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N.C. State - ST - 432
Helicopter Survey of Mule DeerStratified Random Sampling Very Widely Used in Wildlife ResearchMule Deer Helicopter Example from Kufeld et al (1980) Journal ofWildlife Management, 44, 632-639.8 strata of different sizes based on different regions in di
N.C. State - ST - 432
ST 432 Kenneth H PollockDraft Notes January 23, 2009NC Wildlife Commission 2007-2008 Hunter Survey Design and AnalysisKenneth H. Pollock, Professor, and Zhi Wen, Graduate StudentDepartments of Biology (and Statistics)North Carolina State UniversityB
N.C. State - ST - 432
Lecture 2 Sampling TheoryLast Lecture RecapIntroductory Remarks (Ch 1)Finite Populations and SamplesImportant Basic Sampling DesignsSimple Random SamplingEstimation of Population Mean and TotalReview of Some Properties of theSample MeanThe sample
N.C. State - ST - 432
Lecture 15-16: Cluster and Multi-StageSampling DesignsImportant Group Meeting-Time is getting Short for the first two groupsespecially.Lecture 15-16 OutlineExamples of Nested Multi-Level Sampling UnitsCluster and Two-Stage Sampling-Cluster- all sec
N.C. State - ST - 432
IntroductionExample on a TransectSmall Population ExampleRelationship to Cluster SamplingProblems with Systematic Random SamplingVariancesCyclic patternsReplicated Systematic Random SamplingSimple random sampling is the basis of our sampling theor
N.C. State - ST - 432
Lecture 19 Double and Two PhaseSamplingIntroductionRatio EstimatorRegression Estimator (Very Brief)Stratification and Adjusting for Non ResponseCluster Sampling (Very Brief)Ecological ExamplesSummary RemarksSampling Rare and Clustered Populations
N.C. State - ST - 432
4/10/2009History of Mail Surveys (Erodos, 1970) First recorded mail survey conducted in England in1839 Journal of Royal Statistical SocietyMail SurveysAnthony DoveMadeline KamalKatherine LindahlAmy McRimmanMail Surveys in the U.SU.S Census Bure
N.C. State - ST - 432
Regression MethodsImprove Precision if AuxiliaryVariable x is availableLinear Regression thru OriginRatio Estimator (7)Estimators in TextModelyi = xi +iyxRegression thru origin with errors increasingwith x. Discussed in class and text.r = N r
N.C. State - ST - 432
432 SamplingLecture 1Kenneth H. PollockBiology, Statistics and BiomathematicsNorth Carolina State University,My Introduction: AustraliaRural New South WalesSydney University: B Sc.Cornell University, Ithaca NY: MS & Ph D.MY SCIENCE PHILOSOPHYDev
N.C. State - ST - 432
SENSITIVETOPICSNicoleMack,NathanSmith,KrystalStrader,ChristineWuIntroductionWhatareSensitiveSubjects?SensitiveSubjects/Topicsarethosedealingwithissuesinwhichwewishtokeepprivateincludingreceiptofwelfare,income,alcoholanddruguse,criminalhistory,andso
N.C. State - ST - 432
Stratified Random SamplingSummary Notes in Progress 2-23-09Lecture 13- Basic Estimation Methods (withinstrata and overall), Examples, SamplingAllocation Rules.Lecture 14- Sampling Allocation Rules,Optimal Allocation Proof Outline, Example ofCalcula
N.C. State - ST - 432
ST 432 Kenneth H. Pollock 1-23-09.Overview: The Estimation of Subpopulation (or Domain) Parameters in FinitePopulation Sampling based on Simple Random Sampling without replacement ofthe Population.In virtually all surveys interest will not be only in
N.C. State - ST - 432
James HedgesJamesJeff JacksonSarah LikshisDevon SheppardIntroductionTelephone surveying is defined as a systematiccollection of data from a sample population using astandardized questionnaire.Today well discuss theHistoryUse of RDD to attain a
UNC - STOR - 664
CHAPTER 8ANALYSIS OF DESIGNEDEXPERIMENTSDiscuss experiments whose main aim is to studyand compare the eects of treatments (diets,varieties, doses) by measuring response (yield,weight gain) on plots or units (points, subjects, patients). In general t
UNC - STOR - 664
STOR 664 Homework 4SolutionPart A. Exercises (Faraway book)Ch.7 Ex.3library(MASS)library(faraway)data(ozone)a <- boxcox(lm(O3~., data = ozone), lambda = seq(0, .5, by = .05)95%1376137813821380logLikelihood13741372>>>>Read in data, t th
UNC - STOR - 664
STOR 664 Homework 5SolutionPart A. Exercises (Faraway book)Ch.9 Ex.4>>>>>>Set up the data and compute training and test RMSEs for each model.library(faraway)data(fat)index <- seq(10, 250, by = 10)train <- fat[-index, -c(1,3,8)]test <- fat[i
UNC - STOR - 664
Homework 5Reading:(A)S & Y book: Chap 7 & 8;due Tuesday 12/6Faraway book: Chap 6 & 9Faraway book: Chap 9, Exercise 4; see HW5-faraway.pdf for the description.(B) S & Y book: Chap 7, Exercises 1 & 3(C) S & Y book: Chap 8, Exercise 11
UNC - STOR - 435
Lecture 1: Equally Likely OutcomesSTOR 435, Spring 20121/10/2012435-2012equally likelyIntroductionGoal: probabilistic thinking, quantication of uncertainty,problem solvingTerms:random experimentoutcome (outcome space)event A , special events: (
UNC - STOR - 435
Lecture 2: DistributionsSTOR 435, Spring 20121/12/2012435-2012distributionDenition of probabilityPitman: Section 1.3Relationships between (among) events:A B: If A occurs, so does B. Note: A = B if and only ifA B and B A.A B: It means A or B occu
UNC - STOR - 435
Lecture 3: Conditional Probability and IndependenceSTOR 435, Spring 20121/17/2012435-2012conditioningMore rulesPitman: Sections 1.4, 1.5, 1.6Division rule (conditional probability):P(A|B) =P(AB)P(B) ,0,if P(B) > 0;if P(B) = 0.Multiplication
UNC - STOR - 435
Lecture 4: Binomial Random Variable and DistributionSTOR 435, Spring 20121/24/2012435-2012binomialBernoulli trialsPitman: Section 2.1A random experiment consists of n repeated trials, eachhaving two possible outcomes success (S) and failure(F ).
UNC - STOR - 435
Lecture 5: Normal Random Variable and DistributionSTOR 435, Spring 20121/26/2012435-2012normalBell curve 1Histogram vs Density Curve435-2012normaldensityContinuous RV (random variable) XDensity curve f (x) for X that satises:f (x) 0,xIRf (x
UNC - STOR - 435
Lecture 6: Binomial Approximated by Normal or PoissonSTOR 435, Spring 20121/31/2012435-2012approximating binomialnormal approximation to binomialHistogram vs Density Curve435-2012approximating binomialnormal approximation to binomial continuedWh
UNC - STOR - 435
Lecture 7: Discrete Random VariablesSTOR 435, Spring 20122/2/2012435-2012discreteunivariatePitman: Section 3.11D discrete models: Bin(n, p), Poisson(), Uniform()where is a nite subset of I , etc.Rrange (or support) of RV X : the set of all possi
UNC - STOR - 435
Lecture 8: ExpectationsSTOR 435, Spring 20122/7/2012435-2012expectationIntroductionPitman: Section 3.2Many ways to compute expectations: denition, properties,indicator, tail-sum formula, plug-in formula, etc.Denition:E(X ) =x P(X = x).xGenera
UNC - STOR - 435
Lecture 9: Standard Deviation and NormalApproximationSTOR 435, Spring 20122/9/2012435-2012Var and SDBasic factsPitman: Section 3.3Variance: Var(X ) = E(X )2 = E(X 2 ) 2 = 2 where = E(X ).Standard deviation: SD(X ) = Var(X ) = .Some properties:
UNC - STOR - 435
Lecture 10: Geometric Distribution and ExtensionsSTOR 435, Spring 20122/14/2012435-2012geometric etcGeom(p)Pitman: Section 3.4Innite Sum Rule: If a countable collection of eventscfw_A1 , A2 , . partition A, thenP(A) =P(Ak ).k=1Geometric distri
UNC - STOR - 435
Lecture 11: Density and ExpectationSTOR 435, Spring 20122/21/2012435-2012density and expectationHG to density curveHistogram vs Density Curve435-2012density and expectationdensityPitman: Section 4.1Continuous RV XDensity curve f (x) for X that
UNC - STOR - 435
Lecture 12: Exponential and Gamma DistributionsSTOR 435, Spring 20122/23/2012435-2012exp and gammaExponential distributionPitman: Section 4.2Let lifetime X Exp() with density f (x) = ex , x 0,where > 0 is a constant parameter.Survival function: P
UNC - STOR - 435
Lecture 13: CDFSTOR 435, Spring 20122/28/2012435-2012cdfCDFPitman: Section 4.5Denition: F (x) = P(X x), x I . Every RV X has a cdf,Rregardless of its type: discrete, or continuous, or others.Case 1: If X has a discrete distribution, then F is a
UNC - STOR - 435
Lecture 14: Multivariate Continuous DistributionsSTOR 435, Spring 20123/13/2012435-2012multivariateBasicsPitman: Section 5.2For random vector (X1 , ., Xn ), joint cdfF (x1 , ., xn ) = P(X1 x1 , ., Xn xn ); and joint densityf (x1 , ., xn ) that sa
UNC - STOR - 435
Lecture 15: Uniform DistributionsSTOR 435, Spring 20123/15/2012435-2012multivariate uniformvolume proportionPitman: Section 5.1A random vector (X1 , ., Xn ) is said to be uniformlydistributed over a bounded domain D I n ifRP(X1 , ., Xn ) A) =vo
UNC - STOR - 435
Lecture 16: Independent Normal Random VariablesSTOR 435, Spring 20123/20/2012435-2012independent normalFacts and Example 1Pitman: Section 5.3Facts:If X = Z + with parameters I and > 0, thenRZ N (0, 1) X N (, 2 ).Let X1 , ., Xn be independent wi
UNC - STOR - 435
Lecture 17: TransformationsSTOR 435, Spring 20123/22/2012435-2012transformationsFacts and Example 1Pitman: Section 5.4Note: Special care is required in performing multipleintegration with some variables dened over a restricteddomain.Example 1: L
UNC - STOR - 435
Lecture 18: Covariance and CorrelationSTOR 435, Spring 20124/3/2012435-2012correlationDenition and propertiesPitman: Section 6.4; also see Lecture 8 for the denition.Covariance:Cov(X , Y ) = E[(X X )(Y y )] = E(XY ) E(X ) E(Y ).Correlation: Corr(
UNC - STOR - 435
Lecture 19: More on Covariance and CorrelationSTOR 435, Spring 20124/5/2012435-2012more on correlationDenition and propertiesPitman: Section 6.4Example 1: (similar to Lecture 8, Example 4)Let X Uniform[2, 2], and Y = X 2 . Then EX = 0 andE(XY ) =
UNC - STOR - 890
Lecture 17American Options in a BS MarketAmerican option pricing in continuous-time is based on the same idea as in discrete-time, i.e.formulated as an optimal stopping problem. However, the technicalities involved are much morecomplicated. The introd
UNC - COMP - 541
COMP541CombinationalLogicIMontekSinghJan11,20121Today Basicsofdigitallogic(review) Basicfunctions Booleanalgebra GatestoimplementBooleanfunctions2BinaryLogic Binaryvariables Canbe0or1(TorF,loworhigh) Variablesnamedwithsinglelettersinexamples
UNC - COMP - 541
COMP541CombinationalLogicIIMontekSinghJan18,20121Today BasicsofBooleanAlgebra(review) IdentitiesandSimplification BasicsofLogicImplementation Mintermsandmaxterms Goingfromtruthtabletologicimplementation2Identities Useidentitiestomanipulatefun
UNC - COMP - 541
COMP541HierarchicalDesign&VerilogMontekSinghJan25,20121TopicsTopics HierarchicalDesign VerilogPrimer2DesignHierarchyDesignHierarchy Justlikewithlargeprogram,todesignalargechipneedhierarchy DivideandConquer Tocreate,test,andalsotounderstand
UNC - COMP - 541
COMP541StateMachinesMontekSinghFeb8,20121TodaysTopicsTodaysTopics StateMachines Howtodesignmachinesthatgothroughasequenceofevents sequentialmachines Basicallyclosethefeedbackloopinthispicture:2SynchronousSequentialLogicSynchronousSequentialL
UNC - COMP - 541
COMP541MoreonVerilog;DebouncingswitchesMontekSinghFeb15,20121FirstTopicFirstTopic MoreVerilog2HierarchyHierarchy Alwaysmakeyourdesignmodular easiertoreadanddebug easiertoreuse beforeyouwriteevenonelineofVerilog drawapicture blackboxes
UNC - COMP - 541
COMP541FlipFlopTimingMontekSinghFeb27,20121TopicsTopics Lab6: Feedback VGADisplayTimingGenerator Timinganalysis flipflops sequentialsystems clockskew2Lab6:HelpfulVGALinksLab6:HelpfulVGALinksVGATiming Recommended:http:/tinyvga.com/vgatimi
UNC - COMP - 541
COMP541MemoriesIMontekSinghFeb29,20121TopicsTopics Thisweeksassignments Homework#1&Lab#6 Everyonefinishedwiththem? FirstTest LateMarch willannouncedateafterSpringBreak OverviewofMemoryTypes ReadOnlyMemory(ROM):PROMs,FLASH,etc. RandomAccessM
UNC - COMP - 541
COMP541ArithmeticCircuitsMontekSinghMar26,20121Test#1:TakeHomeTest#1:TakeHome WillassignitWednesday,3/28 Giveyoufivedaystoworkonit(due4/2) CoverstopicsuptoLecture15(MemoriesII)2TodaysTopicsToday Addercircuits Howtosubtract Whycomplementedre
UNC - COMP - 541
COMP541DatapathsIMontekSinghMar28,20121TopicsTopics Overnext2classes:datapaths HowALUsaredesigned Howdataisstoredinaregisterfile Lab9:Startbuildingadatapath!2Whatiscomputerarchitecture?Whatiscomputerarchitecture?3Architecture(ISA)Architect
UNC - COMP - 541
COMP541DatapathsII&SingleCycleMIPSMontekSinghApr2,20121TopicsTopics Completethedatapath Addcontroltoit CreateafullsinglecycleMIPS! Reading Chapter7 ReviewMIPSassemblylanguage Chapter6ofcoursetextbook Or,PattersonHennessy(insideflap)2TopLe
UNC - COMP - 541
COMP541InputDevices:Keyboards,MiceandJoysticksMontekSinghApr16,20121KeyboardInterfaceKeyboardInterfacePS/2Keyboard Usesasynchronousserialprotocol Whatdoesthatmean? Eachsymbolistransmittedbitbybit 8databits+3controlbits3the COM port will prod
UNC - COMP - 541
COMP541Interrupts,DMA,SerialI/OMontekSinghApril18,20121Interrupts Twomainkinds Internal Errorwhenexecutinganinstruction Floatingpointexception Virtualmemorypagefault Tryingtoaccessprotectedmemory Invalidopcode! Systemcallrequestedbysoftware
UNC - COMP - 541
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILLComp 541 Digital Logic and Computer DesignSpring 2012Lab #1: Getting StartedIssued Fri. 1/20/12; Due Wed 1/25/12 (beginning of class)This lab assignment consists of two parts. For the first part, detaile
UNC - COMP - 541
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILLComp 541 Digital Logic and Computer DesignSpring 2012Lab #2: Hierarchical Design & Verilog PracticeIssued Fri. 1/27/12; Due Wed 2/1/12 (beginning of class)This lab assignment consists of several steps, e
UNC - COMP - 541
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILLComp 541 Digital Logic and Computer DesignSpring 2012Lab #3: Sequential Design: CountersIssued Wed 2/1/12; Due Wed 2/8/12 (beginning of class)This lab assignment consists of several steps, each building
UNC - COMP - 541
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILLComp 541 Digital Logic and Computer DesignSpring 2012Lab #5: A Stop WatchIssued Fri 2/17/12; Due Fri 2/24/12 (demo in lab)You will learn the following in this lab:Driving a multi-digit 7-segment display
UNC - COMP - 541
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILLComp 541 Digital Logic and Computer DesignSpring 2012Lab #7: Working with Memories (RAM)Issued Thu. 3/15/12; Due Fri.3/23/12 (demo in lab)You will learn the following in this lab:Designing a simple memo
UNC - COMP - 541
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILLComp 541 Digital Logic and Computer DesignSpring 2012Lab #8: A Full Display UnitIssued Fri 3/23/12; Due Fri 3/30/12 (demo in lab)You will learn the following in this lab:Designing a module with multiple
UNC - COMP - 541
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILLComp 541 Digital Logic and Computer DesignSpring 2012Lab #9: A Basic DatapathIssued Thu. 3/30/12; Due (see note below)You will learn the following in this lab:Designing a multi-ported memory (3-port reg
UNC - COMP - 541
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILLComp 541 Digital Logic and Computer DesignSpring 2012Lab #10: A Full Single-Cycle MIPS ProcessorIssued Wed 4/4/12; Due Fri 4/13/12 (demo in lab)You will learn the following in this lab:Integrating ALU,
UNC - COMP - 541
COMP541, Test #1 SAMPLE QUESTIONSWednesday, March 28, 2012Notes:This exam will be take-home. Your work is due at the beginning of class on Monday,April 2, 2012.This exam is open-book, open-notes, though you may not consult other people orresources o
UNC - ECON - 771
Economics 771Spring 2010David GuilkeyFinal Exam1.Given the following model:Yti X ti i ti2Where t=1,2,T and i=1,2,N. The Xs are non-stocastic and i ~ N (0, ) and i ~ N (0, 2 ) .a. Derive the TN x TN covariance matrix of the disturbance term.b. S
UNC - ECON - 771
Economics 771David GuilkeySpring 2011Final Exam1. We have quarterly data on the yield on the 90 day Treasury for 1970 to 2010.a. Provide an interpretation of the following STATA command and the accompanyinggraph:rate90,lags(20)- 0 .5 0Autocorre l