第一篇：伍德里奇計量經(jīng)濟學英文版各章總結

CHAPTER 1 TEACHING NOTES You have substantial latitude about what to emphasize in Chapter 1.I find it useful to talk about the economics of crime example(Example 1.1)and the wage example(Example 1.2)so that students see, at the outset, that econometrics is linked to economic reasoning, even if the economics is not complicated theory.I like to familiarize students with the important data structures that empirical economists use, focusing primarily on cross-sectional and time series data sets, as these are what I cover in a first-semester course.It is probably a good idea to mention the growing importance of data sets that have both a cross-sectional and time dimension.I spend almost an entire lecture talking about the problems inherent in drawing causal inferences in the social sciences.I do this mostly through the agricultural yield, return to education, and crime examples.These examples also contrast experimental and nonexperimental(observational)data.Students studying business and finance tend to find the term structure of interest rates example more relevant, although the issue there is testing the implication of a simple theory, as opposed to inferring causality.I have found that spending time talking about these examples, in place of a formal review of probability and statistics, is more successful(and more enjoyable for the students and me).CHAPTER 2 TEACHING NOTES This is the chapter where I expect students to follow most, if not all, of the algebraic derivations.In class I like to derive at least the unbiasedness of the OLS slope coefficient, and usually I

derive the variance.At a minimum, I talk about the factors affecting the variance.To simplify the notation, after I emphasize the assumptions in the population model, and assume random sampling, I just condition on the values of the explanatory variables in the sample.Technically, this is justified by random sampling because, for example, E(ui|x1,x2,…,xn)= E(ui|xi)by independent sampling.I find that students are able to focus on the key assumption SLR.4 and subsequently take my word about how conditioning on the independent variables in the sample is harmless.(If you prefer, the appendix to Chapter 3 does the conditioning argument carefully.)Because statistical inference is no more difficult in multiple regression than in simple regression, I postpone inference until Chapter 4.(This reduces redundancy and allows you to focus on the interpretive differences between simple and multiple regression.)You might notice how, compared with most other texts, I use relatively few assumptions to derive the unbiasedness of the OLS slope estimator, followed by the formula for its variance.This is because I do not introduce redundant or unnecessary assumptions.For example, once SLR.4 is assumed, nothing further about the relationship between u and x is needed to obtain the unbiasedness of OLS under random sampling.CHAPTER 3 1 TEACHING NOTES For undergraduates, I do not work through most of the derivations in this chapter, at least not in detail.Rather, I focus on interpreting the assumptions, which mostly concern the population.Other than random sampling, the only assumption that involves more than population considerations is the assumption about no perfect collinearity, where the possibility of perfect collinearity in the sample(even if it does not occur in the population)should be touched on.The more important issue is perfect collinearity in the population, but this is fairly easy to dispense with via examples.These come from my experiences with the kinds of model specification issues that beginners have trouble with.The comparison of simple and multiple regression estimates – based on the particular sample at hand, as opposed to their statistical properties – usually makes a strong impression.Sometimes I do not bother with the “partialling out” interpretation of multiple regression.As far as statistical properties, notice how I treat the problem of including an irrelevant variable: no separate derivation is needed, as the result follows form Theorem 3.1.I do like to derive the omitted variable bias in the simple case.This is not much more difficult than showing unbiasedness of OLS in the simple regression case under the first four Gauss-Markov assumptions.It is important to get the students thinking about this problem early on, and before too many additional(unnecessary)assumptions have been introduced.I have intentionally kept the discussion of multicollinearity to a minimum.This partly indicates my bias, but it also reflects reality.It is, of course, very important for students to understand the potential consequences of having highly correlated independent variables.But this is often beyond our control, except that we can ask less of our multiple regression analysis.If two or more explanatory variables are highly correlated in the sample, we should not expect to precisely estimate their ceteris paribus effects in the population.I find extensive treatments of multicollinearity, where one “tests” or somehow “solves” the multicollinearity problem, to be misleading, at best.Even the organization of some texts gives the impression that imperfect multicollinearity is somehow a violation of the Gauss-Markov assumptions: they include multicollinearity in a chapter or part of the book devoted to “violation of the basic assumptions,” or something like that.I have noticed that master’s students who have had some undergraduate econometrics are often confused on the multicollinearity issue.It is very important that students not confuse multicollinearity among the included explanatory variables in a regression model with the bias caused by omitting an important variable.I do not prove the Gauss-Markov theorem.Instead, I emphasize its implications.Sometimes, and certainly for advanced beginners, I put a special case of Problem 3.12 on a midterm exam, where I make a particular choice for the function g(x).Rather than have the students directly compare the variances, they should 2 appeal to the Gauss-Markov theorem for the superiority of OLS over any other linear, unbiased estimator.CHAPTER 4 TEACHING NOTES

At the start of this chapter is good time to remind students that a specific error distribution played no role in the results of Chapter 3.That is because only the first two moments were derived under the full set of Gauss-Markov assumptions.Nevertheless, normality is needed to obtain exact normal sampling distributions(conditional on the explanatory variables).I emphasize that the full set of CLM assumptions are used in this chapter, but that in Chapter 5 we relax the normality assumption and still perform approximately valid inference.One could argue that the classical linear model results could be skipped entirely, and that only large-sample analysis is needed.But, from a practical perspective, students still need to know where the t distribution comes from because virtually all regression packages report t statistics and obtain p-values off of the t distribution.I then find it very easy to cover Chapter 5 quickly, by just saying we can drop normality and still use t statistics and the associated p-values as being approximately valid.Besides, occasionally students will have to analyze smaller data sets, especially if they do their own small surveys for a term project.It is crucial to emphasize that we test hypotheses about unknown population parameters.I tell my students that they will be punished if they write something like ? = 0 on an exam or, even worse, H0:.632 = 0.H0:?1One useful feature of Chapter 4 is its illustration of how to rewrite a population model so that it contains the parameter of interest in testing a single restriction.I find this is easier, both theoretically and practically, than computing variances that can, in some cases, depend on numerous covariance terms.The example of testing equality of the return to two-and four-year colleges illustrates the basic method, and shows that the respecified model can have a useful interpretation.Of course, some statistical packages now provide a standard error for linear combinations of estimates with a simple command, and that should be taught, too.One can use an F test for single linear restrictions on multiple parameters, but this is less transparent than a t test and does not immediately produce the standard error needed for a confidence interval or for testing a one-sided alternative.The trick of rewriting the population model is useful in several instances, including obtaining confidence intervals for predictions in Chapter 6, as well as for obtaining confidence intervals for marginal effects in models with interactions(also in Chapter 6).The major league baseball player salary example illustrates the difference between individual and joint significance when explanatory variables(rbisyr and hrunsyr in this case)are highly correlated.I tend to emphasize the R-squared form of the F statistic because, in practice, it is applicable a large percentage of the time, and it is much more readily computed.I do regret that this example is biased toward students in countries where baseball is played.Still, it is one of the better examples of multicollinearity that I have come across, and students of all backgrounds seem to get the point.CHAPTER 5 TEACHING NOTES Chapter 5 is short, but it is conceptually more difficult than the earlier chapters, primarily because it requires some knowledge of asymptotic properties of estimators.In class, I give a brief, heuristic description of consistency and asymptotic normality before stating the consistency and asymptotic normality of OLS.(Conveniently, the same assumptions that work for finite sample analysis work for asymptotic analysis.)More advanced students can follow the proof of consistency of the slope coefficient in the bivariate regression case.Section E.4 contains a full matrix treatment of asymptotic analysis appropriate for a master’s level course.An explicit illustration of what happens to standard errors as the sample size grows emphasizes the importance of having a larger sample.I do not usually cover the LM statistic in a first-semester course, and I only briefly mention the asymptotic efficiency result.Without full use of matrix algebra combined with limit theorems for vectors and matrices, it is very difficult to prove asymptotic efficiency of OLS.I think the conclusions of this chapter are important for students to know, even though they may not fully grasp the details.On exams I usually include true-false type questions, with explanation, to test the students’ understanding of asymptotics.[For example: “In large samples we do not have to worry about omitted variable bias.”(False).Or “Even if the error term is not normally distributed, in large samples we can still compute approximately valid confidence intervals under the Gauss-Markov assumptions.”(True).]

CHAPTER 6 TEACHING NOTES I cover most of Chapter 6, but not all of the material in great detail.I use the example in Table 6.1 to quickly run through the effects of data scaling on the important OLS statistics.(Students should already have a feel for the effects of data scaling on the coefficients, fitting values, and R-squared because it is covered in Chapter 2.)At most, I briefly mention beta coefficients;if students have a need for them, they can read this subsection.The functional form material is important, and I spend some time on more complicated models involving logarithms, quadratics, and interactions.An important point for models with quadratics, and especially interactions, is that we need to evaluate the partial effect at interesting values of the explanatory variables.Often, zero is not an interesting value for an explanatory variable and is well outside the range in the sample.Using the methods from Chapter 4, it is easy to obtain confidence intervals for the effects at interesting x values.As far as goodness-of-fit, I only introduce the adjusted R-squared, as I think using a slew of goodness-of-fit measures to choose a model can be confusing to novices(and does not reflect empirical practice).It is important to discuss how, if we fixate on a high R-squared, we may wind up with a model that has no interesting ceteris paribus interpretation.I often have students and colleagues ask if there is a simple way to predict y when log(y)has been used as the dependent variable, and to obtain a goodness-of-fit measure for the log(y)model that can be compared with the usual R-squared obtained when y is the dependent variable.The methods described in Section 6.4 are easy to implement and, unlike other approaches, do not require normality.The section on prediction and residual analysis contains several important topics, including constructing prediction intervals.It is useful to see how much wider the prediction intervals are than the confidence interval for the conditional mean.I usually discuss some of the residual-analysis examples, as they have real-world applicability.CHAPTER 7 TEACHING NOTES

This is a fairly standard chapter on using qualitative information in regression analysis, although I try to emphasize examples with policy relevance(and only cross-sectional applications are included.).In allowing for different slopes, it is important, as in Chapter 6, to appropriately interpret the parameters and to decide whether they are of direct interest.For example, in the wage equation where the return to education is allowed to depend on gender, the coefficient on the female dummy variable is the wage differential between women and men at zero years of education.It is not surprising that we cannot estimate this very well, nor should we want to.In this particular example we would drop the interaction term because it is insignificant, but the issue of interpreting the parameters can arise in models where the interaction term is significant.In discussing the Chow test, I think it is important to discuss testing for differences in slope coefficients after allowing for an intercept difference.In many applications, a significant Chow statistic simply indicates intercept differences.(See the example in Section 7.4 on student-athlete GPAs in the text.)From a practical perspective, it is important to know whether the partial effects differ across groups or whether a constant differential is sufficient.I admit that an unconventional feature of this chapter is its introduction of the linear probability model.I cover the LPM here for several reasons.First, the LPM is being used more and more because it is easier to interpret than probit or logit models.Plus, once the proper parameter scalings are done for probit and logit, the estimated effects are often similar to the LPM partial effects near the mean or median values of the explanatory variables.The theoretical drawbacks of the LPM are often of secondary importance in practice.Computer Exercise C7.9 is a good one to illustrate that, even with over 9,000 observations, the LPM can deliver fitted values strictly between zero and one for all observations.If the LPM is not covered, many students will never know about using econometrics to explain qualitative outcomes.This would be especially unfortunate for students who might need to read an article where an LPM is used, or who might want to estimate an LPM for a term paper or senior thesis.Once they are introduced to purpose and interpretation of the LPM, along with its shortcomings, they can tackle nonlinear models on their own or in a subsequent course.A useful modification of the LPM estimated in equation(7.29)is to drop kidsge6(because it is not significant)and then define two dummy variables, one for kidslt6 equal to one and the other for kidslt6 at least two.These can be included in place of kidslt6(with no young children being the base group).This allows a diminishing marginal effect in an LPM.I was a bit surprised when a diminishing effect did not materialize.CHAPTER 8 TEACHING NOTES

This is a good place to remind students that homoskedasticity played no role in showing that OLS is unbiased for the parameters in the regression equation.In addition, you probably should mention that there is nothing wrong with the R-squared or adjusted R-squared as goodness-of-fit measures.The key is that these are estimates of the population R-squared, 1 – [Var(u)/Var(y)], where the variances are the unconditional variances in the population.The usual R-squared, and the adjusted version, consistently estimate the population R-squared whether or not Var(u|x)= Var(y|x)depends on x.Of course, heteroskedasticity causes the usual standard errors, t statistics, and F statistics to be invalid, even in large samples, with or without normality.By explicitly stating the homoskedasticity assumption as conditional on the explanatory variables that appear in the conditional mean, it is clear that only heteroskedasticity that depends on the explanatory variables in the model affects the validity of standard errors and test statistics.The version of the Breusch-Pagan test in the text, and the White test, are ideally suited for detecting forms of heteroskedasticity that invalidate inference obtained under homoskedasticity.If heteroskedasticity depends on an exogenous variable that does not also appear in the mean equation, this can be exploited in weighted least squares for efficiency, but only rarely is such a variable available.One case where such a variable is available is when an individual-level equation has been aggregated.I discuss this case in the text but I rarely have time to teach it.As I mention in the text, other traditional tests for heteroskedasticity, such as the Park and Glejser tests, do not directly test what we want, or add too many assumptions under the null.The Goldfeld-Quandt test only works when there is a natural way to order the data based on one independent variable.This is rare in practice, especially for cross-sectional applications.Some argue that weighted least squares estimation is a relic, and is no longer necessary given the availability of heteroskedasticity-robust standard errors and test statistics.While I am sympathetic to this argument, it presumes that we do not care much about efficiency.Even in large samples, the OLS estimates may not be precise enough to learn much about the population parameters.With substantial heteroskedasticity we might do better with weighted least squares, even if the weighting function is misspecified.As discussed in the text on pages 288-289, one can, and probably should, compute robust standard errors after weighted least squares.For asymptotic efficiency comparisons, these would be directly comparable to the heteroskedasiticity-robust standard errors for OLS.Weighted least squares estimation of the LPM is a nice example of feasible GLS, at least when all fitted values are in the unit interval.Interestingly, in the LPM examples in the text and the LPM computer exercises, the heteroskedasticity-robust standard errors often differ by only small amounts from the usual standard errors.However, in a couple of cases the differences are notable, as in Computer Exercise C8.7.CHAPTER 9 TEACHING NOTES

The coverage of RESET in this chapter recognizes that it is a test for neglected nonlinearities, and it should not be expected to be more than that.(Formally, it can be shown that if an omitted variable has a conditional mean that is linear in the included explanatory variables, RESET has no ability to detect the omitted variable.Interested readers may consult my chapter in Companion to Theoretical Econometrics, 2001, edited by Badi Baltagi.)I just teach students the F statistic version of the test.The Davidson-MacKinnon test can be useful for detecting functional form misspecification, especially when one has in mind a specific alternative, nonnested model.It has the advantage of always being a one degree of freedom test.I think the proxy variable material is important, but the main points can be made with Examples 9.3 and 9.4.The first shows that controlling for IQ can substantially change the estimated return to education, and the omitted ability bias is in the expected direction.Interestingly, education and ability do not appear to have an interactive effect.Example 9.4 is a nice example of how controlling for a previous value of the dependent variable – something that is often possible with survey and nonsurvey data – can greatly affect a policy conclusion.Computer Exercise 9.3 is also a good illustration of this method.I rarely get to teach the measurement error material, although the attenuation bias result for classical errors-in-variables is worth mentioning.The result on exogenous sample selection is easy to discuss, with more details given in Chapter 17.The effects of outliers can be illustrated using the examples.I think the infant mortality example, Example 9.10, is useful for illustrating how a single influential observation can have a large effect on the OLS estimates.With the growing importance of least absolute deviations, it makes sense to at least discuss the merits of LAD, at least in more advanced courses.Computer Exercise 9.9 is a good example to show how mean and median effects can be very different, even though there may not be “outliers” in the usual sense.CHAPTER 10 TEACHING NOTES

Because of its realism and its care in stating assumptions, this chapter puts a somewhat heavier burden on the instructor and student than traditional treatments of time series regression.Nevertheless, I think it is worth it.It is important that students learn that there are potential pitfalls inherent in using regression with time series data that are not present for cross-sectional applications.Trends, seasonality, and high persistence are ubiquitous in time series data.By this time, students should have a firm grasp of multiple regression mechanics and inference, and so you can focus on those features that make time series applications different from cross-sectional ones.I think it is useful to discuss static and finite distributed lag models at the same time, as these at least have a shot at satisfying the Gauss-Markov assumptions.Many interesting examples have distributed lag dynamics.In discussing the time series versions of the CLM assumptions, I rely mostly on intuition.The notion of strict exogeneity is easy to discuss in terms of feedback.It is also pretty apparent that, in many applications, there are likely to be some explanatory variables that are not strictly exogenous.What the student should know is that, to conclude that OLS is unbiased – as opposed to consistent – we need to assume a very strong form of exogeneity of the regressors.Chapter 11 shows that only contemporaneous exogeneity is needed for consistency.Although the text is careful in stating the assumptions, in class, after discussing strict exogeneity, I leave the conditioning on X implicit, especially when I discuss the no serial correlation assumption.As this is a new assumption I spend some time on it.(I also discuss why we did not need it for random sampling.)

Once the unbiasedness of OLS, the Gauss-Markov theorem, and the sampling distributions under the classical linear model assumptions have been covered – which can be done rather quickly – I focus on applications.Fortunately, the students already know about logarithms and dummy variables.I treat index numbers in this chapter because they arise in many time series examples.A novel feature of the text is the discussion of how to compute goodness-of-fit measures with a trending or seasonal dependent variable.While detrending or deseasonalizing y is hardly perfect(and does not work with integrated processes), it is better than simply reporting the very high R-squareds that often come with time series regressions with trending variables.CHAPTER 11 TEACHING NOTES

Much of the material in this chapter is usually postponed, or not covered at all, in an introductory course.However, as Chapter 10 indicates, the set of time series applications that satisfy all of the classical linear model assumptions might be very small.In my experience, spurious time series regressions are the hallmark of many student projects that use time series data.Therefore, students need to be alerted to the dangers of using highly persistent processes in time series regression equations.(Spurious regression problem and the notion of cointegration are covered in detail in Chapter 18.)

It is fairly easy to heuristically describe the difference between a weakly dependent process and an integrated process.Using the MA(1)and the stable AR(1)examples is usually sufficient.When the data are weakly dependent and the explanatory variables are contemporaneously exogenous, OLS is consistent.This result has many applications, including the stable AR(1)regression model.When we add the appropriate homoskedasticity and no serial correlation assumptions, the usual test statistics are asymptotically valid.The random walk process is a good example of a unit root(highly persistent)process.In a one-semester course, the issue comes down to whether or not to first difference the data before specifying the linear model.While unit root tests are covered in Chapter 18, just computing the first-order autocorrelation is often sufficient, perhaps after detrending.The examples in Section 11.3 illustrate how different first-difference results can be from estimating equations in levels.Section 11.4 is novel in an introductory text, and simply points out that, if a model is dynamically complete in a well-defined sense, it should not have serial correlation.Therefore, we need not worry about serial correlation when, say, we test the efficient market hypothesis.Section 11.5 further investigates the homoskedasticity assumption, and, in a time series context, emphasizes that what is contained in the explanatory variables determines what kind of heteroskedasticity is ruled out by the usual OLS inference.These two sections could be skipped without loss of continuity.CHAPTER 12 TEACHING NOTES

Most of this chapter deals with serial correlation, but it also explicitly considers heteroskedasticity in time series regressions.The first section allows a review of what assumptions were needed to obtain both finite sample and asymptotic results.Just as with heteroskedasticity, serial correlation itself does not invalidate R-squared.In fact, if the data are stationary and weakly dependent, R-squared and adjusted R-squared consistently estimate the population R-squared(which is well-defined under stationarity).Equation(12.4)is useful for explaining why the usual OLS standard errors are not generally valid with AR(1)serial correlation.It also provides a good starting point for discussing serial correlation-robust standard errors in Section 12.5.The subsection on serial correlation with lagged dependent variables is included to debunk the myth that OLS is always inconsistent with lagged dependent variables and serial correlation.I do not teach it to undergraduates, but I do to master’s students.9 Section 12.2 is somewhat untraditional in that it begins with an asymptotic t test for AR(1)serial correlation(under strict exogeneity of the regressors).It may seem heretical not to give the Durbin-Watson statistic its usual prominence, but I do believe the DW test is less useful than the t test.With nonstrictly exogenous regressors I cover only the regression form of Durbin’s test, as the h statistic is asymptotically equivalent and not always computable.Section 12.3, on GLS and FGLS estimation, is fairly standard, although I try to show how comparing OLS estimates and FGLS estimates is not so straightforward.Unfortunately, at the beginning level(and even beyond), it is difficult to choose a course of action when they are very different.I do not usually cover Section 12.5 in a first-semester course, but, because some econometrics packages routinely compute fully robust standard errors, students can be pointed to Section 12.5 if they need to learn something about what the corrections do.I do cover Section 12.5 for a master’s level course in applied econometrics(after the first-semester course).I also do not cover Section 12.6 in class;again, this is more to serve as a reference for more advanced students, particularly those with interests in finance.One important point is that ARCH is heteroskedasticity and not serial correlation, something that is confusing in many texts.If a model contains no serial correlation, the usual heteroskedasticity-robust statistics are valid.I have a brief subsection on correcting for a known form of heteroskedasticity and AR(1)errors in models with strictly exogenous regressors.CHAPTER 13 TEACHING NOTES

While this chapter falls under “Advanced Topics,” most of this chapter requires no more sophistication than the previous chapters.(In fact, I would argue that, with the possible exception of Section 13.5, this material is easier than some of the time series chapters.)

Pooling two or more independent cross sections is a straightforward extension of cross-sectional methods.Nothing new needs to be done in stating assumptions, except possibly mentioning that random sampling in each time period is sufficient.The practically important issue is allowing for different intercepts, and possibly different slopes, across time.The natural experiment material and extensions of the difference-in-differences estimator is widely applicable and, with the aid of the examples, easy to understand.Two years of panel data are often available, in which case differencing across time is a simple way of removing g unobserved heterogeneity.If you have covered Chapter 9, you might compare this with a regression in levels using the second year of data, but where a lagged dependent variable is included.(The second approach only requires collecting information on the dependent variable in a previous year.)These often give similar answers.Two years of panel data, collected before and after a policy change, can be very powerful for policy analysis.Having more than two periods of panel data causes slight complications in that the errors in the differenced equation may be serially correlated.(However, the traditional assumption that the errors in the original equation are serially uncorrelated is not always a good one.In other words, it is not always more appropriate to used fixed effects, as in Chapter 14, than first differencing.)With large N and relatively small T, a simple way to account for possible serial correlation after differencing is to compute standard errors that are robust to arbitrary serial correlation and hetero-skedasticity.Econometrics packages that do cluster analysis(such as Stata)often allow this by specifying each cross-sectional unit as its own cluster.CHAPTER 14 TEACHING NOTES

My preference is to view the fixed and random effects methods of estimation as applying to the same underlying unobserved effects model.The name “unobserved effect” is neutral to the issue of whether the time-constant effects should be treated as fixed parameters or random variables.With large N and relatively small T, it almost always makes sense to treat them as random variables, since we can just view the unobserved ai as being drawn from the population along with the observed variables.Especially for undergraduates and master’s students, it seems sensible to not raise the philosophical issues underlying the professional debate.In my mind, the key issue in most applications is whether the unobserved effect is correlated with the observed explanatory variables.The fixed effects transformation eliminates the unobserved effect entirely whereas the random effects transformation accounts for the serial correlation in the composite error via GLS.(Alternatively, the random effects transformation only eliminates a fraction of the unobserved effect.)As a practical matter, the fixed effects and random effects estimates are closer when T is large or when the variance of the unobserved effect is large relative to the variance of the idiosyncratic error.I think Example 14.4 is representative of what often happens in applications that apply pooled OLS, random effects, and fixed effects, at least on the estimates of the marriage and union wage premiums.The random effects estimates are below pooled OLS and the fixed effects estimates are below the random effects estimates.Choosing between the fixed effects transformation and first differencing is harder, although useful evidence can be obtained by testing for serial correlation in the first-difference estimation.If the AR(1)coefficient is significant and negative(say, less than ?.3, to pick a not quite arbitrary value), perhaps fixed effects is preferred.Matched pairs samples have been profitably used in recent economic applications, and differencing or random effects methods can be applied.In an equation such as(14.12), there is probably no need to allow a different intercept for each sister provided that the labeling of sisters is random.The different intercepts might be needed if a certain feature of a sister that is not included in the observed controls is used to determine the ordering.A statistically significant intercept in the differenced equation would be evidence of this.CHAPTER 15 TEACHING NOTES

When I wrote the first edition, I took the novel approach of introducing instrumental variables as a way of solving the omitted variable(or unobserved heterogeneity)problem.Traditionally, a student’s first exposure to IV methods comes by way of simultaneous equations models.Occasionally, IV is first seen as a method to solve the measurement error problem.I have even seen texts where the first appearance of IV methods is to obtain a consistent estimator in an AR(1)model with AR(1)serial correlation.The omitted variable problem is conceptually much easier than simultaneity, and stating the conditions needed for an IV to be valid in an omitted variable context is straightforward.Besides, most modern applications of IV have more of an unobserved heterogeneity motivation.A leading example is estimating the return to education when unobserved ability is in the error term.We are not thinking that education and wages are jointly determined;for the vast majority of people, education is completed before we begin collecting information on wages or salaries.Similarly, in studying the effects of attending a certain type of school on student performance, the choice of school is made and then we observe performance on a test.Again, we are primarily concerned with unobserved factors that affect performance and may be correlated with school choice;it is not an issue of simultaneity.The asymptotics underlying the simple IV estimator are no more difficult than for the OLS estimator in the bivariate regression model.Certainly consistency can be derived in class.It is also easy to demonstrate how, even just in terms of inconsistency, IV can be worse than OLS if the IV is not completely exogenous.At a minimum, it is important to always estimate the reduced form equation and test whether the IV is partially correlated with endogenous explanatory variable.The material on multicollinearity and 2SLS estimation is a direct extension of the OLS case.Using equation(15.43), it is easy to explain why multicollinearity is generally more of a problem with 2SLS estimation.Another conceptually straightforward application of IV is to solve the measurement error problem, although, because it requires two measures, it can be hard to implement in practice.Testing for endogeneity and testing any overidentification restrictions is something that should be covered in second semester courses.The tests are fairly easy to motivate and are very easy to implement.While I provide a treatment for time series applications in Section 15.7, I admit to having trouble finding compelling time series applications.These are likely to be found at a less aggregated level, where exogenous IVs have a chance of existing.(See also Chapter 16.)

CHAPTER 16 TEACHING NOTES

I spend some time in Section 16.1 trying to distinguish between good and inappropriate uses of SEMs.Naturally, this is partly determined by my taste, and many applications fall into a gray area.But students who are going to learn about SEMS should know that just because two(or more)variables are jointly determined does not mean that it is appropriate to specify and estimate an SEM.I have seen many bad applications of SEMs where no equation in the system can stand on its own with an interesting ceteris paribus interpretation.In most cases, the researcher either wanted to estimate a tradeoff between two variables, controlling for other factors – in which case OLS is appropriate – or should have been estimating what is(often pejoratively)called the “reduced form.”

The identification of a two-equation SEM in Section 16.3 is fairly standard except that I emphasize that identification is a feature of the population.(The early work on SEMs also had this emphasis.)Given the treatment of 2SLS in Chapter 15, the rank condition is easy to state(and test).Romer’s(1993)inflation and openness example is a nice example of using aggregate cross-sectional data.Purists may not like the labor supply example, but it has become common to view labor supply as being a two-tier decision.While there are different ways to model the two tiers, specifying a standard labor supply function conditional on working is not outside the realm of reasonable models.Section 16.5 begins by expressing doubts of the usefulness of SEMs for aggregate models such as those that are specified based on standard macroeconomic models.Such models raise all kinds of thorny issues;these are ignored in virtually all texts, where such models are still used to illustrate SEM applications.SEMs with panel data, which are covered in Section 16.6, are not covered in any other introductory text.Presumably, if you are teaching this material, it is to more advanced students in a second semester, perhaps even in a more applied course.Once students have seen first differencing or the within transformation, along with IV methods, they will find specifying and estimating models of the sort contained in Example 16.8 straightforward.Levitt’s example concerning prison populations is especially convincing because his instruments seem to be truly exogenous.CHAPTER 17 TEACHING NOTES I emphasize to the students that, first and foremost, the reason we use the probit and logit models is to obtain more reasonable functional forms for the response probability.Once we move to a nonlinear model with a fully specified conditional distribution, it makes sense to use the efficient estimation procedure, maximum likelihood.It is important to spend some time on interpreting probit and logit estimates.In particular, the students should know the rules-of-thumb for comparing probit, logit, and LPM estimates.Beginners sometimes mistakenly think that, because the probit and especially the logit estimates are much larger than the LPM estimates, the explanatory variables now have larger estimated effects on the response probabilities than in the LPM case.This may or may not be true.I view the Tobit model, when properly applied, as improving functional form for corner solution outcomes.In most cases it is wrong to view a Tobit application as a data-censoring problem(unless there is true data censoring in collecting the data or because of institutional constraints).For example, in using survey data to estimate the demand for a new product, say a safer pesticide to be used in farming, some farmers will demand zero at the going price, while some will demand positive pounds per acre.There is no data censoring here;some farmers find it optimal to use none of the new pesticide.The Tobit model provides more realistic functional forms for E(y|x)and E(y|y > 0,x)than a linear model for y.With the Tobit model, students may be tempted to compare the Tobit estimates with those from the linear model and conclude that the Tobit estimates imply larger effects for the independent variables.But, as with probit and logit, the Tobit estimates must be scaled down to be comparable with OLS estimates in a linear model.[See Equation(17.27);for an example, see Computer Exercise C17.3.]

Poisson regression with an exponential conditional mean is used primarily to improve over a linear functional form for E(y|x).The parameters are easy to interpret as semi-elasticities or elasticities.If the Poisson distributional assumption is correct, we can use the Poisson distribution compute probabilities, too.But over-dispersion is often present in count regression models, and standard errors and likelihood ratio statistics should be adjusted to reflect this.Some reviewers of the first edition complained about either the inclusion of this material or its location within the chapter.I think applications of count data models are on the rise: in microeconometric fields such as criminology, health economics, and industrial organization, many interesting response variables come in the form of counts.One suggestion was that Poisson regression should not come between the Tobit model in Section 17.2 and Section 17.4, on censored and truncated regression.In fact, I put the Poisson regression model between these two topics on purpose: I hope it helps emphasize that the material in Section 17.2 is purely about functional form, as is Poisson regression.Sections 17.4 and 17.5 deal with underlying linear models, but where there is a data-observability problem.Censored regression, truncated regression, and incidental truncation are used for missing data problems.Censored and truncated data sets usually result from sample design, as in duration analysis.Incidental truncation often arises from self-selection into a certain state, such as employment or participating in a training program.It is important to emphasize to students that the underlying models are classical linear models;if not for the missing data or sample selection problem, OLS would be the efficient estimation procedure.CHAPTER 18 TEACHING NOTES

Several of the topics in this chapter, including testing for unit roots and cointegration, are now staples of applied time series analysis.Instructors who like their course to be more time series oriented might cover this chapter after Chapter 12, if time permits.Or, the chapter can be used as a reference for ambitious students who wish to be versed in recent time series developments.The discussion of infinite distributed lag models, and in particular geometric DL and rational DL models, gives one particular interpretation of dynamic regression models.But one must emphasize that only under fairly restrictive assumptions on the serial correlation in the error of the infinite DL model does the dynamic regression consistently estimate the parameters in the lag distribution.Computer Exercise C18.1 provides a good illustration of how the GDL model, and a simple RDL model, can be too restrictive.Example 18.5 tests for cointegration between the general fertility rate and the value of the personal exemption.There is not much evidence of cointegration, which sheds further doubt on the regressions in levels that were used in Chapter 10.The error correction model for holding yields in Example 18.7 is likely to be of interest to students in finance.As a class project, or a term project for a student, it would be interesting to update the data to see if the error correction model is stable over time.The forecasting section is heavily oriented towards regression methods and, in particular, autoregressive models.These can be estimated using any econometrics package, and forecasts and mean absolute errors or root mean squared errors are easy to obtain.The interest rate data sets(for example, INTQRT.RAW)can be updated to do much more recent out-of-sample forecasting exercises.CHAPTER 19 TEACHING NOTES

This is a chapter that students should read if you have assigned them a term paper.I used to allow students to choose their own topics, but this is difficult in a first-semester course, and places a heavy burden on instructors or teaching assistants, or both.I now assign a common topic and provide a data set with about six weeks left in the term.The data set is cross-sectional(because I teach time series at the end of the course), and I provide guidelines of the kinds of questions students should try to answer.(For example, I might ask them to answer the following questions: Is there a marriage premium for NBA basketball players? If so, does it depend on race? Can the premium, if it exists, be explained by productivity differences?)The specifics are up to the students, and they are to craft a 10-to 15-page paper on their own.This gives them practice writing the results in a way that is easy-to-read, and forces them to interpret their findings.While leaving the topic to each student’s discretion is more interesting, I find that many students flounder with an open-ended assignment until it is too late.Naturally, for a second-semester course, or a senior seminar, students would be expected to design their own topic, collect their own data, and then write a more substantial term paper.15

第二篇：計量經(jīng)濟學課程總結

經(jīng)過一個學期對計量經(jīng)濟學的學習，我收獲了很多，也懂得了很多。通過以計量經(jīng)濟學為核心，以統(tǒng)計學，數(shù)學，經(jīng)濟學等學科為指導，輔助以一些軟件的應用，從這些之中我都學到了很多知識。同時對這門課程有了新的認識，計量經(jīng)濟學對我們的生活很重要，它對我國經(jīng)濟的發(fā)展有重要的影響。

計量經(jīng)濟學對我們研究經(jīng)濟問題是很好的方法和理論。學習計量經(jīng)濟學給我印象和幫助最大的主要對EVIES軟件的熟練操作與應用,初步投身于計量經(jīng)濟學，通過利用Eviews軟件將所學到的計量知識進行實踐，讓我加深了對理論的理解和掌握，直觀而充分地體會到老師課堂講授內(nèi)容的精華之所在。在實驗過程中我們提高了手動操作軟件、數(shù)量化分析與解決問題的能力，還可以培養(yǎng)我在處理實驗經(jīng)濟問題的嚴謹?shù)目茖W的態(tài)度，并且避免了課堂知識與實際應用的脫節(jié)。雖然在實驗過程中出現(xiàn)了很多錯誤，但這些經(jīng)驗卻錘煉了我們發(fā)現(xiàn)問題的眼光，豐富了我們分析問題的思路。

計量經(jīng)濟學的定義為：用數(shù)學方法探討經(jīng)濟學可以從好幾個方面著手，但任何一個方面都不能和計量經(jīng)濟學混為一談。計量經(jīng)濟學與經(jīng)濟統(tǒng)計學絕非一碼事;它也不同于我們所說的一般經(jīng)濟理論，盡管經(jīng)濟理論大部分具有一定的數(shù)量特征;計量經(jīng)濟學也不應視為數(shù)學應用于經(jīng)濟學的同義語。經(jīng)驗表明，統(tǒng)計學、經(jīng)濟理 論和數(shù)學這三者對于真正了解現(xiàn)代經(jīng)濟生活的數(shù)量關系來說，都是必要的，但本身并非是充分條件。三者結合起來，就是力量，這種結合便構成了計量經(jīng)濟學?？巳R因(R.Klein)：“計量經(jīng)濟學已經(jīng)在經(jīng)濟學科中居于最重要的地位”，“在大多數(shù)大學和學院中，計量經(jīng)濟學的講授已經(jīng)成為經(jīng)濟學課程表中最有權威的一部分”

計量經(jīng)濟學關心統(tǒng)計工具在經(jīng)濟問題與實證資料分析上的發(fā)展和應用，經(jīng)濟學理論提供對于經(jīng)濟現(xiàn)象邏輯一致的可能解釋。因為人類行為和決策是復雜的過程，所以一個經(jīng)濟議題可能存在多種不同的解釋理論。當研究者無法進行實驗室的實驗時，一個理論必須透過其預測與事實的比較來檢驗,計量經(jīng)濟學即為檢驗不同的理論和經(jīng)濟模型的估計提供統(tǒng)計工具。

在計量經(jīng)濟學一元線性回歸模型，我認識到：變量間的關系及回歸分析的基本概念，主要包括：

其次有一元線形回歸模型的參數(shù)估計及其統(tǒng)計檢驗與應用，包括： 這個公式得給出，以及樣本回歸函數(shù)的隨機形式?？偟恼f來，這一節(jié)留給我印象最深刻的，便是根據(jù)樣本回歸函數(shù)SRF，估計總體回歸函數(shù)PRF，即總體回歸線與樣本回歸線之間的關系。除此以外，我也學會了參數(shù)的最大似然估計法語最小二乘法。對于最小二乘法，當從模型總體隨機抽取n組樣本觀測值后，最合理的參數(shù)估計量應該使得模型能最好的擬合樣本數(shù)據(jù)，而對于最大似然估計法，當從模型總體隨機抽取n組樣本觀測值后，最合理的參數(shù)估計量應該使得從模型中抽取該n組樣本觀測值的概率最大。顯然，這是從不同原理出發(fā)的兩種參數(shù)估計方法。即：

1.一元回歸模型：

關于擬合優(yōu)度的檢驗，也就是檢驗模型對樣本觀測值的擬合程度。被解釋變量Y的觀測值圍繞其均值的總離差平方和可分解為兩個部分：一部分來自于回歸線，另一部分來自于隨機勢力。所以，我們用來自回歸線的回歸平方和占Y的總離差的平方和的比例來判斷樣本回歸線與樣本觀測值的擬合優(yōu)度。這個比例，我們也較它可決系數(shù)，它的取值范圍是0<=R2<=1。

關于變量的顯著性檢驗，是要考察所選擇的解釋變量是否對被解釋變量有顯著的線性影響。所應用的方法是數(shù)理統(tǒng)計學中的假設檢驗。關于置信區(qū)間估計。當我們要判斷樣本參數(shù)的估計值在多大程度上可以“近似”的替代總體參數(shù)的真值，往往需要通過構造一個以樣本參數(shù)的估計值為中心的“區(qū)間”，來考察它以多大的概率包含這真是的參數(shù)值。這樣的方法就是我們所說的參數(shù)檢驗的置信區(qū)間估計。當我們希望縮小置信區(qū)間時，可以采用的方法有增大樣本容量和提高模型的擬合優(yōu)度。

2.多元回歸模型

多元回歸分析與一元回歸分析的幾點不同：

關于修正的可絕系數(shù)。我們可于發(fā)現(xiàn)，在樣本容量一定的情況下，增加解釋變量必定使得自由度減少，所以調(diào)整的思路是:將殘差平方和與總離差平方和分別除以各自的自由度，以剔除變量個數(shù)對擬合優(yōu)度的影響。這樣就引出了我們這里說的調(diào)整的可絕系數(shù)。

關于對多個解釋變量是否對被解釋變量有顯著線性影響關系的聯(lián)合性F檢驗。F檢驗的思想來自于總離差平方和的分解式：TSS=ESS+RSS。通過比較F值與臨界值的大小來判定原方程總體上的線性關系是否顯著成立。計量經(jīng)濟學是一門比較難的課程，其中涉及大量的公式，不容易理解且需要大量的運算，其中需要很好的數(shù)學基礎、統(tǒng)計基礎和自己的分析思考能力，以及良好的計量軟件應用能力，所以在學習的過程中我遇到了很多困難。例如異方差的實驗，異方差通常發(fā)生于橫截面數(shù)據(jù)中，一般是有解釋變量的方差與隨機誤差項的方差成比例。要發(fā)現(xiàn)這一問題，我們學習了很多檢驗，包括park test，Goldfeld-Quant test，White test等。要糾正異方差，常用的方法是WLS，通過對數(shù)據(jù)的處理能夠有效消除異方差的問題。自相關的問題一般見于時間序列數(shù)據(jù)中，一階序列相關是指當前的誤差項與以前的誤差項線性相關。在發(fā)生自相關的情況下，我們在進行變量的顯著性檢驗時更傾向于拒絕虛擬假設。發(fā)現(xiàn)一階自相關問題的最重要檢驗是Durbin-Watson test，這一檢驗的特點是存在未決區(qū)域。糾正自相關的問題，我們學會了GLS和Cochrane-Orcutt迭代法，并在計算機應用中學習了其操作，受益匪淺。但通過這次的實驗，我對課上所學的最小二乘法有了進一步的理解，在掌握理論知識的同時，將其與實際的經(jīng)濟問題聯(lián)系起來。

在目前的學術現(xiàn)狀下，要求研究者必須掌握計量的研究方法，這是實證研究最好的工具。用計量的工具，我們才能夠把經(jīng)濟現(xiàn)象肢解開來，找到其中的脈絡，進而分析得更加清晰。

第三篇：各章習題總結

范圍管理

8、工作分解結構中的每一項都被標以一個獨特的標示符，標示符的名稱是什么? A、質(zhì)量檢測標示符 B、帳目圖表

C、項目活動編碼

D、帳目編碼

9、編制項目范圍說明書時不需要包含以下哪項? A、成本/利益分析 B、項目歷史 C、項目可交付成果 D、可測量的目標

11、范圍說明是重要的，因為范圍說明 A、為制定未來項目的決策提供依據(jù) B、提供了項目的簡潔概要 C、替項目干系人批準項目 D、提供衡量項目成本的標準

14、為了有效的管理項目，應該將工作分解為小塊，以下各項中哪項不能說明任務應該分 解到什么程度? A、可以在80 小時以下完成 B、不能再進一步進行邏輯細分了 C、可由一個人完成

D、可以進行實際估算

17、客戶要求進行范圍變更。為了分析變更對項目的影響，項目經(jīng)理應該回顧工作分解結 構、變更請求、范圍管理計劃和‐‐‐‐‐‐‐? A、績效報告 B、職責分配矩陣 C、帕累托圖

D、蒙特卡洛模擬

20.在項團隊會議上，一個小組成員建議擴大工作范圍，他的建議已經(jīng)超越了項目章

程中的范圍。這時，項目經(jīng)理指出項目團隊應該集中精力完成僅限于需要完成的所有工作。這是一個什么樣的例子? A、范圍定義 B、范圍管理 C、項目章程

D、范圍分解

22、創(chuàng)建工作分解結構的過程可以產(chǎn)生什么? A、項目進度計劃 B、小組外購

C、項目完工日期

D、風險清單

23、項目經(jīng)理可以使用‐‐‐‐‐‐來保證項目團隊清楚的了解到他們的每一項任務包含的工作。

A、項目工作范圍 B、項目章程 C、WBS 詞典

D、風險管理計劃

30、以下哪一項工具或技術用于項目啟動? A、確認替代方案 B、配置管理 C、決策模式 D、分解

35、項目章程最少應該：

A、描述項目經(jīng)理和職能經(jīng)理的職責和權利 B、探討項目的風險和限制以及針對這些問題的計劃 C、指定項目的組織結構 D、說明執(zhí)行組織的商業(yè)目標 40 以下哪項不是項目啟動的輸入? A、產(chǎn)品或服務說明 B、組強戰(zhàn)略計劃 C、項目篩選計劃

D、項目章程

42、項目章程應該由誰發(fā)布? A、項目經(jīng)理

B、執(zhí)行組織的領導 C、項目外的一名經(jīng)理

D、項目發(fā)起人

47.上個星期你還舒舒服服地在海邊休假，今天你卻不得不埋頭于工作。有個項目經(jīng)理的位置目前空缺，因為前任經(jīng)理決定退休并且要在阿肯色州開辦一個農(nóng)場，而你接管了這個項

目，現(xiàn)在要檢查一堆關于這個項目的范圍變更請求。為了評估這個項目將在什么程度上變 更，你需要將這些變更要求跟哪一個項目文件的要求作比較? A、范圍說明

B、工作分解結構 C、項目計劃 D、管理計劃范圍

系統(tǒng)的維護不算在項目的生命周期中

53.公司是一個雞肉食品公司，目前正在實施一個項目，目的是完全消除產(chǎn)品中沙門氏菌的 威脅。你是該項目的項目經(jīng)理。你已經(jīng)完成了項目的構思階段。構思階段的成果是： A、項目計劃 B、工作說明 C、項目章程

D、資源電子數(shù)據(jù)表

62.你在負責管理一個視頻游戲的項目。上個月客戶已經(jīng)簽署項目需求說明和范圍說明。但 是現(xiàn)在她提出了一項范圍變更要求。她希望把這個游戲做成一種電視和電腦上都能玩的互 動游戲。這種范圍變更至少會表現(xiàn)在哪一個方面? A、修改工作分解結構已經(jīng)確定的項目范圍 B、導致所有項目基線的變更 C、需要對成本 時間 質(zhì)量以及其他目標進行調(diào)整

D、得到一個經(jīng)驗教訓

65.在項目生命周期的概念階段，管理層表示希望每個新項目的效益應超過開發(fā)成本。這 是以下什么的例子： A、假定

B、限制條件

C、通過約束優(yōu)化選擇項目

D、一個技術要求

68.你所在的公司原來主要生產(chǎn)是一家處于領先地位的食品供應商。為了增加公司收入，管 理者有意開拓新的市場和產(chǎn)品。你現(xiàn)在領導著一個負責開發(fā)產(chǎn)品的團隊。由于你的背景和 對信息技術的興趣，你建議公司開發(fā)無線通信產(chǎn)品。但當你將建議提交審議的時候，管理 層認為這項產(chǎn)品和公司的核心競爭力不符合。你需要返回規(guī)劃委員會推薦其他產(chǎn)品，并把 管理層的指導方針作為 A、一條假設 B、一項約束 C、一個規(guī)范

D、一項技術要求

70.各種項目的檔案資料可以用于

A、將目前的業(yè)績和預期獲得的教訓與之相比較 B、準備干系人管理計劃 C、篩選項目團隊成員

D、作為項目開始的輸入項

73.在項目生命周期中的哪一個階段遇到的不確定性最大? A、概念階段

B、計劃編制階段 C、實施階段 D、收尾階段

79.項目失敗的理論原因是

A、缺少項目式的或者強大的矩陣結構，不良的范圍定義，以及缺少項目計劃

B、缺少上級管理部門的支持和承諾，項目團隊不和諧，以及項目經(jīng)理缺少領導能力 C、客戶需要的不良定位，項目團隊工作位置上的分散，以及在整個項目進程中缺乏 與客戶的溝通

D、組織結構因素，客戶需要的不良定位，不合適的項目具體要求，以及不良的計劃編 制和控制

82.你所在項目的技術主管提出了一項會給項目帶來增值的請求，但是這個請求同時也會導 致項目范圍的擴大。為了評估實施這一變更可能帶來的影響幅度，你要求在項目中使用凈 值分析法。這種方法代表的是 A、績效評估技術 B、配置管理程序 C、成本核算程序 D、范圍報告機制

項目時間管理

89.可以幫助我們明確哪些工作在規(guī)定的時間必須完成的工具是： A、項目主進度表

B、預算

C、工作分解結構 D、甘特表

91.在項目工作網(wǎng)絡中有幾種類型的浮動期。那些在特定活動中使用并且不影響后來活動的 浮動期被稱作 A、多余的浮動期 B、自由的浮動期 C、總的浮動期 D、預期的浮動期

95.你正計劃指揮你的新的項目管理戶外培訓課程的團隊組建部分，參與者將參加一個生存 試驗來剔除最“弱”團隊成員。獲勝者將得到公司的達爾文獎。因為這個課程只能在綠地 上執(zhí)行，在安排課程的實踐上你只能限制在一年中的幾個特定時間。課程開始的最佳時機 是七月中旬。在你設計項目進度時一個更為普遍的時間限制是 A、不早于開始 B、不晚于結束

C、有一個確定的最晚開始時間 D、有一個確定的最早結束時間

99.項目經(jīng)理在評估項目時間業(yè)績表現(xiàn)時應該關注關鍵的和次關鍵的行為，一個這樣做的方 法就是以浮動時間上升排序分析十個次關鍵的路徑。這種方法是如下哪一個分析管理的一 部分? A、方差分析 B、進度模擬 C、掙值管理 D、趨勢分析

88.在項目發(fā)展過程中，諸如誰來執(zhí)行這個工作，這個工作在那里執(zhí)行，工作的類型以及工 作分解結構(WBS)都是下面哪一個的示例? A、活動屬性 B、限制條件

C、在工作分解結構庫中貯存的數(shù)據(jù) D、定義細化

風險管理：

2、有兩類風險：商務和可保險型，以下哪項可看作可保險型風險 A、薪水冊成本 B、機會成本

C、沉淀成本

D、有擔保的承包商造成的損害

4、針對固定價合同，付款的風險是： A、承包商的實際成本

B、承包商的成本加固定費用 C、在承包商的投標中未公開的應急費用

D、根據(jù)風險評估預測所作的預測成本并用于處理風險

5、獲得可以降低風險量的項目信息的最準確的方法是： A、采用頭腦風暴技術識別風險 B、利用以前類似項目的歷史數(shù)據(jù) C、靈敏度分析 D、Delphi 技術

10、靈敏度分析和頭腦風暴法是兩種不同的風險識別方法，靈敏度分析的優(yōu)點有： A、僅針對公眾確定風險 B、考慮獨立的答案

C、管理層理解可能會有大量不同的結果 D、可以提供項目經(jīng)理可能缺乏的對項目的理解

12、某風險事件已經(jīng)發(fā)生并產(chǎn)生了占總項目成本15%的影響，下列哪些行動是最合適的措 施? A、通知正確的項目干系人 B、更新項目預算

C、控制成本

D、與團隊成員一起采用集體自由討論的方式

19、假設估計幅度的兩端是平均數(shù)的±3 西格瑪，以下哪項幅度估計的風險最低? A、30±5 天，B、22‐30 天

C、最樂觀為26 天，最可能為30 天，最悲觀為33 天 D、A 和B 一樣，風險都低于C

25、有效風險管理的首要要求是 A、決策所需信息的透明度高

B、風險所有關系明確

C、在管理已識別風險的過程中盡早的委任項目經(jīng)理

D、受過風險培訓并能理解風險起因的項目團隊成員幫助創(chuàng)建和實施風險降低策略

27、假如風險事件發(fā)生的機率是85%，而產(chǎn)生的影響是US10000 美元，則US10000 美元代 表什么? A、風險值 B、凈現(xiàn)值 C、期望值

D、應急儲備金

29、某項目經(jīng)理剛剛完成了項目的風險應對計劃，他下一步該做什么? A、確定項目整體風險的比率

B、開始分析哪些在產(chǎn)品中發(fā)現(xiàn)的風險 C、在工作分解結構上增加任務

D、進行項目風險審核

31、項目經(jīng)理正在評估供貨商的標書，兩個供貨商出售類似的電子元件，并且在供貨商方 進行集成，為了避免最大的風險，項目經(jīng)理審查供貨商的： A、價格、銷售額、利潤率

B、價格、交付承諾、檢驗進度計劃 C、價格、經(jīng)驗、交付方式

D、經(jīng)驗、個人技能、材料控制步驟

32、EVMS 報告顯示CV=SV=O，然而，由于遺漏了一個里程碑，整個項目將推遲。以下哪

項報告不充分? A、風險分析報告 B、溝通計劃偏差 C、資源管理計劃 D、關鍵路徑狀態(tài)

33、項目經(jīng)理可能意識到不能滿足某些合同條款和項目目標，要達到某些規(guī)范既費成本又 花時間。這時項目出現(xiàn)風險可能性較高，同客戶協(xié)商降低風險可能性這種手段： A、可以消除所有項目和客戶風險而不需要任何成本 B、可以重新定義風險對客戶發(fā)生的可能性

C、可以使項目范圍減小并改進交付給客戶的產(chǎn)品

D、可能減少支付罰金的成本并且滿足修訂過的規(guī)范達到客戶的最低要求

34、什么是風險的擁有者? A、對風險的識別負責

B、掌握風險來源的某個組織

C、受到風險嚴重影響的某個組織

D、對風險應對策略的實施負責

36、項目經(jīng)理要求項目團隊對其項目風險進行量化和評估，以下幾點不能證明這樣做的好 處的是：

A、徹底理解項目、相關風險、以及風險對項目各部分的影響 B、制訂處理已經(jīng)識別的問題的風險降低策略

C、保證所有已以識別的風險問題納入項目計劃編制

D、識別可能存在的替代方案

37、在做好項目成本結果概率分布后，有15%可能被超過的估算大約‐‐‐一個標準差 A、低于平均數(shù) B、高于中數(shù) C、高于平均數(shù) D、高于中數(shù)

第四篇：各章總結2

2、細胞外信息傳遞方式：（6種）

內(nèi)分泌：其信息分子即為激素，由內(nèi)分泌器官所合成及分泌，經(jīng)血液流經(jīng)全身作用于遠距離的靶器官。

旁分泌，自分泌，近分泌或并置分泌，胞內(nèi)分泌，逆分泌。

3、內(nèi)分泌系統(tǒng)的生理作用：

（1）保證集體內(nèi)環(huán)境的相對穩(wěn)定。A、控制消化道運動及消化腺的分泌； B、控制能量產(chǎn)生；

C、控制細胞外液的組成和容量。

（2）調(diào)節(jié)集體與外界環(huán)境的相對平衡。（3）調(diào)節(jié)生殖功能

4、內(nèi)分泌系統(tǒng)作用的調(diào)節(jié)：

（1）內(nèi)分泌腺功能的相互調(diào)節(jié)：腺體之間通過說分泌的激素表現(xiàn)協(xié)同、拮抗等復雜的相互關系

（2）神經(jīng)系統(tǒng)和內(nèi)分泌系統(tǒng)的相互調(diào)節(jié)

（3）神經(jīng)系統(tǒng)-內(nèi)分泌系統(tǒng)-體液之間的相互調(diào)節(jié)（4）神經(jīng)-內(nèi)分泌-免疫調(diào)節(jié)網(wǎng)絡

5、激素作用的特點：

激素的生理作用是將信息從一個細胞傳遞到其他細胞，作用有四點：（1）特異性：激素在血液循環(huán)過程中雖然廣泛接觸各種組織、細胞，但卻是有選擇性的作用于該激素的靶器官、靶腺體或靶細胞。

（2）高效性：生理狀況下，激素在血液中的含量很低，但卻表現(xiàn)出了強大的生理作用。

（3）協(xié)同性與拮抗性：動物體各內(nèi)分泌腺所分泌的激素之間是相互聯(lián)系、相互影響的，其主要作用表現(xiàn)為相互增強與拮抗性。

（4）激素的作用極為復雜，主要表現(xiàn)在：A、一種激素多種作用；B、一中功能多種激素。

6、激素的分類及其特點：

（1）含氮激素：產(chǎn)生后貯存于該腺體，當機體需要時分泌到鄰近的毛細血管中。

（2）類固醇激素：產(chǎn)生后立即釋放，并不貯存，血液中含有各種此類激素的原因是蛋白類載體與之結合后限制了其擴散。

（3）脂肪酸類激素：目前所知，僅有前列腺素，他在機體需要時分泌，邊分泌邊應用，并不貯存。

7、生殖激素：能直接影響生殖機能的激素稱為生殖激素，例如催乳素、前列腺素、等等。它在哺乳動物的復雜生殖過程中騎著重要的的掉空作用。

由特殊的無管腺和由一定的器官組織合成的化學物質(zhì)，它通過彌散或借助血液循環(huán)的方式運輸?shù)桨薪M織及靶器官而七作用。作用特點：（1）生理效應很強；如前列腺素對(永久)黃體的消除作用，生產(chǎn)上用0.2mg／頭600kg牛；（2）對靶組織和靶器官有高度轉移親和性；（3）借助血液循環(huán)或彌散作用產(chǎn)生生理效應；（4）投藥處距靶器官越近，七作用越強烈（可據(jù)此減少用量）；（5）具有相互協(xié)同或拮抗作用（雌激素對孕激素的協(xié)同作用，孕激素對雄激素的拮抗作用，對動物控制發(fā)情的控制）

幾種生產(chǎn)上常用的生殖激素：

（1）FSH：即促卵泡素，屬于垂體前葉促性腺激素，主要來源：垂體前葉，化學性質(zhì)：糖蛋白，靶器官：卵巢、睪丸（曲細精管），主要作用：促使卵泡發(fā)育成熟，促進精子發(fā)生。

（2）LH／ICSH：即促黃體素或間質(zhì)細胞刺激素，屬于垂體前葉促性腺激素，主要來源：垂體前葉，化學性質(zhì)：糖蛋白，靶器官：卵巢、睪丸（間質(zhì)細胞），主要作用：促使卵泡排卵，形成黃體，促進孕酮、雌激素及雄激素分泌。（3）HCG：即人絨毛膜促性腺激素，屬于胎盤促性腺激素，主要來源：靈長類胎盤絨毛膜，化學性質(zhì)：糖蛋白，靶器官：卵巢、睪丸，主要作用：與LH類似。

（4）eCG／PMSG：即馬絨毛膜促性腺激素或孕馬促性腺激素，屬于胎盤促性腺激素，主要來源：馬胎盤的子宮內(nèi)膜杯化學性質(zhì)：糖蛋白，靶器官：卵巢，主要作用與FSH類似。（PMSG易引起卵巢囊腫）PMSG與FSH相比：一般情況下二者可以相換，前者用藥后半衰期長，后者多用藥兩次，發(fā)情配種后前者近期不易配種，前者多用引起多卵泡發(fā)育。

HCG與LH可完全互換，兩顆稍大。

（5）PGs：即前列腺素，屬于局部刺激素，主要來源：各種組織，化學性質(zhì)：不飽和羥基脂肪酸，靶器官：各種組織和器官，主要作用：具有廣泛的生理作用，PGF2α具有溶黃體作用。

8、母畜生殖功能的發(fā)展階段：

初情期：母畜初次表現(xiàn)發(fā)情并排卵的時期，幼畜發(fā)育到初情期，性腺才真正具有了配子生成和內(nèi)分泌的雙重作用。

性成熟：母畜生長發(fā)育到一定年齡，生殖器官已經(jīng)發(fā)育完全，生殖機能達到了比較成熟的階段，基本具備了正常的繁殖功能，稱為性成熟，但此時身體的生長發(fā)育尚未完成。

繁殖適應齡期：母畜達到性成熟又達到了體成熟（身體已發(fā)育完全并具有雌性動物固有的特征與外貌），開始配種時體重應達到成年體重的70%以上，生產(chǎn)上牛18月齡，350kg；飼養(yǎng)條件較好時，常采用16月齡，300kg。

繁殖年限：限制因素：動物衰老喪失繁殖功能；疾病使生殖器官嚴重受損或功能障礙，反之活動停止。

9、發(fā)情周期：母畜達到初情期以后，其生殖器官及性行為重復發(fā)生一系列明顯的周期性變化稱為發(fā)情周期。

發(fā)情期子宮變化：卵泡生長→雌激素↑導致：A乳房腺管↑，乳房增大；B大腦興奮性↑；C子宮彈性↑，分泌物↑，子宮黏膜潮紅。排卵后，黃體↑→孕酮（拮抗雌激素）→負反饋丘腦導致LH、FSH分泌減少→發(fā)情停止，動物安靜→子宮彈性↓→卵巢彈性↓→產(chǎn)生子宮乳，此時胚胎處于游離狀態(tài)。

10、動物發(fā)情特點與發(fā)情鑒定：

（1）陰道、子宮頸：陰道黏膜潮紅充血，子宮頸弛張1-2倍，便于分泌物排除，精子進入。

（2）卵泡發(fā)育：此時卵巢上有較多各期發(fā)育程度不同的卵泡及黃體。（3）分泌物：陰門黏液：發(fā)情時動物興奮，黏液稀薄透明如蛋清；懷孕時（多在4月后）混沌，粘性稠，可拉長；子宮有炎癥時，分泌物呈絮狀，（條狀）分泌物多是如豆花狀：子宮內(nèi)膜炎時分泌物檢查：夜晚棉簽挑起手電照射下清液中含有粉筆灰狀沉淀物。卵（泡）巢囊腫、永久黃體：長期、大量時變稀薄。

（4）直腸檢查：可見子宮壁緊張，卵泡直徑可達1-2cm。

（5）行為變化：狗四處亂跑找交配，貓怪叫，牛常表現(xiàn)出不安有排尿姿勢，有爬跨動作。有時候表現(xiàn)不明顯。

母畜發(fā)情期生殖器官及性行為周期性變化參見P93。

11、妊娠識別與鑒定：

A、牛妊娠鑒定：直腸檢查參見P146 B、妊娠時間：（從配種時開始算）黃牛：274-291天，平均285天；水牛平均307；奶牛250-305，平均280；山羊146-161，平均152；豬110-123，平均114。

C、母體妊娠識別：黃體和孕酮的作用，參見P109-111。D、妊娠母體變化：

（1）生殖器官變化：卵巢中有黃體的存在；子宮逐漸增大，有被推入腹腔在還納至骨盆腔的過程；子宮中動脈孕側、兩側逐漸變粗出現(xiàn)特有的妊娠脈動；陰道、子宮頸及乳房變化：陰道粘膜蒼白，陰道先變長后變短而粗、充血而水腫；子宮頸縮緊，黏膜增厚，其腔內(nèi)充滿黏液（子宮頸塞）；乳房增大，變實。腺管增生，為泌乳做準備。（2）全身變化：營養(yǎng)狀況良好的動物一般皮毛光亮，驃形較好。到一定的妊娠階段，母畜腹圍增大，胃腸容積減小，排糞尿次數(shù)增加，不喜運動，后肢多水腫。

（3）內(nèi)分泌：大體趨勢為：孕酮從妊娠開始至分娩前短時間，保持較高水平，雌激素則一直保持很低水平，在孕酮水平降低時逐漸升高，分娩后降低為幾乎0。

12、分娩前動物體變化（分娩預兆）：

分娩：妊娠期滿，胎兒發(fā)育成熟，母體將胎兒及其附屬物從子宮排出體外的生理過程。分娩預兆：（1）乳房變化：（乳房進一步增大）乳房極度膨脹、皮膚發(fā)紅，乳頭中充滿白色初乳，乳頭表面被覆一層蠟樣物質(zhì)。

（2）軟產(chǎn)道變化：分娩前1周陰唇開始變軟、腫脹，增大2-3倍，前1-2d子宮頸開始腫大，松軟，封閉宮頸管的黏液軟化流入陰道，有事掉在陰門外，呈透明條索狀（牛）。

（3）骨盆韌帶的變化：骨盆韌帶、薦坐韌帶、薦髂韌帶韌帶變軟，薦骨后端活動性增大。在?？梢娢哺跋孪荨钡那闆r。

（4）精神狀態(tài)的變化：產(chǎn)前精神抑郁，徘徊不安，離群尋找安靜地方分娩，乳牛產(chǎn)前體溫升高至39-39.5℃。（分娩機理：參見P153）。

13、決定分娩過程的要素、分娩過程： 分娩取決于產(chǎn)力、產(chǎn)道及胎兒三者關系。

產(chǎn)力：胎兒從子宮中排出的力量，由子宮（陣縮）肌、腹肌和膈肌節(jié)律性收縮構成。軟產(chǎn)道：由子宮頸、陰道、前庭及陰門這些軟組織構成的管道。

硬產(chǎn)道：即骨盆（骨盆入口、出口，骨盆腔和骨盆軸），助產(chǎn)應在產(chǎn)道充分擴張的情況下進行。胎兒與產(chǎn)道的關系：

胎向：A、縱向：胎兒縱軸與母體縱軸平行，包括正生和倒生；B、橫向：胎兒縱軸與母體縱軸在水平方向垂直；C、豎向：胎兒縱軸與母體縱軸上下垂直。

正常的胎位：正生，上胎位。分娩過程：

（1）子宮開口期：子宮陣縮，此期無努責。母畜表現(xiàn)出尾根抬起，頻做排尿姿勢，脈搏、心跳加快。

（2）胎兒產(chǎn)出期：子宮頸充分開大，胎囊及胎兒前置部分楔入陰道。母畜有后肢踢腹的表現(xiàn)。分娩時母畜多采取側臥后肢挺直努責，便于骨盆開張，胎兒產(chǎn)出。（在胎膜破裂后，應迅速將胎兒拉出，防止腹壓減小，母畜停止努責，分娩中止。）

（3）胎衣（胎膜）排出期：胎兒產(chǎn)出后，陣縮及努責幅度減小，子宮陣縮（努責）將胎衣排出。牛產(chǎn)后不起，可利用胎兒置于其前舔舐而誘導其站起。

14、接產(chǎn)：

（1）準備：產(chǎn)房盡量寬敞、干燥安靜通風良好無賊風。藥械：70%酒精，5%碘酒，消毒液，催產(chǎn)藥液，注射器，無菌棉，體溫計等。人員：接受過訓練的專業(yè)人員。

（2）步驟：臨產(chǎn)檢查，及時助產(chǎn)。

（3）處理新生仔畜：A、擦干羊水；B、處理臍帶（斷臍消毒）；C、幫助吃初乳。

15、產(chǎn)后變化：

行為變化：舔舐仔畜，哺乳，護仔（孕酮激活中樞神經(jīng)有關母性行為的調(diào)節(jié)系統(tǒng)）。

生殖器官變化：胎衣排出后子宮迅速收縮變小；產(chǎn)后卵巢有卵泡開始發(fā)育；陰道、前庭及陰門，骨盆及其韌帶4-5d內(nèi)復原，經(jīng)10d左右妊娠浮腫消失。

牛的誘導分娩：從妊娠265-270d開始，一次肌肉注射20mg地塞米松或5-10mg氟美松，母牛在30-60h后分娩。

16、人工催奶技術：

A、測量母畜體重：胸圍×體斜長／10800；

B、苯甲酸雌二醇（雌激素、促性激素）0.1mg／kg； C、孕激素0.25mg／kg； D、利血平3-5mg／次／d。

B、C二步用至第7天，D步用第6至9天，可擠奶。

1號針劑為苯甲酸雌二醇，5 ml/支；2號針劑為利血平5 ml/支。

二、產(chǎn)科病理學部分：

17、流產(chǎn)原因及處理： 流產(chǎn)原因：

（1）疾病引起的流產(chǎn)：如全身感染，生殖器官疾病等；（2）獸醫(yī)診療錯誤引起；

（3）藥物性流產(chǎn)：A、全身麻醉；B、子宮收縮藥：氨甲酰膽堿，麥角心堿；C、藥物引起：地塞米松等；D、疫苗導致；E、飼料性：長期嚴重的維生素、礦物質(zhì)缺乏（Va、Ve），品質(zhì)不良：過酸、霜凍、霉變、有害、含雌激素過高飼料；F、管理性：打斗、摔倒，運輸驚嚇等刺激等。

處理：排出以上原因，主要在于預防流產(chǎn)的發(fā)生，對于已經(jīng)發(fā)生了流產(chǎn)的母畜，應采取：檢查、分析病因，防流產(chǎn)保胎，促使死胎排出，排出子宮惡露，防止發(fā)生子宮內(nèi)膜炎，生殖道感染。

18、孕畜截癱、浮腫、假孕：

孕畜截癱：妊娠末期孕畜既無導致癱瘓的局部因素（如腰、臀部及后肢損傷），又無明顯的全身癥狀，而后肢無法站立的一種疾病。病因多為Ca、P、Vd缺乏，營養(yǎng)不良，子宮捻轉等。

妊娠浮腫：妊娠末期孕畜腹下及后肢等處發(fā)生水腫，面積小、癥狀輕的是正常的生理現(xiàn)象，癥狀嚴重的才認為是病理狀態(tài)。病因主要是腹下、乳房及后肢靜脈血流滯緩，毛細血管滲透壓增高，血液中水分滲出增多。治療采取強心利尿。

假孕：多見于貓、狗，交配后神經(jīng)刺激或因們刺激后，即行排卵，并產(chǎn)生存在時間較長的黃體，由于孕酮含量與懷孕黃體相同，而且持續(xù)發(fā)揮作用，因而引起一些母狗、母貓，出現(xiàn)類似懷孕的癥狀。狗：60-100d，貓：黃體在持續(xù)20天后開始退化，40-44天完全消失，假孕結束。

癥狀：與正常懷孕相似：乳腺發(fā)育，并能泌乳；行為變化，如搭窩等；母性增強；臨床檢查同正常懷孕一致；食欲減退，厭水；X光，B超等檢查可以確診。

一般不予治療，下一發(fā)情季節(jié)恢復，可肌注PMSG，但易引起卵巢囊腫。

19、陰道脫、子宮脫（內(nèi)翻）：

陰道脫：陰道底壁、側壁和上壁一部分組織和肌肉松弛擴張連帶子宮和子宮頸向后移使松弛的陰道壁形成折襞嵌堵于陰門之內(nèi)或突出于陰門之外。病因：

（1）孕期雌激素原因致陰門松弛；（2）圈舍設計前高后低；（3）母畜年老，肌收縮無力；（4）飼喂霉變飼料，毒素引起韌帶松弛，里急后重。治療：薦尾、尾椎行硬膜外麻醉，對脫出部分清洗消毒，整復回陰道，行結節(jié)縫合，注意流出尿液通路。術后注意觀察，保持后高前低姿勢。子宮腔翻入子宮腔或陰道內(nèi)，稱為子宮內(nèi)翻，子宮全部翻出于陰門之外，稱為子宮脫出。病因：

A、產(chǎn)后強烈怒責如產(chǎn)道及陰門損傷、胎衣不下母畜強烈怒責腹壓增高；B、外力牽引，如胎衣排出時母畜強烈怒責；C、子宮遲緩，可延遲子宮頸閉合時間和子宮角體積縮小速度，更易受腹壁肌收縮和胎衣牽引的影響。治療：

整復法：A、保定；B、清洗；C、麻醉，薦尾硬膜外麻醉，（太深母畜臥下）；D、整復，用飽和明礬水處理子宮縮小體積，從陰門上口小部分開始。側臥保定時注射葡萄糖酸鈣溶液減少瘤胃鼓起。術后護理：強心輸液，對于脫出污染嚴重部分，應行切除。20、難產(chǎn)： 病名：由于各種原因而使分娩的第一階段，尤其是第二階段明顯延長，如不進行人工助產(chǎn)，則母體難于或不能排出胎兒的產(chǎn)科疾病。病因：

普通病因：通過影響母體或胎兒而使正常的分娩過程受阻：遺傳、環(huán)境、內(nèi)分泌、傳染性、外傷性、飼養(yǎng)管理因素等。直接病因：母體性、胎兒性，參見P253。檢查：為了挽救胎兒、母畜生殖力；

A、病史調(diào)查：產(chǎn)期，年齡與胎次，產(chǎn)程，既往病史、繁殖史，胎兒產(chǎn)出情況，是否進行助產(chǎn)等。

B、母畜全身檢查：一般臨床檢查，站立、精神情況；陰門擴張情況等；

C、產(chǎn)科檢查：

（1）外部檢查：視診，檢查乳房，骨盆韌帶，陰門及周圍區(qū)域，陰道分泌物，腹、腹協(xié)部。（2）陰道檢查：檢查產(chǎn)道的松弛度及潤滑程度。（3）胎兒檢查：主要檢查胎向、胎位、胎勢、胎兒大、死活及進入產(chǎn)道的程度。（4）直腸檢查：檢查子宮捻轉的程度，自貢站立和收縮力。

助產(chǎn)準備：母畜保定（盡量采取站立姿勢）；麻醉鎮(zhèn)靜、鎮(zhèn)痛、肌松藥物；消毒用0.1%高錳酸鉀或新潔爾滅擦洗陰門、尾根等部位；潤滑用溫和無刺激的肥皂水、石蠟油等潤滑產(chǎn)道； 胎兒助產(chǎn)：牽引，位置矯正（正生上胎位），截胎術。母畜助產(chǎn)：剖腹產(chǎn)，外陰切開（多用于胎兒過大，胎位不正無法整復），子宮切除（用于難產(chǎn)已久，子宮壁破裂或損傷，胎兒死亡氣腫等）常見助產(chǎn)：

A、子宮遲緩：子宮開口期及胎兒排出期子宮肌層收縮頻率、持續(xù)期及強度不足導致胎兒不能排出。助產(chǎn)：（1）、牽引；（2）、催產(chǎn)素10-20IU肌注或皮下注射。

B、努責過強及破水過早：分娩時子宮壁及腹壁收縮時間過長，間隙短，時間過長，力量強烈，子宮壁肌肉出現(xiàn)痙攣性不協(xié)調(diào)收縮，形成狹窄環(huán)；后者是指子宮頸尚未松軟開張、胎兒姿勢尚未轉正和進入產(chǎn)道胎囊即已破裂，胎水流失。助產(chǎn)：視胎兒情況進行牽引、局部麻醉鎮(zhèn)靜及截胎、剖腹產(chǎn)。

C、子宮捻轉：整個子宮、子宮角或其部分圍繞縱軸發(fā)生扭轉。助產(chǎn)：（1）、產(chǎn)道糾正；（2）、直腸矯正；（3）、母體翻轉；（4）、剖腹矯正及剖腹產(chǎn)。

D、子宮頸開展不全（雙子宮頸）：牛羊子宮頸肌肉十分發(fā)達，產(chǎn)前受雌激素作用發(fā)生漿液性浸潤而變軟過程緩慢，若陣縮過早及早產(chǎn)，則易發(fā)病。助產(chǎn)：牛注射乙烯雌酚50mg，羊5mg，在注射催產(chǎn)素及葡萄糖酸鈣，然后施牽引術；雙子宮的，視情況進行兩子宮間隔膜切開或剖腹產(chǎn)。

E、陰道、陰門及前庭狹窄：常見于首胎配種過早，陰道及陰門腫瘤，骨盆腫脹等，助產(chǎn)：潤滑產(chǎn)道，牽引術，嚴重的施剖腹產(chǎn)。

F、骨盆狹窄：骨盆骨折、異常或損傷引起骨盆腔和大小和形態(tài)異常，妨礙胎兒排出。助產(chǎn)：灌注大量潤滑劑，牽引胎兒排出，適當考慮剖腹產(chǎn)，除營養(yǎng)性及配種過早引起外，其他原因引起不應再做種用。G、子宮疝：子宮通過臍孔、腹壁、腹股溝、膈等破裂口形成各種子宮疝。助產(chǎn)：胎兒無難產(chǎn)的，修復疝，子宮脫出部分壞死的，應行手術解除。

H、胎兒過大：相對較大及絕對過大如巨型胎兒，巨型胎兒人工誘導分娩，在牽引術不能產(chǎn)下時，行陰門切開或剖腹產(chǎn)。

I、雙胎難產(chǎn)：兩個胎兒同時楔入母體骨盆，但二者皆不能通過，同時伴發(fā)胎勢及胎位異常。助產(chǎn)：先推回一個胎兒，拉出一個再使第二個產(chǎn)出。難產(chǎn)時間過久，注射催產(chǎn)素，施行手術剝離。

J、胎兒畸形：常見畸形包括胎兒水腫、胎兒腹腔積水、胎頭積水、肢體不全、頸歪斜、雙頭、雙肢體重復畸形等；助產(chǎn)：胎兒牽引，截胎，剖腹產(chǎn)。

K、胎勢異常：常發(fā)病有腕關節(jié)屈曲、頭頸側彎等；助產(chǎn)：胎兒矯正，牽引產(chǎn)出，截胎，剖腹產(chǎn)等。

L、休克處理：休克是機體在神經(jīng)、內(nèi)分泌、循環(huán)、代謝等系統(tǒng)發(fā)生嚴重障礙時表現(xiàn)出的癥候群，以有效循環(huán)量銳減、微循環(huán)障礙為特征的急性循環(huán)不足，是一種組織灌注不良導致組織缺氧和器官損害的綜合征。

治療：此病主要由腹壓下降過快、疼痛、大量失血、過敏反應引起，治療原則：早期診斷、早期治療。（1）消除病因：終止及矯正休克的發(fā)展；（2）補充血容量：視情況及早采取輸血補液及解除微血管痙攣的藥物，同時還可應用抗壞血酸及鈣制劑等各種綜合措施，或針刺分水、耳尖及尾間等穴，還可輸入右旋糖酐葡萄糖鹽水。（3）防止腹壓過低：胎水流失，胎兒腹部露出體外時，應緩慢拉出胎兒防止腹壓急劇降低。（4）改善心功能，使用提高心肌收縮力的藥物：多巴胺、異丙腎上腺素、洋地黃及皮質(zhì)醇等。（5）調(diào)節(jié)代謝障礙，糾正酸中毒：輕度用生理鹽水，中度用堿性藥物（常用藥物為5％碳酸氫鈉，堿中毒也用此藥）。

（6）外傷性休克伴發(fā)感染時，在休克早期應用廣譜抗生素治療。M、難產(chǎn)預防：（1）避免過早配種；（2）保證發(fā)育期、妊娠期營養(yǎng)供給；（3）妊娠母畜適當運動、使役；（4）產(chǎn)前提前一周或半月將臨產(chǎn)母畜送入產(chǎn)房，適應環(huán)境；（5）產(chǎn)乳牛產(chǎn)前60天干乳；（6）防治難產(chǎn)：臨床檢查（直腸檢查等），及時采取助產(chǎn)措施。正常分娩時間：第一階段（開口期）馬＜4h；牛，綿羊、山羊＜6--12h；犬貓和豬＜6--12h?；虻诙A段（胎兒排出期）馬＜20--40min；牛，綿羊、山羊＜2--3h；犬貓和豬＜2--4h。超過此時間應行助產(chǎn)。

21、剖腹產(chǎn)：

牛的剖腹產(chǎn)切口有6處，臨床常用、實用的是腹側壁切口。A、牛保定：倒牛，繩子保定；

B、選擇腹側壁切口：髖關節(jié)和臍孔連線中部切開，可平移； C、剃毛，消毒，消毒方法碘酒棉球從中間自外；

D、麻醉：2%鹽酸普魯卡因分8點創(chuàng)口皮下注射麻醉；

E、切開皮膚、腹橫肌、橫肌、腹膜，探查子宮及胎兒情況，將子宮拉出體外；

F、固定子宮，作牽引線，盡量避開子葉切開子宮，將胎兒拉出； G、縫合，整復子宮，縫合速度一定要快，控制在5分鐘以內(nèi)； H、關閉腹腔：每逢一針將手探入檢查，保證將腹膜縫合完整，避免將腸壁等內(nèi)臟縫住。

I、快速縫合腹壁肌層，防瘤胃鼓氣導致腹腔關閉困難。J、縫合皮膚，作中部對分縫合，皮膚吻合良好；

K、切口面貼蓋消毒創(chuàng)巾，用圓枕法縫合在皮膚上，防止感染。主意：手術過程要快，減少瘤胃鼓氣帶來的影響；失液過多的，家伙死補液。剖腹產(chǎn)后牛一般不易再孕。

22、產(chǎn)道損傷：陰道陰門及宮頸損傷、子宮破裂等，主要原因是生產(chǎn)過程中撕裂，病理表現(xiàn)為患畜弓背舉尾，作排尿姿勢，不安，有痛苦狀。

治療：殺菌消炎，止血鎮(zhèn)痛，縫合等。

23、胎衣不下：母畜娩出胎兒后在第三產(chǎn)程的生理時限內(nèi)胎衣未能排出，牛超過12h，馬1-1.5h，豬1h，羊4h。原因包括：產(chǎn)后子宮收縮無力，胎盤未成熟或老化，胎盤充血和水腫，胎盤炎癥，胎盤組織構造異常等。

治療：藥物療法：子宮內(nèi)投藥四環(huán)素、土霉素、磺胺藥等1-2g；子宮頸縮小時注射雌激素；肌肉注射抗生素；己烯雌酚20mg，1小時候注射催產(chǎn)素50-100IU，2h后重復一次。

手術療法：人工剝離胎衣，緩慢剝離，不能過多損傷子葉（少于5-8個）行薦尾間隙注射15ml2%鹽酸普魯卡因。

24、子宮內(nèi)膜炎：產(chǎn)后子宮數(shù)天內(nèi)發(fā)生的急性炎癥。轉為慢性炎癥時導致不孕。

治療：患畜子宮49℃消毒液沖洗，應用廣譜抗生素防止毒素自體吸收中毒，如宮內(nèi)注射呋喃唑酮混懸液2-5ml，同時可靜脈注射50IU催產(chǎn)素，PGF2α等。禁止使用雌激素，雖可增加抵抗力但同時增加血流量加速毒素吸收。

25、生產(chǎn)癱瘓、產(chǎn)后截癱：又稱低鈣血癥、乳熱癥，是母畜分娩前后突然發(fā)生的一種嚴重代謝性疾病。

治療：靜脈注射25%硼葡萄糖酸鈣500ml，也可一半皮下一半靜脈注射，6-12h后無效可重復注射，不超過3次，每500ml給藥10min。胰島素及腎上腺皮質(zhì)激素配合高糖及5%碳酸氫鈉連用。

乳房送風法：四個乳區(qū)打滿空氣，乳區(qū)血流減少，血鈣回升，腦缺氧改善等。

26、母畜不孕及其檢查診療：先天及后天不孕，參見P342-366。

27、常見疾病性不孕：參見P391 A、卵巢靜止：發(fā)情情況：無，卵巢觸診：較硬、光滑，子宮觸診：收縮無力，黏液：無，B、永久黃體：無，堅實突起，收縮無力，混濁，粘稠 C、卵泡囊腫：長期發(fā)情，較大卵泡狀物，有時正常 D、黃體囊腫：無，一突起頂部軟，收縮無力。

28、新生仔科學：新生仔環(huán)境變化離開適宜母體保護，接觸復雜外界環(huán)境。營養(yǎng)變化新生仔消化功能不全，營養(yǎng)獲取不足，消化不良。溫度變化：新生仔溫度調(diào)節(jié)能力差，出生后1-2小時內(nèi)體溫下降0.5-1℃，靠哆嗦和肌肉活動供熱。

呼吸和循環(huán)系統(tǒng)變化、消化系統(tǒng)變化、體溫變化、排尿變化、臍帶脫落、代謝與激素變化、血液變化。

29、新生仔疾?。盒律兄舷ⅲ杭偎?，剛出生的仔畜呈現(xiàn)呼吸障礙或無呼吸而僅有心跳，常見于馬和豬。

治療：擦盡鼻孔及口腔內(nèi)羊水，用浸有氨水的棉花放在鼻孔上刺激鼻腔粘膜誘發(fā)呼吸，或者向仔畜身上潑冷水，使用尼可剎米刺激呼吸中樞。

30、乳房炎及其治療：

乳房炎是由各種病因引起的乳房炎癥，其主要特點是乳汁發(fā)生理化性質(zhì)及細菌學變化，如縣組織發(fā)生病例變化。乳房炎治療：（牛站立保定）A、生殖股神經(jīng)封閉：第3、4腰椎直進針，刺透皮膚，針向椎體45°，刺滿（牛腰閃動）退針0.2cm，注射2%鹽酸普魯卡因15-20ml，藥不能過快，退針麻醉淺表神經(jīng)，注射藥物5ml。

B、乳房內(nèi)灌注抗生素法：乳房內(nèi)灌注青霉素、慶大及阿米卡星等加入80-100ml生理鹽水在擠盡乳汁后一次灌入，連續(xù)2-3天。

C、乳房基部封閉：前乳基：乳基部與腹壁之間避開神經(jīng)與血管，沿腹壁切線對準后肢系關節(jié)進針，邊注藥邊退針，注入青鏈霉素50-80W單位。后乳基：距乳中隔1-2cm處，偏向患側以前肢髖關節(jié)為參照，腹壁切線進針，刺滿推藥出針。

D、會陰神經(jīng)封閉：牛尾跟下坐骨聯(lián)合處消毒進針。E、健胃藥＋瓜蔞散

第五篇：中藥學各章總結

第一章作業(yè)

作業(yè)答案

1.什么是中藥 ？什么是中藥學 ？答：中藥，是指在中醫(yī)藥理論指導下認識和使用的藥物，是用以防治疾病的重要武器 中藥學是研究中藥基本理論和各種中藥的來源、采制、性能、功效、臨床應用及其他有關知識的一門學科，是中醫(yī)學的重要組成部分。

2.說明《神農(nóng)本草經(jīng)》、《本草經(jīng)集注》、《本草綱目》、《本草圖經(jīng)》、《證類本草》、《本草綱目拾遺》6部本草的成書時代、作者、藥數(shù)和主要貢獻。答：成書于東漢（不晚于公元2世紀）的《神農(nóng)本草經(jīng)》論載藥365種。《本草經(jīng)集注》（簡稱《集注》）：作者陶弘景為南朝梁代的著名醫(yī)藥學家

1552年至1578年，偉大的醫(yī)藥學家李時珍完成了《本草綱目》，載藥1892種(新增374種)。

18世紀著名的本草學家趙學敏輯成《本草綱目抬遺》10卷，載藥921種《本草綱目》未提及者達716種之多。本草圖經(jīng)》由蘇頌輯成所附的900多幅藥圖。

北宋蜀地名醫(yī)唐慎微所著《經(jīng)史證類備急本草》(后人簡稱《證類本草》)收載礦物藥253種。3.我國被學術界多數(shù)人視為古代世界上最早的藥典著作是什么？共收載多少藥物。答：我國是世界上最早頒行藥典的國家。自唐《新修本草》于公元659年頒行后，歷史上的官修本草曾經(jīng)對中藥學的發(fā)展產(chǎn)生重要影響。書中載藥844種。

4.中醫(yī)的健康標準？（書第8頁）答：1.兩眼有神2.面色紅潤3.語聲宏亮4.呼吸微徐5.情緒穩(wěn)定6.牙齒堅固7.腰腿靈便8.胖瘦適宜9.脈象勻緩10.頭發(fā)潤澤11.記憶良好。

第二章作業(yè)

1.什么是中藥的功效？答：中藥的功效，是在中醫(yī)藥理論指導下，對于藥物治療和保健作用的高度概況，是藥物對于人體醫(yī)療作用在中醫(yī)學范疇內(nèi)的特殊表述形式。中藥功效的作用對象主要是人體的病理狀態(tài)，這是中藥學的性質(zhì)和形成歷史所決定的。其在理論上、內(nèi)容上和形式上都有別于其他醫(yī)藥學對藥物作用的認識和表述，具有明顯的自身特色。

2.什么是中藥的基本作用？答：中醫(yī)理論認為，人體在健康狀態(tài)下，臟腑經(jīng)絡的生理活動正常，并與外界環(huán)境之間保持著“陰平陽秘”的動態(tài)平衡狀態(tài)。當各種致病因素影響人體后，便會破壞這種協(xié)調(diào)和諧的關系，導致邪盛正衰，陰陽氣血失常，臟腑經(jīng)絡功能紊亂等病理改變，發(fā)生疾病。針對不同的病機，使用相應的中藥，或祛除病邪，或扶助正氣，或協(xié)調(diào)臟腑功能，糾正陰陽的盛衰，使機體恢復或重建其陰平陽秘的正常狀態(tài)，這就是中藥的基本作用。

3.五行與五臟六腑的關系？（書33）答：五臟是指肝(木)、心(火)、脾(土)、肺(金)腎(水)六腑：膽、小腸、胃、大腸、膀胱、三焦

4.解釋陰陽學說的含義？（書26）答：陰和陽，既可以表示相互對立的事物，又可用來分析一個事物內(nèi)部所存在著的相互對立的兩個方面。一般來說，凡是劇烈運動著的、外向的、上升的、溫熱的、明亮的，都屬于陽；相對靜止著的、內(nèi)守的、下降的、寒冷、晦暗的，都屬于陰。以天地而言，天氣輕清為陽，地氣重濁為陰；以水火而言，水性寒而潤下屬陰，火性熱而炎上屬陽。任何事物均可以陰陽的屬性來劃分，但必須是針對相互關聯(lián)的一對事物，或是一個事物的兩個方面，這種劃分才有實際意義。如果被分析的兩個事物互不關聯(lián)，或不是統(tǒng)一體的兩個對立方面，就不能用陰陽來區(qū)分其相對屬性及其相互關系。事物的陰陽屬性，并不是絕對的，而是相對的。這種相對性，一方面表現(xiàn)為在一定的條件下，陰和陽之間可以發(fā)生相互轉化，即陰可以轉化為陽，陽也可以轉化為陰。另一方面，體現(xiàn)于事物的無限可分性。第三章作業(yè)

1.簡述藥物四氣、五味的含義、作用，并舉例說明其對臨床用藥的指導意義答：四氣，是指藥物的寒、熱、溫、涼四種藥性，又稱為四性。四氣主要用以反映藥物影響人體寒熱病理變化的作用性質(zhì)，是藥物最主要的性能。

意義：一般屬于寒性或涼性；能夠減輕或消除寒證的藥物，一般屬于溫性或熱性。寒涼性質(zhì)藥物，大多有清熱作用，如清熱、瀉火、涼血、解毒、攻下、滋陰等功效，主要用于陽證、熱證；溫熱性質(zhì)藥物，大多有散寒作用，如散寒、溫里、行氣、活血、補氣、助陽等功效，主要用于陰證、寒證。

最初，五味的本義是指辛、甘、苦、酸、咸五種口嘗而直接感知的真實滋味。滋味實際上不止此五種，為了能與五行學說相結合，前人將淡味視為甘味的“余味”，而附于甘味；又將澀味視為酸味的“變味”，而附于酸味。因此，一直習稱五味。在性能理論中，藥物的五味除了用以表示其實際滋味以外，主要是用以反映該藥的作用特點。1．辛能散、能行 用辛味表示藥物具有發(fā)散、行氣、活血等方面的作用。所以，能發(fā)散表邪的解表藥，消散氣滯血淤的行氣藥和活血化淤藥，一般都標以辛味。2．甘能補、能緩、能和 用甘味表示藥物有補虛、緩急止痛、緩和藥性或調(diào)和藥味等方面的作用。所以，補虛藥(包括補氣、補陽、補血、補陰、健脾、生津和潤燥等)及具有緩急止痛，緩和毒烈藥性，并可調(diào)和藥味的甘草、蜂蜜等藥，都標以甘味，實際上這些藥物都是補虛之藥。3．苦能泄、能燥 泄的含義主要有三：一是降泄，使壅逆向上之氣下降而復常。如杏仁、葶藶子能降壅遏上逆的肺氣而止咳平喘；枇杷葉、代赭石能降上逆的胃氣而止嘔吐呃逆。二是指通泄，能通便瀉下。三是與寒性相結合，表示清泄，能清除火熱邪氣。燥是指燥濕，若干苦味藥能祛濕邪，治療濕證。結合藥性來看，燥濕作用又有苦溫燥濕和苦寒燥濕（又稱清熱燥濕）之分。所以，止咳平喘藥、止嘔逆藥、攻下藥、清熱藥及燥濕藥，一般標以苦味。4．酸與澀都能收能澀 用酸味或澀味表示藥物有收斂固澀作用。所以，能治療滑脫不禁證候的斂肺、澀腸、止血、固精、斂汗藥，一般標以酸味或澀味。習慣上多將滋味本酸的收澀藥多標為酸味，其滋味不酸者，多標以澀味；因為澀附于酸，故經(jīng)常又酸味與澀味并列。5．咸能軟能下 表示藥物有軟堅散結或瀉下作用，所以，能治療癥瘕、痰核、癭瘤等結塊的牡蠣、鱉甲、昆布等藥，多標以咸味；以上結塊多與淤血、氣滯、痰凝相關，故軟堅散結藥亦多辛味之品。因為瀉下通便是苦能通泄所表示的作用特點，咸能下之說與之交叉重復。所以，咸能下的使用十分局限，相沿僅指芒硝等少數(shù)藥的瀉下特點。實際上各論中藥物后的咸味，更多用以反映動物藥、海洋藥的滋味特征。6．淡能滲能利 表示藥物有滲濕利水作用。雖然利尿藥物甚多，但習慣上只將茯苓、豬苓等部分利水藥標以淡味，而且往往甘味與淡味并列；多數(shù)利水藥的藥味并無規(guī)律性。

2.何謂引經(jīng)藥？它們與臟腑經(jīng)絡的關系？舉例幾味中藥。（書66）答：前人認為一些藥物對某一臟腑經(jīng)絡具有特殊作用，其選擇性特別強，并且可以引導與之同用的其他藥物達于病所，而提高臨床療效，因而將此稱為引經(jīng)（或稱引經(jīng)報使、主治引使、響導、各歸引用等），又將這類藥物稱為引經(jīng)藥。臟腑，是中醫(yī)學中特有的定位概念，其與解剖上的實際臟器有較大的區(qū)別，不能與之混淆。對于藥物歸經(jīng)的理解，也不一定是指藥物有效成分實際到達的部位，而主要是藥物產(chǎn)生效應的部位所在。川芎，柴胡等引經(jīng)藥。

3． 何謂藥物升降浮沉？與藥物四氣五味及功效有何關系？答：升降浮沉是用以表示藥物作用趨向的一種性能。升是上升，表示作用趨向于上；降是下降，表示作用趨向于下；浮是發(fā)散，表示作用趨向于外；沉是收束閉藏，表示作用趨向于內(nèi)。

藥物升降浮沉作用趨向性的形成，雖然與藥物在自然界生成稟賦不同，形成藥性不同有關，并受四氣、五味、炮制、配伍等

諸多因素的影響但更主要是與藥物作用于在體所產(chǎn)生的不同療效、所表現(xiàn)出的不同作用趨向密切相關。

影響藥物升降浮沉的因素主要與四氣五味、及藥物質(zhì)地輕重有密切關系，并受到炮制

和配伍的影響。

藥物的升降浮沉與四氣五味有關：王好古云：“夫氣者天也，溫熱天之陽；寒涼天之

陰，陽則升，陰則降；味者地也，辛甘淡地之陽，酸苦咸地之陰，陽則浮，陰則沉”。

一般來講，凡味屬辛、甘，氣屬溫、熱的藥物，大都是升浮藥，如麻黃、升麻、黃芪

等藥；凡味屬苦、酸、咸、性屬寒、涼的藥物，大都是沉降藥，如大黃、芒硝、山楂等。.什么是中藥的毒性及影響毒性的因素？答：毒性是藥物對機體的傷害性，是用以反映藥物安全程度的性能。毒性反應會造成臟腑組織損傷，引起功能障礙，使機體發(fā)生病理變化，甚至死亡。毒性雖然是普遍的，而引起毒性反應則是不多的。藥物毒性的大小是相對的，是否出現(xiàn)毒性反應，主要取決于用量。前述國務院令中確定的毒性中藥有砒石、砒霜、水銀、生馬錢子、生川烏、生草烏、生附子、生白附子、生半夏、生南星、生巴豆、斑蝥、青娘蟲、紅娘蟲、生甘遂、生狼毒、生藤黃、生千金子、生天仙子、鬧羊花、雪上一枝蒿、紅升丹、白降丹、蟾酥、洋金花、紅粉、輕粉及雄黃等28種。對于這些毒藥，哪怕是毒性最大的砒霜，只要在安全有效的劑量內(nèi)合理使用，是不會引起中毒的。而歷代指為無毒的人參、五加皮、火麻仁等，因服用

過量，亦有致人中毒，甚至死亡的報道。

第四章作業(yè) ．中藥炮制目的有哪些？舉例說明。答：㈠增強藥物作用，提高臨床療效

增強藥物的某一作用，提高其臨床療效，是中藥炮制最常見的炮制目的。如在中藥炮制時，經(jīng)常要加入一些輔助藥料（簡稱輔料），其具體作用雖然互不相同，但一般均是為了增效?，F(xiàn)代研究還發(fā)現(xiàn)一些藥物經(jīng)過炮制有利于穩(wěn)定藥效。如含苷類有效成分的藥物經(jīng)加熱處理以后，其相應的酶被破壞或失去活性，可防止苷類水解而避免重要的有效成分含量下降，如人參、黃芩等。㈡降低或消除藥物的毒性或副作用，保證用藥安全一些有毒性或明顯副作用的藥物，如馬錢子、天南星、烏頭及常山等，不經(jīng)炮制而直接生用，即使在常用的有效劑量內(nèi)，也容易產(chǎn)生毒性反應和副作用。如經(jīng)過特殊的炮制處理，可以明顯降低甚至消除某些毒副反應，確保臨床用藥安全。因天南星含有苛辣性毒素，對口、舌、咽喉等有較強的刺激性，可引起口舌麻木，聲音嘶啞，甚至粘膜糜爛和壞死，若與白礬、生姜水共浸并煮透后，則基本無此毒性。

㈢改變藥物的性能功效,使其更加適應病情或擴大應用范圍中藥固有的寒熱、升降、補瀉等性能和功效，在有的情況下不一定完全適合病情的需要，經(jīng)過特殊的炮制處理，將這些性能和功效適當?shù)馗淖?，就可以更加與病情相符合。如豨薟草具有祛風濕，通經(jīng)活絡的功效，但性味苦寒，與風濕寒痹不盡相宜，經(jīng)拌入黃酒蒸制后，其性偏于辛溫，則更能對證。藥物炮制改變性能和功效后，還可以在原藥物的基礎上擴大應用范圍。如生地黃性寒而主要用以清熱涼血，經(jīng)蒸制為熟地黃后，變?yōu)闇匦灾?，則能補血而治療血虛證。㈣ 改變藥材的某些性狀，便于貯存和（或）制劑藥材大都可以隨采隨用，不少動植物藥使用鮮品療效更佳。但因產(chǎn)地季節(jié)等因素的制約，皆要干燥后貯存?zhèn)溆?。一般藥材都可以采用陰干、曬干或烘烤使之干燥。有的藥材則必須經(jīng)過特殊的炮制，才能貯存和運輸。如馬齒莧柔嫩多汁，必須入沸水 單后才能干燥。桑螵蛸、五倍子必須蒸制以殺死蟲卵或蚜蟲。否則桑螵蛸可因蟲卵孵化而失效，而且生用還有滑腸之弊。此外，將植物藥切制成一定規(guī)格的飲片，礦物藥的煅、淬、砸、搗，均是便于制劑和調(diào)配。㈤ 使藥材純凈，保證藥材質(zhì)量和稱量準確，藥材在采收、貯存和銷售過程中，往往帶有一些非藥用部分及雜質(zhì)（如肉桂之栓皮、枳殼之瓤等）、砂土甚至變質(zhì)者，既影響藥材質(zhì)量，又造成稱量的不準確。經(jīng)過修制或特殊處理，則完全可以避免因此造成的不良影響。㈥ 矯味矯臭，便于服用，一些藥物（如乳香、沒藥、地龍等）具有臭氣、異味或刺激性，患者難于接受，服藥后還易引起惡心、嘔吐等不適反應，經(jīng)過炮制不僅可使作用增強，亦可減少不適反應，其效良而不至“苦”口。

2．十八反、十

九

畏的具

體

內(nèi)

容

是

什

么？

（書

68）

答

十八反：烏頭反半夏、瓜蔞、貝母、白蘞及白及；甘草反海藻、大戟、甘遂及芫花；藜蘆反人參、沙參、玄參、丹參、苦參、細辛及芍藥。本草明言十八反 貝蔞半蘞及攻烏 藻戟遂芫具戰(zhàn)草 諸參辛芍叛藜蘆

十九畏：硫黃畏樸硝，水銀畏砒霜，狼毒畏密陀僧，巴豆畏牽牛，丁香畏郁金，牙硝畏三棱，川烏、草烏畏犀角，人參畏五靈脂，官桂畏赤石脂。??硫黃本是火中精，樸硝一見便相爭； 水銀莫與砒霜見，狼毒最怕密陀僧??巴豆性烈最為上，偏與牽牛不順情； 丁香莫與郁金見，牙硝難合荊三棱??川烏草烏不順犀，人參最怕五靈脂； 官桂善能調(diào)冷氣，若遇石脂便相欺??大凡修合看順逆

。。

3．舉例說明煎藥法中先煎、后下、包煎、另煎、烊化的意義。

答：1.先煎 有效成分不容易煎出的藥，與不宜久煎的藥同用，人湯劑時，有效成分不易煎出的藥應先煎一定時間后，再納入其余藥物同煎。一般來說，動物角（如水牛角、山羊角、鹿角等）、甲（如龜甲、鱉甲等）、殼（如海蛤殼、石決明、牡蠣及珍珠母等）類藥物和礦物類藥物（如石膏、花蕊石、灶心土、磁石、代赭石及龍骨等），大多需要先煎30分鐘左右，再納入其他藥同煎。植物藥中，苦楝皮等有效成分難溶于水的藥，與一般藥同入湯劑時，也需先煎。另外，有的藥久熬可使其毒性降低（如川烏、草烏、附子及雷公藤等），亦應先煎。制川烏、制草烏、制附子應先煎0.5～1 小時（至人口無麻味為度），雷公藤應先煎1～2小時，再納入他藥同煎，以確保用藥安全。2.后下 含揮發(fā)性有效成分，久煎易揮發(fā)失效的藥物（如金銀花、連翹、魚腥草、肉桂、沉香、檀香及解表藥、化濕藥中的大部分藥），或有效成分不耐煎煮，久煎容易破壞的藥（如青蒿、大黃、番瀉葉、臭梧桐、麥芽、谷芽、神曲、白芥子、杏仁及鉤藤等），與一般藥同入湯劑時，宜后下微煎，待他藥煎煮一定時間后，再納入這類藥同煎一定時間。有的藥甚至只需用開水浸泡即可，不必入煎（如大黃、番瀉葉用于瀉下通便）。3.包煎 藥材有毛對咽喉有刺激性及漂浮水面不便于煎煮者（如辛夷、旋覆花等），或藥材呈粉未狀及煎煮后容易使煎液混濁者（如蠶沙、海金沙、蒲黃、灶心土、五靈脂及兒茶等），以及煎煮后藥液粘稠不便于濾取藥汁者（如車前子等），入湯劑時都應當用紗布包裹入煎。4.另煎 部分貴重藥材（如人參、西洋參、羚羊角等）與他藥同用，人湯劑時宜另煎取汁，再與其他藥的煎液兌服，以免煎出的有效成分被其他藥的藥渣吸附，造成貴重藥材的浪費。5.烊化 膠類藥材（如阿膠、鹿角膠、龜甲膠等）與他藥同煎，容易粘鍋、熬焦，或粘附于其他藥渣上，既造成膠類藥材的浪費，又影響其他藥的有效成分的溶出，因此應當單獨烊化（將膠類藥物放入水中或己煎好的藥液中加熱溶化）兌服。

4．中藥的用量有何特點？用量多少與哪些因素有關？舉例說明之。答：《中藥學》討論的劑量，主要指為達到一定的治療目的，所應用的單味藥的劑量，又稱用量。教材中各具體藥物用量項下所標用量，系單昧藥的常用有效量。這是臨床確定單味藥用量時的重要參考依據(jù)。用量項下的用量，除特別注明者外，都是指干燥飲片在湯劑中，成人一天內(nèi)服的常用有效量。鮮品人藥及藥物人丸散時的用量則另加注明。劑量的單位：斤;兩;錢;克

5．婦女妊娠為何忌用破血、活血及有毒中藥？各列舉十種妊娠禁用和慎用藥。答：婦女在妊娠期間，除為了中斷妊娠、引產(chǎn)外，禁忌使用某些藥物，稱為妊娠用藥禁忌。妊娠用藥禁忌的理由：避免引起墮胎是禁忌的主要理由。除此之外，凡對母體不利、對胎兒不利、對產(chǎn)程不利、對產(chǎn)后兒童生長發(fā)育不利的藥物，對妊娠婦女均當盡量避免使用?？偟恼f來，凡對妊娠期的母親和胎兒不安全及不利于優(yōu)生優(yōu)育的藥物，均屬妊娠禁忌藥。一般將妊娠禁忌藥分為禁用藥和慎用藥。禁用藥包括劇毒藥、墮胎作用較強的藥及藥性作用峻猛的藥，如砒石、水銀、馬錢子、川烏、草烏、斑蝥、輕粉、雄黃、巴豆、甘遂、大戟、芫花、牽牛子、商陸、藜蘆、膽礬、瓜蒂、干漆、水蛭、虻蟲、三棱、莪術及麝香等。慎用藥主要是活血化淤藥、行氣藥、攻下藥及溫里藥等章節(jié)中的部分藥，如牛膝、川芎、紅花、桃仁、姜黃、枳實、枳殼、大黃、番瀉葉、蘆薈、芒硝、附子及肉桂等。第五章作業(yè)

1.何謂解表藥？簡述解表藥的作用和適應證。答：以發(fā)散表邪為主要功效，常用以治療表征的藥物，稱為解表藥。解表藥可主治外感表證，癥見發(fā)熱，惡寒或惡風，頭身疼痛，無汗或有汗而不暢，脈浮，或有鼻塞流涕、咽癢、咳喘等表現(xiàn)者。發(fā)散風寒藥與發(fā)散風熱藥，除主治風寒表證（感冒風寒）和風熱表證（風熱感冒）外，風邪所致的頭昏頭痛、目赤咽痛、皮膚瘙癢等，亦多選用；此外，本類藥還分別兼有止痛及透疹等其他多種功效，因而又有其相應的主治病證。這些內(nèi)容將分述于以下兩節(jié)的概述之中。

2.何為中藥的君臣佐使，舉例說明。（書70）答：君藥：是針對主病或主證起主要治療作用的藥物。臣藥：是協(xié)助主藥以加強治療作用的藥物。佐藥：一是治療兼證或次要癥狀的藥物。二是用于主藥有毒，或藥性峻烈須加以制約者。三是反佐藥，即與君藥藥性相反而又能在治療中起相反作用的藥物。使藥：一是引經(jīng)藥，即引導它藥直達病所的藥物。二是調(diào)和藥性的藥物，如方劑中常用甘草，大棗以調(diào)和藥性等。? 麻黃9克 發(fā)汗解表以散風寒，宣利肺氣以平喘咳，為君藥? 桂枝6克 發(fā)汗解肌，溫經(jīng)散寒助麻黃解表又除肌體疼痛，為臣藥.杏仁9克 宣暢肺氣助麻黃平喘，為佐藥? 炙甘草3克 調(diào)和諸藥，為使藥 3.解表藥分為哪幾類？其作用和適應證有何不同？答：根據(jù)解表藥的藥性和功效主治差異，常將其分為:發(fā)散風寒藥與發(fā)散風熱藥兩類。有時又有稱為辛溫解表藥與辛涼解表藥者。發(fā)散風寒藥性味辛溫，故又稱辛溫解表藥。其辛能外散風邪，溫可祛寒，以發(fā)散肌表風寒邪氣為主要功效，主治風寒表征，癥見惡寒發(fā)熱，頭身疼痛，口不渴、苔白而潤，脈浮緊，或兼咳喘，鼻塞流涕者。本類藥物，性偏濕燥，多能開腠發(fā)汗，忌用于燥熱內(nèi)盛者；平素陰虛津虧，表虛不固而外感風寒者，亦當慎用。以發(fā)散風熱為主要功效，常用以治療風熱表證及溫熱病衛(wèi)分證的藥物，稱為發(fā)散風熱藥?；蚍Q辛涼解表藥。其實，辛涼解表主要指本類藥物對風熱表證的治療作用；而發(fā)散風熱還包括對風熱頭暈頭痛、風熱目疾、風熱咽痛、風熱皮膚瘙癢等證的治療作用，故稱發(fā)散風熱藥更為允當。發(fā)散風熱藥性味多辛苦而偏寒涼，辛以祛風，苦寒則清熱；其作用趨向升浮為主，多兼苦寒沉降。其發(fā)散之力較為緩和。

4.麻黃與桂枝皆能發(fā)汗解表，效果有何不同？答：麻黃：【性味歸經(jīng)】辛、微苦，溫。歸肺、膀胱經(jīng)?！竟πА堪l(fā)汗解表，平喘，利尿?！緫谩坑糜陲L寒表實證，喘咳證，水腫

桂枝：【性味歸經(jīng)】辛、甘,溫。歸肺、心、腎、肝經(jīng)?！竟πА堪l(fā)汗解表，溫通經(jīng)脈，溫助陽氣?！緫谩坑糜陲L寒表證，寒凝血淤及風寒痹證等多種里寒證，陽虛證。

5.比較桑葉與菊花的功效異同？答：相同點：都可以疏散風熱，清肺熱，清肝。不同點：黃菊可以潤燥，明目。桑葉可以平肝。6.介紹詳細介紹麻黃現(xiàn)代醫(yī)學治療哪些疾病？（書71）答：現(xiàn)代醫(yī)學常用來治療感冒，支氣管哮喘，喘息性支氣管炎，腎炎水腫，低血壓等。1.感冒：以麻黃為主的復方制劑，如麻黃湯，答青龍湯等常用于治療普通感官，流行性感冒等。2.哮喘性支氣管炎，支氣管哮喘：以麻黃為主配伍的止咳平喘的方劑，如麻杏石甘場，麻黃定喘湯，小青龍湯等，治療哮喘性支氣管炎，支氣管哮喘等證，療效滿意，麻黃堿可以用于預防和治療慢性輕癥支氣管哮喘。3.腎炎，水腫：麻黃為主的方劑，如麻黃連翹赤小豆湯腎炎所致全身水腫，小便不利等癥狀有一定效果。4.鼻塞：0.5%~1%麻黃素液滴鼻，可消除鼻粘膜腫脹起的鼻塞。第六章作業(yè)

1．試述清熱藥的含義、分類、使用注意及各類的性能特點和主要適應癥。答：含義:凡藥性寒涼,具有清泄里熱作用的藥物,稱為清熱藥分類：清熱瀉火藥，清熱凉火藥，清熱解毒藥，清熱燥濕藥，清虛熱藥。本類藥物主要用于各種熱證.所謂熱證是一個很廣泛的概念,它不僅指體溫升高的發(fā)熱,而且也泛指體溫雖正?；蚪咏?患者常具有某些熱證癥狀,如口干

使用注意：使用清熱藥，應辨清熱證的階段、部位及虛實，選擇相宜的藥物。如熱在氣分用清熱瀉火藥，熱在營血分用清熱涼血藥；胃熱用清胃熱藥，肺熱用清肺熱藥，心熱用清心熱藥，肝熱用清肝熱藥；濕熱證用清熱燥濕藥；陰虛內(nèi)熱證用清虛熱藥等。對于宜用本類藥物之證，亦不可寒涼清泄太過，以免其損傷陽氣，影響脾胃或化燥傷陰。使用清熱藥還必須以《本經(jīng)》“療熱以寒藥”的原則為指導，忌用于寒證，對于真寒假熱者，尤應辨清，決不能誤用。脾胃氣虛、食少、便溏者，亦應慎用。

性能特點： 清熱藥是用以治療熱證的，根據(jù)藥性確定的原則，相對于病性來說，其藥性皆為寒性。按照苦能清泄的五味理論，清熱藥都可標以苦味；兼能養(yǎng)陰生津者，活血祛淤者，尚有甘或辛味。清熱藥的作用趨向是以沉降為主的。

2．比較石膏與知母，黃芩、黃連與黃柏，鮮地黃與干地黃，金銀花與連翹，丹皮與赤芍，銀柴胡和柴胡性能、功效與應用之異同點。答：石膏：

能清熱瀉火，除煩止渴。用于外感熱病，高熱煩渴，肺熱喘咳，胃火亢盛，頭痛，牙痛等。斂瘡生肌，用于瘡瘍潰而不斂、濕疹、水火燙傷等（外用）內(nèi)服只用于實證，虛證不宜用。煅石膏嚴禁內(nèi)服。脾胃虛寒、陰虛內(nèi)熱忌服主要成分為含水硫酸鈣，此外還含有人體所需常量的Al、Mn以及Fe、Zn、Cu等微量元素。具有解熱、增強機體免疫功能、止渴、提高肌肉和外周神經(jīng)的興奮性等作用。知母：用于外感熱病，高熱煩渴，肺熱燥咳，內(nèi)熱消渴，腸燥便秘等。本品含多種甾體皂苷，并含多量的粘液質(zhì)。具有抗菌、解熱、降血糖、影響神經(jīng)體液調(diào)節(jié)功能、抑制Na+-K+-ATP酶活性、降低組織耗氧量及抗血小板聚集等作用。黃柏、黃芩、黃連三藥，都是苦寒的藥品，均能清熱燥濕、瀉火解毒。但黃柏瀉腎火而退虛熱，且能除下焦?jié)駸?；黃芩則以清肺熱為專長，又能安胎；黃連瀉心火而除煩，善止嘔逆。這是三藥不同之點。因此，一般所謂黃芩治上焦、黃連治中焦、黃柏治下焦的說法，就是根據(jù)黃芩清肺火、黃連止嘔逆、黃柏瀉腎火的特點而來的。但是，現(xiàn)在臨床上作為清熱解毒藥應用時，芩、連、柏三藥都是通用的，沒有上述這樣嚴格的區(qū)分。金銀花：用于癰腫療瘡，喉痹，丹毒，熱毒血痢，風熱感冒，溫病發(fā)熱。連翹：治溫熱，丹毒，斑疹，癰瘍腫毒，小便淋閉；咽喉腫痛，風疹。鮮地黃：清熱生津，涼血，止血。干地黃：清熱涼血，養(yǎng)陰，生津。牡丹皮：用于溫毒發(fā)斑，吐血，夜熱早涼，無汗骨蒸，經(jīng)閉痛經(jīng)，癰腫瘡毒，跌撲傷痛。赤芍：清熱涼血，活血化淤?！緫谩坑糜跍責岵崛胙?，淤血證。

3．石膏配知母，黃連配木香，知母配黃柏各有什么意義。答;石膏配知母的意義：清熱而不傷陰液。

黃連配木香的意義：黃連與木香配伍即是方劑香連丸。濕熱瀉痢即是濕熱之邪壅滯腸中，以致氣機不暢，傳導失常，而致腹痛、里急后重等癥狀。黃連苦寒，可清熱燥濕、瀉火解毒，以清瀉腸胃之濕熱；木香辛苦、溫，有行氣、調(diào)中止痛之功效，配黃連可清熱止痢，行氣止痛而達到治療目的。知母配黃柏的意義：黃柏、知母均味苦同，性寒，入腎經(jīng)，同具清熱瀉火功效，相互配伍，可以增強清相火，退虛熱的功效。

4.黃芩、黃連、黃柏在清臟腑方面各有何特長 答：黃芩：黃芩能清實熱，瀉肺火。黃芩能瀉上焦肺火。黃連： 黃連清熱燥濕的作用很強 黃柏：與黃芩、黃連相似，但以除下焦之濕熱為佳。黃柏燥濕瀉火解毒的功效頗好。

5.從青蒿到青蒿素看現(xiàn)代中藥的研究意義 答：在現(xiàn)代科學技術飛速發(fā)展的今天，通過現(xiàn)代科學技術對中醫(yī)藥的科學內(nèi)涵進行證明和闡述，將不斷提高中醫(yī)藥的學術水平，拓展自身的生存空間。在繼承的同時進行創(chuàng)新，以獲取和保護知識產(chǎn)權。中藥現(xiàn)代化科技產(chǎn)業(yè)行動的成功，對現(xiàn)代科學相關學科的發(fā)展將會產(chǎn)生巨大的啟迪和促進作用。

6.何謂清熱瀉火藥、清熱燥濕藥、清熱解毒藥、清熱涼血藥、清虛熱藥 答：清熱瀉火藥：寒涼性突出，善入氣分，既可清解里熱以治本，又可解肌以退熱以治標，清熱瀉火為主要作用，善治療氣分實熱證。清熱燥濕藥：藥性偏于苦燥清泄，以清熱燥濕為主要作用，善應用于濕熱內(nèi)蘊或濕邪化熱的病癥。清熱涼血藥：善入血分，以清熱涼血為主要作用，善治療血分實熱證。清熱解毒藥：以清熱解毒為主要作用，善治療熱毒，火毒證。清虛熱藥：多入陰份，以清虛熱，退骨蒸為主要作用。善治療熱邪傷陰及陰虛潮熱。

第七章作業(yè)

1.大黃的正確用法是：生大黃瀉下力 強，故欲攻下者宜 生 用，入湯劑應 后 下；久煎則瀉下力 減弱。酒制大黃 活血 作用較好，宜于 活血祛瘀 證。大黃炭則偏于止血，多用于 涼血止血 證。

2.何謂瀉下藥？使用時應注意哪些問題？答：凡以瀉下通便為主要功效，常用以治療便秘證或其他里實積滯證的藥物，稱為瀉下藥。使用注意：攻下藥與峻下藥容易操作正氣或脾胃，故小兒、老人及體虛患者慎用，必要時應攻補兼施。對體壯里實者，亦應攻邪而不傷正，中病即止，一般得瀉即可，切勿過劑。婦女妊娠期忌用、月經(jīng)期及哺乳期慎用攻下和峻下藥，以免損害胎兒和孕婦。對于峻猛而有毒的瀉下藥，應嚴格注意其炮制、配伍禁忌、用法及用量的特殊要求，確保用藥安全而有效。

3.大黃、巴豆均可瀉下，二者的適應證有何異同？答：大黃：【功效】攻下積滯，瀉火解毒，涼血止血，活血祛淤，清泄實熱?！緫谩坑糜诒忝丶捌渌改c積滯證，溫熱病高熱神昏或臟腑火熱上炎證，血熱妄行的出血證，熱毒瘡瘍及燒燙傷，淤血熱，濕熱黃疸及濕熱淋證。巴豆：【功效】攻下冷積，逐水退腫，祛痰利咽?！緫谩坑糜诤e便秘腹痛或食積阻結腸胃之證，臌脹腹水，喉痺痰涎壅盛、呼吸不利。

4.現(xiàn)代醫(yī)學大黃用于哪些疾??？（書73）答：1.便秘 2.急腹癥 3.急性腸炎，菌痢，慢性結腸炎 4.黃疸肝炎 5.上消化道出血 6.產(chǎn)后腹痛，血瘀經(jīng)閉。第8-11章作業(yè)

1.填空：附子、肉桂、干姜均能 溫里散寒止痛。干姜長于溫里散寒健運脾陽而止嘔，附子、肉桂 撒寒止痛 力強，又能 補火助陽。肉桂還能 引火歸原，溫經(jīng)通脈，附子、干姜能 回陽救逆。干姜還能 溫肺化飲。

2.溫里藥在臨床上宜如何配伍使用？ 答：使用本類藥物應根據(jù)不同證候作適當配伍。若外寒內(nèi)侵，而表寒未解者，須與發(fā)散風寒藥配伍，以表里雙解。寒主收引，氣機易于郁滯，兼見氣滯者，常與行氣藥配伍，以溫通氣機。寒性凝滯，寒凝經(jīng)脈，兼見血淤者，宜與活血祛淤藥配伍，以溫通經(jīng)脈。寒與濕合，寒濕內(nèi)阻者，宜與芳香化濕或苦溫燥濕藥配伍，以溫散寒濕。寒性主痛，寒凝疼痛較甚者，當與止痛藥配伍，以散寒止痛。寒為陰邪，易傷陽氣，虛寒相兼，可與補陽藥配伍，以溫陽散寒。若陽虛氣脫者，須與大補元氣藥配伍，以補氣回陽固脫。

3.試比較茯苓與薏苡仁功效、主治病證的共同點與不同點。答：茯苓：【功效】利水滲濕，健脾補中，寧心安神?！緫谩坑糜谒疂袼碌男”悴焕⑺[、泄瀉、痰飲、帶下等證，脾虛證，心神不寧證。

薏苡仁：【功效】利水滲濕，健脾，舒筋，清熱排膿?！緫谩坑糜谒疂袼碌男”悴焕?，泄瀉、帶下等證，風濕痹證，肺癰，腸癰。

4.附子與烏頭來源相同，其功效和主治有何區(qū)別？答：川烏：【性味歸經(jīng)】辛、苦，熱。有大毒。歸肝、腎、脾經(jīng)?！竟πА快铒L濕，散寒止痛。【應用】用于寒痹疼痛，寒凝疼痛證。

附子：【性味歸經(jīng)】辛、甘，熱。有毒。歸腎、心、肝、脾經(jīng)?！竟πА炕仃柧饶妫a火助陽，散寒止痛?！緫谩坑糜谕鲫栕C，陽虛證，寒凝疼痛。.清熱燥濕藥、祛風濕藥、芳香化濕藥、利水滲濕藥的功效和和適應證有何不同？ 答：相同點是：均為祛濕藥

不同處：清熱燥濕藥治療濕熱性質(zhì)的疾?。混铒L濕藥治療風濕、類風濕性 疾病；化濕藥治療脾虛生濕，重點在于健脾；利水滲濕藥治療水濕內(nèi)停證，重點在于利尿祛濕。

第12~14章作業(yè)

1.中藥炮制的目的是什么？答：中藥炮制的目的是降低或消除藥物毒性或副作用，改變或緩和藥性；提高療效；改變或增加藥物作用的部位和趨向；便于調(diào)劑和制劑；保證藥物潔凈度，利于貯藏；有利于服用。

2.山楂其味酸甘，具有收斂和補虛的作用嗎？為什么答：：山楂雖有酸甘之味，但并不具有收斂固澀和補益正氣的作用。因為山楂具有消食散瘀兼行氣之功，主治食積證、血瘀、疝氣等，山楂的效用顯示其性散而不收。而其酸甘之味保留了山楂的口嚐滋味，同時也表示，山楂味酸可入肝經(jīng)，味甘可入脾經(jīng)，而達到消食化積，散瘀行氣之功。

3.最適用于小兒蛔蟲病的藥物是什么，為什么。答：使君子。使君子可單獨炒香，令小兒嚼服，一來小兒宜于服用，二來使君子驅(qū)殺蛔蟲療效確切。

4.木香、香附均可理氣，其臨床應用有何不同？答：木香：【功效】行氣止痛?！緫谩坑糜谄⑽笟鉁雇醋C，大腸氣滯、瀉痢后重，肝膽氣滯證。香附：【功效】疏肝理氣，調(diào)經(jīng)止痛。【應用】用于肝郁氣滯證，月經(jīng)不調(diào)、痛經(jīng)、乳房脹痛。5.應用驅(qū)蟲藥時應注意哪些問題？

答：本類藥物一般宜空腹時服用，使藥物充分作用于蟲體而保證療效。應用毒性較大的驅(qū)蟲藥要注意用量、用法，以免中毒或損傷正氣；同時孕婦、年老體弱者亦當慎用。蟲證而腹痛劇烈者，通常以安蟲為主，待疼痛緩解后，再行驅(qū)蟲。對發(fā)熱患者，亦宜先治其發(fā)熱，待癥狀緩解或消失，再使用驅(qū)蟲藥物。6.香附醋制的作用？答：香附醋炙止痛力增強。第15~18章作業(yè)

1.舉一個例子介紹既能活血，又能涼血，并能養(yǎng)血的藥物。答;丹參既能活血調(diào)經(jīng)，治療瘀血阻滯之月經(jīng)不調(diào)和其他病證，又能涼血消癰以治瘡瘍癰腫，并能養(yǎng)血安神以治熱入營血，煩躁不寐，及血不養(yǎng)心之心悸失眠等。

2.化痰藥因藥性之不同而有何區(qū)別？各用于何證？答：藥性有偏溫，有的偏寒。而藥味多根據(jù)藥物的某些作用特點，并結合實際滋味來確定。如部分藥物具有辛麻味，或兼有宣肺、利氣之功則標辛味；部分藥物來源于海生植物及動物貝殼，并有消痰散結之功，則標咸味。“肺為貯痰之器”，故本章藥物主歸肺經(jīng)；部分藥物因可主治心、肝、脾之證，則可兼歸以上三經(jīng)。少部分化痰藥具有毒性。

3.陳皮有何功效？說明其作用機理？ 答：

1、陳皮有助于消化，因為陳皮含有類檸檬苦素，這種類檸檬苦素味平和，易溶解于水，此外，陳皮含有揮發(fā)油、橙皮甙、維生素B、C等成分，它所含的揮發(fā)油對胃腸道有溫和刺激作用，可促進消化液的分泌，排除腸管內(nèi)積氣，增加食欲。

2、陳皮的苦味可以與其他味道相互協(xié)調(diào)，因此可以用于烹制菜肴改善味道，不但辟去魚肉的膻腥味，且使菜肴特別可口;在涼果、食品方面，新會陳皮梅、陳皮鴨、陳皮酒，其色、香、味都具特色。此外，制作綠豆沙、紅豆粥等甜品，若加入一點陳皮，味道分外芳香。

3、陳皮也是一味常用中藥，味辛苦、性溫，具有通氣的健脾、燥濕化痰、解膩留香、降逆止嘔的功效。中醫(yī)的“陳皮半夏湯”、“二陳湯”是主要靠陳皮治病的，以陳皮為主要成分配制的中成藥，如川貝陳皮、蛇膽陳皮、甘草陳皮、陳皮膏、陳皮末等，是化痰下氣、消滯健胃的良藥。適合胃部脹滿、消化不良、食欲不振、咳嗽多痰等癥狀的人食用。4.簡述瓜蔞的功效、主治證及注意事項。答：【功效】清熱化痰，寬胸散結，潤腸通便。【應用】用于熱痰咳嗽、燥熱痰咳之證，胸痹、痰熱互結之胸脘脹滿證，腸燥便秘。

5.百部的主治病證有哪些？答：用于多種咳嗽。外用用于蟯蟲、陰道滴蟲、頭虱，疥癬等。蜜百部潤肺止咳。用于陰虛勞嗽。6.止血藥分為哪幾類？其作用和適應證有何不同？答：1.收斂止血藥以止血為主要功效，并兼能收澀，且性較平和的藥物，稱為收斂止血藥。本類藥物大多味澀。其性多平，或雖有微寒之性，但實無清熱之功，可用于多種無明顯邪氣的失血證。然本類藥物味澀收斂，易留淤戀邪，故應用當以出血而無明顯邪氣和血淤者為宜，且多與化淤止血藥或活血化淤藥配伍使用。屬正氣虛衰者，當配伍補虛藥，以標本兼治。對于收兼治。對于收斂性較強的收斂止血藥，有淤血及實邪者用之當慎。2.涼血止血藥本類藥物既能清熱涼血，針對血熱妄行的病因而收間接止血之效，又能直接止血。藥性均為寒涼；味多苦、甘，若表示清泄，其甘多與滋味有關；因入血分涼血止血而歸肝經(jīng)。適用于血熱妄行的出血證。原則上不宜于虛寒性出血證，但亦有某些藥物，或通過炮制（炒炭），或通過配伍，亦可使用。本類藥性寒凝滯，易涼遏傷陽而留淤，不宜過用。3.化淤止血藥 既可止血，又能活血化淤的藥物，稱為化淤止血藥。本類藥物既能直接止血，又能活血化淤，以使血脈通暢，最適用于因淤血內(nèi)阻而血不循經(jīng)之出血證。此種出血，淤血不去則血不歸經(jīng)而出血不止，故宜以化淤止血藥為主治之。亦可配伍其他各類止血藥，用于各種內(nèi)外出血證，同樣有止血而不留淤的優(yōu)點。又因其能化淤而消腫止痛，亦常用于跌打損傷及多種淤滯疼痛等。根據(jù)辛能行的理論，本類藥多為辛味；其性可偏溫，或偏寒；主要歸肝、心二經(jīng)。4.溫經(jīng)止血藥 既可止血，又能溫里散寒的藥物，稱為溫經(jīng)止血藥。本類藥藥性溫熱，既能溫通血脈，消散凝滯，促進血液循經(jīng)運行，并扶助陽氣，統(tǒng)攝血液，而有利于止血，又具獨立的止血作用。主要適用于脾陽虛不能統(tǒng)血或沖脈失固之虛寒性出血證，癥見出血日久，血色暗淡，且有全身虛寒表現(xiàn)者。本類藥物又是溫里之藥，尚能溫中以止瀉、止嘔，或溫經(jīng)散寒以調(diào)經(jīng)、止痛等，故又可主治多種里寒證。

7.活血化瘀藥配伍作用和適應證？ 答：功效與主治 活血化淤藥均能促進血行，消散淤血，主治各種淤血證。對活血祛淤藥，按其作用強度的不同常有不同的稱謂。如“和血”、“和營”多指活血作用較弱，藥力平和；“活血”、“化淤”、“祛淤”、“消淤”較“和血”、“和營”作用強，然力量強度又不及“破血”、“破淤”、“逐淤”等功效。后者活血化淤作用強，藥力峻猛。當然，藥物活血作用的強度是相對的，如劑量多少可改變其強度。由于本章藥物數(shù)量較多，為了便于學習掌握，今按其作用特點和主治的不同，相對地將其分為活血止痛藥、活血調(diào)經(jīng)藥、活血療傷藥及破血消癥藥4類。配伍應用活血祛淤藥的使用，應針對病情，并根據(jù)藥物寒溫、猛緩之性或止痛、通經(jīng)、療傷、消癥等專長，加以選擇，并作適當?shù)呐湮?。由于人體氣血之間的密切關系，氣滯可導致血淤，血淤也常兼氣滯，故本類藥物常需與行氣藥同用，以增強活血化淤的功效；寒凝血淤者，當配伍溫里藥以溫通血脈，助活血化淤藥以消散淤滯；若熱灼營血而致血淤者，當配伍清熱涼血藥；痹證、瘡癰，則應與祛風濕藥或清熱解毒藥同用；癥瘕痞塊，應同化痰軟堅之品配伍；淤血而兼正虛，又當配伍相應的補虛藥，以通補兼施。如淤血兼血虛或陰虛者，當同補血藥或養(yǎng)陰藥同用，陰血充足，則淤血易去；同樣，若淤血而兼氣虛者，當與補氣藥同用，氣為血帥，氣足則血易行，淤易去。淤血而出血者，宣配伍止血藥，不可單純止血或單純活血。

第19~21章作業(yè)

1.簡述安神藥、平肝潛陽藥、息風止痙藥的含義和分類？答:安神藥: 以寧心安神為主要作用，常用以治心神不寧之證的藥物，稱為安神藥。平肝潛陽藥: 以平抑上亢之肝陽為主要作用，常用以治療肝陽上亢證的藥物，稱平肝潛陽藥，或稱平抑肝陽藥，簡稱平肝藥等。息風止痙藥: 以平息肝風，制止痙攣抽搐為主要作用，常用以治肝風內(nèi)動證的藥物，稱息風止痙藥?？珊喎Q息風藥或止痙藥。

2.遠志和酸棗仁均有安神之功，如何區(qū)別使用？ 答:酸棗仁: 【性味歸經(jīng)】甘、酸，平。歸心、肝經(jīng)?！竟πА筐B(yǎng)心安神，斂汗?！緫谩坑糜谛纳癫粚幹C，體虛多汗。遠志: 【性味歸經(jīng)】苦、辛，微溫。歸心、腎、肺經(jīng)?！竟πА繉幮陌采?，化痰開竅?！緫谩坑糜谛纳癫粚幹C，癲狂、癇證，咳嗽痰多。

3.羚羊角、天麻與鉤藤三藥功效、主治病證，如何區(qū)別使用？答: 相同點：三藥均能平肝息風、平抑肝陽，均可治肝風內(nèi)動，肝陽上亢之證。不同點：羚羊角咸寒，清熱力強，除善治熱極生風證外，又能清心解毒，多用于高熱神昏，熱毒、發(fā)斑等。鉤藤性涼，輕清透達，長于清熱息風，用治高熱驚風輕證為宜。天麻甘平柔潤，清熱之力不及羚羊角、鉤藤，但治肝風內(nèi)動、驚癇抽搐之證，虛實寒熱皆可選用。又兼祛風通絡，用治肢體麻木，手足不遂，風濕痹痛。4.牛黃簡述的功效及適應證？答: 【功效】息風止痙，清心肝熱，化痰開竅，清熱解毒。

【應用】用于溫熱病熱級生風、小兒肝熱驚風等肝風內(nèi)動證，溫熱病熱入心包、中風等竅閉神昏證，咽喉腫痛，外科瘡癰等。第22~23章作業(yè)

1.簡述開竅藥的性能特點及適應證？答：

一、含義 以開通心竅，啟閉醒神為主要作用，常用以治療閉證神昏的藥物開竅藥。

二、功效與主治 開竅藥均具有開竅醒神功效，主治閉證神昏之證。閉證是指各種實邪阻閉心竅導致神志昏迷的一類證候。閉證神昏多由熱邪內(nèi)陷心包，或痰濕、穢濁、淤血等實邪阻閉心竅，致使心所主之神明失用，而見神志昏迷，不省人事，牙關緊閉，兩手固握有力，或譫語等實證表現(xiàn)。主要用于溫熱病、中風、驚風、癇證、中暑、胸痹及食物不潔等病證之神志昏迷。

三、性能特點 本章藥物的藥性與主治病證間無明顯對應關系，但歷來將大多數(shù)藥標溫性，以表示其溫通之效。開竅藥大多具有濃郁的芳香之氣，并能醒神復蘇，故標辛味。“心主神明”，邪氣閉阻心竅則神昏，本類藥主歸心經(jīng)。開竅辛香走竄，而具升浮之性。除蟾酥、樟腦有毒外，其余藥物在規(guī)定劑量范圍內(nèi)且短時間應用，一般視為無毒。

2.枸杞子的臨床應用有哪些？答：枸杞子可調(diào)節(jié)機體免疫功能、能有效抑制腫瘤生長和細胞突變、具有延緩衰老、抗脂肪肝、調(diào)節(jié)血脂和血糖等方面的作用。因此，枸杞子對糖尿病、血脂異常癥、肝功能異常、胃炎等都有一定的治療作用。3.簡述補陽藥與溫里藥有何不同？答：同：藥性都是溫熱的 異：凡能溫里祛寒，用以治療里寒癥候的藥物，稱為溫里藥

補陽藥就是能補益人體陽氣，消除改善陽虛病證的藥物。從概念上就有區(qū)別，但是通常補陽藥均帶有溫里的作用，而溫里藥只要溫里作用，沒有補益陽氣的作用。

4.簡述熟地黃與生地黃功用的異同點？答：共同點：養(yǎng)陰，同可用治陰虛潮熱，津傷口渴，消渴證。

不同點：生地黃又可清熱、涼血、止血，常用治熱入營血，舌絳煩渴，斑疹吐衄，及溫病后期，余熱未盡之夜熱早涼，舌紅脈數(shù)者醫(yī)`學敎育網(wǎng)搜`集整理。而熟地黃又可養(yǎng)血，填精益髓，常用治血虛萎黃，眩暈，心悸，失眠及月經(jīng)不調(diào)，崩中漏下，或精髓虧虛之腰膝酸軟、遺精、盜汗、耳鳴、耳聾、須發(fā)早白及消渴者。

5.詳細介紹人參的作用？答： 1 調(diào)節(jié)中樞神經(jīng)系統(tǒng)：人參能調(diào)節(jié)中樞神經(jīng)系統(tǒng)，改善大腦的興奮與抑制過程，使之趨于平衡；能提高腦力與體力勞動的能力，提高工作效率，并有抗疲勞的作用。2 促進大腦對能量物質(zhì)的利用，可以提高學習記憶能力人參中增強學習和記憶能力的有效成分為人參皂苷，其中人參皂苷Rb1和Rg1，對學習和記憶功能均有良好影響。人參根皂苷對正常大鼠學習、記憶過程有促進作用，而人參莖葉皂苷對電休克所致的大鼠記憶障礙有明顯的改善作用。兩者均使正常大鼠不同腦區(qū)的單胺類遞質(zhì)含量明顯增多。這些研究工作，對合理應用人參植物資源有一定參考價值。3 改善心臟功能：人參能增加心肌收縮力，減慢心率，增加心輸出量與冠脈血流量，可抗心肌缺血與心律失常。對心臟功能、心血管、血流都有一定的影響。人參有明顯的耐缺氧作用，其制劑可有效地對抗竇性心率失常。人參皂苷可加快脂質(zhì)代謝，并具有明顯降低高膽固醇的作用。小劑量人參可使麻醉動物血壓輕度上升，大劑量則使血壓下降。不同的人參制劑對離體蟾蜍心臟及在體兔、貓、犬心臟皆有增強其功能的作用，并可改善其心室纖顫時的心肌無力。

羚羊角的作用：平肝息風，清肝明目，涼血解毒的功效，主治肝風內(nèi)動，驚癇抽搐，譫語發(fā)狂，肝陽頭痛眩暈，肝火目赤腫痛，血熱出血，溫病發(fā)斑等。杏仁和蘇子的作用有什么區(qū)別：都能止咳平喘，潤腸通便合作用，都可用于咳喘、腸燥便秘等癥。但它們不下列不同之處：(1）杏仁：苦杏仁既能降氣平喘，又可呈肺化濕，適用咳喘實證；甜杏仁又能滋陰，可月于肺陰虛久咳、燥咳。(2）蘇子：性味辛溫，偏于溫中消痰，適用戶肺寒多痰咳喘，如《 別錄》 說：“主下氣，除寒溫戶?！贝竺鞅静荨?說：“止嗽，潤心肺，消痰氣?！?/p>