Exiting from QE. Fumio Hayashi and Junko Koeda. for presentation at SF Fed Conference. March 28, 2014

Size: px
Start display at page:

Download "Exiting from QE. Fumio Hayashi and Junko Koeda. for presentation at SF Fed Conference. March 28, 2014"

Transcription

1 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 1 / 29 Exiting from QE Fumio Hayashi an Junko Koa for prsntation at SF F Confrnc March 28, 214

2 To gt start... h^ : Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 2 / 29

3 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 3 / 29 What This Papr Dos Stuy th ffct of QE on macro variabls (inflation an GDP) on Japans ata using SVAR (structural VAR) Japan has, by our count, 13 QE months (as of Dc. 212) Inclus actual lift-off. Uniqu in two rspcts: Obsrvabl an nognous rgims (unlik in th hin-stat Markov switching mol) IR (impuls rspons) to rgim changs.

4 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 4 / 29 Plan of Talk Intifying th Rgim Th Mol Estimation Rsults IR (impuls rspons) an Countr-factual Analyss Conclusions about BOJ s Zro-Rat/QE Policy

5 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 5 / 29 Thr QE Splls Z (zro-rat rgim) if r (th policy rat) <.5% (5 basis points). P (normal rgim) othrwis. Z = QE bcaus xcss rsrvs > whn Z. Prios of Z (Zro-Rat/QE) Rgim QE1: March July 2 QE2: March 21 - Jun 26 QE3: Dcmbr 28 to at Agrs with BOJ annoucmnts. For xampl, July 14, 26:... th BOJ ci... to chang th guilin for mony markt oprations... Th BOJ will ncourag th uncollatraliz ovrnight rat to rmain at aroun.25 prcnt.

6 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 6 / 29 Policy Rat (r) in Japan, prcnt pr yar Jan 9 Jan 95 Jan Jan 5 Jan 1

7 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 7 / 29 Th Exit Conition (to rpat) Prios of Zro-Rat/QE Rgim QE1: March July 2 QE2: March 21 - Jun 26 QE3: Dcmbr 28 to at During QE s, BOJ ma th inflation commitmnt. For xampl Sptmbr 21, 1999: Th BOJ... is xplicitly committ to continu this policy [th zro-rat policy] until flationary concrns subsi.

8 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 8 / 29 Th Mol, stp 1 of 4: a txtbook block-rcursiv SVAR Point of partur: txtbook 3-variabl SVAR (s Stock an Watson, J. of Econ. Prspctivs, 21) (p, x, r), p = inflation rat, x = output gap, r = policy rat. Th first two quations ar ruc forms in (p, x). Th thir quation is th Taylor rul, rlating r to contmporanous (p, x). Taylor rul: with π t 1 12 (p t + + p t 11 ), r t = ρ r rt + (1 ρ r )r t 1 + σ r v rt, rt αr + β r (1 2) [ π t x t ] }{{} sir Taylor rat, v rt N (, 1). Th inflation rat in th Taylor rul is th yar-on-yar (12-month) inflation rat π.

9 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 9 / 29 Th Mol, stp 2 of 4: A th Zro Lowr Boun Impos th lowr boun: r t = max [ ρ r rt + (1 ρ r )r t 1 + σ r v }{{ rt, ], v } rt N (, 1). shaow Taylor rat Equivalntly, shaow Taylor rat if s t = P, r t = if s t = Z. P if shaow Taylor rat >, s t = Z othrwis. Not: Th rgim st is nognous. st = Z if an only if r t = (th conomtrician osn t hav to obsrv th shaow Taylor rat).

10 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 1 / 29 Th Mol, stp 3 of 4: Introuc Exit Conition If s t 1 = Z, P if shaow Taylor rat > an π t > π + σ π v πt, }{{} s t = targt inflation rat Z othrwis. If s t 1 = P, as bfor, i.., P if shaow Taylor rat >, s t = Z othrwis. (rminr) π t 1 12 (p t + + p t 11 ), shaow Taylor rat = ρ r rt + (1 ρ r )r t 1 + σ r v rt, rt αr + β r (1 2) [ π t x t ] }{{} sir Taylor rat.

11 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 11 / 29 Th Mol, stp 4 of 4: A m A m to (p, x, r). if s t = P, m t = max [ m st, ], v mt N (, 1) if s t = Z, whr m st α s + β s [ π t x t ] + γ s m t 1 + σ s v st, (rminr) p monthly inflation rat, π t 1 12 (p t + + p t 11 ), x output ( gap, m log actual rsrvs rquir rsrvs ).

12 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 12 / 29 Plan of Talk Intifying th Rgim Th Mol Estimation Rsults IR (impuls rspons) an Countr-factual Analyss Conclusions about BOJ s Zro-Rat/QE Policy

13 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 13 / 29 Estimation: Th Monthly Data p (monthly CPI inflation rat) π (12-month inflation rat) m (xcss rsrv rat): m t 1 log ( actual rsrvst rquir rsrvs t ). r (policy rat): collatraliz ovrnight intrbank rat. x (GDP gap): monthly intrpolation of official quartrly stimat.

14 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 14 / Month Inflation Rat (π), prcnt pr yar Jan 9 Jan 95 Jan Jan 5 Jan 1

15 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 15 / 29 GDP, log actual GDP potntial log GDP Jan 9 Jan 95 Jan Jan 5 Jan 1

16 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 16 / 29 Estimation: Th Equations bivariat (p, x) ruc form: allow to shift btwn (lagg) P (normal rgim) an (lagg) Z (zro-rat/qe rgim) by Lucas. Taylor rul: stimat on P + Z, rgim nognity takn into account. Excss rsrv supply quation: on Z = {QE1, QE2, QE3}, but QE1 ropp bcaus QE1 looks iffrnt.

17 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 17 / 29 Estimation: (p, x) Ruc Form (t valu in brackts) sampl prio is January Dcmbr 212 subsampl P (s t 1 = P, sampl siz = 123) p. var. const. p t 1 x t 1 r t 1 m t 1 R 2 p t.23 [.9] x t.2 [ 1.4].1 [1.1]. [.1].14 [1.7].93 [21].39 [3.4].2 [.3] subsampl Z (s t 1 = QE2+QE3, sampl siz = 112).19.8 p. var. const. p t 1 x t 1 r t 1 m t 1 R 2 p t.15 [.3] x t 1.21 [ 3.3].22 [2.4].2 [.3].16 [1.8].77 [14].2 [.1].52 [2.6].11.75

18 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 18 / 29 Things to Not about Estimat Ruc Form subsampl s t 1 = P: lagg r cofficint in p t quation positiv an significant. subsampl s t 1 = Z (QE2+QE3): lagg m cofficint in pt an x t quations positiv. Intrcpts lowr for Z than for P.

19 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 19 / 29 Plan of Talk Intifying th Rgim Th Mol Estimation Rsults IR (impuls rspons) an Countr-factual Analyss Conclusions about BOJ s Zro-Rat/QE Policy

20 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 2 / 29 m-ir an r-ir m-ir (IR to a chang in m): E ( y t+k s t = Z, (p t, x t,, m t + δ m ),... ) }{{} (p t, x t, r t, m t ) in altrnativ history E ( y t+k s t = Z, (p t, x t,, m t ),... ), y = p, x, r, m. }{{} (p t, x t, r t, m t) in baslin history r-ir (IR to a chang in r): ( E t yt+k s t = P, (p t, x t, r t + δ r, ),... ) }{{} (p t, x t, r t, m t ) in altrnativ history ( E t yt+k s t = P, (p t, x t, r t, ),... ), y = p, x, r, m. }{{} (p t, x t, r t, m t ) in baslin history Aaptation of GRT (Gallant-Rossi-Tauchn, Economtrica, 1993) IR.

21 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 21 / 29 m-ir, Fbruary 24.5 Monthly Inflation (p) 1.5 Output Gap (x) 1 % annual rat % Policy Rat (r) 1 Excss Rsrv Rat (m) % annual rat.5 %

22 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 22 / 29 Focus on Exiting from QE2 (March 21 - Jun 26) Wining-own of QE2, March to August 26: March April May Jun July August rgim (P for normal, Z for Z Z Z Z P P zro-rat/qe) ratio of actual to rquir rsrvs m, log of th abov ratio (%) r, th policy rat (% pr yar) π, yar-on-yar inflation rat (%) x, output gap (%) shaow Taylor rat (% pr yar)

23 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 23 / 29 IR with Rgim Chang E ( y t+k s t = Z, (p t, x t,, mt ),... ) }{{} (p t, x t, r t, m t) in altrnativ history E ( y t+k s t = P, (p t, x t, r t, ),... ) }{{} (p t, x t, r t, m t) in baslin history m t th m to b xpct givn history up to (p t, x t ). Can b calculat from th stimat xcss rsrv quation. St t = July 26 (BOJ xit from QE2 in July 26).

24 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 24 / 29 If BOJ han t xit in July Monthly Inflation (p) 1.5 Output Gap (x) 1 % annual rat % Policy Rat (r) 1 Excss Rsrv Rat (m) % annual rat.5 %

25 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 25 / 29 IR with Rgim Chang, in opposit irction E ( y t+k s t = P, (p t, x t,, ),... ) }{{} (p t, x t, r t, m t) in altrnativ history E ( y t+k s t = Z, (p t, x t,, m t ),... ) }{{} (p t, x t, r t, m t) in baslin history Hr, m t in baslin is th actual m.

26 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 26 / 29 If BOJ ha xit on month arlir, in Jun Monthly Inflation (p) 1.5 Output Gap (x) 1 % annual rat % Policy Rat (r) 1 Excss Rsrv Rat (m) % annual rat.5 %

27 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 27 / 29 If BOJ ha xit in April Monthly Inflation (p) 1.5 Output Gap (x) 1 % annual rat % Policy Rat (r) 1 Excss Rsrv Rat (m) % annual rat.5 %

28 Conclusions about BOJ s Zro-Rat/QE Policy Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 28 / 29 Incrass in rsrvs unr QE rais both inflation an output. Exiting from QE2 on July 26 was xpansionary. Bttr to hav n QE2 arlir, in April 26 or May 26, vn though th ratio of actual to rquir rsrvs was as high as Cavats: Th pric puzzl Wining-own taks tim.

29 Fumio Hayashi an Junko Koa Exiting from QE March 28, 214, 29 / 29 r-ir, January Monthly Inflation (p) 1.5 Output Gap (x) 1 % annual rat % Policy Rat (r) 1 Excss Rsrv Rat (m) % annual rat.5 %

A Regime-Switching SVAR Analysis of ZIRP

A Regime-Switching SVAR Analysis of ZIRP Fumio Hayashi and Junko Koeda A Regime-Switching SVAR Analysis of ZIRP June 25, 2013, 1 / 32 A Regime-Switching SVAR Analysis of ZIRP Fumio Hayashi and Junko Koeda Prepared for CIGS Conference on Macroeconomic

More information

The Peril of the Inflation Exit Condition

The Peril of the Inflation Exit Condition Fumio Hayashi (GRIPS) The Peril of the Inflation Exit Condition 1 / 24 The Peril of the Inflation Exit Condition Fumio Hayashi (GRIPS) JEA Presidential Address, 9 September 2018 Fumio Hayashi (GRIPS) The

More information

Additional Math (4047) Paper 2 (100 marks) y x. 2 d. d d

Additional Math (4047) Paper 2 (100 marks) y x. 2 d. d d Aitional Math (07) Prpar b Mr Ang, Nov 07 Fin th valu of th constant k for which is a solution of th quation k. [7] Givn that, Givn that k, Thrfor, k Topic : Papr (00 marks) Tim : hours 0 mins Nam : Aitional

More information

Exiting from QE by Fumio Hayshi and Junko Koeda

Exiting from QE by Fumio Hayshi and Junko Koeda Exiting from QE by Fumio Hayshi and Junko Koeda Federal Reserve Bank of San Francisco March 28 th 2014 Roger E. A. Farmer, Distinguished Professor, UCLA What this Paper Does Uses a structural VAR with

More information

What are those βs anyway? Understanding Design Matrix & Odds ratios

What are those βs anyway? Understanding Design Matrix & Odds ratios Ral paramtr stimat WILD 750 - Wildlif Population Analysis of 6 What ar thos βs anyway? Undrsting Dsign Matrix & Odds ratios Rfrncs Hosmr D.W.. Lmshow. 000. Applid logistic rgrssion. John Wily & ons Inc.

More information

Thomas Whitham Sixth Form

Thomas Whitham Sixth Form Thomas Whitham Sith Form Pur Mathmatics Unit C Algbra Trigonomtr Gomtr Calculus Vctor gomtr Pag Algbra Molus functions graphs, quations an inqualitis Graph of f () Draw f () an rflct an part of th curv

More information

Multiple Short Term Infusion Homework # 5 PHA 5127

Multiple Short Term Infusion Homework # 5 PHA 5127 Multipl Short rm Infusion Homwork # 5 PHA 527 A rug is aministr as a short trm infusion. h avrag pharmacokintic paramtrs for this rug ar: k 0.40 hr - V 28 L his rug follows a on-compartmnt boy mol. A 300

More information

4. Money cannot be neutral in the short-run the neutrality of money is exclusively a medium run phenomenon.

4. Money cannot be neutral in the short-run the neutrality of money is exclusively a medium run phenomenon. PART I TRUE/FALSE/UNCERTAIN (5 points ach) 1. Lik xpansionary montary policy, xpansionary fiscal policy rturns output in th mdium run to its natural lvl, and incrass prics. Thrfor, fiscal policy is also

More information

First order differential equation Linear equation; Method of integrating factors

First order differential equation Linear equation; Method of integrating factors First orr iffrntial quation Linar quation; Mtho of intgrating factors Exampl 1: Rwrit th lft han si as th rivativ of th prouct of y an som function by prouct rul irctly. Solving th first orr iffrntial

More information

Chapter 13 Aggregate Supply

Chapter 13 Aggregate Supply Chaptr 13 Aggrgat Supply 0 1 Larning Objctivs thr modls of aggrgat supply in which output dpnds positivly on th pric lvl in th short run th short-run tradoff btwn inflation and unmploymnt known as th Phillips

More information

Case Study 4 PHA 5127 Aminoglycosides Answers provided by Jeffrey Stark Graduate Student

Case Study 4 PHA 5127 Aminoglycosides Answers provided by Jeffrey Stark Graduate Student Cas Stuy 4 PHA 527 Aminoglycosis Answrs provi by Jffry Stark Grauat Stunt Backgroun Gntamicin is us to trat a wi varity of infctions. Howvr, u to its toxicity, its us must b rstrict to th thrapy of lif-thratning

More information

CARF Working Paper CARF-F-322. A Regime-Switching SVAR Analysis of Quantitative Easing

CARF Working Paper CARF-F-322. A Regime-Switching SVAR Analysis of Quantitative Easing CARF Working Paper CARF-F-322 A Regime-Switching SVAR Analysis of Quantitative Easing Fumio Hayashi Hitotsubashi University Junko Koeda The University of Tokyo July 2013 CARF is presently supported by

More information

Thomas Whitham Sixth Form

Thomas Whitham Sixth Form Thomas Whitham Sith Form Pur Mathmatics Cor rvision gui Pag Algbra Moulus functions graphs, quations an inqualitis Graph of f () Draw f () an rflct an part of th curv blow th ais in th ais. f () f () f

More information

Diploma Macro Paper 2

Diploma Macro Paper 2 Diploma Macro Papr 2 Montary Macroconomics Lctur 6 Aggrgat supply and putting AD and AS togthr Mark Hays 1 Exognous: M, G, T, i*, π Goods markt KX and IS (Y, C, I) Mony markt (LM) (i, Y) Labour markt (P,

More information

Chapter 14 Aggregate Supply and the Short-run Tradeoff Between Inflation and Unemployment

Chapter 14 Aggregate Supply and the Short-run Tradeoff Between Inflation and Unemployment Chaptr 14 Aggrgat Supply and th Short-run Tradoff Btwn Inflation and Unmploymnt Modifid by Yun Wang Eco 3203 Intrmdiat Macroconomics Florida Intrnational Univrsity Summr 2017 2016 Worth Publishrs, all

More information

VALUING SURRENDER OPTIONS IN KOREAN INTEREST INDEXED ANNUITIES

VALUING SURRENDER OPTIONS IN KOREAN INTEREST INDEXED ANNUITIES VALUING SURRENDER OPTIONS IN KOREAN INTEREST INDEXED ANNUITIES Changi Kim* * Dr. Changi Kim is Lcturr at Actuarial Studis Faculty of Commrc & Economics Th Univrsity of Nw South Wals Sydny NSW 2052 Australia.

More information

Inflation and Unemployment

Inflation and Unemployment C H A P T E R 13 Aggrgat Supply and th Short-run Tradoff Btwn Inflation and Unmploymnt MACROECONOMICS SIXTH EDITION N. GREGORY MANKIW PowrPoint Slids by Ron Cronovich 2008 Worth Publishrs, all rights rsrvd

More information

Math 34A. Final Review

Math 34A. Final Review Math A Final Rviw 1) Us th graph of y10 to find approimat valus: a) 50 0. b) y (0.65) solution for part a) first writ an quation: 50 0. now tak th logarithm of both sids: log() log(50 0. ) pand th right

More information

Pipe flow friction, small vs. big pipes

Pipe flow friction, small vs. big pipes Friction actor (t/0 t o pip) Friction small vs larg pips J. Chaurtt May 016 It is an intrsting act that riction is highr in small pips than largr pips or th sam vlocity o low and th sam lngth. Friction

More information

Radiation Physics Laboratory - Complementary Exercise Set MeBiom 2016/2017

Radiation Physics Laboratory - Complementary Exercise Set MeBiom 2016/2017 Th following qustions ar to b answrd individually. Usful information such as tabls with dtctor charactristics and graphs with th proprtis of matrials ar availabl in th cours wb sit: http://www.lip.pt/~patricia/fisicadaradiacao.

More information

EXITING FROM QE. Fumio Hayashi and Junko Koeda. Hitotsubashi University, Japan, and National Bureau of Economic Research. Waseda University, Japan

EXITING FROM QE. Fumio Hayashi and Junko Koeda. Hitotsubashi University, Japan, and National Bureau of Economic Research. Waseda University, Japan EXITING FROM QE by Fumio Hayashi and Junko Koeda Hitotsubashi University, Japan, and National Bureau of Economic Research Waseda University, Japan November 2014 Abstract We develop a regime-switching SVAR

More information

2008 AP Calculus BC Multiple Choice Exam

2008 AP Calculus BC Multiple Choice Exam 008 AP Multipl Choic Eam Nam 008 AP Calculus BC Multipl Choic Eam Sction No Calculator Activ AP Calculus 008 BC Multipl Choic. At tim t 0, a particl moving in th -plan is th acclration vctor of th particl

More information

y = 2xe x + x 2 e x at (0, 3). solution: Since y is implicitly related to x we have to use implicit differentiation: 3 6y = 0 y = 1 2 x ln(b) ln(b)

y = 2xe x + x 2 e x at (0, 3). solution: Since y is implicitly related to x we have to use implicit differentiation: 3 6y = 0 y = 1 2 x ln(b) ln(b) 4. y = y = + 5. Find th quation of th tangnt lin for th function y = ( + ) 3 whn = 0. solution: First not that whn = 0, y = (1 + 1) 3 = 8, so th lin gos through (0, 8) and thrfor its y-intrcpt is 8. y

More information

EXST Regression Techniques Page 1

EXST Regression Techniques Page 1 EXST704 - Rgrssion Tchniqus Pag 1 Masurmnt rrors in X W hav assumd that all variation is in Y. Masurmnt rror in this variabl will not ffct th rsults, as long as thy ar uncorrlatd and unbiasd, sinc thy

More information

Full Waveform Inversion Using an Energy-Based Objective Function with Efficient Calculation of the Gradient

Full Waveform Inversion Using an Energy-Based Objective Function with Efficient Calculation of the Gradient Full Wavform Invrsion Using an Enrgy-Basd Objctiv Function with Efficint Calculation of th Gradint Itm yp Confrnc Papr Authors Choi, Yun Sok; Alkhalifah, ariq Ali Citation Choi Y, Alkhalifah (217) Full

More information

Fumio Hayashi and Junko Koeda. National Graduate Institute for Policy Studies, Japan, and National Bureau of Economic Research

Fumio Hayashi and Junko Koeda. National Graduate Institute for Policy Studies, Japan, and National Bureau of Economic Research EXITING FROM QE by Fumio Hayashi and Junko Koeda National Graduate Institute for Policy Studies, Japan, and National Bureau of Economic Research Waseda University, Japan February 2016 Abstract We develop

More information

Addition of angular momentum

Addition of angular momentum Addition of angular momntum April, 0 Oftn w nd to combin diffrnt sourcs of angular momntum to charactriz th total angular momntum of a systm, or to divid th total angular momntum into parts to valuat th

More information

The Open Economy in the Short Run

The Open Economy in the Short Run Economics 442 Mnzi D. Chinn Spring 208 Social Scincs 748 Univrsity of Wisconsin-Madison Th Opn Economy in th Short Run This st of nots outlins th IS-LM modl of th opn conomy. First, it covrs an accounting

More information

Case Study Vancomycin Answers Provided by Jeffrey Stark, Graduate Student

Case Study Vancomycin Answers Provided by Jeffrey Stark, Graduate Student Cas Stuy Vancomycin Answrs Provi by Jffry Stark, Grauat Stunt h antibiotic Vancomycin is liminat almost ntirly by glomrular filtration. For a patint with normal rnal function, th half-lif is about 6 hours.

More information

2. Laser physics - basics

2. Laser physics - basics . Lasr physics - basics Spontanous and stimulatd procsss Einstin A and B cofficints Rat quation analysis Gain saturation What is a lasr? LASER: Light Amplification by Stimulatd Emission of Radiation "light"

More information

Addition of angular momentum

Addition of angular momentum Addition of angular momntum April, 07 Oftn w nd to combin diffrnt sourcs of angular momntum to charactriz th total angular momntum of a systm, or to divid th total angular momntum into parts to valuat

More information

Chapter 1. Chapter 10. Chapter 2. Chapter 11. Chapter 3. Chapter 12. Chapter 4. Chapter 13. Chapter 5. Chapter 14. Chapter 6. Chapter 7.

Chapter 1. Chapter 10. Chapter 2. Chapter 11. Chapter 3. Chapter 12. Chapter 4. Chapter 13. Chapter 5. Chapter 14. Chapter 6. Chapter 7. Chaptr Binomial Epansion Chaptr 0 Furthr Probability Chaptr Limits and Drivativs Chaptr Discrt Random Variabls Chaptr Diffrntiation Chaptr Discrt Probability Distributions Chaptr Applications of Diffrntiation

More information

Ch. 24 Molecular Reaction Dynamics 1. Collision Theory

Ch. 24 Molecular Reaction Dynamics 1. Collision Theory Ch. 4 Molcular Raction Dynamics 1. Collision Thory Lctur 16. Diffusion-Controlld Raction 3. Th Matrial Balanc Equation 4. Transition Stat Thory: Th Eyring Equation 5. Transition Stat Thory: Thrmodynamic

More information

In this lecture... Subsonic and supersonic nozzles Working of these nozzles Performance parameters for nozzles

In this lecture... Subsonic and supersonic nozzles Working of these nozzles Performance parameters for nozzles Lct-30 Lct-30 In this lctur... Subsonic and suprsonic nozzls Working of ths nozzls rformanc paramtrs for nozzls rof. Bhaskar Roy, rof. A M radp, Dpartmnt of Arospac, II Bombay Lct-30 Variation of fluid

More information

Inheritance Gains in Notional Defined Contributions Accounts (NDCs)

Inheritance Gains in Notional Defined Contributions Accounts (NDCs) Company LOGO Actuarial Tachrs and Rsarchrs Confrnc Oxford 14-15 th July 211 Inhritanc Gains in Notional Dfind Contributions Accounts (NDCs) by Motivation of this papr In Financial Dfind Contribution (FDC)

More information

Y 0. Standing Wave Interference between the incident & reflected waves Standing wave. A string with one end fixed on a wall

Y 0. Standing Wave Interference between the incident & reflected waves Standing wave. A string with one end fixed on a wall Staning Wav Intrfrnc btwn th incint & rflct wavs Staning wav A string with on n fix on a wall Incint: y, t) Y cos( t ) 1( Y 1 ( ) Y (St th incint wav s phas to b, i.., Y + ral & positiv.) Rflct: y, t)

More information

Exchange rates in the long run (Purchasing Power Parity: PPP)

Exchange rates in the long run (Purchasing Power Parity: PPP) Exchang rats in th long run (Purchasing Powr Parity: PPP) Jan J. Michalk JJ Michalk Th law of on pric: i for a product i; P i = E N/ * P i Or quivalntly: E N/ = P i / P i Ida: Th sam product should hav

More information

Prod.C [A] t. rate = = =

Prod.C [A] t. rate = = = Concntration Concntration Practic Problms: Kintics KEY CHEM 1B 1. Basd on th data and graph blow: Ract. A Prod. B Prod.C..25.. 5..149.11.5 1..16.144.72 15..83.167.84 2..68.182.91 25..57.193.96 3..5.2.1

More information

Differentiation of Exponential Functions

Differentiation of Exponential Functions Calculus Modul C Diffrntiation of Eponntial Functions Copyright This publication Th Northrn Albrta Institut of Tchnology 007. All Rights Rsrvd. LAST REVISED March, 009 Introduction to Diffrntiation of

More information

6.1 Integration by Parts and Present Value. Copyright Cengage Learning. All rights reserved.

6.1 Integration by Parts and Present Value. Copyright Cengage Learning. All rights reserved. 6.1 Intgration by Parts and Prsnt Valu Copyright Cngag Larning. All rights rsrvd. Warm-Up: Find f () 1. F() = ln(+1). F() = 3 3. F() =. F() = ln ( 1) 5. F() = 6. F() = - Objctivs, Day #1 Studnts will b

More information

Problem Set 6 Solutions

Problem Set 6 Solutions 6.04/18.06J Mathmatics for Computr Scinc March 15, 005 Srini Dvadas and Eric Lhman Problm St 6 Solutions Du: Monday, March 8 at 9 PM in Room 3-044 Problm 1. Sammy th Shark is a financial srvic providr

More information

dr Bartłomiej Rokicki Chair of Macroeconomics and International Trade Theory Faculty of Economic Sciences, University of Warsaw

dr Bartłomiej Rokicki Chair of Macroeconomics and International Trade Theory Faculty of Economic Sciences, University of Warsaw dr Bartłomij Rokicki Chair of Macroconomics and Intrnational Trad Thory Faculty of Economic Scincs, Univrsity of Warsaw dr Bartłomij Rokicki Opn Economy Macroconomics Small opn conomy. Main assumptions

More information

u x v x dx u x v x v x u x dx d u x v x u x v x dx u x v x dx Integration by Parts Formula

u x v x dx u x v x v x u x dx d u x v x u x v x dx u x v x dx Integration by Parts Formula 7. Intgration by Parts Each drivativ formula givs ris to a corrsponding intgral formula, as w v sn many tims. Th drivativ product rul yilds a vry usful intgration tchniqu calld intgration by parts. Starting

More information

Inference Methods for Stochastic Volatility Models

Inference Methods for Stochastic Volatility Models Intrnational Mathmatical Forum, Vol 8, 03, no 8, 369-375 Infrnc Mthods for Stochastic Volatility Modls Maddalna Cavicchioli Cá Foscari Univrsity of Vnic Advancd School of Economics Cannargio 3, Vnic, Italy

More information

Higher order derivatives

Higher order derivatives Robrto s Nots on Diffrntial Calculus Chaptr 4: Basic diffrntiation ruls Sction 7 Highr ordr drivativs What you nd to know alrady: Basic diffrntiation ruls. What you can larn hr: How to rpat th procss of

More information

Unit 6: Solving Exponential Equations and More

Unit 6: Solving Exponential Equations and More Habrman MTH 111 Sction II: Eonntial and Logarithmic Functions Unit 6: Solving Eonntial Equations and Mor EXAMPLE: Solv th quation 10 100 for. Obtain an act solution. This quation is so asy to solv that

More information

That is, we start with a general matrix: And end with a simpler matrix:

That is, we start with a general matrix: And end with a simpler matrix: DIAGON ALIZATION OF THE STR ESS TEN SOR INTRO DUCTIO N By th us of Cauchy s thorm w ar abl to rduc th numbr of strss componnts in th strss tnsor to only nin valus. An additional simplification of th strss

More information

10. Limits involving infinity

10. Limits involving infinity . Limits involving infinity It is known from th it ruls for fundamntal arithmtic oprations (+,-,, ) that if two functions hav finit its at a (finit or infinit) point, that is, thy ar convrgnt, th it of

More information

A. Limits and Horizontal Asymptotes ( ) f x f x. f x. x "±# ( ).

A. Limits and Horizontal Asymptotes ( ) f x f x. f x. x ±# ( ). A. Limits and Horizontal Asymptots What you ar finding: You can b askd to find lim x "a H.A.) problm is asking you find lim x "# and lim x "$#. or lim x "±#. Typically, a horizontal asymptot algbraically,

More information

The Transfer Function. The Transfer Function. The Transfer Function. The Transfer Function. The Transfer Function. The Transfer Function

The Transfer Function. The Transfer Function. The Transfer Function. The Transfer Function. The Transfer Function. The Transfer Function A gnraliation of th frquncy rsons function Th convolution sum scrition of an LTI iscrt-tim systm with an imuls rsons h[n] is givn by h y [ n] [ ] x[ n ] Taing th -transforms of both sis w gt n n h n n

More information

SPH4U Electric Charges and Electric Fields Mr. LoRusso

SPH4U Electric Charges and Electric Fields Mr. LoRusso SPH4U lctric Chargs an lctric Fils Mr. LoRusso lctricity is th flow of lctric charg. Th Grks first obsrv lctrical forcs whn arly scintists rubb ambr with fur. Th notic thy coul attract small bits of straw

More information

General Notes About 2007 AP Physics Scoring Guidelines

General Notes About 2007 AP Physics Scoring Guidelines AP PHYSICS C: ELECTRICITY AND MAGNETISM 2007 SCORING GUIDELINES Gnral Nots About 2007 AP Physics Scoring Guidlins 1. Th solutions contain th most common mthod of solving th fr-rspons qustions and th allocation

More information

are given in the table below. t (hours)

are given in the table below. t (hours) CALCULUS WORKSHEET ON INTEGRATION WITH DATA Work th following on notbook papr. Giv dcimal answrs corrct to thr dcimal placs.. A tank contains gallons of oil at tim t = hours. Oil is bing pumpd into th

More information

Chapter 13 GMM for Linear Factor Models in Discount Factor form. GMM on the pricing errors gives a crosssectional

Chapter 13 GMM for Linear Factor Models in Discount Factor form. GMM on the pricing errors gives a crosssectional Chaptr 13 GMM for Linar Factor Modls in Discount Factor form GMM on th pricing rrors givs a crosssctional rgrssion h cas of xcss rturns Hors rac sting for charactristic sting for pricd factors: lambdas

More information

Unit 7 Charge-to-mass ratio of the electron

Unit 7 Charge-to-mass ratio of the electron Unit 7 Charg-to-ass ratio of th lctron Kywords: J. J. Thoson, Lorntz Forc, Magntic Filds Objctiv: Obsrv th rsults of lctron ba influncd by th agntic fild and calculat th charg-to-ass ratio of th lctron.

More information

22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches.

22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches. Subjct Chmistry Papr No and Titl Modul No and Titl Modul Tag 8/ Physical Spctroscopy / Brakdown of th Born-Oppnhimr approximation. Slction ruls for rotational-vibrational transitions. P, R branchs. CHE_P8_M

More information

2F1120 Spektrala transformer för Media Solutions to Steiglitz, Chapter 1

2F1120 Spektrala transformer för Media Solutions to Steiglitz, Chapter 1 F110 Spktrala transformr för Mdia Solutions to Stiglitz, Chaptr 1 Prfac This documnt contains solutions to slctd problms from Kn Stiglitz s book: A Digital Signal Procssing Primr publishd by Addison-Wsly.

More information

Chemistry 342 Spring, The Hydrogen Atom.

Chemistry 342 Spring, The Hydrogen Atom. Th Hyrogn Ato. Th quation. Th first quation w want to sov is φ This quation is of faiiar for; rca that for th fr partic, w ha ψ x for which th soution is Sinc k ψ ψ(x) a cos kx a / k sin kx ± ix cos x

More information

As the matrix of operator B is Hermitian so its eigenvalues must be real. It only remains to diagonalize the minor M 11 of matrix B.

As the matrix of operator B is Hermitian so its eigenvalues must be real. It only remains to diagonalize the minor M 11 of matrix B. 7636S ADVANCED QUANTUM MECHANICS Solutions Spring. Considr a thr dimnsional kt spac. If a crtain st of orthonormal kts, say, and 3 ar usd as th bas kts, thn th oprators A and B ar rprsntd by a b A a and

More information

Partial Derivatives: Suppose that z = f(x, y) is a function of two variables.

Partial Derivatives: Suppose that z = f(x, y) is a function of two variables. Chaptr Functions o Two Variabls Applid Calculus 61 Sction : Calculus o Functions o Two Variabls Now that ou hav som amiliarit with unctions o two variabls it s tim to start appling calculus to hlp us solv

More information

LR(0) Analysis. LR(0) Analysis

LR(0) Analysis. LR(0) Analysis LR() Analysis LR() Conlicts: Introuction Whn constructing th LR() analysis tal scri in th prvious stps, it has not n possil to gt a trministic analysr, caus thr ar svral possil actions in th sam cll. I

More information

Problem Set 4 Solutions Distributed: February 26, 2016 Due: March 4, 2016

Problem Set 4 Solutions Distributed: February 26, 2016 Due: March 4, 2016 Probl St 4 Solutions Distributd: Fbruary 6, 06 Du: March 4, 06 McQuarri Probls 5-9 Ovrlay th two plots using Excl or Mathatica. S additional conts blow. Th final rsult of Exapl 5-3 dfins th forc constant

More information

3) Use the average steady-state equation to determine the dose. Note that only 100 mg tablets of aminophylline are available here.

3) Use the average steady-state equation to determine the dose. Note that only 100 mg tablets of aminophylline are available here. PHA 5127 Dsigning A Dosing Rgimn Answrs provi by Jry Stark Mr. JM is to b start on aminophyllin or th tratmnt o asthma. H is a non-smokr an wighs 60 kg. Dsign an oral osing rgimn or this patint such that

More information

TEMASEK JUNIOR COLLEGE, SINGAPORE. JC 2 Preliminary Examination 2017

TEMASEK JUNIOR COLLEGE, SINGAPORE. JC 2 Preliminary Examination 2017 TEMASEK JUNIOR COLLEGE, SINGAPORE JC Prliminary Eamination 7 MATHEMATICS 886/ Highr 9 August 7 Additional Matrials: Answr papr hours List of Formula (MF6) READ THESE INSTRUCTIONS FIRST Writ your Civics

More information

Constants and Conversions:

Constants and Conversions: EXAM INFORMATION Radial Distribution Function: P 2 ( r) RDF( r) Br R( r ) 2, B is th normalization constant. Ordr of Orbital Enrgis: Homonuclar Diatomic Molculs * * * * g1s u1s g 2s u 2s u 2 p g 2 p g

More information

MCB137: Physical Biology of the Cell Spring 2017 Homework 6: Ligand binding and the MWC model of allostery (Due 3/23/17)

MCB137: Physical Biology of the Cell Spring 2017 Homework 6: Ligand binding and the MWC model of allostery (Due 3/23/17) MCB37: Physical Biology of th Cll Spring 207 Homwork 6: Ligand binding and th MWC modl of allostry (Du 3/23/7) Hrnan G. Garcia March 2, 207 Simpl rprssion In class, w drivd a mathmatical modl of how simpl

More information

Problem Set #2 Due: Friday April 20, 2018 at 5 PM.

Problem Set #2 Due: Friday April 20, 2018 at 5 PM. 1 EE102B Spring 2018 Signal Procssing and Linar Systms II Goldsmith Problm St #2 Du: Friday April 20, 2018 at 5 PM. 1. Non-idal sampling and rcovry of idal sampls by discrt-tim filtring 30 pts) Considr

More information

Sundials and Linear Algebra

Sundials and Linear Algebra Sundials and Linar Algbra M. Scot Swan July 2, 25 Most txts on crating sundials ar dirctd towards thos who ar solly intrstd in making and using sundials and usually assums minimal mathmatical background.

More information

Where k is either given or determined from the data and c is an arbitrary constant.

Where k is either given or determined from the data and c is an arbitrary constant. Exponntial growth and dcay applications W wish to solv an quation that has a drivativ. dy ky k > dx This quation says that th rat of chang of th function is proportional to th function. Th solution is

More information

AFM. Advanced Financial Management (AFM) Strategic Professional Options. Friday 7 December The Association of Chartered Certified Accountants

AFM. Advanced Financial Management (AFM) Strategic Professional Options. Friday 7 December The Association of Chartered Certified Accountants Stratgic Profssional Options Avanc Financial Managmnt (AFM) Friay 7 Dcmbr 2018 AFM ACCA Tim allow: 3 hours 15 minuts AFM This qustion papr is ivi into two sctions: Sction A This ONE qustion is compulsory

More information

NARAYANA I I T / P M T A C A D E M Y. C o m m o n P r a c t i c e T e s t 1 6 XII STD BATCHES [CF] Date: PHYSIS HEMISTRY MTHEMTIS

NARAYANA I I T / P M T A C A D E M Y. C o m m o n P r a c t i c e T e s t 1 6 XII STD BATCHES [CF] Date: PHYSIS HEMISTRY MTHEMTIS . (D). (A). (D). (D) 5. (B) 6. (A) 7. (A) 8. (A) 9. (B). (A). (D). (B). (B). (C) 5. (D) NARAYANA I I T / P M T A C A D E M Y C o m m o n P r a c t i c T s t 6 XII STD BATCHES [CF] Dat: 8.8.6 ANSWER PHYSIS

More information

PHYS ,Fall 05, Term Exam #1, Oct., 12, 2005

PHYS ,Fall 05, Term Exam #1, Oct., 12, 2005 PHYS1444-,Fall 5, Trm Exam #1, Oct., 1, 5 Nam: Kys 1. circular ring of charg of raius an a total charg Q lis in th x-y plan with its cntr at th origin. small positiv tst charg q is plac at th origin. What

More information

5.80 Small-Molecule Spectroscopy and Dynamics

5.80 Small-Molecule Spectroscopy and Dynamics MIT OpnCoursWar http://ocw.mit.du 5.80 Small-Molcul Spctroscopy and Dynamics Fall 008 For information about citing ths matrials or our Trms of Us, visit: http://ocw.mit.du/trms. Lctur # 3 Supplmnt Contnts

More information

Differential Equations

Differential Equations UNIT I Diffrntial Equations.0 INTRODUCTION W li in a world of intrrlatd changing ntitis. Th locit of a falling bod changs with distanc, th position of th arth changs with tim, th ara of a circl changs

More information

Slide 1. Slide 2. Slide 3 DIGITAL SIGNAL PROCESSING CLASSIFICATION OF SIGNALS

Slide 1. Slide 2. Slide 3 DIGITAL SIGNAL PROCESSING CLASSIFICATION OF SIGNALS Slid DIGITAL SIGAL PROCESSIG UIT I DISCRETE TIME SIGALS AD SYSTEM Slid Rviw of discrt-tim signals & systms Signal:- A signal is dfind as any physical quantity that varis with tim, spac or any othr indpndnt

More information

Recall that by Theorems 10.3 and 10.4 together provide us the estimate o(n2 ), S(q) q 9, q=1

Recall that by Theorems 10.3 and 10.4 together provide us the estimate o(n2 ), S(q) q 9, q=1 Chaptr 11 Th singular sris Rcall that by Thorms 10 and 104 togthr provid us th stimat 9 4 n 2 111 Rn = SnΓ 2 + on2, whr th singular sris Sn was dfind in Chaptr 10 as Sn = q=1 Sq q 9, with Sq = 1 a q gcda,q=1

More information

The van der Waals interaction 1 D. E. Soper 2 University of Oregon 20 April 2012

The van der Waals interaction 1 D. E. Soper 2 University of Oregon 20 April 2012 Th van dr Waals intraction D. E. Sopr 2 Univrsity of Orgon 20 pril 202 Th van dr Waals intraction is discussd in Chaptr 5 of J. J. Sakurai, Modrn Quantum Mchanics. Hr I tak a look at it in a littl mor

More information

2. Finite Impulse Response Filters (FIR)

2. Finite Impulse Response Filters (FIR) .. Mthos for FIR filtrs implmntation. Finit Impuls Rspons Filtrs (FIR. Th winow mtho.. Frquncy charactristic uniform sampling. 3. Maximum rror minimizing. 4. Last-squars rror minimizing.. Mthos for FIR

More information

Bifurcation Theory. , a stationary point, depends on the value of α. At certain values

Bifurcation Theory. , a stationary point, depends on the value of α. At certain values Dnamic Macroconomic Thor Prof. Thomas Lux Bifurcation Thor Bifurcation: qualitativ chang in th natur of th solution occurs if a paramtr passs through a critical point bifurcation or branch valu. Local

More information

11/11/2018. Chapter 14 8 th and 9 th edition. Aggregate Supply and the Short-run Tradeoff Between Inflation and Unemployment.

11/11/2018. Chapter 14 8 th and 9 th edition. Aggregate Supply and the Short-run Tradeoff Between Inflation and Unemployment. Chaptr 14 8 th and 9 th dition Aggrgat Supply and th Short-run Tradoff Btwn Inflation and Unmploymnt W covr modls of aggrgat supply in which output dpnds positivly on th pric lvl in th short run. th short-run

More information

1973 AP Calculus AB: Section I

1973 AP Calculus AB: Section I 97 AP Calculus AB: Sction I 9 Minuts No Calculator Not: In this amination, ln dnots th natural logarithm of (that is, logarithm to th bas ).. ( ) d= + C 6 + C + C + C + C. If f ( ) = + + + and ( ), g=

More information

1 Minimum Cut Problem

1 Minimum Cut Problem CS 6 Lctur 6 Min Cut and argr s Algorithm Scribs: Png Hui How (05), Virginia Dat: May 4, 06 Minimum Cut Problm Today, w introduc th minimum cut problm. This problm has many motivations, on of which coms

More information

When Does The Universe Become Transparent? Last updated 24 June 2007

When Does The Universe Become Transparent? Last updated 24 June 2007 Chaptr 9 Last updatd Jun 007. Introduction In Chaptr 8 w saw that bfor 85,000 yars th majority of th ordinary matrial in th univrs was in th form of fr lctrons and protons. In othr words, th ordinary mattr

More information

Exam 1. It is important that you clearly show your work and mark the final answer clearly, closed book, closed notes, no calculator.

Exam 1. It is important that you clearly show your work and mark the final answer clearly, closed book, closed notes, no calculator. Exam N a m : _ S O L U T I O N P U I D : I n s t r u c t i o n s : It is important that you clarly show your work and mark th final answr clarly, closd book, closd nots, no calculator. T i m : h o u r

More information

The Ramsey Model. Reading: Firms. Households. Behavior of Households and Firms. Romer, Chapter 2-A;

The Ramsey Model. Reading: Firms. Households. Behavior of Households and Firms. Romer, Chapter 2-A; Th Ramsy Modl Rading: Romr, Chaptr 2-A; Dvlopd by Ramsy (1928), latr dvlopd furthr by Cass (1965) and Koopmans (1965). Similar to th Solow modl: labor and knowldg grow at xognous rats. Important diffrnc:

More information

Abstract Interpretation: concrete and abstract semantics

Abstract Interpretation: concrete and abstract semantics Abstract Intrprtation: concrt and abstract smantics Concrt smantics W considr a vry tiny languag that manags arithmtic oprations on intgrs valus. Th (concrt) smantics of th languags cab b dfind by th funzcion

More information

Sec 2.3 Modeling with First Order Equations

Sec 2.3 Modeling with First Order Equations Sc.3 Modling with First Ordr Equations Mathmatical modls charactriz physical systms, oftn using diffrntial quations. Modl Construction: Translating physical situation into mathmatical trms. Clarly stat

More information

Finite element discretization of Laplace and Poisson equations

Finite element discretization of Laplace and Poisson equations Finit lmnt discrtization of Laplac and Poisson quations Yashwanth Tummala Tutor: Prof S.Mittal 1 Outlin Finit Elmnt Mthod for 1D Introduction to Poisson s and Laplac s Equations Finit Elmnt Mthod for 2D-Discrtization

More information

Chemical Physics II. More Stat. Thermo Kinetics Protein Folding...

Chemical Physics II. More Stat. Thermo Kinetics Protein Folding... Chmical Physics II Mor Stat. Thrmo Kintics Protin Folding... http://www.nmc.ctc.com/imags/projct/proj15thumb.jpg http://nuclarwaponarchiv.org/usa/tsts/ukgrabl2.jpg http://www.photolib.noaa.gov/corps/imags/big/corp1417.jpg

More information

Superposition. Thinning

Superposition. Thinning Suprposition STAT253/317 Wintr 213 Lctur 11 Yibi Huang Fbruary 1, 213 5.3 Th Poisson Procsss 5.4 Gnralizations of th Poisson Procsss Th sum of two indpndnt Poisson procsss with rspctiv rats λ 1 and λ 2,

More information

4.2 Design of Sections for Flexure

4.2 Design of Sections for Flexure 4. Dsign of Sctions for Flxur This sction covrs th following topics Prliminary Dsign Final Dsign for Typ 1 Mmbrs Spcial Cas Calculation of Momnt Dmand For simply supportd prstrssd bams, th maximum momnt

More information

EEO 401 Digital Signal Processing Prof. Mark Fowler

EEO 401 Digital Signal Processing Prof. Mark Fowler EEO 401 Digital Signal Procssing Prof. Mark Fowlr Dtails of th ot St #19 Rading Assignmnt: Sct. 7.1.2, 7.1.3, & 7.2 of Proakis & Manolakis Dfinition of th So Givn signal data points x[n] for n = 0,, -1

More information

MEMORIAL UNIVERSITY OF NEWFOUNDLAND

MEMORIAL UNIVERSITY OF NEWFOUNDLAND MEMORIAL UNIVERSITY OF NEWFOUNDLAND DEPARTMENT OF MATHEMATICS AND STATISTICS Midtrm Examination Statistics 2500 001 Wintr 2003 Nam: Studnt No: St by Dr. H. Wang OFFICE USE ONLY Mark: Instructions: 1. Plas

More information

Mathematics 1110H Calculus I: Limits, derivatives, and Integrals Trent University, Summer 2018 Solutions to the Actual Final Examination

Mathematics 1110H Calculus I: Limits, derivatives, and Integrals Trent University, Summer 2018 Solutions to the Actual Final Examination Mathmatics H Calculus I: Limits, rivativs, an Intgrals Trnt Univrsity, Summr 8 Solutions to th Actual Final Eamination Tim-spac: 9:-: in FPHL 7. Brought to you by Stfan B lan k. Instructions: Do parts

More information

Kernels. ffl A kernel K is a function of two objects, for example, two sentence/tree pairs (x1; y1) and (x2; y2)

Kernels. ffl A kernel K is a function of two objects, for example, two sentence/tree pairs (x1; y1) and (x2; y2) Krnls krnl K is a function of two ojcts, for xampl, two sntnc/tr pairs (x1; y1) an (x2; y2) K((x1; y1); (x2; y2)) Intuition: K((x1; y1); (x2; y2)) is a masur of th similarity (x1; y1) twn (x2; y2) an ormally:

More information

INTEGRATION BY PARTS

INTEGRATION BY PARTS Mathmatics Rvision Guids Intgration by Parts Pag of 7 MK HOME TUITION Mathmatics Rvision Guids Lvl: AS / A Lvl AQA : C Edcl: C OCR: C OCR MEI: C INTEGRATION BY PARTS Vrsion : Dat: --5 Eampls - 6 ar copyrightd

More information

MSLC Math 151 WI09 Exam 2 Review Solutions

MSLC Math 151 WI09 Exam 2 Review Solutions Eam Rviw Solutions. Comput th following rivativs using th iffrntiation ruls: a.) cot cot cot csc cot cos 5 cos 5 cos 5 cos 5 sin 5 5 b.) c.) sin( ) sin( ) y sin( ) ln( y) ln( ) ln( y) sin( ) ln( ) y y

More information

[ ] 1+ lim G( s) 1+ s + s G s s G s Kacc SYSTEM PERFORMANCE. Since. Lecture 10: Steady-state Errors. Steady-state Errors. Then

[ ] 1+ lim G( s) 1+ s + s G s s G s Kacc SYSTEM PERFORMANCE. Since. Lecture 10: Steady-state Errors. Steady-state Errors. Then SYSTEM PERFORMANCE Lctur 0: Stady-tat Error Stady-tat Error Lctur 0: Stady-tat Error Dr.alyana Vluvolu Stady-tat rror can b found by applying th final valu thorm and i givn by lim ( t) lim E ( ) t 0 providd

More information

10. The Discrete-Time Fourier Transform (DTFT)

10. The Discrete-Time Fourier Transform (DTFT) Th Discrt-Tim Fourir Transform (DTFT Dfinition of th discrt-tim Fourir transform Th Fourir rprsntation of signals plays an important rol in both continuous and discrt signal procssing In this sction w

More information

4. (5a + b) 7 & x 1 = (3x 1)log 10 4 = log (M1) [4] d = 3 [4] T 2 = 5 + = 16 or or 16.

4. (5a + b) 7 & x 1 = (3x 1)log 10 4 = log (M1) [4] d = 3 [4] T 2 = 5 + = 16 or or 16. . 7 7 7... 7 7 (n )0 7 (M) 0(n ) 00 n (A) S ((7) 0(0)) (M) (7 00) 8897 (A). (5a b) 7 7... (5a)... (M) 7 5 5 (a b ) 5 5 a b (M)(A) So th cofficint is 75 (A) (C) [] S (7 7) (M) () 8897 (A) (C) [] 5. x.55

More information