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506, Stat Homework set #3 Due Monday April 21, 2008 From Casella and Berger. 3.3; 3.7; 3.9; 3.20 and 3.23 Solution to Casella and Berger 3.3 Let Xi be the indicator function of the event a car is passing during the i-th second , where we start counting time when the pedestrian arrives. The assumptions given imply that X1 , X2 , . . . is a sequence of Bernoulli trials with parameter p. We are also told that the pedestrian will take 3 seconds to cross. We are asked for the probability that the pedestrian has to wait 4 seconds before starting to cross. There is information missing in the statement of this problem. Do we want the probability that the pedestrian makes a safe crossing? Can the pedestrian see whether a car is coming and so he will know that a car will be crossing a few seconds from now? If the answer to this last question is yes, how far can the pedestrian see? If the pedestrian can t see what is coming or we don t care whether the pedestrian makes a safe crossing then the probability is p4 . On the other hand if the pedestrian can see far enough for incoming cars, or if we care for a safe crossing, then the probability is p4 (1 p)3 . Solution to Casella and Berger 3.7 X, which denotes the number of chips, is a Poisson random variable with parameter . Remember that for a Poisson random variable E(X) = . We want the smallest such that P (X 2) .99, or equivalently the smallest such that P (X < 2) .01. Since P (X < 2) = P (X = 0) + P (X = 1) = (1 + )e , then we want the smallest such that (1 + )e .01. The function (1 + )e is a decreasing function of and so the solution we are looking for is the root of the equation (1+ )e = .01. Using some numerical method, e.g., bisection, secant or Newton-Raphson, we get that 6.64. Solution to Casella and Berger 3.9 (a) In the rst paragraph of this problem it states ... reported that its incoming kindergarten class contained ve sets of twins. The model suggested in part (a) for X, the number of twins in a group of 60 students, is that of a binomial random variable with parameters n = 60 1 and p = 1/90. This model is not consistent with the above quote. The binomial model assumes independence of all 60 students. On the other hand the quote talks about sets of twins, not about individual children; don we t have independence. Nevertheless let us solve this problem accepting the binomial model. Then 60 P (X 5) = i=5 4 60 i 60 i p (1 p)60 i = 1 p (1 p)60 i 0.000556628 i i i=0 One then would say that this is a rare event. (b) Having accepted the above calculation let Y denote the number of schools in New York state that have at least 5 twins. Now we may ask the question, what is the chance that at least one school will have at least 5 twins? The presence of twins may be considered independent from school to school, so it is reasonable to model Y as binomial with parameters n = 310 and p = P (X 5) 0.000556628: P (Y 1) = 1 (1 p)310 0.1585281 i.e., almost a 16% chance. (c) For this last case we increase n to about 15000 and so P (Y 1) 0.999764, i.e., an event almost sure to occur. What is the di erence between the meaning of the probabilities that we obtained in (a), (b) and (c)? The rst one would apply if for a given xed school we asked what is the probability that in THAT school we nd 5 children that are twins? . For the last two cases we are asking what is the probability that in some xed district school, and for some de ned period of time, there is at least one school which at some time had 5 twins? The probability computed in part (c) shows that is very likely to occur; its occurrence should not surprise anybody. Solution to Casella and Berger 3.20 (a) See solution to Problem 2.11 part (b). (b) The gamma density is cx 1 e x where c is a constant. Then we need 2 a transformation that gets rid of the square in the part e x of the density f (x). We can try Y = X 2 /2 (we could use any multiple), which give a Gamma distribution with parameters = 1 and = 1/2. Solution to Casella and Berger 3.23 2 Let f (x) = x ( +1) for x > (where , > 0). (a) First f (x) 0 for all x since an exponential function is always positive. f (x) dx = x ( +1) dx = x =1 (b) and (c) We can consider all moments: E(X n ) = x ( +1 n) dx This integral converges only if n < and for those cases we get E(X n ) = n /( n). In particular, if > 2, then E(X) = /( 1), and E(X 2 ) = 2 /( 2). Then use Var(X) = E(X 2 ) E 2 (X). 3
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hw5.pdf
Path: Washington >> STAT >> 506 Spring, 2008
Description: Computer Environments for Social Scientists CSSS 506 Professor: Mark S. Handcock Solutions to Homework 5 Due Tuesday, March 5, 2002 1) The data frame hills contains the results from the Scottish hill races. The data set is taken from Staudte and Shea...
512 Info.doc
Path: Washington >> STAT >> 512 Fall, 2008
Description: STAT512:STATISTICALINFERENCE AUTUMN2007 Instructor: MichaelPerlman,Dept.ofStatistics,Box354322 Office:B310PadelfordHall(mailboxinB313) Phone:5437735 email:michael@stat.washington.edu Officehours:afterclassorbyappointment MWF10:3011:20,Sieg225. Th10:3...
Rcode.txt
Path: Washington >> STAT >> 516 Fall, 2008
Description: Example R Sessionx <- 1:5xy <- 11:15x+yz <- c(10,6,3,5,1)zhelp(seq) seq(from=3,to=7,by=2)seq(3,17)seq(14,2)seq(1,2,.1)help(rep)rep(1,5)rep(1:5,3)temp <- c(2,1,5,9)temp.3 <- rep(temp,3)temp.3rep(c(seq(2,10,2),seq(1,9,2),2)temp.3[3] temp.3[5:7]temp.3[c...
Spatial4.ppt
Path: Washington >> STAT >> 518 Spring, 2008
Description: SpatialStatisticsIV Stat518Sp08 Estimationof variograms Recall (v) = s (1 r (v) Methodofmoments:squareofallpairwise differences,smoothedoverlagbins 2 (h) = 1 N(h) i,j N(h) (Z(s i ) Z(s j )2 N(h) = (, : ) + 2 2 Problems:Notn...
spatial1.ppt
Path: Washington >> STAT >> 518 Spring, 2008
Description: Spatialstatistics STAT518Sp08 Researchgoalsin airqualityresearch Calculateairpollutionfieldsforhealth effectstudies Assessdeterministicairqualitymodels againstdata Interpretandsetairqualitystandards Improvedunderstandingof complicatedsystems Predict...
Spacetime.ppt
Path: Washington >> STAT >> 518 Spring, 2008
Description: Spacetimestatistics STAT518 Sp08 Spacetimeprocesses Separablecovariancestructure: Cov(Z(x,t),Z(y,s)=C1(x,y)C2(s,t) Nonseparablealternatives Temporallyvaryingspatial covariances Fourierapproach Completelymonotonefunctions SARMAPstudy Spatialcor...
524-07s.pdf
Path: Washington >> STAT >> 524 Spring, 2008
Description: ...
CATHEEK STAT 534 HW #1.txt
Path: Washington >> STAT >> 534 Fall, 2008
Description: #-#-#Cathee Kneeling #STAT 534 #Homework #1, Due 4/11/2006 #--#-# Pull in source files directory <- \"http:/www.stat.washington.edu/catheek/stat534/HW#1\" lib.filename <- \"home1-make-data-lib-3-30-05.txt\" lib.pathname <- paste(directory, lib.filename, ...
CATHEEK STAT 534 HW #5.txt
Path: Washington >> STAT >> 534 Fall, 2008
Description: #-#-#Cathee Kneeling #STAT 534, Werner Stuetzle #Homework #5, Due 5/16/2006 #--#-#-#--# cost function calculates minimum total cost # and completes knowncost matrix # i = index of vector v1; j = index of vector v2 #-#--cost = function(i, j, v1, v2) {...
syllabus.pdf
Path: Washington >> STAT >> 535 Fall, 2008
Description: Course Syllabus: BIOSTAT/STAT 535 Statistical Computing Autumn Quarter 2008 Adrian Dobra, Assistant Professor, Statistics, CS&SS and BNHS adobra@u.washington.edu September 30, 2008 Class room The class meets Tuesday and Thursday between 11:30am and ...
535-07au.pdf
Path: Washington >> STAT >> 535 Fall, 2008
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538-08wi.pdf
Path: Washington >> STAT >> 538 Fall, 2008
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550-07au.pdf
Path: Washington >> STAT >> 550 Fall, 2008
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560syll.pdf
Path: Washington >> STAT >> 560 Fall, 2008
Description: Preliminary Battle Plan CSSS/POLS/STAT 560: Hierarchical Modeling for the Social Sciences Professor: Kevin Quinn, Political Science and CSSS Winter Quarter 2002 Class Room Oce 2:30-4:00 PM Tuesday and Thursday 313 Savery C-14-C Padelford Hall Phone...
lec3bw.pdf
Path: Washington >> STAT >> 560 Fall, 2008
Description: 1 CSSS 560 Lecture 3: Review of the Linear Regression Model (Part II) Kevin Quinn University of Washington 2 Outline Residual Diagnostics Leverage and Inuence Example 3 Residual Diagnostics In deriving the sampling properties of the OLS es...
lec1bw.pdf
Path: Washington >> STAT >> 560 Fall, 2008
Description: 1 CSSS 560 Lecture 1: What is Clustered Data and How Should it be Analyzed? Kevin Quinn University of Washington 2 Examples of Clustered Data Multilevel (contextual) data Longitudinal (panel) data Repeated measures data Data collected via cl...
FigHW3.pdf
Path: Washington >> STAT >> 567 Fall, 2008
Description: Earnings of Black Women 0.06 0.06 Earnings of Black Women 0.05 default=SheatherJones 0.05 h=2 h=10 0.04 Probability Probability 0 20 40 60 80 0.03 0.02 0.01 0.00 0.00 0 0.01 0.02 0.03 0.04 20 40 60 80 Earnings (thousands of doll...
lec2plots.pdf
Path: Washington >> STAT >> 567 Fall, 2008
Description: Betty Dick Sally Harry Tom Sally Tom Harry Dick Betty ...
friends.pdf
Path: Washington >> STAT >> 567 Fall, 2008
Description: Krackhardts Hightech Managers friends network q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Trace of var1 10 Density of var1 Density 0e+00 3e+05 Iterations 6e+05 5 10 15 20 0.00 20 0.02 0.04 0.06 0 0.08 ...
FigHW5.3.pdf
Path: Washington >> STAT >> 567 Fall, 2008
Description: Linear Quantile Regression 11.5 11.5 Spline Quantile Regression 11.0 10.5 Logearnings 10.0 Logearnings observed quantiles quantile regression lines 9.5 9.5 10.0 10.5 11.0 observed quantiles quantile regression splines 9.0 9.0 70 75 80...
570-Day-2.pdf
Path: Washington >> STAT >> 570 Fall, 2008
Description: Stemming Tokenizing suffixes irrelevant Porter Stemmer, simple and efficient Website: http:/www.tartarus.org/...
573.pdf
Path: Washington >> STAT >> 573 Fall, 2008
Description: ...
576-07s.pdf
Path: Washington >> STAT >> 576 Fall, 2008
Description: BIOSTAT/STAT 576 Statistical Methods for Survival Data MW 9:00am - 10:20am, Spring 2007; HST T530 Instructor: Ying Qing Chen Tel: 206.667.7051 Email: yqchen@u.washington.edu Oce hour: Th 2:30pm - 3:30pm; H657 Course Web Site: http:/www.scharp.org/u...
576.pdf
Path: Washington >> STAT >> 576 Fall, 2008
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579-07au.pdf
Path: Washington >> STAT >> 579 Fall, 2008
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579-08su.pdf
Path: Washington >> STAT >> 579 Fall, 2008
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579-08sp.pdf
Path: Washington >> STAT >> 579 Fall, 2008
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581.day1.08.pdf
Path: Washington >> STAT >> 581 Fall, 2008
Description: STATISTICS 581: Advanced Theory of Statistical Inference Fall, 2008 Time: Place: Professor: Oce: Phone: e-mail: Oce Hours: Texts: 10:30 - 11:20 MWF (lecture) MEB 245 Jon A. Wellner B320 Padelford 206-543-6207 jaw@stat.washington.edu 1:30 - 3:30 MWF...
ref.08.pdf
Path: Washington >> STAT >> 581 Fall, 2008
Description: References for Statistics 581, Fall 2008 Analysis: Bartle, R. G., The Elements of Integration. Rudin, W., Principles of Mathematical Analysis. Royden, H. L., Real Analysis. Luenberger, D., Optimization by Vector Space Methods. Probability: Will...
ch2.figs-epsf.pdf
Path: Washington >> STAT >> 581 Fall, 2008
Description: Statistics 581, Chapter 2 Empirical Distribution Function and Empirical Process Figures Wellner; 10/24/2008 1 0.8 0.6 0.4 0.2 0.2 0.4 0.6 0.8 1 Figure 1: Uniform Empirical Distribution Function, n = 50. 1 0.75 0.5 0.25 0.2 -0.25 0.4 0.6...
exam1.06.pdf
Path: Washington >> STAT >> 581 Fall, 2008
Description: Statistics 581, Midterm Exam Wellner; 11/06/2006 This exam is to be taken without any books or notes. 1. (24 points) Dene any three of the following ve terms. (a) A uniformly integrable sequence of random variables. (b) Convergence in rth mean of a...
mt03.pdf
Path: Washington >> STAT >> 582 Fall, 2008
Description: Stat 582 W03 Midterm exam Please give as complete solutions as possible. More paper is available if needed. 1. Let (X,Y) be bivariate normal, mean zero, variance 1, correlation r. Find a minimal sufficient statistic for r. Is it complete? 2. Suppo...
final2.89.pdf
Path: Washington >> STAT >> 582 Fall, 2008
Description: STAT 582 FINAL EXAM 1. Let T (F) = m 2 = ( xdF(x)2 . Find, using the asymptotic theory for statistical functionals, the limiting F distribution of T (F n ) when m F 0. 2. Let (X 1 , Y 1 ), . . . , (X n , Y n ) be independent random variables with ...
gamma.pdf
Path: Washington >> STAT >> 582 Fall, 2008
Description: ...
hw1.sln.doc
Path: Washington >> STAT >> 583 Fall, 2008
Description: STAT583 Sp04 Homework1solution 1. (a)(c) E X i - m = 2 xdF(x) = d 2 2 s (1+ 2e) and (1- e)s + 3es ) = p p 0 E(X - m) 2 = s 2 (1+ 8e) .Standardasymptotictheoryhasthat n ( s2 - s 2 (1+ 8e) N(0,t 2 ) where 2 = E(X - m) 4 - E 2 (X - m) 2 = s 4 (3...
notes1.pdf
Path: Washington >> STAT >> 583 Fall, 2008
Description: STAT 583 SPRING 2008 Lecture Notes 1 Statistical Functionals The Gteux derivative of a statistical functional T(F) is the limit T (F + (G F ) T(F) . d1T(F;G F) = lim If Q( ) = T (F + (G F ) has a McLaurin expansion, we get an expansion (the von...
hw3.sln.pdf
Path: Washington >> STAT >> 583 Fall, 2008
Description: STAT 583 Sp08 Solutions, Homework 3 1. (a) T (F! ) = F + !(G \" F ) so IC(x) = x ! F . (b) By the same calculation as that for the median we get p ! 1(x < F !1 ( p) IC(x) = , x \" F !1 ( p) !1 f (F ( p) d 1 T (F + !(\" x # F) = (c) Using (b) d! 1 ...
HW2.pdf
Path: Washington >> STAT >> 583 Fall, 2008
Description: STAT 583 Sp08 Homework 2 Due April 16. 1. Let F be a cdf on [0,1], and define for ! > 1 T (F) = x\"[0,1] # ( F(x) ! F(x!) $ . (a) Compute the Gateux derivative of T at the uniform distribution U on [0,1]. (b) Show that nR1,n = n(T (Fn ) ! T (U ...
homework.pdf
Path: Washington >> STAT >> 592 Fall, 2008
Description: Homework problems (more problems will be added as we go along) Stat 592, W09 1. For a stationary random field Z(s); s !D \" R 2 , observed at sites s1,.,sn, derive the unbiased linear estimator with the smallest variance. Hint: Use a Lagrange multipli...
Lec1.ppt
Path: Washington >> STAT >> 592 Fall, 2008
Description: NRCSE SpatialStatisticalMethods peter@stat.washington.edu www.stat.washington.edu/peter/592 STAT592A(UW)526(UBCV) 8904(SFU) Coursecontent 1.Kriging 1.Gaussianregression 2.Simplekriging 3.Ordinaryanduniversalkriging 4.Effectofestimatedcovariance 5....
syllabus.pdf
Path: Washington >> STAT >> 593 Fall, 2008
Description: Course Syllabus: STAT 593 Modern Topics in Discrete Multivariate Analysis Spring Quarter 2008 Time and Location When: 10:30am-11:50am Tuesday and Thursday Where: TBA Instructor Adrian Dobra Oce: Padelford Hall B-303 E-mail: adobra@u.washington.edu ...
LECT 14.ppt
Path: Washington >> HUM >> 202 Fall, 2008
Description: Lecture 14: October 27, 2006 Questions Reading Lyrical Ballads (1798), published Anonymously; 3 poems by Coleridge; 20 poems by Wordsworth Preface added in 1800 2nd edition Lyrical Ballads: not just an experiment but a poeti...
2007-2008 HUM 204 Syllabus.pdf
Path: Washington >> HUM >> 204 Fall, 2008
Description: HUM 204 The Role of Perspective in History, Science, and Design Fall 2007 Lecture Meeting Times: Monday and Wednesday 12:30-1:50 Lecture Classroom: Kane 110 Instructors: Axel Roesler, Interaction Design Email: roesler@u.washington.edu Office Hours:...
MolCellEndo_2000_Spermatogonial_Transplantation_Update_Millennium.pdf
Path: Washington >> HUM >> 207 Winter, 2008
Description: Molecular and Cellular Endocrinology 161 (2000) 117 120 www.elsevier.com/locate/mce Spermatogonial transplantation an update for the millennium Lonnie D. Russella *, Michael D. Griswoldb a Department of Physiology, School of Medicine, Southern Il...
2007-2008 HUM 208 syllabus.pdf
Path: Washington >> HUM >> 208 Fall, 2008
Description: HUMANITIES 208: VIOLENCE, MYTH, AND MEMORY (SPRING 2008) Tuesday and Thursday 1:00-2:20, Johnson Hall 102 Francisco Benitez (Comp Lit) and Laurie Sears (History) TAs: Cheryll Alipio, Katrina Hagen, William Mitchell \"Violence, Myth, and Memory\" is bui...
lect36_overheads.pdf
Path: Washington >> SWED >> 101 Fall, 2008
Description: Lecture 36 Anthropogenic Effects on Climate It is well-documented that globally averaged land and sea-surface temperatures have increased 0.5 C in the last century. Is this the beginning of manmade global warming? Two major anthropogenic forcings on ...
JFQA-402-JPPW-Appendices.pdf
Path: Washington >> T C >> 402 Winter, 2008
Description: Appendices to JFQA, Vol. 40, No. 2, June 2005, \"Horses and Rabbits? Trade-Off Theory and Optimal Capital Structure,\" by Nengjiu Ju, Robert Parrino, Allen M. Poteshman, and Michael S. Weisbach Horses and Rabbits? Trade-Off Theory and Optimal Capital ...
TC 403 Final.pdf
Path: Washington >> T C >> 403 Fall, 2008
Description: Kyle Kyros Starr TC 403 May 18, 2008 Do You Know the Way to San Jose? Exploring the Future During the course of this class, I have looked through a lens back at who I was in the past and evaluated that person against the person that I am now. In t...
MarxExcerpts.doc
Path: Washington >> T C >> 493 Fall, 2008
Description: Excerpts from Volume 1 of Capital (text from http:/www.marxists.org/archive/marx/works/1867-c1/index.htm) Introductory Note: Well discuss this in class, but just so you have it in front of you, Marx develops an analysis of capitalist production stres...
wave_properties.pdf
Path: Washington >> T C >> 505 Fall, 2008
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twodimensional.pdf
Path: Washington >> T C >> 505 Fall, 2008
Description: QYEW f U Y Y W Y pp U PTXRXgRY HrHPteTSUb pERF GBQPHyUD`Y \"vE i~ U g Y ewf ERU TS ERGBQPHcsTQBU cFpWwdwfYh evdRY GRvXPP GR`Uvv8rBEsBEbbHP GSlCyEWb gfpBh vGRXPuUIEbERQPB GB ySXHmkgcWuhpBY dYGDcvP keER x GBQPHTcFewf XR(UY e~ q nY Ew e ...
intro.pdf
Path: Washington >> T C >> 509 Fall, 2008
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midterm_soln.pdf
Path: Washington >> T C >> 509 Fall, 2008
Description: GFD I: Winter 2007 Midterm Solutions Professor: Chris Bretherton TA: Tom Connolly \\) As A.\' ( ynOt/e g iawardCg- n\"a ir\",{ ina f r{ r,\\6rI V c\\.,r P *e*^p *ropi cc,t {cto nP- r {n 3ce t,( tt 17o r* lo t% scr rq- Av-ops {r\" ^ l o \\ o ,*b ...
hw7_soln.pdf
Path: Washington >> T C >> 509 Fall, 2008
Description: GFDI f HW io^t Sotto{ P.of, Ch.i e hn the\"r{o^ TA\' J[vu\"\'G^nolt1 f)\". No\',-\\incar SWt PV Conrrvr^4ion %:4) D+v _ .P/ )+ +- H *{-zv (,\"tlr, Ass,.^,- 5/r , , \\/3\'rth, q, 1-tr.) \'(\'i-r.)+H {t1)[r -efo,\"J TtlTm-ll1 x (+iir:Lr H T(Ti l-l 3=bll...
ch4.pdf
Path: Washington >> T C >> 516 Fall, 2008
Description: { S Uv Y v d U Y Up U Y v aS Uv v d S fS U d p g6pXYg6qgXp`g`XW8qSPv`oXpogXd`XdvXdgrqpiYPPr26pXYhU@vX2c`S Uv U Y fv v Y U p dw USvv vS Yv {p n m ki S Uvd d U p S U a w gXWBa6Wxip`gaXWPvrPvPrrPrqpiYqU`rxS BlFrqpiYPxg`iWgxpXY`gpsPbv`qSeYa SpS v...
ps4.pdf
Path: Washington >> T C >> 521 Winter, 2008
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ps7.pdf
Path: Washington >> T C >> 521 Winter, 2008
Description: ...
sol.ps3.pdf
Path: Washington >> T C >> 521 Winter, 2008
Description: P k i n k j e ddFd y ti f 7 x x e h p i 7 ti f yl b x n k E@ X Cdd i f yl x V f DdztUCdS7v ` e x e riwzyCDGUutUsq v Ec g @ X t h @ H a gc w @B v hc H h p a e E h H g E @ e E @ f @ e E c @ q c...
1970_statewide-television-address-on-citizen-unrest.pdf
Path: Washington >> T C >> 598 Fall, 2008
Description: a) \\<\\:J \' .\\t \\ rf -: \'t .t\\, a : A O \\ \\ l R1 \\: q : F dl tr- (t) r-,\\i \'\\\'x A .i I 5 ;: H z f r\'l F (h F \'6; ar Y H ()Fr q aa frl Z F r\') 7 3 iJa -{ \'r1 a EK ,ii +) F v 6)x \\J e Y H ^ .H 14 F] (I a5 A 6 z ^ r- - z...
1972_the-day-state-services-were-suspended.pdf
Path: Washington >> T C >> 598 Fall, 2008
Description: The Dav State Services \\WereSuspende d by Governor Dan Evans What if 24 hours? State services \\tere suspended for The federal government has been called local governments have been called omnipotent, and of American politics Ehe \"dark contine...
Porter1976.pdf
Path: Washington >> THAI >> 302 Fall, 2008
Description: ...
mt2_sol.pdf
Path: Washington >> IND E >> 101 Fall, 2008
Description: Name: _Section/TA Name: _ Atmospheric Science 101, Spring 2003 Midterm 2 Thursday 22 May 2003 Version A Multiple Choice (2 points each) Choose the best answer and mark it on a Scantron sheet. 1. Choose the words which best complete the following sent...
101.doc
Path: Washington >> IND E >> 101 Fall, 2008
Description: ABET Course Description IND E 101 Introduction to Industrial Engineering Elective Course Catalog course description: Examines the basic concepts and methods of industrial engineering through team-based hands-on activities. Explores the profession of ...
250.doc
Path: Washington >> IND E >> 250 Fall, 2008
Description: ABET Course Description IND E 250 Fundamentals of Engineering Economy Required Course Catalog course description: Basics of industrial cost analysis and accounting. Application of engineering economics to decision making. Analysis of engineering alte...
316.doc
Path: Washington >> IND E >> 316 Fall, 2008
Description: ABET Course Description IND E 316 Design of Experiments and Regression Analysis Required Course Catalog course description: Introduction to the analysis of data from planned experiments. Analysis of variance for multiple factors and applications of o...
351.doc
Path: Washington >> IND E >> 351 Spring, 2008
Description: ABET Course Description IND E 351 Human Factors in Design Elective Course Catalog Course Description: Engineering considerations of the abilities and limitations of the human aspect in the design of operational systems and components. Functional, psy...
410_UA_AAUH_syllabus.doc
Path: Washington >> IND E >> 410 Fall, 2008
Description: UNIVERSITY OF OREGON Department of History Spring, 2006 Professor Quintard Taylor Office: 364 McKenzie Hall Email: qtaylor@uoregon.edu Phone: 346-6160 Web: http:/faculty.washington.edu/qtaylor/ Office Hours: 2:00-3:30 Tuesday History 410/510 Urban Ar...
412.doc
Path: Washington >> IND E >> 412 Fall, 2008
Description: ABET Course Description IND E 412 Integer and Dynamic Programming Elective Course Catalog course description: Modeling and optimization of problems and dynamic programming approach to optimization. Topics include: integer programming formulation tech...
424.doc
Path: Washington >> IND E >> 424 Winter, 2008
Description: ABET Course Description IND E 424 Simulation Elective Course Catalog course description: In this course, students will learn the processes, tools, and techniques for performing effective simulation analyses, specifically: i) (the basic underlying pri...
426.doc
Path: Washington >> IND E >> 426 Spring, 2008
Description: ABET Course Description IND E 426 Reliability Engineering and System Safety Elective Course Catalog Course Description: Reliability and system safety measures. Life distributions and their applications in reliability. System reliability models. Desig...
