# Register now to access 7 million high quality study materials (What's Course Hero?) Course Hero is the premier provider of high quality online educational resources. With millions of study documents, online tutors, digital flashcards and free courseware, Course Hero is helping students learn more efficiently and effectively. Whether you're interested in exploring new subjects or mastering key topics for your next exam, Course Hero has the tools you need to achieve your goals.

16 Pages

### chap2.4

Course: BST 466, Fall 2009
School: Rochester
Rating:

Word Count: 2544

#### Document Preview

test McNemars By employing essentially the same procedure as for comparing two conditional proportions (p1 and p2), but taking into account b b the dependence between p2+ and p+2, we can develop a chisquare test statistic: (n12 n21)2 2. n12 + n21 This statistic is known as McNemars test. A version of McNemars test after correction of continuity is: (|n12 n21| 1)2 2. n12 + n21 Sign test. If the sample size is...

Register Now

#### Unformatted Document Excerpt

Coursehero >> New York >> Rochester >> BST 466

Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Course Hero has millions of student submitted documents similar to the one below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more. Course Hero has millions of course specific materials providing students with the best way to expand their education.

Below is a small sample set of documents:

Rochester - BST - 466
Measures of Association for Cross Classifications III: Approximate Sampling Theory Leo A. Goodman; William H. Kruskal Journal of the American Statistical Association, Vol. 58, No. 302. (Jun., 1963), pp. 310-364.Stable URL: http:/links.jstor.org/sici
Rochester - BST - 466
Rochester - BST - 466
Chapter 3. Regression Models for Categorical Response 3.1 Introduction In Chapter 2, we discussed how to make inference about association between two variables with stratication (sets of contingency table) and without stratication (a single contingen
Rochester - BST - 466
5.4. Parametric methods The approaches described above for life-tables and the MantelCox test are non-parametric methods. Non-parametric methods do not assume any analytic or parametric distribution, and thus apply to survival data arising in most ap
Rochester - BST - 466
Chapter 4. Loglinear model This chapter introduces the loglinear model, a subclass of the generalized linear models, and its applications to count response and multi-way contingency table analysis. The application of loglinear model to count response
Rochester - BST - 466
Applied Discrete Outcome AnalysisWan Tang, Hua He and Xin Tu Copyrighted by the authors, June 22, 2007ContentsPreface 1 Introduction 1.1 Clarication of Discrete Outcome . . . . . . 1.2 Overview . . . . . . . . . . . . . . . . . . . 1.3 Review of
Rochester - BST - 466
Introduction G.O.F. Test for Logistic Regression with Continuous Covariates Comparison Between Dierent TestsGoodness-of-t Test for Logistic Regression with Continuous CovariatesPresented by Zhen Chen March 26, 2008Goodness-of-t Test for Logisti
Rochester - CSC - 256
Outline Background Protection and Isolation Recent Software InnovationsProtection and Extension in the Microsoft Singularity Operating SystemMichael Spear csc 256/456 25 April 2007 Singularity End to End System Trust -vs- Verification
Rochester - CSC - 256
Operating Systems3/20/2007Recap of the Last Class: Disk StorageMore on Disk Storage and File SystemCS 256/456 Dept. of Computer Science, University of RochesterDisk drivemechanical parts (cylinders, tracks, sectors) and how they move to acc
Rochester - CSC - 256
Operating Systems4/19/2006Multiprocessor HardwareMultiprocessor OSA computer system in which two or more CPUs share full access to the main memory Each CPU might have its own cache and the coherence among multiple cache is maintainedwrite op
Rochester - CSC - 256
Operating Systems2/23/2005Recap of the Last ClassMore on Virtual MemoryVirtual memory separation of user logical memory from physical memory.Only part of the program address space needs to be in physical memory for execution. Copy-on-write:
Rochester - CSC - 256
Operating Systems3/21/2005File System LayoutMore on File SystemPartition table MBRentire disk Disk partitionsCS 256/456 Dept. of Computer Science, University of RochesterBoot blk Super blk Free space mgmt I-nodes Root dir Files &amp; directo
Rochester - CSC - 256
Operating Systems2/15/2004Recap of Last ClassIntroduction to NachosnClassic synchronization problemsq qBounded buffer (producer/consumer) Dining philosopher Monitor Condition variables (Hoare/Mesa semantics) OS kernel synchronization Use
Rochester - CSC - 257
Computer Networks9/5/2007Computer Networks IntroductionOutline: General course information. What are computer networks. Network architecture (Internet and OSI). Socket programming.General Course InformationCourse Web page Course email addres
Rochester - CSC - 256
Operating Systems1/25/2006Recap of the Last ClassProcesses &amp; ThreadsHardware protection System componentskernel and user mode process management, memory management, I/O system, file and storage, networking, monolithic architecture, microke
Rochester - CSC - 573
Kai Shen1/24/2008Some Queueing TheoryPerformance Modeling of Servers - Queueing SystemsIf the facts dont match the theory, change the facts. - Albert Einstein Network server model in queueing systemsarrival process (inter-arrival time distri
Rochester - CSC - 573
Kai Shen1/29/2008Basic Queueing TheoryPerformance Modeling of Servers - Queueing Systems (cont.)Stochastic processes Markov processes/chains Birth-death processes Poisson processes Consider n(t) in an M/M/1 queue h = r/u (traffic intensity or
Rochester - CSC - 257
Computer Networks11/20/2007Multimedia and Quality of ServiceMultimedia NetworkingMultimedia applications: network audio and video (continuous media)Kai Shen Dept. of Computer Science, University of Rochesternetwork provides application wi
Rochester - CSC - 256
Rochester - CSC - 256
Operating Systems2/22/2006SegmentationVirtual MemoryOne-dimensional address space with growing pieces At compile time, one table may bump into another Segmentation:generate segmented logical address at compile time segmented logical address
Rochester - CSC - 256
Operating Systems2/13/2007Basic Memory ManagementBasic Memory ManagementProgram must be brought into memory and placed within a process for it to be run. Mono-programmingrunning a single user program at a timeNeed for multi-programminguti
Rochester - CSC - 257
Computer Networks9/17/2007Types of Multiple Access ProtocolsEthernetThree broad classes: Channel partitioningdivide channel into smaller pieces (time slots, frequency, code) allocate piece to node for exclusive useKai Shen Dept. of Compute
Rochester - CSC - 256
Operating Systems2/2/2004Recap of the Last ClassCPU SchedulingnProcessq q qProcess concept OS data structure for a process Operations on processes Thread concept Compared with processn nnThreadq qCS 256/456 Dept. of Computer Scie
Rochester - CSC - 257
Computer Networks9/17/2007Link Layer Multiple Access ProtocolsRecap of Last ClassLink layerhandle data transfer between neighboring network elements Encoding encode binary data into electromagnetic signals Framing encapsulate data into frame,
Rochester - CSC - 256
Operating Systems2/28/2007Recap of the Last ClassMore on Virtual MemoryVirtual memory separation of user logical memory from physical memory.Transparent page sharing: Only part of the program address space needs to be in physical memory for
Rochester - CSC - 256
Operating Systems2/20/2006Paging: Address Translation SchemePaging and SegmentationA logical address is divided into:Page number (p) used as an index into a page table which contains base address of each page in physical memory.CS 256/456
Rochester - CSC - 573
Kai Shen1/28/2003Web Server and Performance Measurement BasicsAssignment #1n nnnImplement a Web crawler Crawl a list of URLs at http:/tallinn:8080 all Internet drafts (150MB) After retrieving them, your program should save them to the l
Rochester - CSC - 573
Kai Shen2/13/2003Cluster Load Balancing Cluster-based Internet Services: ClusterLoad Balancing and Request DistributionnLoad indexn nnBroadcast policyn nnumber of active service requests CPU/memory/IO load let each node announce its wo
Rochester - CSC - 257
Computer Networks10/17/2007TCP: OverviewTCPconnection-oriented: pipelined:handshaking (exchange of control msgs) to initialize sender, receiver state before data exchange multiple in-flight segments bi-directional data flow in same connecti
Rochester - CSC - 256
Operating Systems1/18/2006General Course InformationIntroductionCS 256/456 Dept. of Computer Science, University of RochesterCourse Web page: www.cs.rochester.edu/~kshen/csc256 Course-related announcement/correspondence: Broadcast email: c
Rochester - CSC - 256
Operating Systems1/26/2005Recap of the Last ClassCPU SchedulingProcessProcess concept A processs image in the computer Operations on processesThreadCS 256/456 Dept. of Computer Science, University of RochesterThread concept Compared wi
Rochester - CSC - 256
Operating Systems4/5/2006Process QueuesCPU SchedulingCS 256/456 Dept. of Computer Science, University of RochesterReady queue set of all processes ready for execution. Device queues set of processes waiting for an I/O device. Process migr
Rochester - CSC - 256
Operating Systems3/20/2006Recap of the Last Class: Storage SystemsFile SystemDisk Structuremechanical parts (cylinders, tracks, sectors) and how they move to access disk data electronic part (disk controller main) exposes an onedimensionally
Rochester - ISSUE - 4
Margot Bouman - The Temporality of the Public Sphere: Orpheus Descending's Loop between Art and CultureBack to Issue 4The Temporality of the Public Sphere: Orpheus Descendings Loop between Art and Cultureby Margot Bouman 2002 Over the summer of
Rochester - ISSUE - 4
Introduction - To incorporate practice - issue 4Back to Issue 4Introduction: To Incorporate Practiceby T'ai Smith 2002 When the topic for this issue was initially formulatedto investigate the processes of work in distinction from the productthe
Rochester - PSC - 204
Loading Print07/07/2005 03:43 PMdialogues Does Abortion Prevent Crime? By Steven Levitt and Steve Sailer Updated Tuesday, Aug. 24, 1999, at 4:24 PM PTFrom: Steven Levitt To: Steve SailerPosted Monday, Aug. 23, 1999, at 5:32 PM PT In recent we
Rochester - PSC - 201
PSC 201 Spring 2009Solution Set #3Chapter 4 review exercises: 1. # 1 (a) The average () of the N = 6 numbers can be found by plugging x them in the formula x= N i=1 xiN=41 + 48 + 50 + 50 + 54 + 57 = 50 6The standard deviation (SD) is eq
Rochester - ISSUE - 9
Issue 9 - IntroBack to Issue 9Introduction: Nature Lovingby Lisa Uddin and Peter Hobbs 2005In the opening sequences of Luc Jacquets recent film for National Geographic, March of The Penguins (2005), audiences are shown spectacular vistas of a
Rochester - ISSUE - 6
Catherine Zuromskis - Issue 6: IntroductionBack to Issue 6Introduction: Visual Publics, Visible Publicsby Catherine Zuromskis 2003Our theoretical understanding of public is much changed since Jurgen Habermas first put forth his notion of the
Rochester - ISSUE - 7
hobbsBack to Issue 7Leaflet Drop: The Paper Landscapes of Warby Jennifer Gabrys 2004War. The possibility at last exists that war may be defeated on the linguistic plane. If war is an extreme metaphor, we may defeat it by devising metaphors tha
Rochester - WEB - 1
Low Gain v. High Gain ADC1000LvHadcL1S7 LvHadcL1S7 Entries Entries 4673 4673 Mean x 108.9 Mean y Mean y 86.8 RMS x RMS x 108.7 108.7 RMS y 28.54 28.548006004002000 02004006008001000
Rochester - WEB - 1
Low Gain v. High Gain ADC1000LvHadcL3S4 LvHadcL3S4 Entries Entries 4673 4673 Mean x 276.3 Mean y Mean y 97.96 RMS x RMS x 306.8 306.8 RMS y 72.19 72.198006004002000 02004006008001000
Rochester - WEB - 1
Low Gain v. High Gain ADC1000LvHadcL2S1 LvHadcL2S1 Entries Entries 4673 4673 Mean x 82.98 Mean y Mean y 64.76 RMS x RMS x 91.41 91.41 RMS y 26.05 26.058006004002000 02004006008001000
Rochester - WEB - 1
Low Gain v. High Gain ADC1000LvHadcL2S3 LvHadcL2S3 Entries Entries 4674 4674 Mean x 216.2 Mean y Mean y 98.38 RMS x RMS x 223.9 223.9 RMS y 41.91 41.918006004002000 02004006008001000
Rochester - WEB - 1
Low Gain v. High Gain ADC1000LvHadcL2S4 LvHadcL2S4 Entries Entries 4674 4674 Mean x 228.4 Mean y Mean y 102.1 RMS x RMS x 233.8 233.8 RMS y 45.9 45.98006004002000 02004006008001000
Rochester - WEB - 1
Low Gain v. High Gain ADC1000LvHadcL3S5 LvHadcL3S5 Entries Entries 4673 4673 Mean x 204.9 Mean y Mean y 80.58 RMS x RMS x 227.3 227.3 RMS y 38.89 38.898006004002000 02004006008001000
Rochester - WEB - 1
Low Gain v. High Gain ADC200 180 160 140 120 100 80 60 40 20 0 0 200 400 600LvHadcL3S7 LvHadcL3S7 Entries Entries 4674 4674 Mean 85.95 Mean y Mean y 78.81 78.81 RMS RMS 26.85 26.85 RMS y 4.531 4.531 2 / ndf 2 / ndf 30.97 / 17 30.97 / 17 Prob Prob
Rochester - WEB - 1
Low Gain v. High Gain ADC700 600 500 400 300 200 100 0 0 200 400 600LvHadcL2S7 LvHadcL2S7 Entries Entries 4673 4673 Mean 94.89 Mean y Mean y 83.27 83.27 RMS RMS 89.14 89.14 RMS y 28.97 28.97 2 / ndf 2 / ndf 86.52 / 38 86.52 / 38 Prob Prob 1.199e-
Rochester - WEB - 1
Low Gain v. High Gain ADC900 800 700 600 500 400 300 200 100 0 0 200 400 600LvHadcL2S3 LvHadcL2S3 Entries Entries 4674 4674 Mean 216.2 Mean y Mean y 98.38 98.38 RMS RMS 223.9 223.9 RMS y 41.91 41.91 2 / ndf 2 / ndf 83.87 / 58 83.87 / 58 Prob Prob
Rochester - WEB - 1
Low Gain v. High Gain ADC700 600 500 400 300 200 100 0 0LvHadcL3S4 LvHadcL3S4 Entries Entries 4673 4673 Mean 276.3 Mean y Mean y 97.96 97.96 RMS RMS 306.8 306.8 RMS y 72.19 72.19 2 / ndf 2 / ndf 75.36 / 58 75.36 / 58 Prob Prob 0.06242 0.06242 Slop
Rochester - WEB - 1
Low Gain v. High Gain ADC500400LvHadcL1S5 LvHadcL1S5 Entries Entries 4673 4673 Mean 285.8 Mean y Mean y 120.6 120.6 RMS RMS 306.3 306.3 RMS y 71.76 71.76 2 / ndf 2 / ndf 80.72 / 58 80.72 / 58 Prob Prob 0.02595 0.02595 Slope 0.1473 0.0004 0.147
Rochester - WEB - 1
Low Gain v. High Gain ADC900 800 700 600 500 400 300 200 100 0 0 200 400 600Entries Entries Mean Mean y Mean y RMS RMS RMS y 2 / ndf 2 / ndf Prob Prob Slope Offset OffsetLvHadcL3S1 LvHadcL3S1 4673 4673 106.9 89.88 89.88 111 111 35.63 35.63 95.41
Rochester - WEB - 1
Low Gain v. High Gain ADC900 800 700 600 500 400 300 200 100 0 0 200 400 600LvHadcL3S5 LvHadcL3S5 Entries Entries 4673 4673 Mean 204.9 Mean y Mean y 80.58 80.58 RMS RMS 227.3 227.3 RMS y 38.89 38.89 2 / ndf 2 / ndf 80.82 / 58 80.82 / 58 Prob Prob
Rochester - WEB - 1
Low Gain v. High Gain ADC900 800 700 600 500 400 300 200 100 0 0 200 400 600LvHadcL2S5 LvHadcL2S5 Entries Entries 4674 4674 Mean 272.5 Mean y Mean y 108.2 108.2 RMS RMS 295.2 295.2 RMS y 60.18 60.18 2 / ndf 2 / ndf 95 / 58 95 / 58 Prob Prob 0.0015
Rochester - WEB - 1
Low Gain v. High Gain ADC1000800600LvHadcL1S4 LvHadcL1S4 Entries Entries 4674 4674 Mean 270.1 Mean y Mean y 111.6 111.6 RMS RMS 286 286 RMS y 61.02 61.02 2 / ndf 2 / ndf 82.83 / 58 82.83 / 58 Prob Prob 0.01785 0.01785 Slope 0.1426 0.0003 0.14
Rochester - WEB - 1
Low Gain v. High Gain ADC1000800600LvHadcL2S2 LvHadcL2S2 Entries Entries 4673 4673 Mean 176.9 Mean y Mean y 77.4 77.4 RMS RMS 213.2 213.2 RMS y 39.36 39.36 2 / ndf 2 / ndf 80.35 / 58 80.35 / 58 Prob Prob 0.02767 0.02767 Slope 0.1402 0.0003 0.
Rochester - WEB - 1
Low Gain v. High Gain ADC900 800 700 600 500 400 300 200 100 0 0 200 400 600LvHadcL2S6 LvHadcL2S6 Entries Entries 4674 4674 Mean 218.5 Mean y Mean y 102 102 RMS RMS 264 264 RMS y 59.23 59.23 2 / ndf 2 / ndf 72.46 / 58 72.46 / 58 Prob Prob 0.09588
Rochester - WEB - 12
Rochester - WEB - 1
Light Loss in Y-11 Optical FiberHoward Budd* and Jesse Chvojka MINERA Note 001 November 2004 AbstractThe MINERvA experiment will use optical fiber for light collection. This fiber travels from the scintillator to the photosensor and must be bent al
Rochester - WEB - 1
Ofine Software UpdateSep 5, 07Move to new Gaudi/SLF4 Has been long awaited LHCb considers release that we are usingobsolete and removed (some of) the old binaries from their ftp archive which is near its end of life old version was only avail
Rochester - AST - 461
13 LECTURE 3 Einstein Coecients Kirchos law relating emission to absorption for a thermal emitter must involve microscopic physics. Consider system with two energy states with statistical weights g 1 and g2 respectively. Transition from 2 to 1 is b