Consumer Theory - Utility Representation & Ordinary Demand
|
|
- Alaina Ferguson
- 6 years ago
- Views:
Transcription
1 Cosumer Theory - tlty Represetato & Ordary Demad tlty Represetato tlty Represetato, () - use fucto to represet prefereces so we ca use optmzato for proofs; fucto cotas formato about prefereces but fucto tself ("utls") mea othg Formally - fucto () represets prefereces R f () (y) R y Ordal - rags matter; orderg based o fucto must be the same as wth prefereces Not Cardal - magtudes (values) do't matter; e.g., () (y) meas othg other tha () > (y); also () - (y) meas othg other tha > or < for rag fte Number of Fuctos - f we fd oe fucto that wors there wll be a fte umber of fuctos that wor because of trasformatos tlty Trasformato - doma s R (e.g., (,,..., )), rage s a umber; trasformato s aother fucto V() F(()) that does somethg to the utlty umber (ot ) Eamples - V() a() b (lear); V() [()] ; V() [()] ; V() l () creasg Trasformato - eample of type of trasformato that s vald for utlty represetatos because t preserves the rags; two deftos (use ether): ) () > (y) F(()) > F((y)) ) dfd > (but eed to mae sure F(()) ests) Eamples - lear trasformato vald f a > ; other three trasformato lsted above vald oly f () > Vald ropertes - oly propertes that ca be obtaed from utlty represetato are those that hold for all utlty represetatos (.e., varat or ordal); these are propertes that rely o the preferece relato R (.e., rags); also called ordal property V F Frst Dervatve - >... loo at V() F(()): > for all creasg trasformatos V() (dfd > ) ay property based o frst dervatve s vald Secod Dervatve - >... loo at V() F(()): V F( ) F ( ) F( ) ; ow the secod term s postve for all creasg trasformatos, but do't ow aythg about the frst term; could be > or < ay property based o secod dervatve s ot vald Rato of Trasformatos - ths s vald; utlty trasformato does't chage rato of frst dervatves (.e., slopes of dfferece curves) F V V F Cocavty - f s cocave, that meas level curves are creasg at a decreasg rate, but we oly care that they are creasg; rate does't matter; ca have a trasformato that eeps level curves creasg, but has them at a creasg rate (.e., cove); ca 3; V, ; V ; V of
2 chec hessas; f H s egatve defte, do't have to have H V be egatve defte cocavty s ot vald Quascocavty - cocavty shows the relatoshp betwee level cures (.e., how to label them), but quascocavty shows the shape of the level curves (.e., set of pots above the level curve s cove); so QC oly depeds o prefereces so propertes based o QC are vald (proved ths the log way HW) () (y) ( ( - )y) (y).e., R y ( ( - )y) R y dfferece Curves dfferece Map - set of dfferece curves dfferece Curve - C() {y: y }; useful to llustrate argumets ad results Defto sg refereces - C() boudary of R () Defto sg tlty Represetato - C() {: () costat} ropertes - to be equvalet to prefereces wth the fve propertes must have: a C goes through every budle ad Cs are o-tersectg, dowward slopg, cotuous, "th" les that are cove to the org roof (of o-tersectg): Assume two Cs tersect as show pcture y ad z By trastvty of prefereces y z z But by mootocty of prefereces y z because y has more of both goods C caot tersect y Ordary Demads Budget Set, B(,) - specfc class of alteratve set A; B(,) {: ad } amout spet o good ; come;... ropertes - Cove - B(,) ad y B(,) ( - )y B(,) Closed - boudares are cluded because of ad defto Bouded - f < ad > (or demad < supply at ) Demad Fucto, o (,) - o (,) C(B(,),R); t's a fucto f prefereces are strctly cove (.e., C(B(,),R) s uque budle), otherwse t's called a demad correspodece because there ca be multple budles () for each prce Observable - we care about demad because t ca be observed (ule prefereces) ropertes - 5 propertes mae demad fucto equvalet to "stadard cosumer". Complete - o (,) defed for all > ad < < Assumg s fed (for ow) ad depedet of. "Sort of" Cotuous - f prefereces (R) are strctly cove, the o (,) s cotuous Small chage budget le from w strctly cove pref. does't chage optmal soluto much D(,) s cotuous D D of
3 3. Addg p roperty - f prefereces (R) are strctly cove, the o (,) mplcato - demads ot urelated; f you ow frst - demads, the last demad s gve by solvg formula above for o (,): o (, ) o o (, ) (, ) Secod mplcato - lmts o demad: o (,) 4. Homogeeous of Degree rces ad come - proportoal chages prces ad come have o effect o demad; o (t,t) o (,) roof: B(,) ; B(t,t) t t t t B(,) B(t,t) 5. Cove - f prefereces (R) are cove, the o (,) s cove set; f R s strctly cove, the o (,) s sgle budle Other ropertes - there are others (e.g., dowward slopg); come from comparatve statcs (hard way) or through dualty optmzato Comparatve Statcs - start wth choce set based o prefereces: C(B(,),R) whch ca be wrtte as optmzato of utlty represetato: Ma () s.t., ad Lagraga - L () - ( - ) K-T Codtos - gore frst order codtos for goods wth zero value; also ow > because mootocty of prefereces says soluto o boudary L L ( ),,,..., ad Ecoomc terpretatos - L MB MC - margal utlty (beeft) C Slope Budget Le Slope - Margal Rate of Substtuto - - tmes slope of dfferece curve; tae total dfferetal of (, ) ad solve for d d : d d d d Slope of Budget Le -... ; tae total dfferetal wth respect to ad : to fd d d (slope of budget le wth respect to ad ): d d d d margal cost s $ lost ($ut) s value of $ lost (utls$) Lst K-T Codtos - equatos, uows (wll come bac to these for comparatve statcs wrt ) () - () of
4 4 of Comparatve Statc for come - Totally dfferetate K-T codtos wth respect to (whch goes to ad ) Note: The last le says t's mpossble for all goods to be feror se frst order codto ad substtute for last term frst equatos ad all terms ( ) st equato Wrte wth matrces: ) ( Frst matr s a bordered hessa! Solve for usg Cramer's Rule: BH Techcally requremet for quascocave s (-) BH, but part s problem here so we usually assume suffcet codto (.e., (-) BH > ), ths case ad (-) (-) sg of s determed by sg of umerator:
5 ( ) Varable Case - sg of s same as sg of Could try to terpret ths by usg substtutescomplmets (propertes of ) ad dmshg margal utlty ( ), but secod dervatve s ot varat property so these coclusos would't hold for all utlty trasformatos we ca oly say that overall sg of ( - ) s ordal (varat to utlty trasformato) ad the same as sg of Drectly - feror Good - quatty demaded decles as come creases; < come Cosumpto Curve (CC) - shows how cosumpto chages wth come; f t slopes bac towards the vertcal as, s feror that rage; f t slopes dow towards the horzotal as, good s feror that rage; Note: ths also meas ca't have all goods be feror. CC feror feror maes budget les shft up (slope does't chage) dfferece Curve for feror Good - the Cs pctured here are cotuous, dowward slopg "th" les that do't tersect ad are cove to org; there are smlar Cs that go through every budle there est prefereces that lead to feror goods Real World - arrowly defed goods lead to feror goods (e.g., relatvely less epesve cuts of meat; relatvely cheap compact cars); more broadly defed goods wll ot be feror (e.g., food, trasportato); does't have to be feror everywhere (e.g., cheap car could go from ormal to feror ad bac to ormal) Share of come - share of come spet o good : s Necessary Good - amout spet as percet of come decles as come rses; ds d < ; techcally ths would clude feror goods, but usually do t clude them (.e., also requre > ) Luury Good - amout spet as percet of come creases as come creases; ds d > Homothetc Good - amout spet as percet of come stays costat whe come chages; has utary come elastcty Chage Share - tae dervatve of s ( s fucto of so use cha rule) ormal (d ca feror ormal 45 o CC 5 of Stay o 45 o le s homothetc below meas s luury ( ecessty); above meas s ecessty ( luury)
6 6 of d ds Multply frst term by ( )( ) ( ), d ds ε ε, > maes ds d > (luury) ad ε, < maes ds d < (ecessty) ds d ε, feror Good < < < Necessty > < (,) Homothetc > Luury > > > Lmted come - s so sum of s 's equals costat, f some s creases (.e., luury good wth ds d > ), the some other s must decrease (.e., ecessary or feror good);.e., f there s a luury good there has to be a ecessary or feror good Comparatve Statc for rces - Result - most mportat result of cosumer theory; prce chage has both come ad substtuto effects Ta Cut Eample - substtuto effect people wor more; come effect people wor less; do t ow whch effect s domat ad t's dffcult to measure emprcally because tme horzo (come effects may tae loger to observe, but by the ta codes chages aga) Totally dfferetate K-T codtos (lsted earler) wth respect to (geeralzes to ay ); Note: sce comparatve statcs loos at optmal soluto, eters to fuctos for optmal decso varables (.e., (,) ad (,)) (, 3,..., ) se frst order codto ad substtute for last term frst equatos ad all terms ( ) st equato; the frst eq move the to the rght had sde: (, 3,..., ) come elastcty product rule product rule
7 7 of Multply last eq by ad put frst term o other sde: Wrte wth matrces: ) ( Frst matr s a bordered hessa; t wll always be a BH for costraed optmzato... the trc s fgurg out what the other terms loo le Solve for usg Cramer's Rule: BH Epad by cofactors usg the frst colum: BH BH ) ( ) ( The frst term s the ow substtuto effect (S ) ad the secod s smply tmes : Slutsy Equato: S geeral: S Ow Substtuto Effect (S ) - frst term from above; tmes prcpal mor of BH dvded by determat of BH; prcpal mor has rowcol removed so t has opposte sg as BH ad S < (because we assume () s quascocave) Substtuto Effect come Effect
8 c More formally - S geeral: S.e., effect of prce o good whle holdg utlty costat Ha Compesato - order to eep utlty costat wth a chagg prce, we eed a crease come to offset the hgher prce; do't ow how much t s uless we ow full dfferece map Cross Substtuto Effect (S ) - effect of prce o good ; smlar to S above oly t's a dfferet row ad col used to epad the dervatve so the term the umerator s ot a prcpal mor S s ucerta The sg of depeds o ; for a ormal good ( > ), t s clear that < (.e.,... stadard law of demad); for a feror good, however, < so the effect of prce s determate; usually the substtuto effect domates so the law of demad holds, but there s the possblty that t wo't Gffe Good - feror good whch come effect domates so > (.e., volates law of demad); amed after frst perso to "observe" ad ; stuato was relad durg potato fame; Brtsh law essetally prohbted mports; ths lmted substtutes so S would be small; for potatoes relad would be large; potatoes are a feror good so Gffe's story s plausble, but "Gffe dd't say t ad t dd't happe, but t's plausble"; Gffe goods are hard to observe ad are more a rrtat to theorsts by mag results ambguous Other roblems - there are other cases wth upwards lopg demad curves (.e., > ); usually they are thgs whch demad s based o perceved qualty through prce so utlty s't ust (), but (,) whch we 8 of c chose to gore our aalyss because we assumed ad were depedet; eample: damods Slutsy Equato Graphcally - whe, the budget le gets steeper (assumg o the horzotal as) come Effect - the ew budget le s below the old oe so the orgal budle s ow feasble; essetally the cosumer s ow "poorer" Substtuto Effect - used to have ; sce, that o loger holds ad cosumers wll substtute away from good to reestablsh equlbrum Coceptual Vew - pcture creasg come to offset hgher prce (e.g., socal securty cost of lvg allowace); o the graph, that meas move the ew budget le up utl t's taget to the orgal dfferece curve; ths would be pot C; the move from pot C to pot B s the come effect; the move from pot A to pot C s the substtuto effect Quatty Matters - tal amout of good beg cosumed matters to dvdual because the more he cosumes, the more he'll be mpacted by a chage prce Not Measurable - ths coceptual vew s't operatoal because we'd eed to ow the cosumer's full dfferece map; all we ow s a specfc pot, but there could be lots of dfferece curves through that pot (each havg a dfferet come ad substtuto effect) Coceptual budget le wll hgher prce (stepper slope), but come off sets so cosumer s o same dfferece curve B C ' A erso who cosumes more of wll requre greater compesato to mae up for '
9 Substtutes - goods that could be used place of each other Gross - defed by cludg the come effect; > (.e., ) Net - does't clude come effect; also called Ha substtutes; S > Complmets - goods used together; defed as gross ad et ust le substtutes ecept ad S < Formal Deftos - formal defto of substtutes s debated; wat somethg that's ordal, symmetrc ( sub for sub for ), ad ubased (goods are equally lely to be substtutes or complmets) Classcal Substtutes - based o ; ot good because t's based o secod dervatve whch s ot varat (.e., ot cosstet for prefereces) Ha Substtutes - defed usg S ; ths s ordal ad symmetrc (S S... taes lots of algebra to prove); based because f there are ust two goods, the best you ca get s S (depedet) eve f the goods are perfect complmets (ca ever get S < ) Gross Substtutes - chec for symmetry: does? use Slutsy equato? S S S cacels S ; cross the ad ad multply both sdes by to get?? ε, ε, gross substtute defto s symmetrc oly f the goods have the same come elastctes (.e., homothetc prefereces); t's possble that come elastctes are so dfferet that & have dfferet sgs dfferece Curves - close to straght les are substtutes ad close to rght agles are complmets Nothg's erfect - o defto really wors so pc oe based o the cotet (e.g., whchever wors best whe tryg to "say somethg terestg" comparatve statcs) Ha Terms (S ) - ot drectly observable; do't ow how much to compesate, but ca compute them drectly by rewrtg Slutsy eq: S ; s drectly observed, the other terms are computed Slutsy Matr - matr of all the Ha Terms; puts restrcto o demad; () dagoal elemets ; () symmetrc; (3) egatve sem-defte S S S S Checg Demad ropertes - loo at fve propertes lsted earler ad the chec the Slutsy matr... but ths s hard stuff erfect complmets, S 9 of
10 Elastcty (ε y, ) - elastcty of y wth respect to ; percetage chage y dvded by percetage chage Dvde By What? - y y - y... do we dvde by y or y? some say to dvde by the average (arc elastcty) ot Elastcty - y ; related to slope, but ot the same % y y dy d dy ε y, % y y y d Why se t? - depedet of uts (does't chage whe you chage uts le slope does); sometmes comes up comparatve statcs of
Mean is only appropriate for interval or ratio scales, not ordinal or nominal.
Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot
More informationThird handout: On the Gini Index
Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationCHAPTER 4 RADICAL EXPRESSIONS
6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube
More information901 Notes: 8.doc John E. Walker Department of Economics Clemson University
9 Notes: 8.doc Joh E. Walker Departmet of Ecoomcs Clemso versty THE COPLETE EPIRICAL IPLICATIONS OF THE THEORY OF CONSER BEHAVIOR The Law of Demad s largely a emprcal fact of ature. I practce we have a
More informationLecture Notes 2. The ability to manipulate matrices is critical in economics.
Lecture Notes. Revew of Matrces he ablt to mapulate matrces s crtcal ecoomcs.. Matr a rectagular arra of umbers, parameters, or varables placed rows ad colums. Matrces are assocated wth lear equatos. lemets
More informationSTA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1
STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal
More informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More informationChapter 9 Jordan Block Matrices
Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.
More informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More informationLecture 07: Poles and Zeros
Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto
More informationThe Mathematical Appendix
The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.
More informationChapter 5 Properties of a Random Sample
Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample
More information7.0 Equality Contraints: Lagrange Multipliers
Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse
More informationLecture 3 Probability review (cont d)
STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto
More informationSummary of the lecture in Biostatistics
Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the
More informationQR Factorization and Singular Value Decomposition COS 323
QR Factorzato ad Sgular Value Decomposto COS 33 Why Yet Aother Method? How do we solve least-squares wthout currg codto-squarg effect of ormal equatos (A T A A T b) whe A s sgular, fat, or otherwse poorly-specfed?
More information= 2. Statistic - function that doesn't depend on any of the known parameters; examples:
of Samplg Theory amples - uemploymet househol cosumpto survey Raom sample - set of rv's... ; 's have ot strbuto [ ] f f s vector of parameters e.g. Statstc - fucto that oes't epe o ay of the ow parameters;
More informationMATH 247/Winter Notes on the adjoint and on normal operators.
MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say
More informationTHE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5
THE ROYAL STATISTICAL SOCIETY 06 EAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 The Socety s provdg these solutos to assst cadtes preparg for the examatos 07. The solutos are teded as learg ads ad should
More informationOrdinary Least Squares Regression. Simple Regression. Algebra and Assumptions.
Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos
More informationSimple Linear Regression
Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato
More informationChapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II
CEE49b Chapter - Free Vbrato of Mult-Degree-of-Freedom Systems - II We ca obta a approxmate soluto to the fudametal atural frequecy through a approxmate formula developed usg eergy prcples by Lord Raylegh
More informationCHAPTER VI Statistical Analysis of Experimental Data
Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca
More information1 Onto functions and bijections Applications to Counting
1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of
More information2SLS Estimates ECON In this case, begin with the assumption that E[ i
SLS Estmates ECON 3033 Bll Evas Fall 05 Two-Stage Least Squares (SLS Cosder a stadard lear bvarate regresso model y 0 x. I ths case, beg wth the assumto that E[ x] 0 whch meas that OLS estmates of wll
More informationi 2 σ ) i = 1,2,...,n , and = 3.01 = 4.01
ECO 745, Homework 6 Le Cabrera. Assume that the followg data come from the lear model: ε ε ~ N, σ,,..., -6. -.5 7. 6.9 -. -. -.9. -..6.4.. -.6 -.7.7 Fd the mamum lkelhood estmates of,, ad σ ε s.6. 4. ε
More informationå 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018
Chrs Pech Fal Practce CS09 Dec 5, 08 Practce Fal Examato Solutos. Aswer: 4/5 8/7. There are multle ways to obta ths aswer; here are two: The frst commo method s to sum over all ossbltes for the rak of
More informationLecture Notes Forecasting the process of estimating or predicting unknown situations
Lecture Notes. Ecoomc Forecastg. Forecastg the process of estmatg or predctg ukow stuatos Eample usuall ecoomsts predct future ecoomc varables Forecastg apples to a varet of data () tme seres data predctg
More informationNewton s Power Flow algorithm
Power Egeerg - Egll Beedt Hresso ewto s Power Flow algorthm Power Egeerg - Egll Beedt Hresso The ewto s Method of Power Flow 2 Calculatos. For the referece bus #, we set : V = p.u. ad δ = 0 For all other
More informationPGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation
PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad
More informationLecture Notes Types of economic variables
Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte
More informationApplying the condition for equilibrium to this equilibrium, we get (1) n i i =, r G and 5 i
CHEMICAL EQUILIBRIA The Thermodyamc Equlbrum Costat Cosder a reversble reacto of the type 1 A 1 + 2 A 2 + W m A m + m+1 A m+1 + Assgg postve values to the stochometrc coeffcets o the rght had sde ad egatve
More informationEECE 301 Signals & Systems
EECE 01 Sgals & Systems Prof. Mark Fowler Note Set #9 Computg D-T Covoluto Readg Assgmet: Secto. of Kame ad Heck 1/ Course Flow Dagram The arrows here show coceptual flow betwee deas. Note the parallel
More informationSection l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58
Secto.. 6l 34 6h 667899 7l 44 7h Stem=Tes 8l 344 Leaf=Oes 8h 5557899 9l 3 9h 58 Ths dsplay brgs out the gap the data: There are o scores the hgh 7's. 6. a. beams cylders 9 5 8 88533 6 6 98877643 7 488
More informationChapter Two. An Introduction to Regression ( )
ubject: A Itroducto to Regresso Frst tage Chapter Two A Itroducto to Regresso (018-019) 1 pg. ubject: A Itroducto to Regresso Frst tage A Itroducto to Regresso Regresso aalss s a statstcal tool for the
More information1 Lyapunov Stability Theory
Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may
More informationL5 Polynomial / Spline Curves
L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a
More informationLecture 3. Sampling, sampling distributions, and parameter estimation
Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called
More informationd dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin
Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace
More informationMEASURES OF DISPERSION
MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda
More informationX ε ) = 0, or equivalently, lim
Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece
More informationAnalyzing Control Structures
Aalyzg Cotrol Strutures sequeg P, P : two fragmets of a algo. t, t : the tme they tae the tme requred to ompute P ;P s t t Θmaxt,t For loops for to m do P t: the tme requred to ompute P total tme requred
More informationX X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then
Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers
More informationMu Sequences/Series Solutions National Convention 2014
Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed
More informationbest estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best
Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg
More informationAhmed Elgamal. MDOF Systems & Modal Analysis
DOF Systems & odal Aalyss odal Aalyss (hese otes cover sectos from Ch. 0, Dyamcs of Structures, Al Chopra, Pretce Hall, 995). Refereces Dyamcs of Structures, Al K. Chopra, Pretce Hall, New Jersey, ISBN
More informationMultiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades
STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos
More informationCorrelation and Regression Analysis
Chapter V Correlato ad Regresso Aalss R. 5.. So far we have cosdered ol uvarate dstrbutos. Ma a tme, however, we come across problems whch volve two or more varables. Ths wll be the subject matter of the
More informationLecture Note to Rice Chapter 8
ECON 430 HG revsed Nov 06 Lecture Note to Rce Chapter 8 Radom matrces Let Y, =,,, m, =,,, be radom varables (r.v. s). The matrx Y Y Y Y Y Y Y Y Y Y = m m m s called a radom matrx ( wth a ot m-dmesoal dstrbuto,
More information{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:
Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed
More informationMultiple Choice Test. Chapter Adequacy of Models for Regression
Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to
More informationDiscrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b
CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package
More informationTESTS BASED ON MAXIMUM LIKELIHOOD
ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal
More information1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i.
CS 94- Desty Matrces, vo Neuma Etropy 3/7/07 Sprg 007 Lecture 3 I ths lecture, we wll dscuss the bascs of quatum formato theory I partcular, we wll dscuss mxed quatum states, desty matrces, vo Neuma etropy
More informationPhysics 114 Exam 2 Fall Name:
Physcs 114 Exam Fall 015 Name: For gradg purposes (do ot wrte here): Questo 1. 1... 3. 3. Problem Aswer each of the followg questos. Pots for each questo are dcated red. Uless otherwse dcated, the amout
More informationStatistics: Unlocking the Power of Data Lock 5
STAT 0 Dr. Kar Lock Morga Exam 2 Grades: I- Class Multple Regresso SECTIONS 9.2, 0., 0.2 Multple explaatory varables (0.) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (0.2) Exam 2 Re- grades Re-
More information9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d
9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,
More informationPower Flow S + Buses with either or both Generator Load S G1 S G2 S G3 S D3 S D1 S D4 S D5. S Dk. Injection S G1
ower Flow uses wth ether or both Geerator Load G G G D D 4 5 D4 D5 ecto G Net Comple ower ecto - D D ecto s egatve sg at load bus = _ G D mlarl Curret ecto = G _ D At each bus coservato of comple power
More informationUNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS
Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method
More informationTransforms that are commonly used are separable
Trasforms s Trasforms that are commoly used are separable Eamples: Two-dmesoal DFT DCT DST adamard We ca the use -D trasforms computg the D separable trasforms: Take -D trasform of the rows > rows ( )
More informationCLASS NOTES. for. PBAF 528: Quantitative Methods II SPRING Instructor: Jean Swanson. Daniel J. Evans School of Public Affairs
CLASS NOTES for PBAF 58: Quattatve Methods II SPRING 005 Istructor: Jea Swaso Dael J. Evas School of Publc Affars Uversty of Washgto Ackowledgemet: The structor wshes to thak Rachel Klet, Assstat Professor,
More informationJohns Hopkins University Department of Biostatistics Math Review for Introductory Courses
Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s
More informationChapter 14 Logistic Regression Models
Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as
More informationhp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations
HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several
More informationε. Therefore, the estimate
Suggested Aswers, Problem Set 3 ECON 333 Da Hugerma. Ths s ot a very good dea. We kow from the secod FOC problem b) that ( ) SSE / = y x x = ( ) Whch ca be reduced to read y x x = ε x = ( ) The OLS model
More informationJohns Hopkins University Department of Biostatistics Math Review for Introductory Courses
Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s
More informationsets), (iii) differentiable - Nash equilibrium in gifts (i.e., contributions to public good) from consumer i is g i + G = amount of public good
Prvate Provso o Publc oods Bergstrom, Blume, & Vara. "O the Prvate Provso o Publc oods." Joural o Publc Ecoomcs. Vol. 9, 986, 5-49. BBV - most mportat paper o ths subect; also apples to gvg to ay charty,
More informationECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model
ECON 48 / WH Hog The Smple Regresso Model. Defto of the Smple Regresso Model Smple Regresso Model Expla varable y terms of varable x y = β + β x+ u y : depedet varable, explaed varable, respose varable,
More information12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model
1. Estmatg Model parameters Assumptos: ox ad y are related accordg to the smple lear regresso model (The lear regresso model s the model that says that x ad y are related a lear fasho, but the observed
More informationMOLECULAR VIBRATIONS
MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal
More informationESS Line Fitting
ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here
More informationIII-16 G. Brief Review of Grand Orthogonality Theorem and impact on Representations (Γ i ) l i = h n = number of irreducible representations.
III- G. Bref evew of Grad Orthogoalty Theorem ad mpact o epresetatos ( ) GOT: h [ () m ] [ () m ] δδ δmm ll GOT puts great restrcto o form of rreducble represetato also o umber: l h umber of rreducble
More informationInvestigating Cellular Automata
Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte
More informationIntroduction to local (nonparametric) density estimation. methods
Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest
More informationDescriptive Statistics
Page Techcal Math II Descrptve Statstcs Descrptve Statstcs Descrptve statstcs s the body of methods used to represet ad summarze sets of data. A descrpto of how a set of measuremets (for eample, people
More informationAssignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)
Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form
More informationMMJ 1113 FINITE ELEMENT METHOD Introduction to PART I
MMJ FINITE EEMENT METHOD Cotut requremets Assume that the fuctos appearg uder the tegral the elemet equatos cota up to (r) th order To esure covergece N must satsf Compatblt requremet the fuctos must have
More informationC-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory
ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?
More informationANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK
Far East Joural of Appled Mathematcs Volume, Number, 2008, Pages Ths paper s avalable ole at http://www.pphm.com 2008 Pushpa Publshg House ANALYSIS ON THE NATURE OF THE ASI EQUATIONS IN SYNERGETI INTER-REPRESENTATION
More informationDepartment of Agricultural Economics. PhD Qualifier Examination. August 2011
Departmet of Agrcultural Ecoomcs PhD Qualfer Examato August 0 Istructos: The exam cossts of sx questos You must aswer all questos If you eed a assumpto to complete a questo, state the assumpto clearly
More informationFor combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.
Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted
More informationSpring Ammar Abu-Hudrouss Islamic University Gaza
١ ١ Chapter Chapter 4 Cyl Blo Cyl Blo Codes Codes Ammar Abu-Hudrouss Islam Uversty Gaza Spr 9 Slde ٢ Chael Cod Theory Cyl Blo Codes A yl ode s haraterzed as a lear blo ode B( d wth the addtoal property
More informationCHAPTER 3 POSTERIOR DISTRIBUTIONS
CHAPTER 3 POSTERIOR DISTRIBUTIONS If scece caot measure the degree of probablt volved, so much the worse for scece. The practcal ma wll stck to hs apprecatve methods utl t does, or wll accept the results
More informationWe have already referred to a certain reaction, which takes place at high temperature after rich combustion.
ME 41 Day 13 Topcs Chemcal Equlbrum - Theory Chemcal Equlbrum Example #1 Equlbrum Costats Chemcal Equlbrum Example #2 Chemcal Equlbrum of Hot Bured Gas 1. Chemcal Equlbrum We have already referred to a
More informationComputational Geometry
Problem efto omputatoal eometry hapter 6 Pot Locato Preprocess a plaar map S. ve a query pot p, report the face of S cotag p. oal: O()-sze data structure that eables O(log ) query tme. pplcato: Whch state
More informationNew Trade Theory (1979)
Ne Trade Theory 979 Ne Trade Theory Krugma, 979: - Ecoomes of scale as reaso for trade - Elas trade betee smlar coutres Ituto of model: There s a trade-off betee ecoomes of scale the roducto of good tyes
More informationMidterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes
coometrcs, CON Sa Fracsco State Uversty Mchael Bar Sprg 5 Mdterm am, secto Soluto Thursday, February 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes eam.. No calculators of ay kd are allowed..
More information1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers.
PROBLEMS A real umber s represeted appromately by 63, ad we are told that the relatve error s % What s? Note: There are two aswers Ht : Recall that % relatve error s What s the relatve error volved roudg
More informationThe number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter
LOGISTIC REGRESSION Notato Model Logstc regresso regresses a dchotomous depedet varable o a set of depedet varables. Several methods are mplemeted for selectg the depedet varables. The followg otato s
More informationLecture 9: Tolerant Testing
Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have
More informationUNIVERSITY OF EAST ANGLIA. Main Series UG Examination
UNIVERSITY OF EAST ANGLIA School of Ecoomcs Ma Seres UG Examato 03-4 INTRODUCTORY MATHEMATICS AND STATISTICS FOR ECONOMISTS ECO-400Y Tme allowed: 3 hours Aswer ALL questos from both Sectos. Aswer EACH
More informationENGI 3423 Simple Linear Regression Page 12-01
ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable
More informationConsumer theory. A. The preference ordering B. The feasible set C. The consumption decision. A. The preference ordering. Consumption bundle
Föreläsgsderlag för Gravelle-Rees. Del. Thomas Soesso Cosmer theory A. The referece orderg B. The feasble set C. The cosmto decso A. The referece orderg Cosmto bdle ( 2,,... ) Assmtos: Comleteess 2 Trastvty
More informationExercises for Square-Congruence Modulo n ver 11
Exercses for Square-Cogruece Modulo ver Let ad ab,.. Mark True or False. a. 3S 30 b. 3S 90 c. 3S 3 d. 3S 4 e. 4S f. 5S g. 0S 55 h. 8S 57. 9S 58 j. S 76 k. 6S 304 l. 47S 5347. Fd the equvalece classes duced
More information( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model
Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch
More informationFeature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)
CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.
More informationENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections
ENGI 441 Jot Probablty Dstrbutos Page 7-01 Jot Probablty Dstrbutos [Navd sectos.5 ad.6; Devore sectos 5.1-5.] The jot probablty mass fucto of two dscrete radom quattes, s, P ad p x y x y The margal probablty
More informationPTAS for Bin-Packing
CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,
More information