Chapter04 Basic Motivation Concepts
leadership第四单元翻译
描述了发展的双因素理论在俄亥俄州立大学
讨论了管理方格识别和地方五个锚方式对网格
INTRODUCTION
When traits theory was not productive the focus shifted toward behavior of leaders
“What do leaders actually do and how do they do it?”
组织工作
考虑初始结构是独立的规模
TWO-FACTOR APPROACHES, cont.
Blake and Mouton managerial grid:
Explored the concern for task and concern for relationship
Built upon the work of the Michigan and Ohio State studies
其他研究人员把重点放在分歧的专制（任务）与民主（社会）风格
生产导向与员工导向
工作中心和员工为中心
专制与参与
任务导向与社会情感面向
McGREGOR THEORY X AND THEORY Y
McGregor created two alternative sets of assumptions of human motivation
早期行为的方法
勒温，利比特，和白色（爱荷华大学）发现三个不同的领导风格
专制作风–正式规则，法规，控制
民主作风–合作，参与和参与，互动
–自由放任风格“让”，不干涉，不参与
连续的
EARLY BEHAVIORAL APPROACHES, cont.
General findings:
lat04
CHAPTER 4"Neuters of the Second Declension; Summary of Adjectives; Present Indicative of Sum;Predicate Nouns and Adjectives"Despite its lengthy title, you'll find that much of this chapter only adds incrementally to concepts you've already learned. That's the way it's going to be for most of these chapters. Now that you've learned the basics, the details will be much easier for youto grasp.NEUTERS OF THE SECOND DECLENSIONThe second declension is the pattern of cases ending which has an "-o-" for its thematic vowel. The nominative singular has three possible forms -- "-us", "-er", and "-ir". Sometimes nouns which end in "-er" in the nominative undergo a stem change from the nominative to the genitive singular. To find the real stem of the noun, you simply drop off the genitive ending "-i" from the second entry in the dictionary. Finally, you may remember that the vast majority of nouns ending in "-us", "-er", and "-ir" in the nominative singular are masculine.What you learned in the last chapter was not the whole story on the second declension. The second declension is divided into two parts: the part you know, and a set of endings which you're going to learn now. This second part contains only neuter nouns. This is important to remember. Unlike the first declension and the first part of the second, whose nouns could be either feminine or masculine, all nouns which follow this second part of the second declension are neuter. Next, the endings of this pattern are nearly identical to those of the second declension you already know. The differences are that (1) the nominative singular ending is always "-um"; (2) the stem is found by dropping off nominative "-um" ending and there is never a stem change; (3) the neuter nominative and accusative plural endings are "-a". You don't have to worry about the vocative singular; it's the same as the nominative singular. Remember, the only place in Latin where the vocative differs from the nominative is in the singular of "-us" ending second declension nouns and adjectives.A dictionary entry for a noun of this type will look likethis: "x"um, -i (n) (where "x" is the stem). Since there is nevera stem change, the second entry only gives you the genitivesingular ending so that you can see the declension of the noun.The "-um" of the nominative singular and then the "-i" in thegenitive tell you that the noun is a neuter noun of the seconddeclension, and that it therefore fits into the subcategory of thesecond declension. Here are some examples for you to decline anda second declension noun of the "us" type for comparison:numerus, -i (m) periculum, -i (n) consilium, -ii (n)Nom. ______________ _______________ _______________ Gen. ______________ _______________ _______________ Dat. ______________ _______________ _______________ Acc. ______________ _______________ _______________ Abl. ______________ _______________ _______________ Voc. ______________N/V. ______________ _______________ _______________ Gen. ______________ _______________ _______________ Dat. ______________ _______________ _______________ Acc. ______________ _______________ _______________ Abl. ______________ _______________ _______________ There are a couple of hard and fast rules pertaining to theinflection of all neuter nouns, no matter which declension theybelong to, which you may want to commit to memory: (1) thenominative and accusative forms of neuters nouns are always likeeach other, and (2) the nominative plural -- and hence neuterplural because of rule (1) -- is always a short "-a".ADJECTIVESYou recall that adjectives are words which modify nouns, and thatin Latin an adjective must agree with the noun it's modifying. By "agreeing", we mean it must have the same number, gender, and case.An adjective acquires number and case by declining through adeclension -- just like nouns -- but how does an adjective changegender? An adjective changes gender by using differentdeclensional patterns. If an adjective needs to modify a femininenoun, it uses endings from the first declension; if it has tomodify a masculine noun, it uses the second declension endingswhich are used by "-us" and "-er" ending nouns. So how do youimagine will an adjective modify a neuter noun? Let's look at adictionary entry for a typical adjective: "magnus, -a, -um".The first entry, as you recall, tells you which declension theadjective uses to modify a masculine noun. It tells you by givingyou the nominative singular ending of the declension it uses. Thesecond entry is the nominative singular ending of the declensionthe adjective uses to modify a feminine noun. The third entry isthe nominative singular of the declension the adjective uses tomodify a neuter noun.So how does the adjective "magnus, -a, -um" modify a neuternoun? It uses the "-um" neuter endings of the second declension,so "magnus", when it's modifying a neuter noun, will follow thesame pattern as a noun like "periculum, -i (n). Write out all thepossible forms of the adjective "great". (Check your work against Wheelock, p. 18.)"magnus, -a, -um"MASCULINE FEMININE NEUTER Nom. _______________ _______________ _______________ Gen. _______________ _______________ _______________ Dat. _______________ _______________ _______________ Acc. _______________ _______________ _______________ Abl. _______________ _______________ _______________ Voc. _______________N/V. _______________ _______________ _______________ Gen. _______________ _______________ _______________ Dat. _______________ _______________ _______________ Acc. _______________ _______________ _______________ Abl. _______________ _______________ _______________ THE VERB "TO BE"As in most languages, the verb "to be" in Latin is irregular-- i.e., it doesn't follow the normal pattern of conjugation ofother verbs. Wheelock says it's best just to memorize the forms bysheer effort and rote. That's a perfectly acceptable suggestion.But the verb is actually much more regular than it may firstappear. If you wish, you may try to follow my discussion about theverb to get a glimpse behind its seemingly bizarre appearance. Ifnot, just memorize the forms outright and skip over the paragraphsin between the lines of asterisks.****************************************For those of you going on with me, let's recall a couple ofthings. A verb conjugates by adding personal endings to the stemof the verb. You find the stem of the verb by dropping of the"-re" ending of the infinitive, and what you're left with is thestem. The final vowel of the stem tells you the conjugation of theverb: "-a-" for a first conjugation, "-e-" for the secondconjugation, etc. So let's have a look at the infinitive of theverb "to be" to find its stem. The infinitive is "esse". Whatkind of an infinitive is this?We need to back up a little. Although you were toldotherwise, the real infinitive ending of a Latin verb is not "-re"at all, but "-se". Why does the "-se" become "-re"? It's aninvariable rule of Latin pronunciation that an "-s-" which iscaught between two vowels -- we call it "intervocalic" -- turnsinto a "-r-". So the reason "laudare" is not "laudase" is that theoriginal intervocalic "-s-" became an "-r-". So let's look againat the infinitive for the verb "to be": "esse". If we drop offthe infinitive ending "-se", we're left with the stem "es-" for theverb. But the stem has no final vowel. For this reason we call"esse" an "athematic verb", because its stem ends in a consonant,not a vowel, as other verbs do. To conjugate the verb, we shouldtherefore add the personal endings directly to the final "-s" ofthe stem. This is what the formula should be (don't fill in theconjugated form yet).STEM + PERSONAL ENDING = CONJUGATED FORM 1st es + m = _______________2nd es + s = _______________3rd es + t = _______________1st es + mus = _______________2nd es + tis = _______________3rd es + nt = _______________ Try to pronounce the final form for the first person singular"esm". Do you hear how you're automatically inserting a "u" soundto make the word pronounceable? It sounds like "esum". Try topronounce "esmus". The same thing happens between the "s" and the"m". You almost have to insert a "u". Now pronounce "esnt". Samething, right? This is what happened to these forms. Over time, a"u" sound became a part of the conjugation of the verb, and theinitial "e-" of the stem of all the forms with this "u" was lost.(I can't account for that.) Write out the resulting forms. Nowlook at the remaining forms. Is there any trouble adding an "s" ora "t" to the final "s-" of the stem? No. In fact, in the secondperson singular, the "s" of the personal ending just gets swallowedup by the "s" of the stem: "es + s = es". Where there was nocomplication in pronouncing the forms, the "e-" of the stem stayed.Now write out the remaining forms of "to be" in Latin.****************************************As with other Latin verbs, the basic form of "to be" isconsidered to be the first person singular, and that's how the verbwill be listed in the dictionary, followed by the infinitive: "sum, esse". So when I want to refer to the Latin verb "to be", I'll saythe verb "sum". You can also see why it's going to be important to memorize all these forms well. You can't look up "estis" or "es".You must reduce these conjugated forms to a form that will appearin the dictionary: you must know that these forms are from "sum".THE SENTENCE: SUBJECT AND PREDICATEWe divide sentences into two parts: the subject, which is what'sbeing talked about, and the predicate, what's being said about the subject. Basically, the subject is the subject of the verb, andthe predicate is the verb and everything after it. For example, inthe sentence "Latin drives me crazy because it has so many forms", "Latin" is the subject, and everything else is the predicate. Of course, the full story of subject and predicate is more involvedthan this, but this will get us by for now.PREDICATE NOMINATIVES, TRANSITIVE AND INTRANSITIVE VERBS In Latin the subject of a verb is in the nominative case. You know that. So it may seem to follow that, if the subject of the verb isthe subject of the sentence, that the nominative case should beentirely limited to the subject of the sentence. That is, weshouldn't expect there ever to be a noun in the nominative case inthe predicate. Nouns in the nominative case should be the subjectof verbs, and the subject of verbs is in the subject clause of the sentence, not in the predicate. But we do find nouns in the nominative in the predicate. When we do, we call them, logically enough, "predicate nominatives". How does it happen that a nominative case shows up in the predicate, after the verb?We divided verbs into two broad classes: verbs which transfer action and energy from the subject to something else (the object),and verbs in which there is no movement of energy from one place to another. Consider this sentence: "George kicked the ball". Here George expended energy -- he kicked -- and this energy was immediately applied to an object -- the ball -- which was changedas a result of what George did to it. We call a verb like this a "transitive" verb and the object affected by it the direct object.In Latin, the direct object of a transitive verb is put into the accusative case. Now look at this sentence: "The river is wide".Is the river doing anything in this sentence to anything else?Does the verb "is" imply that the subject is acting on somethingelse? No. There is no movement of activity from the subject to something else. Verbs like this are called "intransitive" anddon't take direct objects. In Latin that means they are not followed by an accusative case. Some more examples of this: "The dog was running away", "We'll all laugh", "The clown didn't seemvery happy".Sometimes it's hard to tell whether a verb in English is transitive or intransitive. A rule of thumb is this. Ask yourself, "Can I 'x' something?" (where "x" is the verb you're investigating). If the answer is "yes" then the verb is transitive; if "no" then it's intransitive. "Can I see something?" Yes; therefore the verb "to see" is transitive. "Can I fall something?" No; therefore "to fall" is intransitive.THE COPULATIVE VERB "SUM"The verb "to be" is obviously an intransitive verb -- there is no movement of energy from the subject to an object -- but it has an interesting additional property. What are we actually doing whenwe use the verb "to be?" We are in effect modifying the subject with something in the predicate. In the sentence "The river is wide", "river" is the subject and "wide" is an adjective in the predicate that is modifying "river". Even though it's on the other side of the verb and in the predicate, it's directly tied to the subject. In Latin, therefore, what case would "wide" be in? Thinkof it this way. "Wide" is an adjective, and it's modifying the "river", even though it's in the predicate. Adjectives in Latinmust agree in number, gender and case with the nouns they modify,so "wide" has to be in the nominative case. It's modifying "river", right? What the verb "to be" does is to tie or link the subject directly to something in the predicate, and for that reason we call the verb "to be" a "linking" or "copulative" verb. This principle has a special application in Latin, which has a full case system. When the verb "sum" links the subject with an adjective in the predicate, the adjective agrees with the subject.Donum est magnum. Dona sunt magna.nominative = nominative nominative=nominative neuter = neuter neuter = neutersingular = singular plural = pluralWhen "sum" links the subject with a noun in the predicate, however, we have a bit of a problem. Nouns have fixed gender, so the noun in the predicate can't agree with the subject noun in quite the same way an adjective can. A noun in the predicate has its own gender which it cannot change. But a noun in the predicate which is tied to the subject by "sum", will agree with the subjectin case. Think of the verb "sum" as an equal sign, with the same case on both sides.Mea vita est bellum (war).nominative =nominativefeminine ~ neutersingular = singularVOCABULARY PUZZLESLook at these two dictionary listings:1. bellum, -i (n) "war"2. bellus, -a, -um "beautiful"The first is an entry for a noun, the second an entry for an adjective. What are the differences? An entry for a noun starts with the nominative singular form, then it gives you the genitive singular. It actually starts to decline the noun for you so that you can tell the noun's declension and whether the noun has any stem changes you should be worried about. The final entry is the gender, since nouns have fixed gender which you must be given. For a noun, therefore you must be given (1) the nominative form, (2) the stem, (3) the declension, and (4) the gender.An entry for an adjective, by contrast, has different information to convey. For an adjective, you must know which declension it'll use to modify nouns of different gender, andthat's what the "-us, -a, -um" is telling you. But there is an important omission from the adjective listing. There is no gender specified, and how could there be, adjectives change their gender. As you'll see later, this is the one sure sign that a word you're looking at is an adjective: if it has declension endings listed butno gender.You may also be concerned that, given the similar appearance of these two words, you may mix them up in your sentences.Certainly there will be some overlap of the two forms. For example, "bella" is a possible form of the noun "bellum" and the adjective "bellus, -a, -um". But there are also many forms which "bellus, -a, -um" can have which "bellum, -i (n)" can never have.For example, "bellarum" can't possibly come from a second declension neuter noun. Neither can "bellae", "bellas", "bellos", "bella", and some others. If you see "bell- something" in your text, first ask yourself whether the case ending is a possible form from the neuter noun for war. If not, then it's from the adjective for "pretty". In the instances where the forms do overlap, you'll have to let context and your good judgment tell you which it is.12/31/92。
英文商务统计学ppt_第四章Ch04
X number of face cards Probabilit y of Face Card T total number of cards
X 12 face cards 3 T 52 total cards 13
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc.
In this chapter, you learn:
Basic probability concepts Conditional probability To use Bayes’ Theorem to revise probabilities
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc.
Joint event
Complement of an event A (denoted A’)
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc.
Chap 4-5
Example of empirical probability
Find the probability of selecting a male taking statistics from the population described in the following table:
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc.
Chap 4-6
计量经济学 ae_ch04
CHAPTER4ASYMPTOTIC THEOR Y FOR LINEAR REGRESSION MODELS WITH I.I.D.OBSER V A TIONSKey words:Asymptotics,Almost sure convergence,Central limit theorem,Conver-gence in distribution,Convergence in quadratic mean,Convergence in probability,I.I.D., Law of large numbers,Slutsky theoremQuestions:The assumptions of classical linear regression models are rather strong and one may have a hard time…nding practical applications where all these assumptions hold exactly. An interesting question is now is whether the estimators and tests which are based on the same principles before still make sense in this more general setting.In particular, what happens to OLS,t and F-tests if any of the following assumptions fails: (i)Strict Exogeneity E("t j X)=0:(ii)conditional homoskedasticity(var("t j X)= 2)?(iii)serial uncorrelatedness(cov("t;"s j X)=0for t=s)?(iv)normality("j X N(0; 2I))?When classical assumptions are violated,we do not know the…nite sample statis-tical properties of the estimators and test statistics anymore.A useful tool to obtain understanding of the properties of estimators and tests in this more general setting is to pretend that we can obtain a limitless number of observations.We can then pose the question how estimators and test statistics would behave when the number of observa-tions increases without limit.This is called asymptotic analysis.In practice,the sample size is always…nite.However,the asymptotic properties translate into results that hold true approximately in…nite samples,provided that the sample size is large enough.We now need to learn some basic analytic tools for asymptotic theory. Introduction to Asymptotic TheoryReferences:Asymptotic Theory for Econometricians,2nd Edition,by Halbert White(1999) Econometrics,Ch.2,by Fumio Hayashi(2000)Convergence Concepts and Limit TheoremsConvergence in mean squares (or in quadratic mean):A sequence of random variables/vectors/matrices Z n ;n =1;2;:::;is said to converge to Z in mean squares as n !1ifE jj Z n Z jj 2!0as n !1;where jj jj is the sum of the absolute value of each component in Z n Z:Remarks:(i)When Z n is a vector/matrix,convergence can be understood as convergence in each element of Z n :(ii)When Z n Z is a n m matrix,then we can de…nejj Z n Z jj 2=n X t =1m X s =1[Z n Z ](t;s ) :Example 1:Suppose f Z t g is i.i.d.( ; 2);and Z n =n 1P n t =1Z t :Then Z n q:m:!Proof:Because E ( Zn )= and var ( Z n )= 2=n;we have E [ Z n ]2=var ( Z n )=varn 1n X t =1Z t!=1n 2var n X t =1Z t !=1n 2n X t =1var (Z t )= 2n !0:Convergence in probability :Z n converges to Z in probability if for any given constant ">0;Pr[jj Z n Z jj >"]!0as n !1:Notations :(i)We can also writeZ n Z p!0or Z n Z =o P (1);(ii)When Z=b is a constant,we can write Z n p!b and b=p lim Z n is called the probability limit of Z n;where o P(1)means“Z n b vanishes to zero in probability.”(iii)Convergence in probability is also called weak convergence.(iv)When Z n!p Z;the probability that the di¤erence j Z n Z j exceeds any small constant"is very small.Example1[W eak Law of Large Numbers(WLLN)]:Suppose f Z t g is i.i.d.( ; 2); and de…ne Z n=n 1P n t=1Z t:Then Z n p! :Proof:For any given">0;we have by Chebyshev’s inequalityPr(j Z n j>") E[ Zn ]2 "2= 2n"2!0as n!1:Hence,Znp! :Remark:This is the so-called weak law of large numbers(WLLN).In fact we can weaken the moment condition.Lemma[WLLN for i.i.d.sample]Suppose f Z t g is i.i.d.with E(Z t)= and E j Z t j<1:De…ne Z n=n 1P n t=1Z t:Then Z n p!A related concept:De…nition[Boundedness in probability]A sequence of random variables/vectors/matrices f Z n g is bounded in probability if for any small constant >0;there exists a constantC such thatP(jj Z n jj>C) :We denoteZ n=O P(1):Interpretation:The probability that jj Z n jj exceeds a very large constant is very small. Example:Suppose Z n N( ; 2)for all n:ThenZ n=O P(1):Answer:For any >0;we always have a su¢ciently large constant C=C( )>0such thatP(j Z n j>C)=1 P( C<Z n<C)=1 P C <Z n <C=1 C + C+;and where (z)=P(Z z):[We can choose C such that [(C )= ] 1 12[ (C+ )= ] 1:]2Please see what happens to C if Z n N(0;1)?Remark:Z n Z q:m:!0implies Z n Z p!0(why?);but the converse may not be true. Example:SupposeZ n=(0with prob1 1n with prob1:Then Z n p!0but E[Z n 0]2=n!1:De…nition[Almost Sure Convergence]:f Z n g converges to Z almost surely ifPr h lim n!1jj Z n Z jj=0i=1:We denoteZ n Z a:s:!0:Interpretation:Recall the de…nition of a random variable,namely any random variable is a mapping from sample space to the real line:Z: !R:Let!be a basic outcome in sample space :De…ne a subset in :Z n(!)=Z(!)g:A c=f!2 :limn!1Then almost sure convergence can be stated asP(A c)=1:In other words,the convergent set has probability one.Example:Let!be uniformly distributed on[0;1];and de…neZ(!)=!for all!2[0;1]:andZ n(!)=!+!n for!2[0;1]: Is Z n Z a:s:!0?Answer:ConsiderA c=f!:limn!1j Z n(!) Z(!)j=0g: Because for any given!2[0;1);we always havelimn!1j Z n(!) Z(!)j=lim n!1!n=0:Also,for!=1;we havelimn!1j Z n(1) Z(1)j=1n=1=0:Thus,A c=[0;1)and P(A c)=1:P(A)=P(!=1)=0:Remark:Almost sure convergence is closely related to pointwise convergence(almost everywhere).Lemma[Strong law of large numbers(SLLN)for i.i.d.samples]:Suppose f Z t g be i.i.d.with E(Z t)= and E j Z t j<1:ThenZ n a:s: ! :Remark:Almost sure convergence implies convergence in probability but not vice versa. Lemma:(i)Suppose a n p!a and b n p!b;and g( )and h( )are continuous functions. Theng(a n)+h(b n)p!g(a)+h(b);g(a n)h(b n)p!g(a)h(b):(ii)Similar results hold for almost sure convergence.Convergence in distribution:Z n converges to Z in distribution ifF n(z)!F(z)as n!1for all continuity points of F ( );namely all points z where F (z )is continuous.Remark:For convergence in probability and almost sure convergence,we can view that convergence of Z n to b occurs element by element (that is,each element of Z n converges to the corresponding element of b ).For convergence in distribution of Z n to Z ,however,element by element convergence does not implies convergence in distribution of Z n to Z;because element-wise convergence in distribution ignores the relationships among thecomponents of Z n :But Z n d !Z implies that each element of Z n converges in distributionto the corresponding element of Z:Example [Central Limit Theorem,CLT):Suppose f Z t g is i.i.d.( ; 2);and Z n =n 1P nt =1Z t :ThenZ n E ( Z n )p var ( Z n )= Z n p 2=n =p n ( Z n )d !N (0;1):Proof:Put Y t =Z t ;and Y n =n 1P n t =1Y t :Then p n ( Z n ) =p n Y n :The characteristic function of p n Y nn (u )=E [exp(iu p n Y n )];i =p 1=E [exp iu p n n X t =1Y t !]=n Y t =1E exp iu p n Y t = Y u p nn :Now considerln n (u )=n ln Y u p n =ln Y u p n n 1!u 2lim n !1 0Y (u=pn ) Y (u=p n )n 1=2=u 22lim n !1 00Y (u=p n ) Y (u=p n ) [ 0Y (u=p n )]2 2Y (u=p n )= u 22;where we have used the fact that Y (0)=1; 0Y (0)=0and 00Y (0)= 1:It follows thatlim n !1n (u )=e 12u 2:This is the characteristic function of N (0;1).By the uniqueness of the characteristic function,the asymptotic distribution ofp n ( Z n )is N (0;1):Lemma [The Cramer-W old Device]:A random vector Z n d!Z if and only if for any nonzero such that 0 = pj =1 2j =1;we have0Z n d ! 0Z:Remark:This lemma is useful for obtaining multivariate asymptotic distribution.The Slutsky Theorem :Let Z n d !Z;a n p !a and b n p !b:Thena n +b n Z n d !a +bZ:Question :If X n !d X and Y n !d Y:Is X n +Y n !d X +Y ?Answer:No.We consider two cases:Case 1:X n and Y n are independent N(0,1).ThenX n +Y n !d N (0;2):Case2:X n=Y n N(0;1):ThenX n+Y n=2X n N(0;4):Delta Method:Suppose p n( Z n )= !d N(0;1),and g( )is continuously di¤eren-tiable with g0( )=0:Thenp n[g( Zn) g( )]!d N(0;[g0( )]2 2):Remark:The Delta method is a Taylor series approximation in a statistical context. It linearizes a smooth(i.e.,di¤erentiable)nonlinear statistic so that the CLT can be applied to the linearized statistic.Therefore,it can be viewed as a generalization of the CLT.This method is very useful when more than one parameter makes up the function to be estimated and more than one random variable is used in the estimator. Proof:First,p n( Z n )= !d N(0;1)implies p n( Z n )= =O P(1):Therefore,we have Z n =O P(n 1=2)=o P(1):Next,by a Taylor expansion,we haveY n=g( Z n)=g( )+g0( n)( Z n );where n= +(1 ) Z n for 2[0;1]:It follows by the Slutsky theorem thatp n g( Z n) g( )=g0( n)p n Z n!d N(0;[g0( )]2);by the Slutsky theorem,where g0( n)!p g0( )given n!p :By the Slutsky theorem again,we havep n[Yn g( )]!d N(0; 2[g0( )]2):Example:Suppose p n( Z n )= !d N(0;1)and =0and0< <1:Find the limiting distribution of p n( Z 1n 1):ANS:De…ne g( Z n)= Z 1n:Because =0;g( )is continuous at :By a Taylor series expansion,we haveg( Z n)=g( )+g0( n)( Z n );Z 1n 1=[ 2n]( Z n )where n= +(1 ) Z n!p given Z n!p and 2[0;1]:It follows thatp n( Z 1n 1)=2np n( Zn )!d N(0; 2= 4):Summary of Probability ToolsWLLN:Suppose f Z t g is i.i.d.with E(Z t)= and E j Z t j<1:ThenZn! in prob.SLLN:Under the same conditions,we haveZn! almost surely.CLT:Suppose f Z t g is i.i.d.( ; 2):Thenp n Z n!N(0;1)in distribution.The Cramer-Wold Device:Z n converges to Z in distribution if0Z n! 0Z in distributionfor any nonzero such that 0 =1:Delta Method:Suppose p n( Z n )= !d N(0;1),and g( )is continuously di¤erentiable with g0( )=0:Thenp n[g( Zn) g( )]!d N(0;[g0( )]2 2):Large Sample Theory for Linear Regression Models with I.I.D.Observations4.1AssumptionsAssumption4.1:f Y t;X0t g0is an i.i.d.sequence such thatY t=X0t 0+"t;t=1;:::;n;for some unknown 0and some unobservable"t:Assumption4.2:E("t j X t)=0a.s.with E("2t)= 2<1:Assumption4.3:The K K matrixE(X t X0t)=Qis nonsingular and…nite:Assumption4.4:The K K matrix V=E(X t"2t X0t)is…nite and positive de…nite.Remarks:(i)In Assumption4.1,put Z t=(Y t;X0t)0:Then IID means that Z t and Z s are independent when t=s,and the Z t have the same distribution for all t:In Assumption 4.2,we have E("t j X)=E("t j X1;X2;:::X t;:::X n)=E("t j X t)=0:(ii)We do not assume normality for"t j X t;and allow for conditional heteroskedastic-ity.(iii)Assumption4.3implies E(X2jt)<1for j=0; ;k:(iv)When X0t=1;Assumption4.4implies E("2t)<1:(v)If E("2t j X t)= 2a.s.,then Assumption4.4can be ensured by the condition E("2t)<1and Assumption4.3:In general,Assumption4.4can be ensured by the moment conditions that E("4t)<1 and E(X4jt)<1for0 j k;because by repeating the Cauchy-Schwarz inequality, we havej E("2t X jt X lt)j [E("4t)]1=2[E(X2jt X2lt)]1=2[E("4t)]1=2[E(X4jt)E(X4lt)]1=4where0 j;l k and1 t n:Questions:Consistency of OLS?Asymptotic normality?Asymptotic e¢ciency?Hypothesis testing?4.2Consistency of OLSSuppose we have a random sample f Y t ;X 0t g n t =1:The OLS estimator:^=(X 0X ) 1X 0Y =X 0X n 1X 0Y n = n 1n X t =1X t X 0t ! 1n 1n Xt =1X t Y t :Substituting Y t =X 0t 0+"t ;we obtain^ = 0+ n 1n X t =1X t X 0t! 1n 1n Xt =1X t "tWe will consider the consistency of ^directly.Theorem [Consistency of OLS]:Under Assumptions 4.1-4.3,as n !1;^ p! 0or ^0=o P (1):Proof:Let C >0be some bounded constant.Also,denote X t =(X 0t ;X 1t ;:::;X kt )0:First,the moment condition holds:for all 0 j k;E j X jt "t j (EX 2jt )12(E"2t )12by the Cauchy-Schwarz inequalityC 12C 12Cwhere E (X 2jt ) C by Assumption 4.3,and E ("2t ) C by Assumption 4.2.It follows by WLLN (with Z t =X t "t )thatn 1n X t =1X t "t p!E (X t "t )=0;whereE (X t "t )=E [E (X t "t j X t )]by the law of iterated expectations=E [X t E ("t j X t )]=E (X t 0)=0:Applying WLLN again(with Z t=X t X0t)and noting thatE j X jt X lt j [E(X2jt)E(X2lt)]12Cby the Cauchy-Schwarz inequality for all(j;l);where0 j;l k;we haven 1nX t=1X t X0t p!E(X t X0t)=Q:It follows that^ 0=n 1nX t=1X t X0t! 1n 1n X t=1X t"tp!Q 1 0=0:This completes the proof.4.3Asymptotic Normality of OLSNext,we derive the asymptotic distribution of^ :Lemma[Central Limit Theorem(CLT)]:Suppose that f Z t g is a sequence of i.i.d. random vectors with E(Z t)=0and var(Z t)=V is…nite and positive de…nite.De…neZ n =n 1n X t=1Z t:Then as n!1;p n Znd!N(0;V)orV 12p n Znd!N(0;I):Remark:What is the variance of p n Z n?Answer:varn 12n X t =1Z t !=E" n 12n X t =1Z t!n 12n X s =1Z s!0#=n 1n X t =1n X s =1E (Z t Z 0s )=n 1n X t =1E (Z t Z 0t )(because Z t and Z s are independent for t =s;by the law of iterated expectations)=E (Z t Z 0t )=VTheorem [Asymptotic Normality of OLS]:Under Assumptions 4.1-4.4,we have p n (^ 0)d !N (0;Q 1V Q 1)as n !1;where V =E (X t X 0t "2t ):Proof:Put the sample average^Q=n 1n X t =1X t X 0t :Then we can writepn (^ 0)=^Q 11p n nXt =1X t "t :First,we consider the second termn12n X t =1X t "t :Noting that E (X t "t )=0in Assumption 4.2,and var (X t "t )=E [(X t X 0t "2t )=V;whichis …nite and positive de…nite by Assumption 4.4.Thus by the CLT for i.i.d.random sequences,we haven 12n X t =1X t "t =p n n 1n X t =1X t "t!=pn Zn d!Z N (0;V ):On the other hand,as shown earlier,we have^Q p!Q;and so^Q 1p!Q 1given that Q is nonsingular so that the inverse function is continuous and well de…ned. It follows thatp n(^ 0)=^Q 1n 12n X t=1X t"td!Q 1Z N(0;Q 1V Q 1)by the Slutsky Theorem.This completes the proof.Remarks:(i)The asymptotic mean of p n(^ 0)is0;(ii)the asymptotic variance of p n(^ 0)is Q 1V Q 1;we denotep n^ )=Q 1V Q 1:avar(Summary:(i)^ 0!p0:(ii)p n(^ 0)!d N(0;Q 1V Q 1):Special Case:Conditional HomoskedasticityAssumption4.5:E("2t j X t)= 2a.s.Theorem:Suppose Assumptions4.1–4.5hold.Thenp n(^ 0)d!N(0; 2Q 1):Remark:The asymptotic variance of p n(^ 0)isp n^ )= 2Q 1:avar(Proof:Under Assumption4.6,we can simplifyV=E(X t X0t"2t)=E[E(X t X0t"2t j X t)]by the law of iterated expectations=E[X t X0t E("2t j X t)]= 2E(X t X0t)= 2Q:The results follow immediately because Q 1V Q 1= 2Q 1.4.4Asymptotic V ariance EstimatorTo construct con…dence interval estimators or hypothesis tests,we need to estimate the asymptotic variance of p n(^ 0),avar(p n^ ):Case I:Conditional HomoskedasticityUnder this case,the asymptotic variance of p n(^ 0)isavar(p n^ )=Q 1V Q 1= 2Q 1:Question:How to estimate Q?Lemma:Suppose Assumptions4.1and4.3hold.Then^Q=n 1nX t=1X t X0t p!Q:Question:How to estimate 2?Recalling that 2=E("2t);we use sample variances2=e0e=(n K)=1n Kn X t=1e2t=1n KnX t=1(Y t X0t^ )2:Is s2consistent for 2?Theorem[Estimator for 2]:Under Assumptions4.1-4.3,s2p! 2:Proof:Given thats2=e0e=(n K)ande t=Y t X0t^="t X0t(^ 0);we haves2=1n KnX t=1["t X0t(^ 0)]2=nn Kn 1nX t=1"2t!+(^ 0)0"(n K) 1n X t=1X t X0t#(^ 0) 2(^ 0)0(n K) 1nX t=1X t"tp!1 2+0 Q 0 2 0 0= 2given that K is a…xed number,where we have made use of the weak law of large numbers in three places.Remark:We can estimate 2Q 1by s2^Q 1:Theorem:[Asymptotic Variance Estimator of p n(^ 0)]:Under Assumptions 4.1-4.5,we haves2^Q 1p! 2Q 1:Remark:The asymptotic variance estimator of p n(^ 0)is s2^Q 1=s2(X0X=n) 1: This is equivalent to saying that the variance of^ 0is approximately s2^Q 1=n= s2(X0X) 1when n!1:This implies that when n!1and there exists conditional homoskedasticity,the variance estimator of^ 0coincides with the form of the variance estimator for^ 0in the classical regression case.CASE II:Conditional HeteroskedesticityIn this case,avar(p n^ )=Q 1V Q 1;which cannot be simpli…ed.This is the so-called White’s heteroskedasticity-consistentestimator for the asymptotic variance of p n^ .Question:How to estimate Q?Answer:Use^Q:Question:How to estimate V=E(X t X0t"2t)? Answer:Use its sample analog^V=n 1nX t=1X t X0t e2t=X0ee0X n:Assumption4.6:(i)E(X4jt)<1for all j=0;:::;k:(ii)E("4t)<1: Lemma:Suppose Assumptions4.1–4.4and4.6hold.Then^V p!V:Proof:Because e t="t (^ 0)0X t;we have^V=n 1nX t=1X t X0t"2t+n 1nX t=1X t X0t(^ 0)0X t X0t(^ 0)2n 1nX t=1X t X0t"t X0t(^ 0)!V+0 2 0; where for the…rst term,we haven 1nX t=1X t X0t"2t p!E(X t X0t"2t)=Vby the law of large numbers and Assumption4.4.For the second term,we haven 1nX t=1X it X0jt(^ 0)0X t X0t(^ 0)=k X l=0k X m=0(^ l 0l)(^ m 0m)n 1n X t=1X it X jt X lt X mt!!p0given^ 0!p0;andn 1nX t=1X it X jt X lt X mt!p E(X it X jt X lt X mt)=O(1)by the WLLN and Assumption4.6.Similarly,for the last term,we haven 1nX t=1X it X0jt"t X0t(^ 0)=k X l=0(^ l 0l)n 1n X t=1X it X0jt X lt"t!!p0given^ 0!p0;andn 1nX t=1X it X jt X lt"t!p E(X it X jt X lt"t)=0by the WLLN.This completes the proof.Theorem[Asymptotic variance estimator fo r p n(^ 0)]:Under Assumptions 4.1–4.4and4.6,we have^Q 1^V^Q 1p!Q 1V Q 1:Remark:This is the so-called White’s heteroskedasticity-consistent variance-covariance matrix of estimator p n(^ 0):It follows that when there exists conditional het-eroskedasticity,the estimator for the variance of^ 0is(X0X) 1^V(X0X) 1=(X0X) 1X0ee0Xn(X0X) 1;which di¤ers from the estimator s2(X0X) 1in the case of conditional homoskedasticity.4.5Hypothesis T estingQuestion:How to construct a test statistic forH0:R 0=r?Case I:Conditional HomoskedasticityTheorem[ 2test]:Suppose that Assumptions4.1–4.5hold.Then under H0;J F (R^ r)0 s2R(X0X) 1R0 1(R^ r)d! 2Jas n!1:Proof:ConsiderR^ r=R(^ 0)+R 0 r:Note thatp n ^ 0 d!N(0; 2Q 1):Under H0;p n(R^ r)=R p n(^ 0)d!N(0; 2RQ 1R0):It follows that the quadratic formp n(R^ r)0 2RQ 1R0 1p n(R^ r)d! 2J: Also,because s2^Q 1p! 2Q 1;we have by the Slutsky theoremp n(R^ r)0 s2R^Q 1R0 1p n(R^ r)d! 2J: or equivalentlyJ (R^ r)0[R(X0X) 1R0] 1(R^ r)=Js2=J F d! 2J;namelyJ F d! 2J:Remarks:We cannot use the F distribution,but we can still compute the F-statistic and the appropriate test statistic is J times the F-statistic,which is asymptotically 2J.That is,J F=(~e0~e e0e)e0e=(n K)d! 2J:An Alternative Interpretation:Because J F J;n K approaches 2Jas n!1;we may interpret that the classical results for the F-test are still approximately valid under conditional homoskedasiticity when n is large.A special Case:Testing for Joint Signi…cance of All Economic Variables Theorem[(n K)R2test]:Suppose Assumption4.1-4.5hold,and we are interested in testing the null hypothesis thatH0: 01= 02= = 0k=0;where the are the regression coe¢cients fromY t= 00+ 01X1t+ + 0k X kt+"t:Let R2be the coe¢cient of determination from the unrestricted regression modelY t=X0t 0+"t:Then under H0;(n K)R2d! 2k;where K=k+1:Proof:First,note thatF=R2=k(1 R2)=(n k 1)=R2=k(1 R2)=(n K);we havek F=(n K)R2=(1 R2)d! 2k under H0:This implies that kF is bounded in probability;that is,(n K)R2=(1 R2)=O P(1):And consequently,given that k is a…xed integer,R2=(1 R2)=O P(n 1)=o P(1)orR2p!0: Therefore,1 R2p!1:By the Slutsky theorem,we then havek F d! 2k(n K)R2(1 R2)d! 2k(n K)R2d! 2k;or asymptotically equivalent,(n K)R2d! 2k:This completes the proof.Case II:Conditional HeteroskedasticityRecall that under H 0;pn (R ^r )=R p n (^ 0)+pn (R 0 r )=R pn (^0)d!N (0;RQ 1V Q 1R 0);whereV =E (X t X 0t "2t ):It follows thatpn (R ^ r )0[RQ 1V Q 1R 0] 1p n (R ^ r )d ! 2J :Because ^Q p !Q and ^V p!V;we have the Wald test statisticW =pn (R ^ r )0[R ^Q 1^V ^Q 1R 0] 1p n (R ^ r )d ! 2Jby the Stusky theorem.We can write W equivalently as follows:W =(R ^r )0[R (X 0X ) 1(X 0ee 0X )(X 0X ) 1R 0] 1(R ^ r );where we have used^V =1n nX t =1X t e t e t X 0t =X 0ee 0X n:Remarks:(i)Under conditional heteroskedasticity,J F and (n K )R 2cannot be used.(ii)Although the general form of the test statistic developed here can be used no matter whether there exists conditional homoskedasticity,the general form of test sta-tistic may perform poorly in small samples.Thus,if one has information that the error term is conditionally homoskedastic,one should use the test statistics derived under conditional homoskedasticity,which will perform better in small sample sizes.Because of this reason,it is important to test whether conditional homoskedasticity holds.4.6T esting Conditional HomoskedasticityQuestion:How to test conditional homoskedasticity?White’s (1980)test:The null hypothesisH0:E("2t j X t)=E("2t);where"t is the regression error in the linear regression modelY t=X0t 0+"t:First,suppose"t were observed,and we consider the auxiliary regression"2t=vech(X t X0t)0 +v t= 0+kX j=1 j X jt+X1 j l k jl X jt X lt+v t= 0vech(X t X0t)+v t;where there is a total of K(K+1)2regressors.Assuming that E("4t j X t)= 4(which implies E(v2t j X t)= 2v under H0);then we can run an OLS regression and obtain(n J)~R2d! 2J;where J=K(K+1)2 1is the number of the regressors except the intercept. Unfortunately,the"2t is not observable.However,we can replace"2t with e t=Y t X0t^ ; and run the auxiliary regressione2t= 0+kX j=1 j X jt+X1 j l k jl X jt X lt+v t= 0vech(X t X0t)+v t;the resulting test statistic(n J)R2d! 2J:Note that it can be shown that the replacement of"2t by e2t has no impact on the asymptotic distribution of nR2:The proof,however,is rather tedious.For the details of the proof,see White(1980).Question:Why the use of e2t in place of"2t has no impact on the asymptotic distribution of(n J)R2?Answer:Decomposee2t=h"t X0t(^ 0)i2="2t+(^ 0)0X t X0t(^ 0) 2(^ 0)0X t"t:For the last term,X t"t is uncorrelated with vech(X t X0t)given E("t j X t)=0:Therefore, this term,after scaled by the factor^ 0;is expected to have negligible impact on the asymptotic distribution of the test statistic.For the second term,X t X0t is perfectly correlated with vech(X t X0t):However,it is scaled by a factor of jj^ 0jj2rather than by jj^ 0jj only.As a consequence,the regression of(^ 0)0X t X0t(^ 0)on vech(X t X0t) also has negligible impact on the asymptotic distribution of(n J)R2: Question:How to test conditional homoskedasticity if E("4t j X t)is not a constant?4.7SummaryAssumptionsAssumption4.1:f Y t;X0t g is an i.i.d.sequence such thatY t=X0t 0+"t;t=1; ;n;for some unknown 0and some unobservable"t:Assumption4.2:E("t j X t)=0a.s.with E("2t)<1:Assumption4.3:The K K matrixE(X t X0t)=Qis nonsingular and…nite:Assumption4.4:V=E("2t X t X0t)is…nite and positive de…nite.Questions:Consistency of OLS?Asymptotic normality?Asymptotic e¢ciency?Hypothesis testing?Consistency of OLSSuppose we have a random sample f Y t;X0t g n t=1:The OLS estimator:^ =(X0X) 1X0Y=^Q 1n 1n X t=1X t Y t:Substituting Y t=X0t 0+"t;we obtain^ 0=^Q 1n 1n X t=1X t"tWe will consider consistency of^ directly.Theorem[Consistency of OLS]:Under Assumptions4.1-4.3,^ p! 0or^ 0=oP(1):Asymptotic Normality of OLSTheorem[Asymptotic Normality of OLS]:Under Assumptions4.1-4.4,we havep n(^ 0)d!N(0;Q 1V Q 1)as n!1;where V=E("2t X t X0t):Remarks:(i)The asymptotic mean of p n(^ 0)is0;(ii)the asymptotic variance of p n(^ 0)is Q 1V Q 1;we denoteavar(p n^ )=Q 1V Q 1:(iii)When Conditional Homoskedasticity holds:Assumption4.5:E("2t j X t)= 2a.s.p n(^ 0)d!N(0; 2Q 1):Asymptotic V ariance EstimatorQuestion:How to estimate the asymptotic variance of p n(^ 0),avar(p n^ ):Case I:Conditional HomokeadasticityUnder this case,the asymptotic variance of p n(^ 0)isavar(p n^ )= 2Q 1:Question:How to estimate Q?^Q p!Q:Question:How to estimate 2?s2=1n Kn X t=1e2t p! 2:It follows thats2^Q 1p! 2Q 1: CASE II:Conditional HeteroskdeasticityIn this case,avar(p n^ )=Q 1V Q 1:Question:How to estimate Q?Answer:Use^Q:Question:How to estimate V=E(X t X0t"2t)? Answer:Use its sample analog^V=n 1nX t=1X t X0t e2t p!Vunder the additional assumption:Assumption4.6:E(X4jt)<1for all j=0; ;k and E("4t)<1: Theorem[Asymptotic variance estimator fo r p n(^ 0)]:Under Assumptions 4.1–4.6,we have^Q 1^V^Q 1p!Q 1V Q 1:Remark:This is the so-called White’s heteroskedasticity-consistent variance-covariance matrix of estimator p n(^ 0):Hypothesis T estingQuestion:How to construct a test statistic forH0:R 0=r?Case I:Conditional HomoskedasticityIn this case,V= 2Q;so we havep n(^ 0)d!N(0; 2RQ 1R0):RTheorem[ 2test]:Suppose that Assumptions4.1-4.5hold.Then under H0;J F d! 2Jas n!1:A special procedureTheorem[(n K)R2test]:Suppose Assumption4.1-4.5hold,and we are interested in testing the null hypothesis thatH0: 01= 01= = 0k:Let R2be the coe¢cient of determination from the unrestricted regression modelY t=X0t 0+u t:Then under H0;nR2d! 2k:Case II:Conditional HeteroskedasticityRecall that under H0;R^ r=R(^ 0)+R 0 r=R(^ 0)p n(^ 0)d!N(0;RQ 1V Q 1R0);RwhereV=E(X t X0t"2t):It follows that(R^ r)0[RQ 1V Q 1R0] 1(R^ r)d! 2J: Because^Q p!Q and^V p!V;we have(R^ r)0[R^Q 1^V^Q 1^R0] 1(R^ r)d! 2Jby the Slutsky theorem.T esting Conditional HomoskedasticityQuestion:How to test conditional homoskedasticity?White’s(1980)test:The null hypothesisH0:E("2t j X t)=E("2t);where"t is the regression error in the linear regression modelY t=X0t 0+"t;where we assume that the…rst component of X t is the intercept.First,suppose"t is observed,and we consider the auxiliary regression"2t=vech(X t X0t)0 +v t= 0+kX j=1 j X jt+X1 j l k jl X jt X lt+v t:Assuming E("4t j X t)= 4(which implies E(v2t j X t)= 2v under H0);then we can run an OLS regression and obtain(n J)~R2d! 2J;where J is the number of the regressors except the intercept.Unfortunately,the"2t is not observable.However,we can replace"2t with e t= Y t X0t^ ;and run the auxiliary regressione2t= 0+kX j=1 j X jt+X1 j l k jl X jt X lt+v t;。
chapter4-basic motivation concept
Chapter 4 Basic Motivation Theory
——A need
means a physiological or psychological deficiency that makes certain outcomes appear attractive.需 要是指生理或心理缺陷，使 得某些成果出现吸引力。
Chapter 4 Basic Motivation Theory
(5) Equity theory公平理论 Equity theory states that employees weigh what they put into a job situation (input) against what they get from it (outcome) and then compare their input: outcome ratio with the input: outcome ratio of relevant others. Three reference categories: others, system, and self 公平理论指出，员工衡量什么对他们得到它（结果）， 然后把他们的工作情况（输入），比较它们的输入： 输入的结果比结果有关的人的比例。 三个参考类别：其他，系统，和自我
Question
你认为工资保密制度好不好？
Chapter 4 Basic Motivation Theory
(4) The Expectancy Theory期望理论 The expectancy theory argues that the strength of a tendency to act in a certain way depends on the strength of an expectation that the act will be followed by a given outcome and on the attractiveness of that outcome to the individual.期望理论认为，一种倾向，以某 种方式行事的实力取决于期望的力量，该行为 将被给定的结果和随后的吸引力，个别的结果。
如何管理时间英语
Sharing techniques for cultivating focus
Practice single task
Dedicate your attention to one task at a time instead of multitasking, which can divide your focus and reduce efficiency
You can adjust the length of the intervals and breaks to suit your needs and preferences, but it's important to maintain a consistent schedule
During breaks, stand up, stretch, walk around, or do something that will help you relax and recharge for the next interval
Use the 80/20 rule to focus on high value tasks
Develop specific executable plan steps
Create a detailed action plan for each task
CH06 Motivation Concepts
Organizational Behavior
14th Edition
Motivation Concepts
Kelli J. Schutte
William Jewell College
Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall
See E X H I B I T S 7-2 and 7-3
Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall
7-6
Criticisms of Two-Factor Theory
Herzberg says that hygiene factors must be met to remove dissatisfaction. If motivators are given, then satisfaction can occur. Herzberg is limited by his methodology
Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall
7-2
Early Theories of Motivation
These early theories may not be valid, but they do form the basis for contemporary theories and are still used by practicing managers. Maslow’s Hierarchy of Needs Theory McGregor’s Theory X and Theory Y Herzberg’s Two-Factor Theory McClelland’s Theory of Needs
组织行为学OB 期末复习题4 (1)
Chapter 6 Basic Motivation Concepts1. What are the three key elements of motivation?a. reactance, congruence and circumstanceb. interest, activity and rewardc. awareness, effort and outcomed. stimulation, progress and achievemente. intensity, direction and persistence2. In Maslow’s hierarchy of needs, what is the term used for the drive to becomewhat one is capable of becoming?a. perfectionb. self-actualizationc. hypo-glorificationd. self-esteeme. attainment3. According to Maslow, when does a need stop motivating?a. when it is substantially satisfiedb. it never stops motivatingc. when one returns to a lower level needd. when one chooses to move to a higher level neede. only when it is completely satisfied4. Which of the following theories was proposed by Douglas McGregor?a. Hierarchy of Needs Theoryb. Theories X and Yc. Two-Factor Theoryd. ERG Theorye. Expectancy Theory5. Who developed ERG theory?a. McClellandb. Maslowc. Alderferd. Ouchie. Dieckmann6. Hunger, thirst, sex, pay, and physical work environment are examples of which ofAlderfer’s needs?a. existenceb. safetyc. growthd. physiologicale. subsistence7. What other name is the two-factor theory known by?a. Theories X and Yb. Motivator-Hygiene Theoryc. Hierarchy of Needs Theoryd. Satisfaction/Dissatisfaction Theory1e. Minimal Justification Theory8. McClelland’s theory of needs concentrates on which three needs?a. achievement, realization and acceptanceb. achievement, power and affiliationc. power, acceptance and confirmationd. affiliation, control and realizatione. control, status and relationships9. According to the goal-setting theory of motivation, highest performance isreached when goals are set to which level?a. impossible but inspirationalb. difficult but attainablec. slightly beyond a person’s actual potentiald. only marginally challenginge. easily attained10. According to Bandura, what is the most important source of increasing self-efficacy?a. arousalb. verbal modelingc. verbal persuasiond. enactive masterye. focused training11. Which of the following is not a predictable choice when employees perceive aninequity?a. change their inputsb. change their outcomesc. choose a different referentd. acquire more tenuree. leave the field12. Which of the following is one of the relationships proposed in expectancy theory?a. reward-satisfaction relationshipb. satisfaction-performance relationshipc. rewards-personal goals relationshipd. effort-satisfaction relationshipe. performance-achievement relationship13. A theory based on “needs” is the premise for theories by all of the followingexcept _____.a. McClellandb. Alderferc. McGregord. Maslowe. VroomTRUE/FALSET 1. Motivation is the result of the interaction of the individual and the situation.F 2. High intensity is unlikely to lead to favorable job-performance outcomes unless2the effort is channeled in a direction that benefits the individual.F 3. According to Maslow, a need that is essentially satisfied no longer motivates.F 4. According to Herzberg, pay strongly motivates.T 5. Individuals with a high need to achieve prefer job situations with personal responsibility, feedback, and an intermediate degree of riskT 6. According to reinforcement theory, behavior is environmentally determined.3。
消费者行为学全英重点
Chapter 1 Consumer Rule1.Consumer behavior(消费者行为):the study of the process involved when individuals or groups select,purchase,use,or dispose of products,services,ideas,or experience to satisfy needs and desires.※2.Relationship marketing(关系营销):interact with customers regularly;give them reasons to maintain a bond with the company.3.Database marketing(数据库营销):tracking specific consumers’buying habits and crafting products and messages tailored precisely to people’s wants.4.C2C e-commerce:(consumer to consumer activity) the new world of virtual brand communities.5.Business ethics(商业道德):are rules of conduct that guide action in the marketplace----the standards against which most people in a culture judge what is right and what is wrong,good or bad.Chapter 2 Perception1.Sensory systems感觉系统Sensation感觉: is the immediate response of our sensory receptors(eyes,ears,nose,mouth,and,fingers)to,basicstimuli(light,color,sound,odor,and texture).Perception知觉:is the process by which sensations are selected,organized,and interpreted.Hedonic Consumption享乐消费: multisensory,fantasy,and emotional aspects of consumers’ interactions with products.2.Sensory marketing感觉营销:companies pay extra attention to the impact of sensations on our product experiences.3.Absolute threshold绝对阈限:the minimum amount of stimulation that can be detected on a given sensory channel.4.Different threshold差别阈限:ability of a sensory system to detect changes or differences between two stimuli/5.Weber’s law韦伯定律: the amount of change required for the perceiver to notice a change is systematically related to the intensity of the original stimulus.K=Ii∆K=a constant(this varies across the senses)i∆=the minimal change in intensity of the stimulus required to produce a j.n.d.I=the intensity of the stimulus where the change occurs.6.Subliminal perception阈下知觉:occurs when stimulus is below the level of the consumer’s awareness.7.Sensory overload感官超载:consumers exposed to far more information than they can process.8.Perceptual selection:people attend to only a small portion of the stimuli to which they are exposed.9.Perceptual vigilance知觉警惕:consumers are more likely to be aware of stimuli that relate to their current needs.10.Perceptual defense知觉防御:people see what they want to see--and don’t see what they don’t want to see. 11.Adaptation适应:the degree to which consumers continue to notice a stimulus over time.12.Semiotics符号学:is the study of the correspondence between signs and symbols and their roles in how we assign meanings.13.Perceptual positioning知觉定位:constitutes the product’s market position,and it may have more to do with our expectations of product performance as communicated by its color,packaging,or styling with the product itself.14.Perceptual map知觉地图:a vivid way to paint a picture of where products or brands are”located”in consumers’ minds.Chapter 3 Learning and memory1.Behavioral learning theories行为学习理论:assume that learning takes place as the result of responses to external events.2.Classical conditioning经典条件反射理论:a stimulus that elicits a response is paired with another stimulus that initially doe not elicit a response on its own.3.Instrumental conditioning(also operant conditioning)工具性条件反射:the individual learns to perform behaviors that produce positive outcomes and to avoid those that yield negative outcomes.4.Stimulus generalization刺激泛化:tendency for stimuli similar to a conditioned stimulus to evoke similar,unconditioned responses.5.Stimulus discrimination刺激辨别:occurs when a UCS does not folloiw a stimulus similar to a CS.指对于相近但不同的刺激学会做出不同反应的过程。
chapter04-数据挖掘概念与技术PPT课件
Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process
E.g., Hotel price: currency, tax, breakfast covered, etc.
When data is moved to the warehouse, it is converted.
5
Data Warehouse—Time Variant
The time horizon for the data warehouse is significantly longer than that of operational systems Operational database: current value data Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years)
1
Chapter 4: Data Warehousing and On-line Analytical Processing
Data Warehouse: Basic Concepts Data Warehouse Modeling: Data Cube and OLAP Data Warehouse Design and Usage Data Warehouse Implementation Data Generalization by Attribute-Oriented
组织行为_06st
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6–3
Theory X and Theory Y (Douglas McGregor)
Theory X
Assumes that employees dislike work, lack ambition, avoid responsibility, and must be directed and coerced to perform.
Core Needs Existence: provision of basic material requirements. Relatedness: desire for relationships. Growth: desire for personal development.
© 2005 Prentice Hall Inc. All rights reserved.
• Knowledge of results • Meaningfulness of work • Personal feelings of responsibility for results
– Increases in these psychological states result in increased motivation, performance, and job satisfaction.
Concepts: More than one need can be operative at the same time. If a higher-level need cannot be fulfilled, the desire to satisfy a lowerlevel need increases.
04Chapter_4_gram
11
1.3 Relation of Co-occurrence CoIt means that words of different sets of clauses may permit, or require, the occurrence of a word of another set or class to form a sentence or a particular part of a sentence.
NP Det N V S VP NP Det N
The girl ate the apple
17
Word-level N=noun A=adjective V=verb P=preposition Det=determiner Adv=adverb Conj=conjunction
Phrasal NP=noun phrase AP=adjective phrase VP=verb phrase PP=preposition phrase S=sentence or clause
the basic sentence, the prepositional phrase, the predicate (verb + object) construction, and the connective (be + complement) construction.
22
The boy smiled. (Neither constituent can smiled. substitute for the sentence structure as a whole.) He hid behind the door. (Neither door. constituent can function as an adverbial.) He kicked the ball. (Neither constituent ball. stands for the verb-object sequence.) verbJohn seemed angry. (After division, the angry. connective construction no longer exists.)
Chapter04_PPT
– If/Then
• Single-selection structure
– If/Then/Else
• Double-selection structure
– Select Case
• Multiple-selection structure
2002 Prentice Hall. All rights reserved.
Fig. 4.2
2002 Prentice Hall. All rights reserved.
Visual Basic keywords.
12
4.4 Control Structures
Private RaiseEvent Rem Select Short Stop SyncLock True Until WithEvents #If...Then...#Else Property ReadOnly RemoveHandler Set Single String Then Try When WriteOnly Protected ReDim Resume Shadows Static Structure Throw TypeOf While Xor -= Public Region Return Shared Step Sub To Unicode With #Const &
2002 Prentice Hall. All rights reserved.
6
4.4 Control Structures
• Transfer of control
– GoTo statement
• It causes programs to become quite unstructured and hard to follow
ch04_Motivation and Values
4-5
Motivational Strength
• Biological vs. Learned Needs:
– Instinct: Innate patterns of behavior universal in a species – Tautology: Circular explanation (e.g. instinct is inferred from the behavior it is supposed to explain)
4-4
Ads Reinforce Desired ates
• This ad for exercise shows men a desired state (as dictated by contemporary Western culture), and suggests a solution (purchase of equipment) to attain it.
4 - 17
Criticisms of Maslow’s Hierarchy
• The application is too simplistic:
– It is possible for the same product or activity to satisfy every need.
• It is too culture-bound:
– The assumptions of the hierarchy may be restricted to Western culture
• It emphasizes individual needs over group needs
– Individuals in some cultures place more value on the welfare of the group (belongingness needs) than the needs of the individual (esteem needs)
组织行为学(双语)课程CH05-Motivation-I-Basic-theory
5-4
Maslow’s Hierarchy of Needs Theon Esteem Social Safety
Psychological
Lower
Copyright ©2010 Pearson Education, Inc.
5-5
Douglas McGregor’s X & Y
attaining a organizational goal
Intensity – the amount of effort put forth to meet the goal
Direction – efforts are channeled toward organizational goals
• McGregor’s Theory X and Theory Y
• Herzberg’s Two-Factor (Motivation-Hygiene) Theory
• McClellan’s Theory of Needs (Three Needs Theory)
Copyright ©2010 Pearson Education, Inc.
Satisfied
Hygiene Factors
Motivation Factors
• Quality of supervision
• Pay • Company policies • Physical working
conditions • Relationships • Job security
• High achievers prefer jobs with:
Personal responsibility Feedback Intermediate degree of risk (50/50)
COST04Job Order Costing(成本管理会计-复旦, 洪剑鞘)
4-3
Learning Objectives
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6 7
Track the flow of costs in a job-costing system Prorate end-of-period under- or overallocated indirect costs using alternative methods Apply variations of normal costing
4-4
成本核算流程－对外报告
1、按照成本计算目的，确定成本核算对象， 如产品种类、产品批别等。 2、划分生产成本和期间费用的界限，生产成 本指应计入产品成本的那部分费用。 3、在生产成本中，划分直接成本和间接成本 ，即为每个成本核算对象确定直接成本项 目（如直接材料、直接人工等）。
4-5
成本核算流程－对外报告
4 - 16
Job-Costing and Process-Costing Systems
In a job-costing system, the cost object is an individual unit, batch, or lot of a distinct product or service called a job. In process costing, the cost object is masses of identical or similar units or a product or service.
4 - 23
General Approach to Job Costing
Step 1: The cost object is Job 100. Step 2: Identify the direct costs of Job 100. Direct material = $45,000 Direct manufacturing labor = $14,000
Chapter 4 Motivation and Values
NEED FOR UNIQUENESS
Assert one’s individual identity Enjoy products that focus on their unique character (perfumes, clothing)
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Motivational Conflicts
• Consumer will: • Approach positive goal • Avoid negative goal
12/2/2014 Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall
4-4
The Motivation Process
• Motivation refers to the process that leads people to
behave as they do
• It occurs when a need is aroused • To understand motivation is to understand why
•
Needs and Motivation
• The degree of arousal is drive • Personal and cultural factors combine to
and direction
create a want – one manifestation of a need
4-17
Levels of Needs in the Maslow Hierarchy:
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1.分步实现目标，不断强化行为； 分步实现目标，不断强化行为； 分步实现目标 2.强化力度要达到最小临界值； 强化力度要达到最小临界值； 强化力度要达到最小临界值 3.奖励要及时，方法要创新； 奖励要及时， 奖励要及时 方法要创新； 4.奖惩结合，以奖为主； 奖惩结合， 奖惩结合 以奖为主； 5.采用多种形式的奖励机制。 采用多种形式的奖励机制。 采用多种形式的奖励机制
Equity Theory (cont’d)
Propositions relating to inequitable pay: 1. Overrewarded employees produce more than equitably rewarded employees. 2. Overrewarded employees produce less, but do higher quality piece work. 3. Underrewarded hourly employees produce lower quality work. 4. Underrewarded employees produce larger quantities of lower-quality piece work than equitably rewarded employees
Management by Objectives (MBO)
Converts overall organizational objectives into specific objectives for work units and individuals Common ingredients:
Goal specificity Explicit time period Performance feedback Participation in decision making
Contingencies in goal-setting theory:
Goal Commitment – public goals better! Task Characteristics – simple & familiar better! National Culture – Western culture suits best!
当代激励理论
强化理论四种策略比较
刺激 正强化 负强化 消除 惩罚 高绩效加薪 刺激 因迟到挨批评 刺激 高绩效排名奖励 刺激 吸烟罚款 强化理论的应用 期望行为 高绩效 期望行为 准时 不期望行为 恶性竞争 不期望行为 上班吸烟 表现出期望行为时 加薪 移去不愉快的结果 不再批评 不呈现结果 忽视 呈现不愉快的结果 罚款 重复期望行为 继续高绩效 重复期望行为 继续准时 减少不期望行为 不再竞争排名 减少不期望行为 偶尔或不吸烟
Need for Affiliation (nAff)
The desire for friendly and close interpersonal relationships
McClelland's High Achievers
High achievers prefer jobs with:
Personal responsibility Feedback Intermediate degree of risk (50/50)
Reinforcement Theory
Concepts: Behavior is environmentally caused. Behavior can be modified (reinforced) by providing (controlling) consequences. Reinforced behavior tends to be repeated.
Contemporary Theories of Motivation
(Goal-setting theory; Reinforcement theory; Equity theory; Expectancy theory)
What Is Motivation?
Motivation is the willingness to do something and is conditioned by this action’s ability to satisfy some need for the individual.
Management by Objectives (MBO)
Reinforcement theory Equity Theory Expectancy Theory
Goal-Setting Theory
Goals increase performance when the goals are:
Specific Difficult, but accepted by employees Accompanied by feedback (especially selfgenerated feedback)
Chapter 4 Basic Motivation Concepts
The basic motivation process Early Theories of Motivation
(Maslow’s hierarchy of needs theory; Theory X and theory Y; Two-factor theory; McClelland’s theory of needs)
developing process of need
Theory X and Theory Y (Douglas McGregor)
Douglas McGregor’s X & Y
Theory X Theory Y
How to motivate?
Carrot and stick Must be coerced, controlled or threatened with punishment View work as being as natural as rest or play Empower: self-direction and self-control if committed to objectives
Is it the best method to motivate through satisfying employees’ need?
Early Theories of Motivation
Maslow’s Hierarchy of Needs Theory McGregor’s Theory X and Theory Y Herzberg’s Two-Factor (MotivationHygiene) Theory McClellan’s Theory of Needs (Three Needs Theory)
Maslow’s Hierarchy of Needs Theory
Self-Actualization
Upper
Esteem Social Safety Psychological
Lower
Maslow’s Hierarchy of Needs Theory
生 理 需 要 安 全 需 要 归 属 需 要 尊 重 需 要 自 我 实 现
Equity Theory and Reactions to Inequitable Pay
Employees are: Paid by: Piece Time Will produce more Produce less output or output of poorer quality
Will produce Over-Rewarded fewer, but higherquality units UnderRewarded Produce large number of low quality units
Hygiene Factors
• Quality of supervision • Pay • Company policies • Physical working conditions • Relationships • Job security
• Promotional opportunities • Opportunities for personal growth • Recognition • Responsibility • Achievement
High achievers are not necessarily good managers High nPow and low nAff is related to managerial success
Contemporary Theories of Motivation
Goal-Setting Theory
Dissatisfied
Not Satisfied
Factors characterizing events on the job that led to extreme job dissatisfaction
Factors characterizing events on the job that led to extreme job satisfaction
Equity Theory
Employees weigh what they put into a job situation (input) against what they get from it (outcome). They compare their input-outcome ratio with the input-outcome ratio of relevant others. My Output My Input Your Output Your Input