Chapter 5: The Keynesian System (I): The Role of Aggregate Demand
|
|
- Godwin Morton
- 6 years ago
- Views:
Transcription
1 LECTURE NOTES Chapter 5: The Keynesian System (I): The Rle f Aggregate Demand 1. The Prblem f Unemplyment Keynesian ecnmics develped in the cntext f the Great Depressin Sharp fall in GDP High rate f unemplyment (25%) Keynes bk was written fr the particular case f the U.K. (but the title is General Thery ) The prblem f high unemplyment is a deficiency in Aggregate Demand Investment was t lw Remember: MV = Py = NGDP = C + I + G + NX Keynesian ecnmics argues that Aggregate Demand deficiency can be cmpensated with gvernment spending n public wrks (expansinary fiscal plicy.) In Keynes s wrds: scialize investment. Linel Rbbins n the treatment f classical ecnmists (emphasis added): On this plane, nt nly is any real knwledge f the classical writer nn-existent but further their place has been taken by a set f mythlgical figures, passing by the same names, but nt infrequently invested with attitudes almst exactly the reverse f the thse which the riginals adpted. These dummies are very malignant creatures indeed [ ] They can cnceive f n functin f the state than that f the night watchman [ ] Hence, when a ppular writer f the day wishes t present his wn pint f view in a specially favurable setting, he has nly t pint the cntrast with the attitude f these reprehensible peple and the desired effect is prduced. Rbbins, L. (1952). The Thery f Ecnmic Plicy. Lndn: Macmillan. p. 5. Page 1 f 14
2 2. The Simple Keynesian Mdel: Cnditins fr Equilibrium Output In Keynesian mdels equilibrium requires utput t equal aggregate demand Y C + Ir (realized investment)+ T [utput] Y = E = C + I (desired investment) + G [AD] Y C + S + T [Incme] Equilibrium cnditins Y = E = C + I (desired investment) + G S + T = I + G Ir = I These tw can differ if inventries changed unexpectedly (I r I) There are n retained earnings, therefre All business prfits g t the husehlds as dividends, wage, etc., incme Husehld s incme is distributed thrugh three channels T business by (1) cnsumptin and (2) t investment thrugh savings T (3) gvernment spending thrugh taxes Cnsumptin is a direct link between husehld s incme and the prductive sectr But there are tw likeages (incme nt ging frm the husehld t the business sectr): Savings in the financial markets (what if investment is in financial assets?) (Net) taxes paid t the gvernment (what if sme tax revenue is nt spent?) Als injectins Business demand fr utput (rather than the husehld) Gvernment spending (if G > T) Page 2 f 14
3 If utput > aggregate demand Y > E C + I r + G > C + I + G I r > I If utput < aggregate demand Y < E C + I r + G < C + I + G I r < I Page 3 f 14
4 3. The Cmpnents f Aggregate Demand Cnsumptin C = a + b Y D, a > 0, 0 < b < 1 a: effect n cnsumptin ther than dispsable incme b = ΔC ΔY D is the marginal prpensity t cnsume (MPC) Als: Y D Y T C + S. Then: S Y D C D sme math S = a + (1 b) Y D 1 b = ΔS ΔY D is the marginal prpensity t save (MPS) MPC + MPS = 1 Thugh ther variables (i.e. wealth) als affect cnsumptin, in this mdel dispsable incme is the main driver f cnsumptin which is the mayr cmpnent f GDP Page 4 f 14
5 Page 5 f 14
6 Investment Cnsumptin is a stable functin f dispsable incme Investment is nt Autnmus cmpnents f AD: determined independently f the level f incme Investment (mre vlatile) Gvernment spending (less vlatile and manageable by plicy makers) AD = cnsumptin + autnmus cnsumptin Investment decisins Similar thery abut interest rates Entrepreneurs linearly extraplate the past int the future Entrepreneurs rely n the beliefs f ther entrepreneurs Then: Investment is subject t big changes due t animal spirits (fears, hpes, etc.) Gvernment spending and taxes Defined by the plicy makers -> unrelated t the level f incme Taxes are als defined by the plicy makers, nt by incme Page 6 f 14
7 4. Determining Equilibrium Incme Equilibrium cnditin Y = Y = E = C + I + G Y = E = a + by D + I + G Y = E = a + by bt + I + G 1 (a bt + I + G) 1 b autnmus autnmus expenditures expenditure multiplier Page 7 f 14
8 Assume Y < AD Inventries fall Then business increase investment Therefre Y increases until equilibrium is reached Assume Y > AD Inventries rise Then business decrease investment Therefre Y decreases until equilibrium is reached Page 8 f 14
9 5. Changes in Equilibrium Incme ΔY = ΔY = 1 = 1 = 1 ΔI ΔG (1 b) 1 MPC MPS Keynesian multiplier: 1 MPS Because 0 < b < 1, Keynesian multiplier > 1 Then: ΔY > ΔI and ΔY > ΔG Equilibrium cnditin after a shck ΔY = ΔC + ΔI ΔY ΔC = ΔI ΔS = ΔI And because S + T = I + G ΔS ΔI = ΔG ΔT ΔS ΔI = ΔG (if net Taxes are cnstant) G needs t cmpensate fr net savings nt invested Change in taxes ΔY = b ΔT 1 b Incme is shifted by b dllars because dispsable incme decreases by ΔT but dispsable incme that ges t cnsumptin is b per dllar Implicatin: If yu have/want t increase incme, better t increase G than reduce T. An increase in gvernment spending financed with taxes ΔY + ΔY = 1 + b = 1 ΔG ΔT 1 b 1 b Fr gvernment spending t have an effect n incme it shuld nt be financed by taxes Page 9 f 14
10 Page 10 f 14
11 Page 11 f 14
12 6. Fiscal Stabilizatin Plicy Use G s stabilize ther vlatile and irratinal (animal spirits) autnmus cnsumptin cmpnents (investment) Ideally: ΔG = ΔI Be careful: The simple Keynesian mdel is designed t restre equilibrium, NOT t increase ptential utput Side nte (be careful hw yu read equatins): Des ΔG ΔY r des ΔY ΔG What is the causal relatin? A mathematical frmulatin takes the causal relatin as given. If yur thery has the wrng causal relatinship yu can have a cnsistent mathematical mdel with the wrng causal relatinship and n sign f the theretical mistake Page 12 f 14
13 Page 13 f 14
14 7. Exprts and Imprts in the Simple Keynesian Mdel Assume nw an pen ecnmy with exprts (X) and imprts (Z) Then: Y = E = C + I + G + X Z Assume nt taxes (fr simplicity) and that: C = a + b Y, a > 0, 0 < b < 1 Z = u + v Y, u > 0, 0 < v < 1 Y = a + b Y + I + G + X u v Y Y = 1 (a + I + G + X u) 1 b+v Keynesian multiplier in pen ecnmies is smaller than Keynesian multiplier in clse ecnmies 1 < 1 1 b+v 1 b Fiscal plicy is less effective in ecnmies with large marginal prpensity t imprt Page 14 f 14
LECTURE NOTES. Chapter 3: Classical Macroeconomics: Output and Employment. 1. The starting point
LECTURE NOTES Chapter 3: Classical Macrecnmics: Output and Emplyment 1. The starting pint The Keynesian revlutin was against classical ecnmics (rthdx ecnmics) Keynes refer t all ecnmists befre 1936 as
More informationMankiw Chapter 11. Aggregate Demand I. Building the IS-LM Model
Mankiw Chapter 11 Building the IS-LM Model 0 IN THIS CHAPTER, WE WILL COVER: the IS curve and its relation to: the Keynesian cross the LM curve and its relation to: the theory of liquidity preference how
More informationInternal vs. external validity. External validity. This section is based on Stock and Watson s Chapter 9.
Sectin 7 Mdel Assessment This sectin is based n Stck and Watsn s Chapter 9. Internal vs. external validity Internal validity refers t whether the analysis is valid fr the ppulatin and sample being studied.
More informationGreen economic transformation in Europe: territorial performance, potentials and implications
ESPON Wrkshp: Green Ecnmy in Eurpean Regins? Green ecnmic transfrmatin in Eurpe: territrial perfrmance, ptentials and implicatins Rasmus Ole Rasmussen, NORDREGIO 29 September 2014, Brussels Green Grwth:
More informationCHAPTER 3 INEQUALITIES. Copyright -The Institute of Chartered Accountants of India
CHAPTER 3 INEQUALITIES Cpyright -The Institute f Chartered Accuntants f India INEQUALITIES LEARNING OBJECTIVES One f the widely used decisin making prblems, nwadays, is t decide n the ptimal mix f scarce
More informationIn the OLG model, agents live for two periods. they work and divide their labour income between consumption and
1 The Overlapping Generatins Mdel (OLG) In the OLG mdel, agents live fr tw perids. When ung the wrk and divide their labur incme between cnsumptin and savings. When ld the cnsume their savings. As the
More informationCAUSAL INFERENCE. Technical Track Session I. Phillippe Leite. The World Bank
CAUSAL INFERENCE Technical Track Sessin I Phillippe Leite The Wrld Bank These slides were develped by Christel Vermeersch and mdified by Phillippe Leite fr the purpse f this wrkshp Plicy questins are causal
More informationUN Committee of Experts on Environmental Accounting New York, June Peter Cosier Wentworth Group of Concerned Scientists.
UN Cmmittee f Experts n Envirnmental Accunting New Yrk, June 2011 Peter Csier Wentwrth Grup f Cncerned Scientists Speaking Ntes Peter Csier: Directr f the Wentwrth Grup Cncerned Scientists based in Sydney,
More informationCHAPTER 24: INFERENCE IN REGRESSION. Chapter 24: Make inferences about the population from which the sample data came.
MATH 1342 Ch. 24 April 25 and 27, 2013 Page 1 f 5 CHAPTER 24: INFERENCE IN REGRESSION Chapters 4 and 5: Relatinships between tw quantitative variables. Be able t Make a graph (scatterplt) Summarize the
More informationFlipping Physics Lecture Notes: Simple Harmonic Motion Introduction via a Horizontal Mass-Spring System
Flipping Physics Lecture Ntes: Simple Harmnic Mtin Intrductin via a Hrizntal Mass-Spring System A Hrizntal Mass-Spring System is where a mass is attached t a spring, riented hrizntally, and then placed
More informationLecture 5: Equilibrium and Oscillations
Lecture 5: Equilibrium and Oscillatins Energy and Mtin Last time, we fund that fr a system with energy cnserved, v = ± E U m ( ) ( ) One result we see immediately is that there is n slutin fr velcity if
More informationINSTRUMENTAL VARIABLES
INSTRUMENTAL VARIABLES Technical Track Sessin IV Sergi Urzua University f Maryland Instrumental Variables and IE Tw main uses f IV in impact evaluatin: 1. Crrect fr difference between assignment f treatment
More informationWe can see from the graph above that the intersection is, i.e., [ ).
MTH 111 Cllege Algebra Lecture Ntes July 2, 2014 Functin Arithmetic: With nt t much difficulty, we ntice that inputs f functins are numbers, and utputs f functins are numbers. S whatever we can d with
More informationComputational modeling techniques
Cmputatinal mdeling techniques Lecture 4: Mdel checing fr ODE mdels In Petre Department f IT, Åb Aademi http://www.users.ab.fi/ipetre/cmpmd/ Cntent Stichimetric matrix Calculating the mass cnservatin relatins
More informationComment on John Taylor: Rules Versus Discretion: Assessing the Debate over the Conduct of Monetary Policy
Cmment n Jhn Taylr: Rules Versus Discretin: Assessing the Debate ver the Cnduct f Mnetary Plicy Octber 13th, 2017 Dnald Khn Rbert V. Rsa Chair in Internatinal Ecnmics Senir Fellw, Ecnmic Studies The Brkings
More informationComputational modeling techniques
Cmputatinal mdeling techniques Lecture 2: Mdeling change. In Petre Department f IT, Åb Akademi http://users.ab.fi/ipetre/cmpmd/ Cntent f the lecture Basic paradigm f mdeling change Examples Linear dynamical
More informationThermodynamics Partial Outline of Topics
Thermdynamics Partial Outline f Tpics I. The secnd law f thermdynamics addresses the issue f spntaneity and invlves a functin called entrpy (S): If a prcess is spntaneus, then Suniverse > 0 (2 nd Law!)
More informationLow growth, institutions and the need for a new regional policy
12.març.2018 Reitria da Universidade Nva de Lisba Cfinanciad pr Cfinanced by Lw grwth, institutins and the need fr a new reginal plicy Andrés Rdríguez-Pse Lndn Schl f Ecnmics 1 Sustained lw grwth 3 12
More informationWRITING THE REPORT. Organizing the report. Title Page. Table of Contents
WRITING THE REPORT Organizing the reprt Mst reprts shuld be rganized in the fllwing manner. Smetime there is a valid reasn t include extra chapters in within the bdy f the reprt. 1. Title page 2. Executive
More informationFive Whys How To Do It Better
Five Whys Definitin. As explained in the previus article, we define rt cause as simply the uncvering f hw the current prblem came int being. Fr a simple causal chain, it is the entire chain. Fr a cmplex
More informationFlipping Physics Lecture Notes: Simple Harmonic Motion Introduction via a Horizontal Mass-Spring System
Flipping Physics Lecture Ntes: Simple Harmnic Mtin Intrductin via a Hrizntal Mass-Spring System A Hrizntal Mass-Spring System is where a mass is attached t a spring, riented hrizntally, and then placed
More informationThis section is primarily focused on tools to aid us in finding roots/zeros/ -intercepts of polynomials. Essentially, our focus turns to solving.
Sectin 3.2: Many f yu WILL need t watch the crrespnding vides fr this sectin n MyOpenMath! This sectin is primarily fcused n tls t aid us in finding rts/zers/ -intercepts f plynmials. Essentially, ur fcus
More informationMODULE 1. e x + c. [You can t separate a demominator, but you can divide a single denominator into each numerator term] a + b a(a + b)+1 = a + b
. REVIEW OF SOME BASIC ALGEBRA MODULE () Slving Equatins Yu shuld be able t slve fr x: a + b = c a d + e x + c and get x = e(ba +) b(c a) d(ba +) c Cmmn mistakes and strategies:. a b + c a b + a c, but
More informationThermodynamics and Equilibrium
Thermdynamics and Equilibrium Thermdynamics Thermdynamics is the study f the relatinship between heat and ther frms f energy in a chemical r physical prcess. We intrduced the thermdynamic prperty f enthalpy,
More informationPattern Recognition 2014 Support Vector Machines
Pattern Recgnitin 2014 Supprt Vectr Machines Ad Feelders Universiteit Utrecht Ad Feelders ( Universiteit Utrecht ) Pattern Recgnitin 1 / 55 Overview 1 Separable Case 2 Kernel Functins 3 Allwing Errrs (Sft
More informationMODULE FOUR. This module addresses functions. SC Academic Elementary Algebra Standards:
MODULE FOUR This mdule addresses functins SC Academic Standards: EA-3.1 Classify a relatinship as being either a functin r nt a functin when given data as a table, set f rdered pairs, r graph. EA-3.2 Use
More informationx 1 Outline IAML: Logistic Regression Decision Boundaries Example Data
Outline IAML: Lgistic Regressin Charles Suttn and Victr Lavrenk Schl f Infrmatics Semester Lgistic functin Lgistic regressin Learning lgistic regressin Optimizatin The pwer f nn-linear basis functins Least-squares
More informationDepartment of Economics, University of California, Davis Ecn 200C Micro Theory Professor Giacomo Bonanno. Insurance Markets
Department f Ecnmics, University f alifrnia, Davis Ecn 200 Micr Thery Prfessr Giacm Bnann Insurance Markets nsider an individual wh has an initial wealth f. ith sme prbability p he faces a lss f x (0
More informationGlobal Sourcing and Relative Wages with A Nontradable Good
Jurnal f Ecnmic Integratin 7(4), December 2002; 70-723 Glbal Surcing and Relative Wages with A Nntradable Gd Yng-Yil Chi Hansung University Abstract In this paper I intrduce a new cncept f a glbal surcing
More informationEdexcel GCSE Physics
Edexcel GCSE Physics Tpic 10: Electricity and circuits Ntes (Cntent in bld is fr Higher Tier nly) www.pmt.educatin The Structure f the Atm Psitively charged nucleus surrunded by negatively charged electrns
More information[COLLEGE ALGEBRA EXAM I REVIEW TOPICS] ( u s e t h i s t o m a k e s u r e y o u a r e r e a d y )
(Abut the final) [COLLEGE ALGEBRA EXAM I REVIEW TOPICS] ( u s e t h i s t m a k e s u r e y u a r e r e a d y ) The department writes the final exam s I dn't really knw what's n it and I can't very well
More informationPart a: Writing the nodal equations and solving for v o gives the magnitude and phase response: tan ( 0.25 )
+ - Hmewrk 0 Slutin ) In the circuit belw: a. Find the magnitude and phase respnse. b. What kind f filter is it? c. At what frequency is the respnse 0.707 if the generatr has a ltage f? d. What is the
More informationIntroduction to Spacetime Geometry
Intrductin t Spacetime Gemetry Let s start with a review f a basic feature f Euclidean gemetry, the Pythagrean therem. In a twdimensinal crdinate system we can relate the length f a line segment t the
More informationThe Keynesian Model of Income Determination (revised)
Economics 32 Menzie D. Chinn Spring 2 Social Sciences 748 University of Wisconsin-Madison The Keynesian Model of Income Determination revised This set of notes outlines the Keynesian model of national
More informationBOUNDED UNCERTAINTY AND CLIMATE CHANGE ECONOMICS. Christopher Costello, Andrew Solow, Michael Neubert, and Stephen Polasky
BOUNDED UNCERTAINTY AND CLIMATE CHANGE ECONOMICS Christpher Cstell, Andrew Slw, Michael Neubert, and Stephen Plasky Intrductin The central questin in the ecnmic analysis f climate change plicy cncerns
More informationSection 5.8 Notes Page Exponential Growth and Decay Models; Newton s Law
Sectin 5.8 Ntes Page 1 5.8 Expnential Grwth and Decay Mdels; Newtn s Law There are many applicatins t expnential functins that we will fcus n in this sectin. First let s lk at the expnential mdel. Expnential
More informationCHEM-443, Fall 2013, Section 010 Midterm 2 November 4, 2013
CHEM-443, Fall 2013, Sectin 010 Student Name Midterm 2 Nvember 4, 2013 Directins: Please answer each questin t the best f yur ability. Make sure yur respnse is legible, precise, includes relevant dimensinal
More information3. Classify the following Numbers (Counting (natural), Whole, Integers, Rational, Irrational)
After yu cmplete each cncept give yurself a rating 1. 15 5 2 (5 3) 2. 2 4-8 (2 5) 3. Classify the fllwing Numbers (Cunting (natural), Whle, Integers, Ratinal, Irratinal) a. 7 b. 2 3 c. 2 4. Are negative
More informationEquilibrium of Stress
Equilibrium f Stress Cnsider tw perpendicular planes passing thrugh a pint p. The stress cmpnents acting n these planes are as shwn in ig. 3.4.1a. These stresses are usuall shwn tgether acting n a small
More informationEngineering Decision Methods
GSOE9210 vicj@cse.unsw.edu.au www.cse.unsw.edu.au/~gs9210 Maximin and minimax regret 1 2 Indifference; equal preference 3 Graphing decisin prblems 4 Dminance The Maximin principle Maximin and minimax Regret
More informationPart One: Heat Changes and Thermochemistry. This aspect of Thermodynamics was dealt with in Chapter 6. (Review)
CHAPTER 18: THERMODYNAMICS AND EQUILIBRIUM Part One: Heat Changes and Thermchemistry This aspect f Thermdynamics was dealt with in Chapter 6. (Review) A. Statement f First Law. (Sectin 18.1) 1. U ttal
More informationKeysight Technologies Understanding the Kramers-Kronig Relation Using A Pictorial Proof
Keysight Technlgies Understanding the Kramers-Krnig Relatin Using A Pictrial Prf By Clin Warwick, Signal Integrity Prduct Manager, Keysight EEsf EDA White Paper Intrductin In principle, applicatin f the
More information, which yields. where z1. and z2
The Gaussian r Nrmal PDF, Page 1 The Gaussian r Nrmal Prbability Density Functin Authr: Jhn M Cimbala, Penn State University Latest revisin: 11 September 13 The Gaussian r Nrmal Prbability Density Functin
More informationFall 2013 Physics 172 Recitation 3 Momentum and Springs
Fall 03 Physics 7 Recitatin 3 Mmentum and Springs Purpse: The purpse f this recitatin is t give yu experience wrking with mmentum and the mmentum update frmula. Readings: Chapter.3-.5 Learning Objectives:.3.
More informationLecture 2: Supervised vs. unsupervised learning, bias-variance tradeoff
Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeff Reading: Chapter 2 STATS 202: Data mining and analysis September 27, 2017 1 / 20 Supervised vs. unsupervised learning In unsupervised
More informationComputational modeling techniques
Cmputatinal mdeling techniques Lecture 11: Mdeling with systems f ODEs In Petre Department f IT, Ab Akademi http://www.users.ab.fi/ipetre/cmpmd/ Mdeling with differential equatins Mdeling strategy Fcus
More informationLecture 7: Damped and Driven Oscillations
Lecture 7: Damped and Driven Oscillatins Last time, we fund fr underdamped scillatrs: βt x t = e A1 + A csω1t + i A1 A sinω1t A 1 and A are cmplex numbers, but ur answer must be real Implies that A 1 and
More informationKinetic Model Completeness
5.68J/10.652J Spring 2003 Lecture Ntes Tuesday April 15, 2003 Kinetic Mdel Cmpleteness We say a chemical kinetic mdel is cmplete fr a particular reactin cnditin when it cntains all the species and reactins
More information22.54 Neutron Interactions and Applications (Spring 2004) Chapter 11 (3/11/04) Neutron Diffusion
.54 Neutrn Interactins and Applicatins (Spring 004) Chapter (3//04) Neutrn Diffusin References -- J. R. Lamarsh, Intrductin t Nuclear Reactr Thery (Addisn-Wesley, Reading, 966) T study neutrn diffusin
More informationChapter 5: Diffusion (2)
Chapter 5: Diffusin () ISSUES TO ADDRESS... Nn-steady state diffusin and Fick s nd Law Hw des diffusin depend n structure? Chapter 5-1 Class Eercise (1) Put a sugar cube inside a cup f pure water, rughly
More informationThe Law of Total Probability, Bayes Rule, and Random Variables (Oh My!)
The Law f Ttal Prbability, Bayes Rule, and Randm Variables (Oh My!) Administrivia Hmewrk 2 is psted and is due tw Friday s frm nw If yu didn t start early last time, please d s this time. Gd Milestnes:
More informationLecture 2: Supervised vs. unsupervised learning, bias-variance tradeoff
Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeff Reading: Chapter 2 STATS 202: Data mining and analysis September 27, 2017 1 / 20 Supervised vs. unsupervised learning In unsupervised
More informationCHEM Thermodynamics. Change in Gibbs Free Energy, G. Review. Gibbs Free Energy, G. Review
Review Accrding t the nd law f Thermdynamics, a prcess is spntaneus if S universe = S system + S surrundings > 0 Even thugh S system
More informationAutumn 2012 CHEM452B Bruce H. Robinson 322 Gould Hall HW 10(A) Homework 10A KEY (there will not be a 10B) 2
Autumn 0 CHEM45B Bruce H. Rbinsn Guld Hall HW 0(A) Hmewrk 0A KEY (there will nt be a 0B) QA) Let c be the speed f sund in air. he square f the speed f sund, () f the gas with respect t the change in the
More informationDifferentiation Applications 1: Related Rates
Differentiatin Applicatins 1: Related Rates 151 Differentiatin Applicatins 1: Related Rates Mdel 1: Sliding Ladder 10 ladder y 10 ladder 10 ladder A 10 ft ladder is leaning against a wall when the bttm
More informationI.S. 239 Mark Twain. Grade 7 Mathematics Spring Performance Task: Proportional Relationships
I.S. 239 Mark Twain 7 ID Name: Date: Grade 7 Mathematics Spring Perfrmance Task: Prprtinal Relatinships Directins: Cmplete all parts f each sheet fr each given task. Be sure t read thrugh the rubrics s
More informationStudy Group Report: Plate-fin Heat Exchangers: AEA Technology
Study Grup Reprt: Plate-fin Heat Exchangers: AEA Technlgy The prblem under study cncerned the apparent discrepancy between a series f experiments using a plate fin heat exchanger and the classical thery
More informationMore Tutorial at
Answer each questin in the space prvided; use back f page if extra space is needed. Answer questins s the grader can READILY understand yur wrk; nly wrk n the exam sheet will be cnsidered. Write answers,
More informationPSU GISPOPSCI June 2011 Ordinary Least Squares & Spatial Linear Regression in GeoDa
There are tw parts t this lab. The first is intended t demnstrate hw t request and interpret the spatial diagnstics f a standard OLS regressin mdel using GeDa. The diagnstics prvide infrmatin abut the
More informationLecture 6: Phase Space and Damped Oscillations
Lecture 6: Phase Space and Damped Oscillatins Oscillatins in Multiple Dimensins The preius discussin was fine fr scillatin in a single dimensin In general, thugh, we want t deal with the situatin where:
More informationThe general linear model and Statistical Parametric Mapping I: Introduction to the GLM
The general linear mdel and Statistical Parametric Mapping I: Intrductin t the GLM Alexa Mrcm and Stefan Kiebel, Rik Hensn, Andrew Hlmes & J-B J Pline Overview Intrductin Essential cncepts Mdelling Design
More informationLecture 02 CSE 40547/60547 Computing at the Nanoscale
PN Junctin Ntes: Lecture 02 CSE 40547/60547 Cmputing at the Nanscale Letʼs start with a (very) shrt review f semi-cnducting materials: - N-type material: Obtained by adding impurity with 5 valence elements
More information20 Faraday s Law and Maxwell s Extension to Ampere s Law
Chapter 20 Faraday s Law and Maxwell s Extensin t Ampere s Law 20 Faraday s Law and Maxwell s Extensin t Ampere s Law Cnsider the case f a charged particle that is ming in the icinity f a ming bar magnet
More informationI. Analytical Potential and Field of a Uniform Rod. V E d. The definition of electric potential difference is
Length L>>a,b,c Phys 232 Lab 4 Ch 17 Electric Ptential Difference Materials: whitebards & pens, cmputers with VPythn, pwer supply & cables, multimeter, crkbard, thumbtacks, individual prbes and jined prbes,
More informationIAML: Support Vector Machines
1 / 22 IAML: Supprt Vectr Machines Charles Suttn and Victr Lavrenk Schl f Infrmatics Semester 1 2 / 22 Outline Separating hyperplane with maimum margin Nn-separable training data Epanding the input int
More informationPhysics 2010 Motion with Constant Acceleration Experiment 1
. Physics 00 Mtin with Cnstant Acceleratin Experiment In this lab, we will study the mtin f a glider as it accelerates dwnhill n a tilted air track. The glider is supprted ver the air track by a cushin
More informationPlan o o. I(t) Divide problem into sub-problems Modify schematic and coordinate system (if needed) Write general equations
STAPLE Physics 201 Name Final Exam May 14, 2013 This is a clsed bk examinatin but during the exam yu may refer t a 5 x7 nte card with wrds f wisdm yu have written n it. There is extra scratch paper available.
More informationBootstrap Method > # Purpose: understand how bootstrap method works > obs=c(11.96, 5.03, 67.40, 16.07, 31.50, 7.73, 11.10, 22.38) > n=length(obs) >
Btstrap Methd > # Purpse: understand hw btstrap methd wrks > bs=c(11.96, 5.03, 67.40, 16.07, 31.50, 7.73, 11.10, 22.38) > n=length(bs) > mean(bs) [1] 21.64625 > # estimate f lambda > lambda = 1/mean(bs);
More informationGetting Involved O. Responsibilities of a Member. People Are Depending On You. Participation Is Important. Think It Through
f Getting Invlved O Literature Circles can be fun. It is exciting t be part f a grup that shares smething. S get invlved, read, think, and talk abut bks! Respnsibilities f a Member Remember a Literature
More informationKinematic transformation of mechanical behavior Neville Hogan
inematic transfrmatin f mechanical behavir Neville Hgan Generalized crdinates are fundamental If we assume that a linkage may accurately be described as a cllectin f linked rigid bdies, their generalized
More information" 1 = # $H vap. Chapter 3 Problems
Chapter 3 rblems rblem At 1 atmsphere pure Ge melts at 1232 K and bils at 298 K. he triple pint ccurs at =8.4x1-8 atm. Estimate the heat f vaprizatin f Ge. he heat f vaprizatin is estimated frm the Clausius
More informationUnit 8 ~ Learning Guide
Unit 8 ~ Learning Guide Name: INSTRUCTIONS Cmplete the fllwing ntes and questins as yu wrk thrugh the related lessns. Yu are required t have this package cmpleted BEFORE yu write yur unit test. D yur best
More informationCoalition Formation and Data Envelopment Analysis
Jurnal f CENTRU Cathedra Vlume 4, Issue 2, 20 26-223 JCC Jurnal f CENTRU Cathedra Calitin Frmatin and Data Envelpment Analysis Rlf Färe Oregn State University, Crvallis, OR, USA Shawna Grsspf Oregn State
More informationALE 21. Gibbs Free Energy. At what temperature does the spontaneity of a reaction change?
Name Chem 163 Sectin: Team Number: ALE 21. Gibbs Free Energy (Reference: 20.3 Silberberg 5 th editin) At what temperature des the spntaneity f a reactin change? The Mdel: The Definitin f Free Energy S
More informationChapter 3 Kinematics in Two Dimensions; Vectors
Chapter 3 Kinematics in Tw Dimensins; Vectrs Vectrs and Scalars Additin f Vectrs Graphical Methds (One and Tw- Dimensin) Multiplicatin f a Vectr b a Scalar Subtractin f Vectrs Graphical Methds Adding Vectrs
More informationFacilitating landlocked and least developed country SMEs participation in trade
Facilitating landlcked and least develped cuntry SMEs participatin in trade by the Hn. Mr. Ousavanh Thiengthepvngsa President f the Yung Entrepreneurs Assciatin f La PDR Email: usavanh@skcrpratin.cm Intrductin
More informationL a) Calculate the maximum allowable midspan deflection (w o ) critical under which the beam will slide off its support.
ecture 6 Mderately arge Deflectin Thery f Beams Prblem 6-1: Part A: The department f Highways and Public Wrks f the state f Califrnia is in the prcess f imprving the design f bridge verpasses t meet earthquake
More informationSupport-Vector Machines
Supprt-Vectr Machines Intrductin Supprt vectr machine is a linear machine with sme very nice prperties. Haykin chapter 6. See Alpaydin chapter 13 fr similar cntent. Nte: Part f this lecture drew material
More informationChemistry 20 Lesson 11 Electronegativity, Polarity and Shapes
Chemistry 20 Lessn 11 Electrnegativity, Plarity and Shapes In ur previus wrk we learned why atms frm cvalent bnds and hw t draw the resulting rganizatin f atms. In this lessn we will learn (a) hw the cmbinatin
More informationCS 477/677 Analysis of Algorithms Fall 2007 Dr. George Bebis Course Project Due Date: 11/29/2007
CS 477/677 Analysis f Algrithms Fall 2007 Dr. Gerge Bebis Curse Prject Due Date: 11/29/2007 Part1: Cmparisn f Srting Algrithms (70% f the prject grade) The bjective f the first part f the assignment is
More informationAP Statistics Practice Test Unit Three Exploring Relationships Between Variables. Name Period Date
AP Statistics Practice Test Unit Three Explring Relatinships Between Variables Name Perid Date True r False: 1. Crrelatin and regressin require explanatry and respnse variables. 1. 2. Every least squares
More informationAdmin. MDP Search Trees. Optimal Quantities. Reinforcement Learning
Admin Reinfrcement Learning Cntent adapted frm Berkeley CS188 MDP Search Trees Each MDP state prjects an expectimax-like search tree Optimal Quantities The value (utility) f a state s: V*(s) = expected
More informationLecture 12: Chemical reaction equilibria
3.012 Fundamentals f Materials Science Fall 2005 Lecture 12: 10.19.05 Chemical reactin equilibria Tday: LAST TIME...2 EQUATING CHEMICAL POTENTIALS DURING REACTIONS...3 The extent f reactin...3 The simplest
More informationA Few Basic Facts About Isothermal Mass Transfer in a Binary Mixture
Few asic Facts but Isthermal Mass Transfer in a inary Miture David Keffer Department f Chemical Engineering University f Tennessee first begun: pril 22, 2004 last updated: January 13, 2006 dkeffer@utk.edu
More informationLand Information New Zealand Topographic Strategy DRAFT (for discussion)
Land Infrmatin New Zealand Tpgraphic Strategy DRAFT (fr discussin) Natinal Tpgraphic Office Intrductin The Land Infrmatin New Zealand Tpgraphic Strategy will prvide directin fr the cllectin and maintenance
More informationInformation for Physics 1201 Midterm I Wednesday, February 20
My lecture slides are psted at http://www.physics.hi-state.edu/~humanic/ Infrmatin fr Physics 1201 Midterm I Wednesday, February 20 1) Frmat: 10 multiple chice questins (each wrth 5 pints) and tw shw-wrk
More informationCOMP 551 Applied Machine Learning Lecture 5: Generative models for linear classification
COMP 551 Applied Machine Learning Lecture 5: Generative mdels fr linear classificatin Instructr: Herke van Hf (herke.vanhf@mail.mcgill.ca) Slides mstly by: Jelle Pineau Class web page: www.cs.mcgill.ca/~hvanh2/cmp551
More informationCOMP 551 Applied Machine Learning Lecture 11: Support Vector Machines
COMP 551 Applied Machine Learning Lecture 11: Supprt Vectr Machines Instructr: (jpineau@cs.mcgill.ca) Class web page: www.cs.mcgill.ca/~jpineau/cmp551 Unless therwise nted, all material psted fr this curse
More informationEcology 302 Lecture III. Exponential Growth (Gotelli, Chapter 1; Ricklefs, Chapter 11, pp )
Eclgy 302 Lecture III. Expnential Grwth (Gtelli, Chapter 1; Ricklefs, Chapter 11, pp. 222-227) Apcalypse nw. The Santa Ana Watershed Prject Authrity pulls n punches in prtraying its missin in apcalyptic
More information**DO NOT ONLY RELY ON THIS STUDY GUIDE!!!**
Tpics lists: UV-Vis Absrbance Spectrscpy Lab & ChemActivity 3-6 (nly thrugh 4) I. UV-Vis Absrbance Spectrscpy Lab Beer s law Relates cncentratin f a chemical species in a slutin and the absrbance f that
More informationWe respond to each of ORR s specific consultation questions in Annex A to this letter.
Je Quill Office f Rail Regulatin One Kemble Street Lndn, WC2B 4AN Hannah Devesn Regulatry Refrm Specialist Netwrk Rail Kings Place, 90 Yrk Way Lndn, N1 9AG Email:hannah.devesn@netwrkrail.c.uk Telephne:
More informationIB Sports, Exercise and Health Science Summer Assignment. Mrs. Christina Doyle Seneca Valley High School
IB Sprts, Exercise and Health Science Summer Assignment Mrs. Christina Dyle Seneca Valley High Schl Welcme t IB Sprts, Exercise and Health Science! This curse incrprates the traditinal disciplines f anatmy
More informationGASES. PV = nrt N 2 CH 4 CO 2 O 2 HCN N 2 O NO 2. Pressure & Boyle s Law Temperature & Charles s Law Avogadro s Law IDEAL GAS LAW
GASES Pressure & Byle s Law Temperature & Charles s Law Avgadr s Law IDEAL GAS LAW PV = nrt N 2 CH 4 CO 2 O 2 HCN N 2 O NO 2 Earth s atmsphere: 78% N 2 21% O 2 sme Ar, CO 2 Sme Cmmn Gasses Frmula Name
More informationChapter 17 Free Energy and Thermodynamics
Chemistry: A Mlecular Apprach, 1 st Ed. Nivald Tr Chapter 17 Free Energy and Thermdynamics Ry Kennedy Massachusetts Bay Cmmunity Cllege Wellesley Hills, MA 2008, Prentice Hall First Law f Thermdynamics
More informationMedium Scale Integrated (MSI) devices [Sections 2.9 and 2.10]
EECS 270, Winter 2017, Lecture 3 Page 1 f 6 Medium Scale Integrated (MSI) devices [Sectins 2.9 and 2.10] As we ve seen, it s smetimes nt reasnable t d all the design wrk at the gate-level smetimes we just
More informationAIP Logic Chapter 4 Notes
AIP Lgic Chapter 4 Ntes Sectin 4.1 Sectin 4.2 Sectin 4.3 Sectin 4.4 Sectin 4.5 Sectin 4.6 Sectin 4.7 4.1 The Cmpnents f Categrical Prpsitins There are fur types f categrical prpsitins. Prpsitin Letter
More informationRelationships Between Frequency, Capacitance, Inductance and Reactance.
P Physics Relatinships between f,, and. Relatinships Between Frequency, apacitance, nductance and Reactance. Purpse: T experimentally verify the relatinships between f, and. The data cllected will lead
More informationGeneral Chemistry II, Unit I: Study Guide (part I)
1 General Chemistry II, Unit I: Study Guide (part I) CDS Chapter 14: Physical Prperties f Gases Observatin 1: Pressure- Vlume Measurements n Gases The spring f air is measured as pressure, defined as the
More information5 th grade Common Core Standards
5 th grade Cmmn Cre Standards In Grade 5, instructinal time shuld fcus n three critical areas: (1) develping fluency with additin and subtractin f fractins, and develping understanding f the multiplicatin
More informationCHEM 116 Electrochemistry at Non-Standard Conditions, and Intro to Thermodynamics
CHEM 116 Electrchemistry at Nn-Standard Cnditins, and Intr t Thermdynamics Imprtant annuncement: If yu brrwed a clicker frm me this semester, return it t me at the end f next lecture r at the final exam
More information