.1
.use wave1
.generate wave=1
.renpfix a
.append using wave2
Listing the?rst20observations shows that we now have a long panel dataset with two time periods.
.sort pid wave
.list pid wave hid sex age mastat in1/20,clean
pid wave hid sex age mastat
1.1000225111000209female91never ma
2.1000449111000381male28never ma
3.1000449122000148male29never ma
4.1000452111000381male26never ma
5.1000452122000148male27never ma
6.1000785711000667female57widowed
7.1000785722000296female59widowed
8.1001457811001221female54married
9.1001457822000369female55married
10.1001460811001221male57married
11.1001460822000369male58married
12.1001681311001418male36married
13.1001681322000504male37married
14.1001684811001418female32married
15.1001684822000504female33married
16.1001793311001507female49married
17.1001793322000717female49married
18.1001796811001507male46married
19.1001796822000717male46married
20.1001905711001604female59never ma
1To create a wide panel we would not remove the pre?xes and would use merge to combine the datasets.
5
2.3Creating a longer panel
When performing the same operations on several waves of data,we can write do-?les more e?ciently using the foreach and forvalues loop commands.Here I use foreach to perform the same commands just substituting the wave pre?x each time.2The command to extract all of the data is shown below.
foreach w in a b c d{
use‘w’hid‘w’pno‘w’sex‘w’age pid‘w’mastat‘w’hgspn‘w’qfachi‘w’fiyr‘w’fiyrl/// using$dir/‘w’indresp
sort‘w’hid
save‘w’ind,replace
clear
use‘w’hid‘w’tenure‘w’hhsize‘w’nkids‘w’fihhyr using$dir/‘w’hhresp
sort‘w’hid
merge‘w’hid using‘w’ind
keep if_merge==3
drop_merge
renpfix‘w’
generate wave=index("abcd","‘w’")
save wave‘w’,replace
}
We start the forvalues comman by de?ning what should be replaced in each iteration of the loop–in this case w,and giving the list of values to substitute(a,b,c,d for the?rst4waves).We then write out the code substituting‘w’where the pre?x would normally be.This reproduces the steps we went through above. The new function used here is index–this returns the position of‘w’in the list abcd and so generates the wave variable.
After this code has been run,we can use a similar loop to append the?les together,and to delete the
?les created in the process:
foreach w in a b c{
append using wave‘w’
}
compress
save BHPS,replace
foreach w in a b c d{
capture erase wave‘w’.dta
capture erase‘w’ind.dta
}
Note that compress ensures that the data is being stored as e?ciently as possible.Sorting and listing the dataset produced shows:
.sort pid wave
.list pid wave hid sex age mastat in1/20,clean
pid wave hid sex age mastat
1.1000225111000209female91never ma
2.1000449111000381male28never ma
3.1000449122000148male29never ma
2The forvalues command would be used when you want to loop over numbers rather than letters or variables.
6
4.1000452111000381male26never ma
5.1000452122000148male27never ma
6.1000452133000192male28never ma
7.1000785711000667female57widowed
8.1000785722000296female59widowed
9.1000785733000257female59widowed
10.1001457811001221female54married
11.1001457822000369female55married
12.1001457833000389female56married
13.1001460811001221male57married
14.1001460822000369male58married
15.1001460833000389male59married
16.1001681311001418male36married
17.1001681322000504male37married
18.1001681333000508male37married
19.1001681344000307male39married
20.1001684811001418female32married
This shows a panel in long format.Note that age mostly increases by one year between waves for each individual(the age variable here is age at interview date which can vary),whilst sex is constant.
In order to perform analysis exploiting the panel dimension of the dataset we must declare the data to be a panel–we do this using xtset,and declaring the panel variable(here pid)and time variable(here wave).Note that the panel variable and time variable must together uniquely identify every observation in the dataset.
.xtset pid wave
panel variable:pid(unbalanced)
time variable:wave,1to4,but with gaps
delta:1unit
This shows that we have an unbalanced panel–as seen in the list above we do not have an observation for every person in every time period.Some useful commands to investigate panel data are xtdescribe,xtsum and xttrans:
.xtdescribe
pid:10002251,10004491,...,47737689n=12350
wave:1,2,...,4T=4
Delta(wave)=1unit
Span(wave)=4periods
(pid*wave uniquely identifies each observation)
Distribution of T_i:min5%25%50%75%95%max
1124444 Freq.Percent Cum.|Pattern
---------------------------+---------
764361.8961.89|1111
10098.1770.06| 1...
679 5.5075.55|11..
596 4.8380.38| (1)
527 4.2784.65|111.
458 3.7188.36|.111
418 3.3891.74|..11
7
290 2.3594.09|.1..
197 1.6095.68|..1.
533 4.32100.00|(other patterns)
---------------------------+---------
12350100.00|XXXX
xtdescribe gives information about the panel structure–we see that there are12,350individuals and4 time periods,and that62%of people have observations in all four time periods.
.xtsum sex age nkids fiyr lwages mastat
Variable|Mean Std.Dev.Min Max|Observations
-----------------+--------------------------------------------+----------------
sex overall| 1.531028.499042712|N=39190 between|.499502312|n=12350
within|0 1.531028 1.531028|T-bar= 3.17328
||
age overall|44.0055918.433861597|N=39190 between|18.933581596.5|n=12350
within| 1.04443532.3389250.33892|T-bar= 3.17328
||
nkids overall|.5951008.976242609|N=39190 between|.937591509|n=12350
within|.2487689-3.154899 3.595101|T-bar= 3.17328
||
fiyr overall|8939.0168929.790287481.8|N=37455 between|8186.3210160891.5|n=11982
within|3631.936-70011.81204307.5|T-bar= 3.12594
||
lwages overall|8.849195 1.166257-3.32173312.56891|N=23609 between| 1.206297-3.32173311.61299|n=8323
within|.4933544 1.25089114.18753|T-bar= 2.8366
||
mastat overall| 2.497844 2.03162606|N=39185 between| 2.04319706|n=12350
within|.5396737-2.002156 6.247844|T-bar= 3.17287
xtsum gives summary statistics and shows variation between individuals and within individuals–so we see that sex does not vary within individuals,and that log wages vary more between individuals than within individuals.We also see the total number of observations(N),number of individuals with observations(n) and average number of time periods for each individual.
.xttrans married,freq
|Married=1
Married=1|01|Total
-----------+----------------------+----------
0|10,669492|11,161
|95.59 4.41|100.00
-----------+----------------------+----------
1|36715,312|15,679
| 2.3497.66|100.00
-----------+----------------------+----------
Total|11,03615,804|26,840
|41.1258.88|100.00
8
xttrans gives an indication of whether there are transitions between groups for categorical variables–for example,we see here that96%of unmarried individuals remain unmarried in the next period,and98%of married individuals remain married in the next period.
3Regression using panel data
Having set up our dataset we can perform some regressions.As previously,I use a local macro to store my list of independent variables:
local xlist"age age2i.sex*i.married nkids i.qfachi"
We can estimate a pooled OLS regression using the regress command seen in the last lecture.We should use robust standard errors clustered by individual.
.xi:regress lwages‘xlist’,vce(cluster pid)
i.sex_Isex_1-2(naturally coded;_Isex_1omitted)
i.married_Imarried_0-1(naturally coded;_Imarried_0omitted)
i.sex*i.married_IsexXmar_#_#(coded as above)
i.qfachi_Iqfachi_1-7(naturally coded;_Iqfachi_1omitted)
Linear regression Number of obs=23520
F(12,8285)=409.27
Prob>F=0.0000
R-squared=0.3092
Root MSE=.96854
(Std.Err.adjusted for8286clusters in pid)
------------------------------------------------------------------------------
|Robust
lwages|Coef.Std.Err.t P>|t|[95%Conf.Interval]
-------------+----------------------------------------------------------------age|.1890312.005557434.010.000.1781373.1999251
age2|-.0022714.0000708-32.100.000-.0024101-.0021327 _Isex_2|-.3258962.0300076-10.860.000-.3847186-.2670737
_Imarried_1|.488806.028615817.080.000.4327118.5449002
_IsexXmar_~1|-.654709.0376179-17.400.000-.7284494-.5809685 nkids|-.2203951.0115555-19.070.000-.2430467-.1977435 _Iqfachi_2|-.1272603.077578-1.640.101-.2793325.024812
_Iqfachi_3|-.1781531.0785097-2.270.023-.3320517-.0242544
_Iqfachi_4|-.4158935.0734584-5.660.000-.5598903-.2718966
_Iqfachi_5|-.5313831.0726375-7.320.000-.6737708-.3889953
_Iqfachi_6|-.617404.080617-7.660.000-.7754335-.4593746
_Iqfachi_7|-.8375914.073551-11.390.000-.9817697-.693413 _cons| 6.046451.129803846.580.000 5.792003 6.300899
------------------------------------------------------------------------------
Fixed and random e?ects regressions are both carried out using the xtreg command.In both cases we should again get cluster robust standard errors.The default is for Stata to estimate random e?ects when xtreg is used–you must specify the option“fe”to get?xed e?ects:
.xi:xtreg lwages‘xlist’,fe vce(cluster pid)
i.sex_Isex_1-2(naturally coded;_Isex_1omitted)
i.married_Imarried_0-1(naturally coded;_Imarried_0omitted)
i.sex*i.married_IsexXmar_#_#(coded as above)
9
i.qfachi_Iqfachi_1-7(naturally coded;_Iqfachi_1omitted)
Fixed-effects(within)regression Number of obs=23520
Group variable:pid Number of groups=8286
R-sq:within=0.0451Obs per group:min=1 between=0.1672avg= 2.8
overall=0.1293max=4
F(11,8285)=38.35
corr(u_i,Xb)=-0.3782Prob>F=0.0000
(Std.Err.adjusted for8286clusters in pid)
------------------------------------------------------------------------------
|Robust
lwages|Coef.Std.Err.t P>|t|[95%Conf.Interval]
-------------+----------------------------------------------------------------age|.2902458.017377516.700.000.2561815.3243102
age2|-.0030951.0002108-14.680.000-.0035083-.0026819 _Isex_2|(dropped)
_Imarried_1|.0473639.0421936 1.120.262-.0353461.1300739
_IsexXmar_~1|-.0574721.0682978-0.840.400-.1913529.0764086 nkids|-.1347352.0193899-6.950.000-.1727443-.0967261 _Iqfachi_2|.0239754.27867750.090.931-.5223023.5702531
_Iqfachi_3|-.2838254.2629136-1.080.280-.7992019.231551
_Iqfachi_4|-.1524719.2703457-0.560.573-.6824172.3774734
_Iqfachi_5|-.4847609.2739852-1.770.077-1.021841.0523187
_Iqfachi_6|-.4576768.3040223-1.510.132-1.053637.138283
_Iqfachi_7|-.3651604.3077017-1.190.235-.9683329.238012 _cons| 3.198992.444527.200.000 2.327621 4.070362
-------------+----------------------------------------------------------------sigma_u| 1.1636036
sigma_e|.59940523
rho|.79029066(fraction of variance due to u_i)
------------------------------------------------------------------------------
Here sex is dropped–this is because it is invariant over time.The lack of variance in quali?cations and marital status explains the imprecise coe?cient estimates here.
.xi:xtreg lwages‘xlist’,re vce(cluster pid)
i.sex_Isex_1-2(naturally coded;_Isex_1omitted)
i.married_Imarried_0-1(naturally coded;_Imarried_0omitted)
i.sex*i.married_IsexXmar_#_#(coded as above)
i.qfachi_Iqfachi_1-7(naturally coded;_Iqfachi_1omitted)
Random-effects GLS regression Number of obs=23520
Group variable:pid Number of groups=8286
R-sq:within=0.0341Obs per group:min=1 between=0.3457avg= 2.8
overall=0.3053max=4
Random effects u_i~Gaussian Wald chi2(12)=4356.47
corr(u_i,X)=0(assumed)Prob>chi2=0.0000
10
(Std.Err.adjusted for8286clusters in pid)
------------------------------------------------------------------------------
|Robust
lwages|Coef.Std.Err.z P>|z|[95%Conf.Interval]
-------------+----------------------------------------------------------------age|.2133805.005807336.740.000.2019984.2247626
age2|-.0025222.0000737-34.220.000-.0026667-.0023778 _Isex_2|-.4601064.0312502-14.720.000-.5213557-.3988571
_Imarried_1|.341099.027577812.370.000.2870475.3951504
_IsexXmar_~1|-.4701444.0385164-12.210.000-.5456351-.3946536 nkids|-.201973.0116565-17.330.000-.2248194-.1791267 _Iqfachi_2|-.0940869.0991879-0.950.343-.2884916.1003178
_Iqfachi_3|-.1635236.0960624-1.700.089-.3518024.0247552
_Iqfachi_4|-.3019628.0911546-3.310.001-.4806225-.1233031
_Iqfachi_5|-.4841929.0903848-5.360.000-.6613438-.3070421
_Iqfachi_6|-.524829.0975616-5.380.000-.7160463-.3336117
_Iqfachi_7|-.785563.0915491-8.580.000-.9649959-.6061301 _cons| 5.485352.146495337.440.000 5.198227 5.772478
-------------+----------------------------------------------------------------sigma_u|.87706892
sigma_e|.59940523
rho|.68163491(fraction of variance due to u_i)
------------------------------------------------------------------------------
Stata reports the standard deviations of the error components estimated in sigma_u and sigma_e.We also see di?erent R2statistics for within and between variation.These can be tabulated if the estimates have been stored.
.esttab POLS FE RE,b se stats(r2r2_o r2_b r2_w)
------------------------------------------------------------
(1)(2)(3)
lwages lwages lwages
------------------------------------------------------------
age0.189***0.290***0.213***
(0.00556)(0.0174)(0.00581)
age2-0.00227***-0.00310***-0.00252***
(0.0000708)(0.000211)(0.0000737)
_Isex_2-0.326***0-0.460***
(0.0300)(0)(0.0313)
_Imarried_10.489***0.04740.341***
(0.0286)(0.0422)(0.0276)
_IsexXmar_~1-0.655***-0.0575-0.470***
(0.0376)(0.0683)(0.0385)
nkids-0.220***-0.135***-0.202***
(0.0116)(0.0194)(0.0117)
_Iqfachi_2-0.1270.0240-0.0941
11
(0.0776)(0.279)(0.0992)
_Iqfachi_3-0.178*-0.284-0.164
(0.0785)(0.263)(0.0961)
_Iqfachi_4-0.416***-0.152-0.302***
(0.0735)(0.270)(0.0912)
_Iqfachi_5-0.531***-0.485-0.484***
(0.0726)(0.274)(0.0904)
_Iqfachi_6-0.617***-0.458-0.525***
(0.0806)(0.304)(0.0976)
_Iqfachi_7-0.838***-0.365-0.786***
(0.0736)(0.308)(0.0915)
_cons 6.046*** 3.199*** 5.485***
(0.130)(0.445)(0.146)
------------------------------------------------------------
r20.3090.0451
r2_o0.1290.305
r2_b0.1670.346
r2_w0.04510.0341
------------------------------------------------------------
Standard errors in parentheses
*p<0.05,**p<0.01,***p<0.001
Estimation can also be easily implemented in?rst di?erences using the regress command and di?erence operator“D.”.We do not need to generate variables in?rst di?erences.The option noconstant is used so that Stata does not add a constant term(which would be di?erenced out).For example:
.regress D.(lwage age age2female married nkids),vce(cluster pid)noconstant
Linear regression Number of obs=14667
F(4,6146)=65.00
Prob>F=0.0000
R-squared=0.0208
Root MSE=.73681
(Std.Err.adjusted for6147clusters in pid)
------------------------------------------------------------------------------
|Robust
D.lwages|Coef.Std.Err.t P>|t|[95%Conf.Interval]
-------------+----------------------------------------------------------------age|
D1.|.3179554.021196115.000.000.2764036.3595071
age2|
D1.|-.0034294.0002499-13.720.000-.0039192-.0029395 female|
D1.|(dropped)
married|
D1.|.0144043.03906050.370.712-.062168.0909767 nkids|
12
D1.|-.0957519.0221907-4.310.000-.1392534-.0522504
------------------------------------------------------------------------------
3.1Hausman test
Stata can easily perform a Hausman test–that is,a test of whether the individual e?ects are random. The null hypothesis is that both?xed and random e?ects are consistent,the alternative hypothesis is that random e?ects is not consistent.We must?rst estimate the?xed and random e?ects models–and without robust standard errors.Then,the Hausman test is conducted using the hausman command.
.quietly xi:xtreg lwages‘xlist’,fe
.estimates store FE1
.quietly xi:xtreg lwages‘xlist’,re
.estimates store RE1
.hausman FE1RE1,sigmamore
----Coefficients----
|(b)(B)(b-B)sqrt(diag(V_b-V_B))
|FE1RE1Difference S.E.
-------------+----------------------------------------------------------------age|.2902458.2133805.0768653.0125061
age2|-.0030951-.0025222-.0005729.0001535
_Imarried_1|.0473639.341099-.293735.0336022
_IsexXmar_~1|-.0574721-.4701444.4126723.0470245
nkids|-.1347352-.201973.0672378.0120169 _Iqfachi_2|.0239754-.0940869.1180623.1387245
_Iqfachi_3|-.2838254-.1635236-.1203018.1676963
_Iqfachi_4|-.1524719-.3019628.1494909.1630687
_Iqfachi_5|-.4847609-.4841929-.000568.1697845
_Iqfachi_6|-.4576768-.524829.0671522.2006431
_Iqfachi_7|-.3651604-.785563.4204026.1869317
------------------------------------------------------------------------------
b=consistent under Ho and Ha;obtained from xtreg
B=inconsistent under Ha,efficient under Ho;obtained from xtreg Test:Ho:difference in coefficients not systematic
chi2(11)=(b-B)’[(V_b-V_B)^(-1)](b-B)
=238.04
Prob>chi2=0.0000
Cameron and Trivedi(2009)recommend using the sigmamore option.Here we see the null hypothesis is clearly rejected with a p-value of0.0000so the random e?ects estimates are not consistent.
4Creating variables to identify changes in variables
We may wish to create a variable which records whether a certain status has changed.For example,whether marital status has changed.Once data is declared to be a panel this is straightforward.Let’s?rst recode mastat so that it has just three categories:
recode mastat(0=.)(1/2=1)(3/5=2)(6=3),generate(ma)
13
Then to?nd changes we generate a new variable which incorporates the lagged value and current value of ma:
generate mach=(10*L.ma)+ma
Having labelled the values we have a useful marital change variable.So we can see that there are352 instances of individuals going from never having been married to having a partner in this sample.
.tabulate mach
marital change|Freq.Percent Cum.
-----------------------------+-----------------------------------
stayed in couple|16,84963.9463.94
partnership ended|360 1.3765.30
partnered->never married!|1130.4365.73
ex-partner->partnership|1800.6866.41
stayed ex-partner|3,62213.7480.16
never married->partnership|352 1.3481.49
never married->ex-partner|140.0581.55
stayed never married|4,86318.45100.00
-----------------------------+-----------------------------------
Total|26,353100.00
References
Cameron,Colin A.and Pravin K.Trivedi,Microeconometrics Using Stata,Texas:Stata Press,2009. Taylor,Marcia Freed,John Brice,Nick Buck,and Elaine Prentice-Lane,“British Household Panel Survey User Manual Volume A:Introduction,Technical Report and Appendices,”ISER,University of Essex,Colchester2009.
14
为了选择最佳的液位传感器，我们不但需要了解被测液体的属性和状态，同时，也要知道不同的检测方式的优点与局限性，从而才能选出最合适的传感器。以下为目前市场上最常见的检测技术。 激光测量：激光类传感器基于光学检测原理，通过物体表面反射光线至接收器进行检测，其光斑较小且集中，易于安装、校准，灵活性好，可应用于散料或液位的连续或者限位报警等;但其不适合应用于透明液体（透明液体容易折射光线，导致光线无法反射至接收器），含泡沫或者蒸汽环境（无法穿透泡沫或者容易受到蒸汽干扰），波动性液体（容易造成误动作），振动环境等。 tdr（时域反射）/ 导波雷达/微波原理测量：其名称在行业内有多种不同的叫法，其具备了激光测量的好处，如：易于安装、校准，灵活性好等，另外其更优于激光检测，如无需重复校准和多功能输出等，其适用于各种含泡沫的液位检测，不受液体颜色影响，甚至可应用于高粘性液体，受外部环境干扰相对小，但其测量高度一般小于6米。 超声波测量：由于其原理为通过检测超声波发送与反射的时间差来计算液位高度，故容易受到超声波传播的能量损耗影响。其亦具备安装容易、灵活性高等特点，通常可安装于高处进行非接触式测量。但当使用于含蒸汽、粉层等环境时，检测距离将会明显缩短，不建议使用在吸波环境，如泡沫等。 音叉振动测量：音叉式测量仅为开关量输出，不能用于连续性监控液体高度。其原理为：当液体或者散料填充两个振动叉时，共振频率改变时，依靠检测频率改变而发出开关信号。其可用于高粘度液体或者固体散料的高度监控，主要为防溢报警、低液位报警等，不提供模拟量输出，另外，多数情况下需要开孔安装于容器侧面。 光电折射式测量：该检测方式通过传感器内部发出光源，光源通过透明树脂全反射至传感器接受器，但遇到液面时，部分光线将折射至液体，从而传感器检测全反射回来光量值的减少来监控液面。该检测方式便宜，安装、调试简单，但仅能应用于透明液体，同时只输出开关量信号。 艾驰商城是国内最专业的MRO工业品网购平台，正品现货、优势价格、迅捷配送，是一站式采购的工业品商城！具有10年工业用品电子商务领域研究，以强大的信息通道建设的优势，以及依托线下贸易交易市场在工业用品行业上游供应链的整合能力，为广大的用户提供了传感器、图尔克传感器、变频器、断路器、继电器、PLC、工控机、仪器仪表、气缸、五金工具、伺服电机、劳保用品等一系列自动化的工控产品。 如需进一步了解图尔克、奥托尼克斯、科瑞、山武、倍加福、邦纳、亚德客、施克等各类传感器的选型，报价，采购，参数，图片，批发信息，请关注艾驰商城https://www.doczj.com/doc/6014997910.html,/
说明：裤子上的尺码，如160/68A，160是指身高，68表示腰围，A代表体型；体型分类：A正常体B偏胖体C肥胖体Y偏瘦体 说明：34号到38号是属于超大尺寸的超大号牛仔裤 尺寸、裤长测量方法： 1、腰围 裤子腰围：两边腰围接缝处围量一周；净腰围：在单裤外沿腰间最细处围量一周，按需要加放尺寸； 2、臀围 裤子臀围：由腰口往下，裤子最宽处横向围量一周；净臀围：沿臀部最丰满处平衡围量一周，按需要加放松度；
3、裤长 由腰口往下到裤子最底边的距离；休闲裤、牛仔裤裤长不含脚口贴边，脚口贴边另预留3-4CM长供自行缭边使用； 4、净裤长 由腰口到您裤子的实际缭边处的距离；男士净裤长标准测量长度在：皮鞋鞋帮 身高裤长对照表 身高(CM) 裤长(市尺) 裤长（CM) 160~165 2尺9寸97 165~170 3尺100 170~175 3尺1寸103 175~180 3尺2寸107 180~185 3尺3寸110 男式衬衫尺码对照表单位（厘米） 身高/胸围尺码身高腰围肩宽胸围衣长袖长165/84Y 37165 94 44 104 78 58 165/88Y 38165 98 45 108 78 59.5
170/92Y 39170 102 46 112 79 59.5 175/96Y 40175 106 47 115 79 60.5 175/100Y 41175 110 48 118 80 60.5 180/104Y 42180 113 49 121 81 61.5 180/108Y 43180 116 50 124 81 61.5 185/112Y 44185 119 51 126 82 62.5 185/116Y 45185 122 51 128 82 62.5 185/120Y 46185 124 52 130 83 64 注：（身高/胸围）为净尺寸。一般实际紧腰围和成衣相差12~22厘米。 女式衬衫尺码对照表单位（厘米） 规格尺码肩宽胸围腰围下摆围后衣长短袖长短袖口长袖长长袖口155/80 3537 86 71 89 56 19.5 30 54 21 155/83 3638 89 74 92 57 19.5 31 55 22 160/86 3739 92 77 95 58 20 32 56 22 160/89 3840 95 80 98 59 20 33 56 23 165/92 3941 98 83 101 60 20.5 34 57 23 165/95 4042 101 86 104 61 20.5 35 57 24 170/98 4143 104 89 107 62 21 36 58 24 170/101 4244 107 92 110 63 21 37 58 25 173/104 4345 110 95 113 64 21.5 38 59 25 注：尺寸表中的规格表示为（身高/胸围净尺寸）的参考尺寸。 男士西服尺码对照表单位（厘米）
外文翻译 专业机械设计制造及其自动化学生姓名刘链柱 班级机制111 学号1110101102 指导教师葛友华
外文资料名称： Design and performance evaluation of vacuum cleaners using cyclone technology 外文资料出处：Korean J. Chem. Eng., 23(6), (用外文写) 925-930 (2006) 附件： 1.外文资料翻译译文 2.外文原文
应用旋风技术真空吸尘器的设计和性能介绍 吉尔泰金，洪城铱昌，宰瑾李， 刘链柱译 摘要：旋风型分离器技术用于真空吸尘器 - 轴向进流旋风和切向进气道流旋风有效地收集粉尘和降低压力降已被实验研究。优化设计等因素作为集尘效率，压降，并切成尺寸被粒度对应于分级收集的50％的效率进行了研究。颗粒切成大小降低入口面积，体直径，减小涡取景器直径的旋风。切向入口的双流量气旋具有良好的性能考虑的350毫米汞柱的低压降和为1.5μm的质量中位直径在1米3的流量的截止尺寸。一使用切向入口的双流量旋风吸尘器示出了势是一种有效的方法，用于收集在家庭中产生的粉尘。 摘要及关键词：吸尘器; 粉尘; 旋风分离器 引言 我们这个时代的很大一部分都花在了房子，工作场所，或其他建筑，因此，室内空间应该是既舒适情绪和卫生。但室内空气中含有超过室外空气因气密性的二次污染物，毒物，食品气味。这是通过使用产生在建筑中的新材料和设备。真空吸尘器为代表的家电去除有害物质从地板到地毯所用的商用真空吸尘器房子由纸过滤，预过滤器和排气过滤器通过洁净的空气排放到大气中。虽然真空吸尘器是方便在使用中，吸入压力下降说唱空转成比例地清洗的时间，以及纸过滤器也应定期更换，由于压力下降，气味和细菌通过纸过滤器内的残留粉尘。 图1示出了大气气溶胶的粒度分布通常是双峰形，在粗颗粒（>2.0微米）模式为主要的外部来源，如风吹尘，海盐喷雾，火山，从工厂直接排放和车辆废气排放，以及那些在细颗粒模式包括燃烧或光化学反应。表1显示模式，典型的大气航空的直径和质量浓度溶胶被许多研究者测量。精细模式在0.18?0.36 在5.7到25微米尺寸范围微米尺寸范围。质量浓度为2?205微克，可直接在大气气溶胶和 3.85至36.3μg/m3柴油气溶胶。
男装、女装衣服尺码对照表
1、男装尺码对照表
身高 （cm）
衬衣尺码 (领围 cm)
西服尺码夹克尺码西裤尺码
(肩宽 (胸围 (腰围
cm)
cm)
cm)
西(腰裤围尺寸码) T
恤尺码
毛衣尺码 内裤尺码 统计比例
160 37(S) 44(S) 80(S) 72
28
S
S
S
0
165 38(M) 46(M) 84(M) 74，76 29
M
M
M
1
170 39(L) 48(L) 88(S) 78
30
L
L
L
2
175 40(XL) 50(XL) 92(M) 80
31
XL
XL
XL
3
180 41(2XL) 52(2XL) 96(L) 82
32
2XL
2XL
2XL
3
185 42(3XL) 54(3XL) 100(XL) 84，86 33
3XL
3XL
3XL
2
190 43(4XL) 56(4XL) 104(4XL) 88
34
4XL
4XL
4XL
1
195 44(5XL)
90
35
5XL
5XL
5XL
0
2、衬衫尺寸(除个别款尺寸，买前询问）
平铺尺寸 M
XL
胸围 97cm 99cm 101cm
肩宽 43cm 44cm 45cm
衣长 67cm 68cm 69cm
袖长 62cm 64cm 65cm
3、裤装尺码为： 26 代表腰围为：“尺” 28 代表腰围为：“尺” 30 代表腰围为：“尺” 32 代表腰围为：“尺” 34 代表腰围为：“尺” 38 代表腰围为：“尺” 42 代表腰围为：“尺” 50 代表腰围为：“尺” 54 代表腰围为：“尺”
27 代表腰围为：“尺” 29 代表腰围为：“尺” 31 代表腰围为：“尺” 33 代表腰围为：“尺” 36 代表腰围为：“尺” 40 代表腰围为：“尺” 44 代表腰围为：“尺” 52 代表腰围为：“尺”
4.女装尺码对照表
上装尺码
“女上装”尺码对照表(cm)
S
M
L
155/80A
160/84A
165/88A
XL 170/92A
超声波传感器 超声波传感器是利用超声波的特性研制而成的传感器。超声波是一种振动频率高于声波的机械波，由换能晶片在电压的激励下发生振动产生的，它具有频率高、波长短、绕射现象小，特别是方向性好、能够成为射线而定向传播等特点。超声波对液体、固体的穿透本领很大，尤其是在阳光不透明的固体中，它可穿透几十米的深度。超声波碰到杂质或分界面会产生显著反射形成反射成回波，碰到活动物体能产生多普勒效应。因此超声波检测广泛应用在工业、国防、生物医学等方面。 以超声波作为检测手段，必须产生超声波和接收超声波。完成这种功 能的装置就是超声波传感器，习惯上称为超声换能器，或者超声探头。 以超声波作为检测手段，必须产生超声波和接收超声波。完成这种功能的装置就是超声波传感器，习惯上称为超声换能器，或者超声探头。 超声波探头主要由压电晶片组成，既可以发射超声波，也可以接收超声波。小功率超声探头多作探测作用。它有许多不同的结构，可分直探头（纵波）、斜探头（横波）、表面波探头（表面波）、兰姆波探头（兰姆波）、双探头（一个探头反射、一个探头接收）等。 超声探头的核心是其塑料外套或者金属外套中的一块压电晶片。构成晶片的材料可以有许多种。晶片的大小，如直径和厚度也各不相同，因此每个探头的性能是不同的，使用前必须预先了解它的性能。 组成部分 超声波探头主要由压电晶片组成，既可以发射超声波，也可以接收超声波。小功率超声探头多作探测作用。它有许多不同的结构，可分直探头（纵波）、斜探头（横波）、表面波探头（表面波）、兰姆波探头（兰姆波）、双探头（一个探头反射、一个探头接收）等。 性能指标
超声探头的核心是其塑料外套或者金属外套中的一块压 超声波传感器 电晶片。构成晶片的材料可以有许多种。晶片的大小，如直径和厚度也各不相同，因此每个探头的性能是不同的，我们使用前必须预先了解它的性能。超声波传感器的主要性能指标包括： 工作频率 工作频率就是压电晶片的共振频率。当加到它两端的交流电压的频率和晶片的共振频率相等时，输出的能量最大，灵敏度也最高。 工作温度 由于压电材料的居里点一般比较高，特别是诊断用超声波探头使用 超声波传感器 功率较小，所以工作温度比较低，可以长时间地工作而不失效。医疗用的超声探头的温度比较高，需要单独的制冷设备。[1] 灵敏度 主要取决于制造晶片本身。机电耦合系数大，灵敏度高；反之，灵敏度低。 主要应用 超声波传感技术应用在生产实践的不同方面，而医学应用是其
裤子尺寸对照表1 裤子尺寸对照表2 说明：裤子上的尺码，如160/68A，160是指身高，68表示腰围，A代表体型；体型分类：A正常体B偏胖体C肥胖体Y偏瘦体 牛仔裤尺码对照表：（以下测量误差在+-2cm） 说明：34号到38号是属于超大尺寸的超大号牛仔裤
尺寸、裤长测量方法： 1、腰围 裤子腰围：两边腰围接缝处围量一周；净腰围：在单裤外沿腰间最细处围量一周，按需要加放尺寸； 2、臀围 裤子臀围：由腰口往下，裤子最宽处横向围量一周；净臀围：沿臀部最丰满处平衡围量一周，按需要加放松度； 3、裤长 由腰口往下到裤子最底边的距离；休闲裤、牛仔裤裤长不含脚口贴边，脚口贴边另预留3-4CM长供自行缭边使用； 4、净裤长 由腰口到您裤子的实际缭边处的距离；男士净裤长标准测量长度在：皮鞋鞋帮和鞋底交接处；
男式衬衫尺码对照表 单位（厘米） 身高/胸围 尺码 身高 腰围 肩宽 胸围 衣长 袖长 165/84Y 37 165 94 44 104 78 58 165/88Y 38 165 98 45 108 78 59.5 170/92Y 39 170 102 46 112 79 59.5 175/96Y 40 175 106 47 115 79 60.5 175/100Y 41 175 110 48 118 80 60.5 180/104Y 42 180 113 49 121 81 61.5 180/108Y 43 180 116 50 124 81 61.5 185/112Y 44 185 119 51 126 82 62.5 185/116Y 45 185 122 51 128 82 62.5 185/120Y 46 185 124 52 130 83 64 注：（身高/胸围）为净尺寸。一般实际紧腰围和成衣相差12~22厘米。
南京理工大学 毕业设计(论文)外文资料翻译 教学点：南京信息职业技术学院 专业：电子信息工程 姓名：陈洁 学号： 014910253034 外文出处：《 Pci System Architecture 》 (用外文写) 附件： 1.外文资料翻译译文；2.外文原文。 指导教师评语： 该生外文翻译没有基本的语法错误，用词准确，没 有重要误译，忠实原文；译文通顺，条理清楚，数量与 质量上达到了本科水平。 签名： 年月日 注：请将该封面与附件装订成册。
附件1：外文资料翻译译文 64位PCI扩展 1.64位数据传送和64位寻址：独立的能力 PCI规范给出了允许64位总线主设备与64位目标实现64位数据传送的机理。在传送的开始，如果回应目标是一个64位或32位设备，64位总线设备会自动识别。如果它是64位设备，达到8个字节（一个4字）可以在每个数据段中传送。假定是一串0等待状态数据段。在33MHz总线速率上可以每秒264兆字节获取（8字节／传送＊33百万传送字／秒），在66MHz总线上可以528M字节／秒获取。如果回应目标是32位设备，总线主设备会自动识别并且在下部4位数据通道上（AD[31::00]）引导，所以数据指向或来自目标。 规范也定义了64位存储器寻址功能。此功能只用于寻址驻留在4GB地址边界以上的存储器目标。32位和64位总线主设备都可以实现64位寻址。此外，对64位寻址反映的存储器目标（驻留在4GB地址边界上）可以看作32位或64位目标来实现。 注意64位寻址和64位数据传送功能是两种特性，各自独立并且严格区分开来是非常重要的。一个设备可以支持一种、另一种、都支持或都不支持。 2.64位扩展信号 为了支持64位数据传送功能，PCI总线另有39个引脚。 ●REQ64#被64位总线主设备有效表明它想执行64位数据传送操作。REQ64#与FRAME#信号具有相同的时序和间隔。REQ64#信号必须由系统主板上的上拉电阻来支持。当32位总线主设备进行传送时，REQ64#不能又漂移。 ●ACK64#被目标有效以回应被主设备有效的REQ64#（如果目标支持64位数据传送），ACK64#与DEVSEL#具有相同的时序和间隔（但是直到REQ64#被主设备有效，ACK64#才可被有效）。像REQ64#一样，ACK64#信号线也必须由系统主板上的上拉电阻来支持。当32位设备是传送目标时，ACK64#不能漂移。 ●AD[64::32]包含上部4位地址／数据通道。 ●C/BE#[7::4]包含高4位命令/字节使能信号。 ●PAR64是为上部4个AD通道和上部4位C/BE信号线提供偶校验的奇偶校验位。 以下是几小结详细讨论64位数据传送和寻址功能。 3.在32位插入式连接器上的64位卡
男装、女装衣服尺码对照表1、男装尺码对照表 2、衬衫尺寸(除个别款尺寸，买前询问）
3、裤装尺码为： 26代表腰围为：“尺” 27代表腰围为：“尺” 28代表腰围为：“尺” 29代表腰围为：“尺” 30代表腰围为：“尺” 31代表腰围为：“尺” 32代表腰围为：“尺” 33代表腰围为：“尺” 34代表腰围为：“尺” 36代表腰围为：“尺” 38代表腰围为：“尺” 40代表腰围为：“尺” 42代表腰围为：“尺” 44代表腰围为：“尺” 50代表腰围为：“尺” 52代表腰围为：“尺” 54代表腰围为：“尺” 4.女装尺码对照表 “女上装”尺码对照表(cm)
“女下装”尺码详细对照表(cm) 其他算法
裤子尺码对照表 26号------1尺9寸臀围2尺632号------2尺6寸臀围3尺2 27号------2尺0寸臀围2尺734号------2尺7寸臀围3尺4 28号------2尺1寸臀围2尺836号------2尺8寸臀围3尺5-6 29号------2尺2寸臀围2尺938号------2尺9寸臀围3尺7-8 30号------2尺3寸臀围3尺040号------3尺0寸臀围3尺9-4尺 31号------2尺4寸臀围3尺142号------3尺1-2寸臀围4尺1-2 牛仔裤尺码对照表 5.尺码换算参照表 女装（外衣、裙装、恤衫、上装、套装） 标准尺码明细 中国 (cm) 160-165 / 84-86 165-170 / 88-90 167-172 / 92-96 168-173 / 98-102 170-176 / 106-110 国际 XS S M L XL
超声波液位传感器是一种常用的测量仪器，被广泛的应用于多个行业当中。超声波传感器是一种利用超声波的特性研制而成的传感器，具有测量精准、检测范围广、使用灵活、维护简便等优点。接下来艾驰商城小编主要来为大家介绍一下超声波液位传感器的结构及工作原理，希望可以帮助到大家。 超声波液位传感器的结构 超声波传感器主要由压电晶片组成，既可以发射超声波，也可以接收超声波。小功率超声探头多作探测作用。它有许多不同的结构，可分直探头（纵波）、斜探头（横波）、表面波探头（表面波）、兰姆波探头（兰姆波）、双探头（一个探头反射、一个探头接收）等。超声传感器的核心是其塑料外套或者金属外套中的一块压电晶片。构成晶片的材料可以有许多种。晶片的大小，如直径和厚度也各不相同，因此每个传感器的性能是不同的，我们使用前必须预先了解它的性能。 超声波液位传感器的工作原理 超声波是一种振动频率高于声波的机械波，由换能晶片在电压的激励下发生振动产生的，它具有频率高、波长短、绕射现象小，特别是方向性好、能够成为射线而定向传播等特点。超声波对液体、固体的穿透本领很大，尤其是在阳光不透明的固体中，它可穿透几十米的深度。超声波碰到杂质或分界面会产生显著反射形成反射成回波，碰到活动物体能产生多普勒效应。因此超声波检测广泛应用在工业、国防、生物医学等方面以超声波作为检测手段，必须产生超声波和接收超声波。完成这种功能的装置就是超声波传感器，习惯上称为超声换能器，或者超声探头。 艾驰商城是国内最专业的MRO工业品网购平台，正品现货、优势价格、迅捷配送，是一站式采购的工业品商城！具有10年工业用品电子商务领域研究，以强大的信息通道建设的优势，以及依托线下贸易交易市场在工业用品行业上游供应链的整合能力，为广大的用户提供了传感器、图尔克传感器、变频器、断路器、继电器、PLC、工控机、仪器仪表、气缸、五金工具、伺服电机、劳保用品等一系列自动化的工控产品。 如需进一步了解图尔克、奥托尼克斯、科瑞、山武、倍加福、邦纳、亚德客、施克等各类传感器的选型，报价，采购，参数，图片，批发信息，请关注艾驰商城https://www.doczj.com/doc/6014997910.html,/
衣服尺寸对照表 女款上装 女裤 尺码XL 3XL
女式内裤 女式泳装 女鞋 2尺4 2尺6 2尺7 2尺8 2尺9 3尺 3尺1 （市尺） 尺码 S M L XL 3XL 光脚长度 女裙对应臀围 尺码 XS/32 S/34 M/36 L/38 XL/40 腰围 63-70 70-76 80-86 86-93 93-100
文胸 80,83,85,88,9 85,88,90,93,95,9 90,93,95,98100,1 95,98,100,103,105,1 103,105,108,110,1 胸 女袜胸 68-72(cm) 73-77(cm) 78-82(cm) 83-87(cm) 88-92(cm) 03 08 13 A,B,C,D,DD A,B,C,D,DD,E 型 A,B,C,D,DD,E A,B,C,D,DD,E B,C,D,DD,E 尺 70A,70B,70C 75A,75B,75C, 80A,80B,80C 85A,85B,85C 90B,90C,90D 码 70D,70DD 75D,75DD,75E 80D,80DD,80E 85D,85DD,85E 90DD,90E 英 32A,32B,32C 34A,34B,34C 36A,36B,36C 38A,38B,38C 40B,40C,40D
女式衬衫 数目为12.5公分者为B 罩杯。以此类推计算，即下胸围尺寸为 75公分者，可容许+/-2.5公分的误差，凡 介于72.5?77.5公分者皆可穿75。假设您的下胸围尺寸为 72.5公分，则建议选购75的胸罩而非70，因 为在 穿着时会比较舒适，二来也比较耐穿。 臀围 型号 80-88cm （约 34 85-93cm （约 36 90-98cm （约 38 100-108cm （约 罩杯 【罩杯的尺寸】上胸围尺寸减去下胸围尺寸之数目为 10.0公分者为A 罩杯。上胸围尺寸减去下胸围尺寸之 式 32D,32DD 34D,34DD,34E 36D,36DD,36E 38D,38DD,38E 40DD,40E XL 范围
预测高速公路建设项目最终的预算和时间 摘要 目的——本文的目的是开发模型来预测公路建设项目施工阶段最后的预算和持续的时间。 设计——测算收集告诉公路建设项目，在发展预测模型之前找出影响项目最终的预算和时间，研究内容是基于人工神经网络(ANN)的原理。与预测结果提出的方法进行比较,其精度从当前方法基于挣值。 结果——根据影响因素最后提出了预算和时间，基于人工神经网络的应用原理方法获得的预测结果比当前基于挣值法得到的结果更准确和稳定。 研究局限性/意义——因素影响最终的预算和时间可能不同,如果应用于其他国家,由于该项目数据收集的都是泰国的预测模型,因此,必须重新考虑更好的结果。 实际意义——这项研究为用于高速公路建设项目经理来预测项目最终的预算和时间提供了一个有用的工具，可为结果提供早期预算和进度延误的警告。 创意/价值——用ANN模型来预测最后的预算和时间的高速公路建设项目,开发利用项目数据反映出持续的和季节性周期数据, 在施工阶段可以提供更好的预测结果。 关键词：神经网、建筑业、预测、道路、泰国 文章类型：案例研究 前言 一个建设工程项普遍的目的是为了在时间和在预算内满足既定的质量要求和其他规格。为了实现这个目标,大量的工作在施工过程的管理必须提供且不能没有计划地做成本控制系统。一个控制系统定期收集实际成本和进度数据,然后对比与计划的时间表来衡量工作进展是否提前或落后时间表和强调潜在的问题(泰克兹,1993)。成本和时间是两个关键参数，在建设项目管理和相关参数的研究中扮演着重要的角色，不断提供适当的方法和
工具，使施工经理有效处理一个项目，以实现其在前期建设和在施工阶段的目标。在施工阶段,一个常见的问题要求各方参与一个项目，尤其是一个所有者,最终项目的预算到底是多少?或什么时候该项目能被完成？ 在跟踪和控制一个建设项目时，预测项目的性能是非常必要的。目前已经提出了几种方法,如基于挣值技术、模糊逻辑、社会判断理论和神经网络。将挣值法视为一个确定的方法，其一般假设，无论是性能效率可达至报告日期保持不变，或整个项目其余部分将计划超出申报日期（克里斯坦森，1992；弗莱明和坎普曼，2000 ；阿萨班尼，1999；维卡尔等人，2000）。然而，挣值法的基本概念在研究确定潜在的进度延误、成本和进度的差异成本超支的地区。吉布利（1985）利用平均每个成本帐户执行工作的实际成本，也称作单位收入的成本，其标准差来预测项目完工成本。各成本帐户每月的进度是一个平均平稳过程标准偏差，显示预测模型的可靠性，然而，接受的单位成本收益在每个报告期在变化。埃尔丁和休斯（1992）和阿萨班尼（1999）利用分解组成成本的结构来提高预测精度。迪克曼和Al-Tabtabai（1992）基于社会判断理论提出了一个方法，该方法在预测未来的基础上的一组线索，源于人的判断而不是从纯粹的数学算法。有经验的项目经理要求基于社会判断理论方法的使用得到满意的结果。Moselhi等人（2006）应用“模糊逻辑”来预测潜在的成本超支和对建设工程项目的进度延迟。该方法的结果在评估特定时间状态的项目和评价该项目的利润效率有作用。这有助于工程人员所完成的项目时间限制和监控项目预算。Kaastra和博伊德（1996）开发的“人工神经网络”,此网络作为一种有效的预测工具，可以利用过去“模式识别”工作和显示各种影响因素的关系，然后预测未来的发展趋势。罗威等人（2006）开发的成本回归模型能在项目的早期阶段估计建筑成本。总共有41个潜在的独立变量被确定，但只有四个变量：总建筑面积，持续时间，机械设备，和打桩，是线性成本的关键驱动因素，因为它们出现在所有的模型中。模型提出了进一步的洞察了施工成本和预测变量的各种关系。从模型得到的估计结果可以提供早期阶段的造价咨询（威廉姆斯（2003））——最终竞标利用回归模型预测的建设项目成本。 人工神经网络已被广泛用在不同的施工功能中，如估价、计划和产能预测。神经网络建设是Moselhi等人（1991）指出，由Hegazy（1998）开发了一个模型，该模型考虑了项目的外在特征，估计加拿大的公路建设成本： ·项目类型 ·项目范围
男装、女装衣服尺码对照表 1、男装尺码对照表 身高（cm）衬衣尺码 (领围cm) 西服尺码 (肩宽cm) 夹克尺码 (胸围cm) 西裤尺码 (腰围cm) 西裤尺码 (腰围寸) T恤尺码毛衣尺码内裤尺码统计比例 16037(S)44(S)80(S)7228S S S0 16538(M)46(M)84(M)74，7629M M M1 17039(L)48(L)88(S)7830L L L2 17540(XL)50(XL)92(M)8031XL XL XL3 18041(2XL)52(2XL)96(L)82322XL2XL2XL3 18542(3XL)54(3XL)100(XL)84，86333XL3XL3XL2 19043(4XL)56(4XL)104(4XL)88344XL4XL4XL1 19544(5XL)90355XL5XL5XL0
2、衬衫尺寸(除个别款尺寸，买前询问） 3、裤装尺码为： 26代表腰围为：“尺”27代表腰围为：“尺”28代表腰围为：“尺”29代表腰围为：“尺”30代表腰围为：“尺”31代表腰围为：“尺”32代表腰围为：“尺”33代表腰围为：“尺”34代表腰围为：“尺”36代表腰围为：“尺”38代表腰围为：“尺”40代表腰围为：“尺”42代表腰围为：“尺”44代表腰围为：“尺”50代表腰围为：“尺”52代表腰围为：“尺”54代表腰围为：“尺” 4.女装尺码对照表
“女下装”尺码详细对照表(cm) 其他算法
裤子尺码对照表 26号------1尺9寸臀围2尺6 32号------2尺6寸臀围3尺2 27号------2尺0寸臀围2尺734号------2尺7寸臀围3尺4 28号------2尺1寸臀围2尺836号------2尺8寸臀围3尺5-6 29号------2尺2寸臀围2尺938号------2尺9寸臀围3尺7-8 30号------2尺3寸臀围3尺040号------3尺0寸臀围3尺9-4尺 31号------2尺4寸臀围3尺142号------3尺1-2寸臀围4尺1-2 牛仔裤尺码对照表 5.尺码换算参照表 女装（外衣、裙装、恤衫、上装、套装） 标准尺码明细 中国(cm) 160-165 / 84-86 165-170 / 88-90 167-172 / 92-96 168-173 / 98-102 170-176 / 106-110
超声波传感器 基本介绍 人们能听到声音是由于物体振动产生的，它的频率在20HZ-20KHZ范围内，超过20KHZ称为超声波，低于20HZ的称为次声波。常用的超声波频率为几十KHZ-几十MHZ。超声波传感器是利用超声波的特性研制而成的传感器。超声波是一种振动频率高于声波的机械波，由换能晶片在电压的激励下发生振动产生的，它具有频率高、波长短、绕射现象小，特别是方向性好、能够成为射线而定向传播等特点。超声波对液体、固体的穿透本领很大，尤其是在阳光不透明的固体中，它可穿透几十米的深度。超声波碰到杂质或分界面会产生显著反射形成反射成回波，碰到活动物体能产生多普勒效应。因此超声波检测广泛应用在工业、国防、生物医学等方面。 以超声波作为检测手段，必须产生超声波和接收超声波。完成这种功能的装置是声波传感器，习惯上称为超声换能器，或者超声探头。 以超声波作为检测手段，必须产生超声波和接收超声波。完成这种功能的装置就是超声波传感器，习惯上称为超声换能器，或者超声探头。 超声波探头主要由压电晶片组成，既可以发射超声波，也可以接收超声波。小功率超声探头多作探测作用。它有许多不同的结构，可分直探头（纵波）、斜探头（横波）、表面波探头（表面波）、兰姆波探头（兰姆波）、双探头（一个探头反射、一个探头接收）等。 超声探头的核心是其塑料外套或者金属外套中的一块压电晶片。构成晶片的材料可以有许多种。晶片的大小，如直径和厚度也各不相同，因此每个探头的性能是不同的，使用前必须预先了解它的性能。 组成部分 超声波探头主要由压电晶片组成，既可以发射超声波，也可以接收超声波。小功率超声探头多作探测作用。它有许多不同的结构，可分直探头（纵波）、斜探头（横波）、表面波探头（表面波）、兰姆波探头（兰姆波）、双探头（一个探头反射、一个探头接收）等。 ①
基于超声波传感器的液位测量 1.摘要 超声波传感器应用广泛，其中液体液位的准确测量是实现生产过程检测和实时控制的重要保障，也是实现安全生产的重要环节。本文主要介绍液位的测量。液体罐内液位测量的方法有很多种，其中超声波传感器由于结构简单、体积小、费用低、信息处理简单可靠，易于小型化与集成化，并且可以进行实时控制，所以超声波测量法得到了广泛的应用。2.超声波概要 超声波是指频率高于20kHz的机械波，一般由压电效应或磁致伸缩效应产生；它沿直线传播，频率越高，绕射能力越弱，但反射能力越强；它还具有强度大、方向性好等特点，为此，利用超声波的这些性质就可制成超声波传感器。超声波传感器是利用超声波在超声场中的物理特性和各种效应研制而成的传感器。超声波传感器按其工作原理可分为压电式、磁致伸缩式、电磁式等，其中以压电式最为常用。压电式超声波传感器常用的材料是压电晶体和压电陶瓷，它是利用压电材料的压电效应来工作的：逆压电效应将高频电振动转换成高频机械震动，从而产生超声波，可作为发射探头；而正压电效应是将超声波振动转换成电信号，可作为接收探头。 3.检测方法选择 从测量范围来说，有的液位计只能测量几十厘米，有的却可达几十米。从测量条件和环境来说，有的非常简单，有的却十分复杂。例如:有的是高温高压，有的是低温或真空，有的需要防腐蚀、防辐射，有的从安装上提出苛刻的限制，有的从维护上提出严格的要求等。 按测量液位的感应元件与被测液体是否接触，液位仪表可以分为接触型和非接触型两大类。接触型液位测量主要有:人工检尺法、浮子测量装置、伺服式液位计、电容式液位计以及磁致伸缩液位计等。它们的共同点是测量的感应元件与被测液体接触，即都存在着与被测液体相接触的测量部件且多数带有可动部件。因此存在一定的磨损且容易被液体沾污或粘住，尤其是杆式结构装置，还需有较大的安装空间，不方便安装和检修。非接触型液位测量主要有超声波液位计、微波雷达液位计、射线液位计以及激光液位计等。顾名思义，这类测量仪表的共同特点是测量的感应元件与被测液体不接触。因此测量部件不受被测介质影响，也不影响被测介质，因而其适用范围较为广泛，可用于接触型测量仪表不能满足的特殊场合，如粘度高、腐蚀性强、污染性强、易结晶的介质。 根据以上几种因素得知，超声波液位计是非接触式液位计中发展最快的一种。超声波在同一种介质中传播速度相对恒定，遇到被测物体表面时会产生反射，基于此原理研制出
衣服尺寸对照表 女款上装 上衣S M L XL XXL XXXL 服装384042444648胸围(cm)78-8182-8586-8990-9394-9798-102腰围(cm)62-6667-7071-7475-7980-8485-89 肩宽(cm)363840424446适合身高(cm)153/157158/162163/167168/172173/177177/180， 女裤 尺码24 26 27 28 29 30 31 对应臀围（市 2尺4 2尺6 2尺7 2尺8 2尺9 3尺3尺1 尺） 对应臀围 80 87 90 93 97 100 103 （cm） 对应腰围（市 1尺8 1尺9 2尺2尺1 2尺2 2尺3 2尺4 尺） 对应腰围 59 63 67 70 74 78 82 （cm） 女式背心 尺码S M L XL 3XL
胸围72-80 79-87 86-94 94-97 98-102 腰围58-64 64-70 69-77 77-85 85-93 臀围82-90 87-95 92-100 97-105 102-110 女式内裤 尺码S M L XL 3XL 型号150-155 155-160 160-165 165-170 170-175 腰围55-61 61-67 67-73 73-79 79-85 臀围80-86 85-93 90-98 95-103 100-108 女式泳装 尺码XS/32 S/34 M/36 L/38 XL/40 腰围63-70 70-76 80-86 86-93 93-100 女鞋 光脚长度 22.0 22.5 23.0 23.5 24.0 24.5 25.0 （cm） 鞋码(中国) 34码35码36码37码38码39码40码 鞋码(美国) 4.5 5 5.5 6 6.5 7 7.5 鞋码(英国) 3.5 4 4.5 5 5.5 6 6.5 女裙 尺码24 26 27 28 29 30 31 对应臀围 2尺4 2尺6 2尺7 2尺8 2尺9 3尺3尺1 （市尺）
2100单词，11500英文字符，4300汉字 出处：Do?an E. New Historicism and Renaissance Culture*J+. Ankara üniversitesiDilveTarih-Co?rafyaFakültesiDergisi, 2005, 45(1): 77-95. 原文 NEW HISTORICISM AND RENAISSANCE CULTURE EvrimDogan Although this new form of historicism centers history as the subject of research, it differs from the "old" in its understanding of history. While traditional historicism regards history as "universal," new historicism considers it to be "cultural." According to Jeffrey N. Cox and Larry J.Reynolds, "new" historicism can be differentiated from "old" historicism "by its lack of faith in 'objectivity' and 'permanence' and its stress not upon the direct recreation of the past, but rather the process by which the past is constructed or invented" (1993: 4). This new outlook on history also brings about a new outlook on literature and literary criticism. Traditional literary historicism holds that the proper aim of literary criticism is to attempt to reconstruct the past objectively, whereas new historicism suggests that history is only knowable in the same sense literature is—through subjective interpretation: our understanding of the past is always conducted by our present consciousness’s. Louis Montrose, in his "Professing the Renaissance," lays out that as critics we are historically bound and we may only reconstruct the histories through the filter of our consciousness: Our analyses and our understandings necessarily proceed from our own historically, socially and institutionally shaped vantage points; that the histories we reconstruct are the textual constructs of critics who are, ourselves, historical subjects (1989: 23). For Montrose, contemporary historicism must recognize that "not only the poet but also the critic exists in history" and that the texts are "inscriptions of history" and furthermore that "our comprehension, representation, interpretation of the texts of the past always proceeds by a mixture of estrangement and appropriation.". Montrose suggests that this kind of critical practice constitutes a continuous dialogue between a "poetics" and a "politics" of culture. In Montrose's opinion, the complete recovery of meanings in a diverse historical outlook is considered necessary since older historical criticism is "illusory," in that it attempts to "recover meanings that are in any final or absolute sense authentic, correct, and complete," because scholarship constantly "constructs and delimits" the objects of study and the scholar is "historically positioned vis-a-vis that object:" The practice of a new historical criticism invites rhetorical strategies by which to foreground the constitutive acts of textuality that traditional modes of literary history efface or misrecognize. It also necessitates efforts to historicize the present as well as the past, and to historicize the dialectic between them—those reciprocal historical pressures by which the past has shaped the present and the present reshapes the past. The new historicist outlook on literary criticism is primarily against literary formalism that excludes all considerations external to the "text," and evaluates it in isolation.The preliminary concern of new historicism is to refigure the relationship between texts and the cultural system in which they were produced. In terms of new historicism, a literary text can only be evaluated in its social, historical, and political contexts. Therefore, new historicism renounces the formalist conception of literature as an autonomous aesthetic order that transcends the needs and interests of