4#厂房土建及钢结构工程量清单预算
新世纪大学英语综合教程4第四册Unit 4
Brainstorming
Directions: What words will occur to you whenever we mention the phrase “choosing a career”. Write down as many words as possible about it.
Operating engineers
Global reading
1 Structure Analysis
2
Table Completion
3
True or False
Structure Analysis
Parts Paras. Main Ideas
The author explains his understanding of work, labor, and play. Whether one is a laborer or a 1~3 worker has little to do with whether he or she is doing a physical or a mental job, but with the attitude he or she takes towards the job he or she does.
1
2
4
In the author’s eyes, the majority of people in a modern technological society are laborers rather than workers. The author stresses the two negative aspects of technology and the division of labor: by eliminating the need for special skills, they turned enjoyable work into boring labor and by increasing productivity, they give people excessive leisure time.
全新版大学英语综合教程4课后翻译（完整版）
全新版大学英语综合教程4课后翻译Unit 11. Mr. Doherty and his family are currently engaged in getting the autumn harvest in on the farm.多尔蒂先生和他的家人目前正在农场忙于秋收。
2. We must not underestimate the enemy. They are equipped with the most sophisticatedweapons.我们不能低估敌人,他们装备了最先进的武器。
3. Having been cut of a job/Not having had a job for 3months, Phil is getting increasinglydesperate.菲尔已经三个月没有找到工作了,正在变得越来越绝望。
4. Sam, as the project manager, is decisive, efficient, and accurate in his judgment.作为项目经理,山姆办事果断,工作效率高,且判断准确。
5. Since the chemical plant was identified as the source of solution, the village neighborhoodcommittee decided to close it down at the cost of 100 jobs.既然已经证实这家化工厂是污染源,村委会决定将其关闭,为此损失了一百个工作岗位。
Unit 21.There was an unusual quietness in the air, except for the sound of artillery in the distance.空气有一种不寻常的寂静,只有远处响着大炮的声音。
2. The expansion of urban areas in some African countries has been causing a significant fall inliving standards and an increase in social problems.在某些非洲国家城市的扩展已经引起生活水平相当大的下降和社会问题的增多。
幼儿园数字4的正确写法
《幼儿园数字 4 的正确写法》
亲爱的小朋友们，今天老师要和大家一起学习数字 4 的正确写法哦。
小朋友们，咱们先来看看数字 4 像什么呀？它呀，有点像小旗子在飘呢。
写数字 4 的时候，咱们要先准备好小铅笔和小本子。
第一步，先在本子上轻轻地画一个小斜线，就像滑梯一样，从左上慢慢滑到右下。
老师给大家讲个小故事吧。
有一天，小猴子去游乐场玩滑梯，它从高高的地方一下子就滑了下来，可开心啦！咱们写数字 4 的这第一步，就像小猴子滑滑梯。
接下来，再画一个竖线，要直直的，像小竹子一样。
然后呀，再画一个小斜线，从右上滑到左下。
最后，把下面封上口，数字 4 就写好啦！
小朋友们看，写好的数字 4 是不是很漂亮呀？
老师再给大家举个例子。
比如说，咱们有 4 个小苹果，就可以用数字 4 来表示。
小朋友们可以一边念着“小滑梯，小竹子，再滑一下封上口”，一边写数字 4 。
咱们班的小明小朋友，一开始写数字 4 总是写不好，但是他不放弃，每天都认真练习，现在写得可漂亮啦！
小朋友们，咱们多练习几次，一定能把数字 4 写得又好看又正确。
等大家都学会写数字 4 了，老师会给大家奖励小贴画哟！
加油，小朋友们，相信你们都能写好数字 4 ！。
Unit 4 词法翻译 - 增词法和省词法
Unit 4. Translation Techniques of Words and Expressions 词法翻译（2）Outline of this unit1. Addition 增词法2. Omission 省词法1. Addition 增词法增补翻译是在原文的基础上，增加一些词语，使译文的语法结构、修辞方式及语言表达符合汉语的行文要求。
要点：增词不增义，增加原文中已省略但汉语表达所需要的词语，不能随心所欲地增加。
增词法的主要类型：语法方面：动词、量词、时态词、名词修辞方面：名词复数、语气词逻辑方面：表示概括性、承上启下、逻辑关联的词语语法方面：动词、量词、时态词、名词上述这些词汇在英语语法中省略了，但在汉语中不可或缺，译文中要增加。
动词：英语动词呈显性；汉语动词在句中是表意的重心，不可少，并且汉语动词不受形态的约束，运用极为灵活，在汉语中备受青睐。
I fell madly in love with her, and she with me.Histories make men wise, poets witty, the mathematics subtle, natural philosophy deep, moral grave, logic and rhetoric able to contend.After the football match, he’s got an important meeting.He dismissed the meeting without a closing speech.练习：I am looking forward to the holidays.We won’t retreat, we never have and never will.Reading makes a full man; conference a ready man; and writing an exact man.The world needn’t be afraid of a possible shortage of coal, oil, natural gas or other sources of fuel for the future.接下来看增加量词的情况。
4的数字代表什么意思有什么含义
4的数字代表什么意思有什么含义 在中国民间，许多⼈都因“四”与“死”的发⾳相近，⾯对“四”有所忌讳，和西⽅社会对“⼗三”的忌讳是类似的。
那么，你知道4的数字代表什么意思吗?接下来就跟着店铺⼀起去看看数字4的含义吧。
数字4的含义(⼀) 数字4的含义：执⾏数 数字四代表物质的坚固性，也就是物质的组成和建造。
⼀个坚固的物质或实体，是它众多构成体的整体组合，同时它⾃⼰⼜是⼆个新的整体。
绝对的⼀以数字四来定义⾃⼰，因爲他同时是⼀个整体和所有构成体(创造)的组合。
埃及⼈运⽤四个简单的现象(⽕、风、⼟、⽔)来形容构成物质所必需的四元素的作⽤⾓⾊。
⽕是活跃的，凝结的法则;⼟是接收的、格式化的法则，风是细微的、沉思的法则，会影响⼒量的交换;⽔是总和，是⼈、⼟、⽔的组合法则，⽔也是⼀种在它们之上的物质。
数字4总是呈现出苦难的特质，它代表着⽣命中那些让你感觉受限、需要收敛⾃我的⽅⾯。
⾯对种种困难应该采取怎样的解决之道，我们在⾯对逆境的时⼜应当如何规范⾃⼰的⼯作⽣活，都需要极强的组织思维与⾃律性，数字4便是带给我们这样的⼈⽣课题。
数字4的含义(⼆) 关键词：限制、规范、服务 象征符号：四⽅型 属性：计算智商型 对应⾊彩;绿⾊ 五⾏：阴⽕ 星座排⾏：巨蟹 这个数字在形态上就像⼀把三⾓量尺，代表精算，中规中矩，同时每⼀笔都是直挺的，见棱见⾓，象征着死板，不知变通。
四季分春夏秋冬，⽅向分东南西北，物质存在的四元素分为⽕、⽔、风、地，数字4代表了完整的秩序。
数字4的物质平⾯结构就像⼀个正⽅盒⼦，坚固、完善、安全。
数字本质都带有原始的意义，1和2的结合创造了3，当组合成⼀个家庭时，就要靠数字4来稳定⽣存的基本“安全”，4的任务就是实现和显化。
正⾯优势 帮助他⼈，实际，秩序，效率，⾃律，组织⼒，可靠，实⼲，诚恳，有勇⽓，任劳任怨，未⾬绸缪，稳重，做事认真，坚定，忠实，逻辑分明 负⾯挑战 ⼼胸狭隘，情绪紧张，不妥协，过分固执，过于谨慎，约束，拒绝改变，⾃我保护，挑剔，嫉妒⼼，吝啬，⼼胸狭窄，缺乏想象⼒，说教，不变通，⽆趣乏味 恐惧 变化，不稳定，⽣活危机 数字4的含义(三) 起初，⼈们都认为“四”最⼀般的象征含义是四⽅和四时。
阅读教程一Unit 4
number of people are not satisfied with their jobs.
___F___ 4. In the author’s opinion, many things have to be
• The conversation would lull for a while.
平静，安静； 平息
lull n. 暂停； 间歇；平静期
• a lull in the fighting / political violence /
conversation / storm
Para. 10
dull adj.
What would you do if you take a job you don’t like? Give reasons to support your idea.
Reading One – Enjoy Your Job
Style of Writing
• Argumentation
Structure
Para. 10
dull adj.
• The coffin closed with a dull thud.
• （声音）不清晰的，隐约的，低沉的
• The pain, usually a dull ache, gets
worse with exercise. • (感觉)不明显的，不强烈的，隐约的
Reading One – Enjoy Your Job
Para. 3 apt adj.
• an apt remark / description / choice
《使命召唤4：现代战争》全流程全收集图文攻略
《使命召唤4：现代战争》全流程全收集图⽂攻略 情报收集1 打开船舱进⼊后，跟着⼩队下楼。
你会在下层舱室看到⼀个醉⿁敌⼈。
就在醉⿁旁边舱室的桌⾯上。
%{p a g e-b r e a k|情报收集1|p a g e-b r e a k}% 情报收集2 船底⼤货仓。
货仓地⾯的栅格⽹上。
%{p a g e-b r e a k|情报收集2|p a g e-b r e a k}% 情报收集3 跟随队友前进时，遇到有敌⼈的路边棚屋。
消灭掉棚屋内的敌⼈。
情报在桌⾯上。
%{p a g e-b r e a k|情报收集3|p a g e-b r e a k}% 情报收集4 灰⽩⾊⽼旧建筑。
进⼊后带上夜视仪，上楼。
楼上房间，马桶旁的桌⼦上。
%{p a g e-b r e a k|情报收集4|p a g e-b r e a k}% 情报收集5 开始时队友在外警戒的临时基地。
下楼进⼊地下室。
地下室⼤厅旁的⼩房间，有3个拳头海报的桌⼦上。
%{p a g e-b r e a k|情报收集5|p a g e-b r e a k}% 情报收集6 巷战，有⽔塔的街道。
前进经过⽔塔后向右转。
⾓落房间⼆楼的桌⼦上。
%{p a g e-b r e a k|情报收集6|p a g e-b r e a k}% 情报收集7 从刚才的房间出去。
向有多个卫星接收器(锅盖)的建筑⽅向⾛。
从蓝⾊的废弃车辆和有3个拳头海报的楼梯⼜上去。
情报就在露台上的桌⼦上。
%{p a g e-b r e a k|情报收集7|p a g e-b r e a k}% 情报收集8 夜视夜战的⼤楼。
⼆楼时会有⼀个等待队友破门⽽⼊的房间。
进去后会看到强光，取下夜视镜就能发现情报。
%{p a g e-b r e a k|情报收集8|p a g e-b r e a k}% 情报收集9 巷战街道，在有⾃⾏车的⾓落右转。
连续右转前进，知道看到有⾹蕉、⽔果箱⼦在前⾯的建筑。
计量经济学第4章课后答案
17CHAPTER 4SOLUTIONS TO PROBLEMS4.2 (i) and (iii) generally cause the t statistics not to have a t distribution under H 0.Homoskedasticity is one of the CLM assumptions. An important omitted variable violates Assumption MLR.3. The CLM assumptions contain no mention of the sample correlations among independent variables, except to rule out the case where the correlation is one.4.3 (i) While the standard error on hrsemp has not changed, the magnitude of the coefficient has increased by half. The t statistic on hrsemp has gone from about –1.47 to –2.21, so now the coefficient is statistically less than zero at the 5% level. (From Table G.2 the 5% critical value with 40 df is –1.684. The 1% critical value is –2.423, so the p -value is between .01 and .05.)(ii) If we add and subtract 2βlog(employ ) from the right-hand-side and collect terms, we havelog(scrap ) = 0β + 1βhrsemp + [2βlog(sales) – 2βlog(employ )] + [2βlog(employ ) + 3βlog(employ )] + u = 0β + 1βhrsemp + 2βlog(sales /employ ) + (2β + 3β)log(employ ) + u ,where the second equality follows from the fact that log(sales /employ ) = log(sales ) – log(employ ). Defining 3θ ≡ 2β + 3β gives the result.(iii) No. We are interested in the coefficient on log(employ ), which has a t statistic of .2, which is very small. Therefore, we conclude that the size of the firm, as measured by employees, does not matter, once we control for training and sales per employee (in a logarithmic functional form).(iv) The null hypothesis in the model from part (ii) is H 0:2β = –1. The t statistic is [–.951 – (–1)]/.37 = (1 – .951)/.37 ≈ .132; this is very small, and we fail to reject whether we specify a one- or two-sided alternative.4.4 (i) In columns (2) and (3), the coefficient on profmarg is actually negative, although its t statistic is only about –1. It appears that, once firm sales and market value have been controlled for, profit margin has no effect on CEO salary.(ii) We use column (3), which controls for the most factors affecting salary. The t statistic on log(mktval ) is about 2.05, which is just significant at the 5% level against a two-sided alternative.18(We can use the standard normal critical value, 1.96.) So log(mktval ) is statistically significant. Because the coefficient is an elasticity, a ceteris paribus 10% increase in market value is predicted to increase salary by 1%. This is not a huge effect, but it is not negligible, either.(iii) These variables are individually significant at low significance levels, with t ceoten ≈ 3.11 and t comten ≈ –2.79. Other factors fixed, another year as CEO with the company increases salary by about 1.71%. On the other hand, another year with the company, but not as CEO, lowers salary by about .92%. This second finding at first seems surprising, but could be related to the “superstar” effect: firms that hire CEOs from outside the company often go after a small pool of highly regarded candidates, and salaries of these people are bid up. More non-CEO years with a company makes it less likely the person was hired as an outside superstar.4.7 (i) .412 ± 1.96(.094), or about .228 to .596.(ii) No, because the value .4 is well inside the 95% CI.(iii) Yes, because 1 is well outside the 95% CI.4.8 (i) With df = 706 – 4 = 702, we use the standard normal critical value (df = ∞ in Table G.2), which is 1.96 for a two-tailed test at the 5% level. Now t educ = −11.13/5.88 ≈ −1.89, so |t educ | = 1.89 < 1.96, and we fail to reject H 0: educ β = 0 at the 5% level. Also, t age ≈ 1.52, so age is also statistically insignificant at the 5% level.(ii) We need to compute the R -squared form of the F statistic for joint significance. But F = [(.113 − .103)/(1 − .113)](702/2) ≈ 3.96. The 5% critical value in the F 2,702 distribution can be obtained from Table G.3b with denominator df = ∞: cv = 3.00. Therefore, educ and age are jointly significant at the 5% level (3.96 > 3.00). In fact, the p -value is about .019, and so educ and age are jointly significant at the 2% level.(iii) Not really. These variables are jointly significant, but including them only changes the coefficient on totwrk from –.151 to –.148.(iv) The standard t and F statistics that we used assume homoskedasticity, in addition to the other CLM assumptions. If there is heteroskedasticity in the equation, the tests are no longer valid.4.11 (i) Holding profmarg fixed, n rdintensΔ = .321 Δlog(sales ) = (.321/100)[100log()sales ⋅Δ] ≈ .00321(%Δsales ). Therefore, if %Δsales = 10, n rdintens Δ ≈ .032, or only about 3/100 of a percentage point. For such a large percentage increase in sales,this seems like a practically small effect.(ii) H 0:1β = 0 versus H 1:1β > 0, where 1β is the population slope on log(sales ). The t statistic is .321/.216 ≈ 1.486. The 5% critical value for a one-tailed test, with df = 32 – 3 = 29, is obtained from Table G.2 as 1.699; so we cannot reject H 0 at the 5% level. But the 10% criticalvalue is 1.311; since the t statistic is above this value, we reject H0 in favor of H1 at the 10% level.(iii) Not really. Its t statistic is only 1.087, which is well below even the 10% critical value for a one-tailed test.1920SOLUTIONS TO COMPUTER EXERCISESC4.1 (i) Holding other factors fixed,111log()(/100)[100log()](/100)(%),voteA expendA expendA expendA βββΔ=Δ=⋅Δ≈Δwhere we use the fact that 100log()expendA ⋅Δ ≈ %expendA Δ. So 1β/100 is the (ceteris paribus) percentage point change in voteA when expendA increases by one percent.(ii) The null hypothesis is H 0: 2β = –1β, which means a z% increase in expenditure by A and a z% increase in expenditure by B leaves voteA unchanged. We can equivalently write H 0: 1β + 2β = 0.(iii) The estimated equation (with standard errors in parentheses below estimates) isn voteA = 45.08 + 6.083 log(expendA ) – 6.615 log(expendB ) + .152 prtystrA(3.93) (0.382) (0.379) (.062) n = 173, R 2 = .793.The coefficient on log(expendA ) is very significant (t statistic ≈ 15.92), as is the coefficient on log(expendB ) (t statistic ≈ –17.45). The estimates imply that a 10% ceteris paribus increase in spending by candidate A increases the predicted share of the vote going to A by about .61percentage points. [Recall that, holding other factors fixed, n voteAΔ≈(6.083/100)%ΔexpendA ).] Similarly, a 10% ceteris paribus increase in spending by B reduces n voteAby about .66 percentage points. These effects certainly cannot be ignored.While the coefficients on log(expendA ) and log(expendB ) are of similar magnitudes (andopposite in sign, as we expect), we do not have the standard error of 1ˆβ + 2ˆβ, which is what we would need to test the hypothesis from part (ii).(iv) Write 1θ = 1β +2β, or 1β = 1θ– 2β. Plugging this into the original equation, and rearranging, givesn voteA = 0β + 1θlog(expendA ) + 2β[log(expendB ) – log(expendA )] +3βprtystrA + u ,When we estimate this equation we obtain 1θ≈ –.532 and se( 1θ)≈ .533. The t statistic for the hypothesis in part (ii) is –.532/.533 ≈ –1. Therefore, we fail to reject H 0: 2β = –1β.21C4.3 (i) The estimated model isn log()price = 11.67 + .000379 sqrft + .0289 bdrms (0.10) (.000043) (.0296)n = 88, R 2 = .588.Therefore, 1ˆθ= 150(.000379) + .0289 = .0858, which means that an additional 150 square foot bedroom increases the predicted price by about 8.6%.(ii) 2β= 1θ – 1501β, and solog(price ) = 0β+ 1βsqrft + (1θ – 1501β)bdrms + u= 0β+ 1β(sqrft – 150 bdrms ) + 1θbdrms + u .(iii) From part (ii), we run the regressionlog(price ) on (sqrft – 150 bdrms ), bdrms ,and obtain the standard error on bdrms . We already know that 1ˆθ= .0858; now we also getse(1ˆθ) = .0268. The 95% confidence interval reported by my software package is .0326 to .1390(or about 3.3% to 13.9%).C4.5 (i) If we drop rbisyr the estimated equation becomesn log()salary = 11.02 + .0677 years + .0158 gamesyr (0.27) (.0121) (.0016)+ .0014 bavg + .0359 hrunsyr (.0011) (.0072)n = 353, R 2= .625.Now hrunsyr is very statistically significant (t statistic ≈ 4.99), and its coefficient has increased by about two and one-half times.(ii) The equation with runsyr , fldperc , and sbasesyr added is22n log()salary = 10.41 + .0700 years + .0079 gamesyr(2.00) (.0120) (.0027)+ .00053 bavg + .0232 hrunsyr (.00110) (.0086)+ .0174 runsyr + .0010 fldperc – .0064 sbasesyr (.0051) (.0020) (.0052) n = 353, R 2 = .639.Of the three additional independent variables, only runsyr is statistically significant (t statistic = .0174/.0051 ≈ 3.41). The estimate implies that one more run per year, other factors fixed,increases predicted salary by about 1.74%, a substantial increase. The stolen bases variable even has the “wrong” sign with a t statistic of about –1.23, while fldperc has a t statistic of only .5. Most major league baseball players are pretty good fielders; in fact, the smallest fldperc is 800 (which means .800). With relatively little variation in fldperc , it is perhaps not surprising that its effect is hard to estimate.(iii) From their t statistics, bavg , fldperc , and sbasesyr are individually insignificant. The F statistic for their joint significance (with 3 and 345 df ) is about .69 with p -value ≈ .56. Therefore, these variables are jointly very insignificant.C4.7 (i) The minimum value is 0, the maximum is 99, and the average is about 56.16. (ii) When phsrank is added to (4.26), we get the following:n log() wage = 1.459 − .0093 jc + .0755 totcoll + .0049 exper + .00030 phsrank (0.024) (.0070) (.0026) (.0002) (.00024)n = 6,763, R 2 = .223So phsrank has a t statistic equal to only 1.25; it is not statistically significant. If we increase phsrank by 10, log(wage ) is predicted to increase by (.0003)10 = .003. This implies a .3% increase in wage , which seems a modest increase given a 10 percentage point increase in phsrank . (However, the sample standard deviation of phsrank is about 24.)(iii) Adding phsrank makes the t statistic on jc even smaller in absolute value, about 1.33, but the coefficient magnitude is similar to (4.26). Therefore, the base point remains unchanged: the return to a junior college is estimated to be somewhat smaller, but the difference is not significant and standard significant levels.(iv) The variable id is just a worker identification number, which should be randomly assigned (at least roughly). Therefore, id should not be correlated with any variable in the regression equation. It should be insignificant when added to (4.17) or (4.26). In fact, its t statistic is about .54.23C4.9 (i) The results from the OLS regression, with standard errors in parentheses, aren log() psoda =−1.46 + .073 prpblck + .137 log(income ) + .380 prppov (0.29) (.031) (.027) (.133)n = 401, R 2 = .087The p -value for testing H 0: 10β= against the two-sided alternative is about .018, so that we reject H 0 at the 5% level but not at the 1% level.(ii) The correlation is about −.84, indicating a strong degree of multicollinearity. Yet eachcoefficient is very statistically significant: the t statistic for log()ˆincome β is about 5.1 and that forˆprppovβ is about 2.86 (two-sided p -value = .004).(iii) The OLS regression results when log(hseval ) is added aren log() psoda =−.84 + .098 prpblck − .053 log(income ) (.29) (.029) (.038) + .052 prppov + .121 log(hseval ) (.134) (.018)n = 401, R 2 = .184The coefficient on log(hseval ) is an elasticity: a one percent increase in housing value, holding the other variables fixed, increases the predicted price by about .12 percent. The two-sided p -value is zero to three decimal places.(iv) Adding log(hseval ) makes log(income ) and prppov individually insignificant (at even the 15% significance level against a two-sided alternative for log(income ), and prppov is does not have a t statistic even close to one in absolute value). Nevertheless, they are jointly significant at the 5% level because the outcome of the F 2,396 statistic is about 3.52 with p -value = .030. All of the control variables – log(income ), prppov , and log(hseval ) – are highly correlated, so it is not surprising that some are individually insignificant.(v) Because the regression in (iii) contains the most controls, log(hseval ) is individually significant, and log(income ) and prppov are jointly significant, (iii) seems the most reliable. It holds fixed three measure of income and affluence. Therefore, a reasonable estimate is that if the proportion of blacks increases by .10, psoda is estimated to increase by 1%, other factors held fixed.。