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Rochester - AST - 142
Astronomy 142 Midterm Exam #1 SolutionsShort Problems (Choose 4 of 6 parts) In the absence of extinction an O5 star has colors U-B =-1.1 and B-V=-0.3. Its absolute magnitude is MV =-5.6. Interstellar extinction has extinction ratio A(B)/A(V) = 1.34
Rochester - AST - 111
Problem Set #7, AST111 Solutions1) The Voyager 1 spacecraft detected nine active volcanoes on Io. Assume that, on average, there are nine volcanoes active on Io, and that the average eruptive rate per volcano is 50km3/yr. The radius of Io is 1820 km
Rochester - AST - 111
Astronomy 111 Lab ManualFall 2004Scaling of Splash Craters and Ejecta TrajectoriesIntroductionThe surface of many bodies in our solar system exhibit evidence for impact craters. Some surfaces, like portions of the Moon appear saturated with cra
Rochester - AST - 111
Overview of the Solar systemThis is a composite image of all the planets in our solar system. From the top is Mercury (taken by the Mariner 10 spacecraft); Venus (taken by the Pioneer Venus Orbiter); a full disk of the Earth (taken by the Apollo 17
Rochester - AST - 142
Today in Astronomy 142: the Milky Way, continuedStellar relaxation time Virial theorem Differential rotation of the stars in the disk The local standard of rest Rotation curves and the distribution of mass The rotation curve of the GalaxyFigure: sp
Rochester - AST - 111
Problem Set #5, AST111due Tuesday Oct 12, beginning of class 1) Calculate the equilibrium temperature of the Moon as a function of latitude (see PS problems 3.5, 3.6). Assume that the moon is a rapid rotator with zero obliquity, has a bond albedo of
Rochester - AST - 111
AST111, Lecture 1bPlanetary properties (overview continued) The Celestial Sphere (our coordinate system).Planetary properties (continued): Measuring Mass The orbital period of a moon about a planet depends on the semi-major axis and on the plane
Rochester - PHY - 103
Student projects for PHY103 (2008)The physics of/and music encompasses a wide range of topics, some that we will cover in labs and lectures in this class. However the diversity of subject matter related to this topic cannot be captured in a single s
Rochester - AST - 111
AST111 - Elementary Astronomy: Origins of the Solar System and BeyondLectures: TR 11:05am -12:20pm, Bausch and Lomb Hall (B+L) Room 270 Workshops: M7-9pm, B+L480, by tradition. Problem sets are due the next day at the beginning of class. Labs: R 6:
Rochester - AST - 111
Problem Set #4, AST111Solutions 1) On Spectral resolution and Absolute magnitude. When you observe with your eye you see light over a large wavelength range (about 4000-7000). We can think of the eye as having spectral resolution R ~ 5. Supposing yo
Rochester - AST - 111
Relected light from dust in AB Aurigas debris diskAST111, Lecture 3bAST111, Lecture 3bThe Dynamics of Small Bodies Dissipative and Radiation ForcesAdditional Forces on Small BodiesFor planets, gravity is the largest force. However small bodi
Rochester - AST - 111
Astronomy 111 Lab ManualFall 2004CCD cameras: basic operation, and the pinhole cameraIntroductionCharge-coupled devices (CCDs) were invented at AT&T Bell Laboratories in the late 1960s. A CCD consists of a silicon wafer patterned in the manner
Rochester - AST - 142
Astronomy 142 Workshop #8: Problems1. The epicyclic frequency. Consider a star in a nearly circular orbit in a galaxy. We can think of the motion of the star as a radial oscillation about a circular orbit of radius R0 . The frequency of radial oscil
Rochester - BST - 466
BST 446 Homework # 3 (Due in one week from 2/13) 1. In the last homework assignment, you have constructed the variableEDULEVEL for theDOS data. Use these variables to answer the following questions. (a) Use the Pearson chi-square statistic to test
Rochester - BST - 466
data dd; set dosSurivivalBygender; nf=effsize-failed; array t[2] nf failed; do i=0 to 1; depression=i; count=t[i+1]; output; end; keep gender Uppertime depression count; run; proc freq data=dd; weight count; tables Uppertime*depression*gender/cmh NOC
Rochester - BST - 466
3.3. The generalized linear models The term generalized linear model (GLM) was rst introduced in a landmark paper by Nelder and Wedderburn (1972), in which a wide range of seemingly disparate problems of statistical modelling and inference were set i
Rochester - BST - 466
BST 446 Homework # 3 (Due in one week from 2/8) 1. A categorical variable X with more than two possible levels is said to follow a multinomial distribution. The multinomial distribution is an extension of the binomial distribution for modeling two-le
Rochester - BST - 466
libname path "C:\"; symbol1 c=blue; proc freq data=path.survival; tables Year*majorminordepp/NOCOL NOCUM NOPERCENT NOROW; run; data one; set path.survival; if majorminordepp=1 and year=0 then delete; if majorminordepp=1 then year=year-1; if gender=.
Rochester - BST - 466
DATA ssd; INPUT gender dep count; DATALINES; 1 0 211 1 1 103 1 3 65 2 0 175 2 1 44 2 3 19 ; proc freq data=ssd; weight count; table gender*dep/all trend cmh; exact fisher chisq trend jt; run;31, 2008 428 The FREQ Procedure Table of gender by dep gen
Rochester - BST - 466
Chapter 1. Introduction This course focuses on modeling relationship among variables that contain discrete outcomes. Clarication of discrete outcomes: 1. binary outcome (yes, no) 2. ordinal (e.g. 5 point Likert scale: strongly disagree, disagree, neu
Rochester - BST - 466
BST 446 Midterm Exam 1. The DDPC data set contains information for those patients in the depression study ofseniors who identied an informant. (a) We are interested in the distribution of the depression status for patients based on the diagnosis, r
Rochester - BST - 466
Journal of Applied Statistics Vol. 33, No. 7, 679 690, August 2006Modifying the Exact Test for a Binomial Proportion and Comparisons with Other ApproachesALAN D. HUTSONUniversity at Buffalo, The State University of New York, New York, USAABSTRA
Rochester - BST - 466
Fisher Exact Test. s Why the distribution of the cell count n11 conditional on the mariginal counts follows the Hypergeometric distribution? Notation: Y Present (1) n11 n21 n+1X Present (1) Absent (0) TotalAbsent (0) n12 n22 n+2Total n1+ n2+ n
Rochester - BST - 466
BST 466 Midterm Exam 1. The PPW data set is from a study about depression in post partum women with N = 198. Each of the 198 women in the study has been screened for depression based on the Edinburgh postnatal depression (EPDS) scale, and undergone a
Rochester - BST - 466
BST 446 Homework # 1 (Due in class 2/04/08) 1. A sample of 32 severely agitated patients diagnosed as having schizophrenic spectrumdisorders was treated by two second-generation oral antipsychotic medications for 5 days. One of the outcomes of inte
Rochester - BST - 466
22 23 24DATA ms; INPUT ms gender count; DATALINES;NOTE: The data set WORK.MS has 4 observations and 3 variables. NOTE: DATA statement used (Total process time): real time 0.00 seconds cpu time 0.00 seconds29 30 31 32; PROC SORT DATA = ms; BY
Rochester - BST - 466
Chapter 5. Analyses of discrete survival time Survival data analysis is widely used in research studies and investigations when interest lies in the time to occurrence of some events of interest. In many applications, including studies involving seri
Rochester - BST - 466
4.7.2 Zero-inated Poisson model In addition to data clustering, another common cause of overdispersion is the excessive amount of zeros in the data. In many biomedical and psychosocial research, the distribution of zeros often exceeds the expected fr
Rochester - BST - 466
3). Goodman-Kruskal Gamma, Kendalls tau-b, Stuarts tau-c and Somers D All these measures consider whether the column variable Y tends to increase as the row variable X increases and vice versa. Each pair of subjects is classied as concordant or disco
Rochester - BST - 466
3.4.3. Models for ordinal responses Ordinal response occurs more frequently than nominal response in practice. For ordinal response, it is natural to model the cumulative response probabilities j = Pr (Y j) , j = Pr (Y = j) , j = 1, . . . , J 1, j
Rochester - BST - 466
Predicting Sexual Risk - 1Predicting Sexual Risk Behaviors Among Adolescent and Young Women Using a Prospective Diary MethodDianne Morrison-Beedy, PhD, APRN 1, Michael P. Carey, PhD 2, Changyong Feng, PhD 3 and Xin M. Tu, PhD 3Correspondence sh
Rochester - BST - 466
Word Count: 4,130 Tables: 2Metabolic Syndrome Prevalence Among Patients Receiving ClozapineJ. Steven Lamberti, M.D. David Olson, Ph.D., R.Ph. John F. Crilly, M.S.W., Ph.D. Telva Olivares, M.D. Geoffrey C. Williams, M.D., Ph.D. Xin Tu, Ph.D. Wan T
Rochester - BST - 466
McNemars test 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
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
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 & 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 & 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
Operating Systems1/29/2007User/Kernel ThreadsCPU SchedulingUser threadsKernel threadsThread data structure is in user-mode memory scheduling/switching done at user mode Thread data structure is in kernel memory scheduling/switching done b
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,