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UMass (Amherst) - BIEP - 540
BE540Topic 1. Summarizing DataComputer Illustration: Epi InfoBE540 - Introduction to Biostatistics Computer Illustration Topic 1 Summarizing Data Software: Epi Info 2002A Visit to Yellowstone National Park, USASource: Chatterjee, S; Handco
UMass (Amherst) - ENGIN - 112
College of Engineering University of Massachusetts AmherstENGIN 112 Introduction to Electrical and Computer Engineering Fall 2008 Discussion A 8. Comparators, Encoders and Multiplexers1 Weve discussed a number of combinational circuits that ar
Kentucky - AEC - 302
University of KentuckyCollege of Agriculture Department of Agricultural EconomicsAEC 302 FALL 2003 Name Section Number EXAM III General Instructions: 1. 2. 3. 4. 5. 6. 7. Circle the appropriate answer on Section I. A calculator may be used. Notes
UMass (Amherst) - HIST - 180
How geologists thinkJohn McPheeIn this passage, author John McPhee is on a field trip with Kenneth Deffeyes, a professor of geology at Princeton University. While digging for rocks, the two men discuss plate-tectonic theory, the modern theory of "c
Kentucky - CHE - 101
Please write your name _1 Please write your student number _Third Midterm Exam CHE 101Answer each question in the space provided please. Use the backs of exam pages for scratch work only, the backs of exam pages will NOT be graded. Remember, that
Kentucky - CHE - 101
Themes From Oct. 31Energy output points from the citric acid cycleNADH and FADH2:Relay runners, passing high energy Hs to the top of a `water wheel' which they will drive, to produce more ATP.GTP, like ATP:explicit energy.The `average' eukar
UMass (Amherst) - POLSC - 356
University of Massachusetts Amherst Fall 2006 THE PROBLEMSPolitical Science 356 M.J. Peterson15 Sept. Exercise: Riot Control research hint: the full text of the CWC is available via http:/disarmament.un.org/TreatyStatus.nsf On August 24th 2006 Po
Kentucky - FIN - 464
Commercial Mortgage-Backed SecuritiesNational University of Singapore July 27, 2001Notes from lecture given by Brent Ambrose at National University of Singapore July 2001July 2001 Brent W. Ambrose, University of Kentucky 1COMMERICAL MORTGAGEBAC
Kentucky - MA - 551
[Munkres, Problem 6, page 181] Problem. Let (X, d) be a metric space. If f : X - X satisfies the condition d(f (x), f (y) = d(x, y) for all x, y X, then f is called an isometry of X. Show that if f is an isometry and X is compact, then f is bijectiv
Kentucky - MA - 321
MA/CS 321:001 MWF 11:0011:50 FB 213 Fall 2004Instructor: Russell Brown Oce: POT741 Phone: 257-3951 russell.brown@uky.eduAnnouncements. Homework 7 will be due on Monday, 1 November 2004. The exam will be delayed until Friday, 5 November 2004. Plea
UMass (Amherst) - BIEP - 540
PubHlth 540Hypothesis TestingPage 1 of 55Unit 7. Hypothesis TestingTopic1. The Logic of Hypothesis Testing . 2. Beware the Statistical Hypothesis Test . 3. Introduction to Type I, II Error and Statistical Power . 4. Normal: Test for , 2 Kno
Taylor IN - COS - 104
Telecommunications (Chapter 6)Thursday, September 26Agenda TWOtestimonies? Video 7:00 PM Thursday & Friday Questions? LectureAnalog vs. DigitalAnalog: signal of continuously varying strength and/or quality Digital: signal represente
Taylor IN - CSE - 121
While WaitingRichard Pattis quotes Programming languages, like pizzas, come in too sizes; too big and too small. The code for a computer system provides the ecology in which [more] code is born, matures, and dies. A well-designed habitat allows fo
Taylor IN - CSE - 280
Lisp-ish quotes while waiting "Lisp is a programmable programming language." - John Foderaro, CACM, September 1991 "One can even conjecture that Lisp owes its survival specifically to the fact that its programs are lists, which everyone, including
Duke - STA - 216
Bayesian Analysis of Structural Equation Models Sperm Motility Example Summary of sperm motility data Outcome Dose Mean SD Y1 0 88.4 9.21 8 76.1 7.54 24 82.1 15.6 72 77.2 13.3 Y2 0 0.219 0.013 8 0.216 0.013 24 0.207 0.012 72 0.206 0.020 Y3 0 25.5 2.7
Duke - STA - 216
STA 216, Generalized Linear Models, Lecture 8September 19, 2008High-dimensional PredictorsData Augmentation for Binary DataAlternatives to SSVSA variety of fast alternatives to SSVS have been proposed Many approaches rely on sparse maximum
Duke - STA - 216
STA 216 Generalized Linear ModelsMeets: 2:50-4:05 T/TH (Old Chem 025)Instructor: David Dunson 219A Old Chemistry, 684-8025 dunson@stat.duke.edu Teaching Assistant: Jenhwa Chu 114 Old Chemistry jenhwa@stat.duke.eduSTA 216 SyllabusTopics to be c
Duke - STA - 103
STA103 Spring 2001Name Circle section: F 8:00, F 9:10, F 10:30, F 11:50Diagnostic QuizSTA103 is more math-intensive than STA101 or STA102; you need to have completed at least MTH31 or its equivalent to do well in the class. The simple problems t
Duke - STA - 294
Pairwise comparison table Calculate all pairwise alignment scores and arrange them in a table S1 S2 S3 S4 S5 2 0 9 1S1 10 5 4 S2 10 25 8 S3 5 25 11 S4 4 8 11 S5 2 0 9 1Convert all score into distances . 1. FengDoolitle : D=log(SSrand)/(SmaxSrand)
Duke - STA - 216
STA 216 Fall 2000 Assignment 4 Refer to the binary regression O-ring example from class. 1. Write down the expression for the working response Z and the weights W for complementary log-log link. 2. Carry out k steps of the Fisher scoring algorithm us
Duke - STA - 216
alpha0alpha1pi[i]Y[i]for(i IN 1 : 24)alpha1 5.55112E-17 -0.2 -0.4 -0.6 10850 10900 10950 iteration 40.0 30.0 20.0 10.0 0.0alpha1 1.0 0.5 0.0 -0.5 -1.0 0 20 lag 40 1.0 0.5 0.0 -0.5 -1.0 2XWSXIURGHIDXRJPRGHO*UDSK
Duke - STA - 103
Multivariate probability distributions Often we are interested in more than 1 aspect of an experiment/trial Will have more than 1 random variable Interest the probability of a combination of events (results of the different aspects of the experim
Duke - STA - 104
STA 104 MTH 135Name: Probability First Test 2:10-3:30 pm Thursday, 3 October 1996This is a closed-book examination, so please do not refer to your notes, the text, or to any other books. If you dont understand something in one of the questions fe
Duke - STA - 216
STA 216 Fall 2000 Assignment 3 Refer to the O-ring example from class and the last assignment. Assume that you have M possible models (M1 , . . . , MM ) for O-ring failure and that you can calculate the posterior probability of each model (Mj |Y ). F
Taylor IN - COS - 381
1IntroductionFor this lab, you are going to begin the construction of your simulated computer. The resulting component of this assignment is a 32 32 register file, that is a set of 32 registers each of which is 32 bits in size. See Figure 5.7 in
Taylor IN - COS - 382
Lexical AnalysisJonathan GeislerFebrurary 8, 2006Jonathan GeislerLexical AnalysisLanguage RecognitionLets use the same grammar as Monday and validate a sentence for that grammar: 1/2.5=Jonathan GeislerLexical AnalysisParse treesThi
Duke - STA - 242
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Duke - STA - 242
' (' c 9rQI S a C S Y A sA A )`VAR%3yeav)wYBSVC%ifVWeC4d4VIi9zVQw9`T4BI9 VI9x3eC%BIH4ea4)iYwss vqUWVSBc4eaBSDBAb`4VIxEABYXDVADeWeS4`BYHVAR3R%BIp%eYBSR%Bfw9ESheapvCb`eAiShi}iYeRY S o S 9 A Sd W A G ( 9 c 9aAW A 9 Cf 9 9Q sr YW Y 9 cQ 9Q 9 Ca 9Q
Duke - STA - 244
STA2444/23/2003Take Home Final ExamDue 5/1/2003 by 5pm This is an open note/open book test. All work must be your own.Study of the growth of plants can be a crucial element in understanding how they compete for resources. For example, soybean
Duke - STA - 244
STA2444/7/2003Homework 7Due 4/14/2001 1. The matrix X(i) X(i) can be written as X(i) X(i) = X X - xi xi where xi is the ith row of X and X(i) is the matrix X with the ith row removed. Use this to show (X(i) X(i) )-1 = (X X)-1 + (X X)-1 xi xi (X
Duke - STA - 244
STA2441/15/2003Homework 2Due 1/22/2003 1. Write the following two way analysis of variance (AOV) model with interactions Yijk = + i + j + ij + with i = 1, 2, 3, j = 1, 2, k = 1, 2 in matrix notation. 2. Suppose we have a k k matrix S partition
Duke - STA - 244
STA2442/28/2005Homework 5Due 3/7/2001 1. For a random vector n , is called exchangeable if has the same distribution as any permutation of the vector . If is exchangeable, prove that E( ) = 1 ( ), and that the Cov( ) = has the forma a b .
Duke - STA - 244
STA2441/15/2001Homework 1Due 1/22/20011. Assume that we have a sample of size n where Y i = 0 + 1 Xi + e i and the errors ei are iid N (0, 2 ). (a) Find the maximum likelihood estimator of 2 , 2 . Hint: let = 2 and maximize. ^ (b) Under
Duke - STA - 103
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Duke - STA - 244
STA2442/5/2005Homework 3Due 2/12/2001 1. Recall from class that a non-central 2 (m, ) can be represented as a Poisson mixture of central 2 random variables, where Y P (/2) and X|Y 2 (m + 2y, 0). Find the mean and variance of a non-central Chi-
Duke - STA - 103
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Duke - STA - 103
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Duke - STA - 103
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Duke - STA - 244
STA2444/9/2002Homework 7Due 4/16/2001 1. Problem 15.7 in CW. To obtain case diagnostics in S-Plus, fit a model using the QR option, i.e. mylm.obj <- lm(Y X1 + X2, data=mydataframe, qr=T) To obtain the case diagnostics, use the function ls.diag(
Duke - STA - 103
g c gycp x c j e d p p x uy e d t r ey c d pc p e ~isq~n"cfreqqg "q%tsmis|q~g c d uc u xe x c d c p | pe re c jl s'}ers weup1Wy~y sfesqk qknk yc d ey c d pc p e g p upp rcyc u t yc t r sc q%qe srs|q~nuc1ux%q Tssxv3s"9 a p p x uy e d
Duke - STA - 103
h7W9AAP Br2rW 014sGC3 d bc2G90'DbcGAVb{b652IIr2B@I65{Dbfb8fD28fS65rS6db`U01`@DrA 3 q l P 37A) w v 3 H7 9 H7 1 3)7 5 PW ')CA97 3 7) H7 v 5 ) iuxhfi xh F 3A w v d w 3 ) d 3 97) H42FtSt2PzBBbH G9BSG942@Dp2tbI@b822@01b01b6501SxSH s 7 PA 9 H7 9A Q9A
Duke - STA - 103
()c 5 q1 )h oefen'a5sf q1 r AeqAdGeC(oC9edPAq0adPA0P#( y f HR cp HR H FE f ( f @ V c H HRp p c 9 H HR D H h ( d FACE@ y CS@0G%dHeQnmeqiC0G0CH0giACBse6T 3E R h f 3 R cp H S 3 ` H FE f S 3E E H FE E 3 D @ c c x } ( Q 5 sf q1 r a5 h
Duke - STA - 244
STA2443/19/2005Homework 6Due 3/28/2002 1. For the usual linear model Y N (X, -1 In ) with prior distributions N (bo , Vo ) independent of and p() 1/: (a) Find the posterior distribution of |. (b) Can you find a closed form expression for th
Duke - STA - 102
ill sandwich "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes" "Yes"
Duke - STA - 102
exposure nma "high" 28 "high" 35 "high" 37 "high" 37 "high" 43.5 "high" 44 "high" 45.5 "high" 46 "high" 48 "high" 48.
Duke - STA - 102
subject fev1 gender 1 2.30 0 2 2.15 1 3 3.50 1 4 2.60 0 5 2.75 0 6 2.82 1 7 4.05 1 8 2.25 1 9 2.68 0
Duke - STA - 244
x1 x2 y1 y2 y3 y4 10 8 8.04 9.14 7.46 6.58 8 8 6.95 8.14 6.77 5.76 13 8 7.58 8.74 12.74 7.71 9 8 8.81 8.77 7.11 8.84 11 8 8.33 9.26 7.81 8.47 14 8 9.96 8.1 8.84 7.04 6 8 7.24 6.13 6.08 5.25 4 19 4.26 3.1 5.39 12.5 12 8 10.84 9.13 8.15 5.56
Duke - STA - 244
D m S WS y 0 10 1 3408 623 0.04 5 1 206.8 680.2 0.1 5 1 1841.2 721.4 0.16 5 1 1223.2 750.4 0.28 5 1 861.2 789.4 0.04 5 2 2810.8 672.2 0.1 5 2 860.8 709.2 0.16 5 2 592.8 731.2 0.28 5 2 2642.8 778.2 0.04 5 3 2399.2 668.4 0.1 5 3 327.2 715.6
Duke - STA - 244
FUEL/POP INC LIC/POP POP TAX VEH/POP VM/VEH 644.147 14.826 0.70923 4041 13 0.911408 11.0684 474.545 21.761 0.549091 550 8 0.669091 10.5625 552.524 16.297 0.660573 3665 18 0.777899 12.2119 683.539 14.218 0.735857 2351 18.7 0.615908 14.0981 501.34
Duke - STA - 244
Pressure Temp 20.79 194.5 20.79 194.3 22.4 197.9 22.67 198.4 23.15 199.4 23.35 199.9 23.89 200.9 23.99 201.1 24.02 201.4 24.01 201.3 25.14 203.6 26.57 204.6 28.49 209.5 27.76 208.6 29.04 210.7 29.88 211.9 30.06 212.2
Duke - STA - 244
STA2444/18/2002Homework 8Due 4/26/2001 Refer to Exercise 11.5 in CW (page 285). Use any appropriate methods covered in class to answer the problem (Bayesian, Frequentist, or compare both). Provide a typed solution describing the problem and how
Duke - STA - 244
STA2442/14/2005Homework 4Due 2/21/2001 1. Consider the linear model Y = X 1 1 + X 2 2 + where X1 is n q and X2 is n (p q), with both matrices of full column rank. Consider the problem of testing N H : 1 = 0. Assume that N (0, 2 In ). (a) Gi
Duke - STA - 244
U X1 X2 Y 0.493151 1 1 0.872302 1.40245 2 1 1.59988 2.31175 3 1 2.4019 3.22104 4 1 3.25942 4.13034 5 1 4.14616 5.03964 6 1 5.04607 5.94894 7 1 5.95154 6.85823 8 1 6.85928 7.76753 9 1 7.76795 8.67683 10 1 8.677 0.0770038 1 2 0.557762 0.986
Duke - STA - 113
Name:Section:STAT 113 Midterm 31 Otis 1979, Journal of Psychology interviewed people waiting to see the space aliens lm Close Encounters of the Third Kind." Each person was asked to state his or her degree of agreement with the statement Life on
Duke - STA - 113
Name:Section:STAT 113 Midterm 21a. 1pt Suppose y is a normally distributed random variable with mean 0 and variance 1.0, i.e. y is standard normal. Find P ,1:0 y 0:5.1b. 2pt Suppose y is normally distributed random variable with mean 10 and va
Duke - STA - 113
Homework 9a SolutionsAs yi iid Bernoullip, then E yi = = p. Setting this expression to its respective sample P y =1 ^ y moment, we obtain: = n , or p = n ^ n! y n,y where K = 8.8 c. For the Binomial experiment, the likelihood function is L = K p
Duke - STA - 113
Name:1a. 2pt Suppose y is normally distributed random variable with mean = 5:0 and variance 2 = 4:0, i.e. y N 5:0; 4:0. Find P 3:0 y 12:0. 1b. 2pt Suppose y is a 2 distributed random variable with = 12 degrees of freedom. Find cuto s c and d, su
Duke - STA - 113
Name:Section:STAT 113 Midterm 31 Otis1 1979 interviewed people waiting to see the space aliens lm Close Encounters of the Third Kind." Each person was asked to state his or her degree of agreement with the statement Life on Earth is being observ
Duke - STA - 113
Name:Section:STAT 113, Spring 99 Midterm 3On all problems, please show your work. Just the correct answer without justi cation and intermediate results is not acceptable.Note:1.In a survey of college students, it was found that X = 69 of th
Duke - STA - 113
SOLUTIONNote: Version B had slightly di erent numbers. But the basic problems were the same. You can recognize Version B by Name" instead of Name:" on the top line, i.e., a missing :" after Name". 1. On questions 1a-f: 2pts for the correct choice;