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Course: STAT 740, Fall 2009
School: Los Angeles Southwest...
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Report Oral guidelines There is not much time to speak during the oral report; I nd it a good idea to focus on one or two of your most interesting topics rather than rush through a comprehensive presentation of your entire report. Do not worry if these elements are not statisticalthe main reason for an oral report is not to demonstrate your statistical prowess, but to obtain practice conveying technical...

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Report Oral guidelines There is not much time to speak during the oral report; I nd it a good idea to focus on one or two of your most interesting topics rather than rush through a comprehensive presentation of your entire report. Do not worry if these elements are not statisticalthe main reason for an oral report is not to demonstrate your statistical prowess, but to obtain practice conveying technical information. Having said that you should concentrate on only one or two topics, take care that you do not spend too much time on these elements. In the past, I have recommended specic elements of their reports for students to discuss. Almost invariably, students go into too much detail on these elements and end up exceeding their time limit. Usually I get the blame for having encouraged them to discuss a topic in detail. Be aware that a short time limit has to be enforced in order that all students have the opportunity to present their talks. John Spurriers referenced page has a lengthy list of dos and donts. The mechanical mistakes I commonly observe in a short presentation mostly involve a failure to engage the audience. Students like to address the overhead projector, talk to the screen, stare out into middle distance, stare at a corner of the roomanything but talk to the audience. Some of these problem can be addressed through adequate practice; students often seem surprised or are caught unaware by their PowerPoint slides. This is a good sign you needed at least one more practice session. You can refer to your overheads or note cards as a reminder, but be sure step to away from the overhead and talk directly to your classmates. In addition, idiosyncrasies that are acceptable for a long lecture should probably be avoided for a short talkdont put your hands in your pockets, balance on the outside edges of your shoes, hold your hands behind your back, rock back and forth, etc. Make sure everyone in your group speaks. I sometimes neglect to mention this in class and nd that a group member is not scheduled to speak, particularly in a large group. I want everyone in the class to have an opportunity to practice oral presentations. The amount of information on any single overhead should be minimal. Copying an entire page of computer output directly to an overhead is never eective; pull out the relevant information only and reproduce it using a large font. Be sure to trust your graphics and your text. If you have put alot of work on the slides, you need to let the slides do your work for youdo not ov...

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Los Angeles Southwest College - STAT - 517
Ch 11 Graphicsq Managing graphics windows q 1-d, 2-d and 3-d plotting q Interactive graphics Fall 2004 Don Edwards and the University of South Carolina1R graphicsq High level of functionality q Like SAS/Graph, number of options can be intimida
Los Angeles Southwest College - STAT - 517
Ch 12 Function writingq Inputting functions q Function writing examplesqPooled t-test qPlotting Fall 2004 Don Edwards and the University of South Carolina1Inputting functionsq Work from a text file q Fixing and sourcing q Re-editing2Poole
Los Angeles Southwest College - STAT - 506
I.2 Examples To Illustrate DOE Conceptst t1. Optimally Feeding Fish 2. Targeting A Process/Reducing Process Variation 3. Improving A Process 4. Weighing Two Objects 5. Baking Bread 6. Mitigating Noise Factors 7. Comparing Tires Sony USA ve
Los Angeles Southwest College - STAT - 740
Notes on Kernel Density EstimationThe R function density handles most of the estimation approaches we studied in class. R also has a library (KernSmooth) of kernel density functions, but it seemed a bit piecemeal and outdated. We can load the datase
Los Angeles Southwest College - STAT - 506
I.4 Implementation Issuest tFundamentals of Problem Solving DOE Implementation Issues DOE Stages Helicopter Experiment Project ReportstProblem Solving/DOELevels of UnderstandingI.4.1The Center for Reliabilty and Quality Sciences Depart
Los Angeles Southwest College - STAT - 506
I.5 Taguchi's Philosophyt t t t tSome Important Aspects Loss Functions Exploiting Nonlinearities Examples Taguchi Comments and Criticisms5.1The Center for Reliabilty and Quality Sciences Department of Statistics The University of South Carolin
Los Angeles Southwest College - STAT - 706
Designs with Randomization Restrictions RCBD with a complete factorial in each block A: Cooling Method B: Temperature Conduct ab experiments in each blockDesigns with Randomization Restrictions All factors are crossedYijk = + i + j + ij
Los Angeles Southwest College - STAT - 706
Population Marginal Means Two factor model with replication Yijk = ij + ijk , k = 1, , nij Yijk nijYij. j k j Yi. = = nij nijn E (Y ) = nij j i . ij jjjij~ = i.Population Marginal MeansE (Yi. ) = E (Y. j . ) = E (Y. ) =n ij j
Los Angeles Southwest College - STAT - 706
Resource Allocation Given N observations, how should we allocate them to a treatments?Minimize V (Y ) subject to N = ni. 1 1aai Use a Lagrange multiplier and minimizea N -n F= + i 1 ni 1 a 2Resource AllocationF =- 2 -=0 ni ni
Los Angeles Southwest College - STAT - 706
Nested Designs Ex 1-Compare 4 states' low-level radioactive hospital waste A: State B: Hospital Rep: Daily wasteNested Designs Ex 2-Compare tobacco yield/acre in 5 counties A: County B: Farm Rep: Field Yield/acre Other Ex-Tick study, Clas
Los Angeles Southwest College - STAT - 706
Estimability Effects model:Yij = i + ij = + i + ij , j = 1, , n i = i - Linear combinations of parameters are estimable only if they can be expressed as linear combinations of the cell means { i}Estimability Linear combinations of cell
Los Angeles Southwest College - STAT - 770
data crab;input color spine width satell weight; if satell=0 then bisat=0; else bisat=1; weight=weight/1000; cards;3 3 28.3 8 30504 3 22.5 0 15502 1 26.0 9 23004 3 24.8 0 21004 3 26.0 4 26003 3 23.8 0 21002 1 26
Los Angeles Southwest College - STAT - 519
StateRegion1991199019802010AreaPopDens MedAge<18>65SectionMaineNE123512281125145335.440.0134.325.113.41NewHampshireNE1105110992115259.4123.2033.325.311.61VermontNE5675635116549.661.3033.425.611.81Massa
Los Angeles Southwest College - STAT - 519
Here are the commands we used in exploring bounds on the error ofestimation in confidence intervals for a mean collected from a simple random sample in Minitab. To look at the sample size neededover a wide range of choices of B and sigma, we need
Los Angeles Southwest College - STAT - 517
These are various college football scoresfrom early in the 2004 season.The first column is the visiting team, the second the visitor's score,then the home team and their score.The fifth column is a note about the game and the sixth is how much a
Los Angeles Southwest College - STAT - 770
m2logL=function(Lambda){2*Lambda-20-20*log(Lambda/10)-qchisq(.95,1)}Lambda=seq(2,20,by=.5)plot(Lambda,m2logL(Lambda)+qchisq(.95,1),type="l",ylab="-2logL")abline(h=qchisq(.95,1)uniroot(m2logL,interval=c(3,10)$rootuniroot(m2logL,interval=c(10,25)$
Los Angeles Southwest College - STAT - 506
macro rse x1 x2 ymcolumn x1 x2 y zmconstant nrandlet nrand=count(x1)rand nrand z;normal 0 .6.let y=18.5+2.376*x1+1.2*x2-.0234375*x1*x1-.02*x2*x2+.0125*x1*x2+zendmacro
Los Angeles Southwest College - STAT - 517
Maalea sail sch 75.00 Maalea sail yac 32.95 Lahaina sail cat 62.00 Maalea power cat 22.00 Maalea sail sch 47.50 Maalea power cat 28.99 Maalea power y
Los Angeles Southwest College - STAT - 770
Final Exam 1. A researcher is testing the use of three different formats for the same product: Print, Online, CD. Respondents indicated their use of the product on an ordinal scale (1=Never, 2=Rarely, 3=Sometimes, 4=Often/Always). The data is availab
Los Angeles Southwest College - STAT - 740
Markov Chain Monte Carlo ExerciseIn this exercise, we will study an implementation of the Metropolis-Hastings algorithm for the Ising model, and work with BRugs on selected machines on which it has been downloaded. I would encourage you to study BRu
Los Angeles Southwest College - STAT - 740
Reading a function into RThe R function buffon is available on the website as a text le. The le actually names the function: buffon=function(n,l,p){ . . . } We use the source command to read the le. We do not have to use an R assignment (= or <-) to
Los Angeles Southwest College - STAT - 770
Section 3.4 Computer Exercise1. We will be working with the Hog data; copy the SAS program (listed as Chapter 2 contingency table on the webpage) into SAS. Add CMH1 as an option to the TABLES command to obtain the M 2 statistic for the default score
Los Angeles Southwest College - STAT - 740
Calling Fortran Subroutines in R on a PCRefer to Brian Habings webpage for downloading and installing Fortran from the MinGW website. The material presented here is consistent with the portion of Brians website that deals with running Fortran subrou
Los Angeles Southwest College - STAT - 740
Bootstrap ExerciseSome nonparametric bootstrap condence interval techniques are suciently easy to implement that you should consider writing your own. Working with the breast cancer data, we can construct 95% condence intervals for the 25% trimmed m
Los Angeles Southwest College - STAT - 706
Midterm 1. In a Complete Randomized Design, 7 dierent levels of baking powder were tested for their eect on relative rise of biscuit dough. 4 biscuits were baked using each recipe. Use = .05 for all tests. (a) Test the cell means model and examine t
Los Angeles Southwest College - STAT - 517
STAT 517: Final ExamProvide code, output, and answers in either Word, PDF, RTF, or text format; try to combine les as much as possible before sending them to me. Work independently. 1. A hostess at a restaurant recorded tips received each day over t
Los Angeles Southwest College - STAT - 517
STAT 517: AppendicesHitchcockGregoSAS and other packages SAS can interact with other packages in a variety of different ways. We will brieydiscuss SPSSX SUDAAN IML SQL will be discussed in more detailUniversity of South CarolinaPage 1
Los Angeles Southwest College - STAT - 706
Homework 1 1. For the data in Problem 3.36, use orthogonal polynomial contrasts to test whether or not a linear model is better than an intercept model; whether or not a quadratic model (with a linear term) is better than an intercept model. Are eith
Los Angeles Southwest College - STAT - 706
Homework 1 1. For the following data, use orthogonal polynomial contrasts to test whether or not a linear model is better than an intercept model; whether or not a quadratic model (with a linear term) is better than an intercept model. Are either of
Los Angeles Southwest College - STAT - 770
Midterm Exam 1. Consider the following 2X2 table with xed row and column marginals: 12 3 15 (a) What is the range of n11 ? (b) Consider the exact test of Ho : = 2 vs. HA : > 2 ( = .05). Find the rejection region and compute a p-value for this test.
Los Angeles Southwest College - STAT - 706
Latin Square Design Traditionally, latin squares have two blocks, 1 treatment, all of size n Yandell introduces latin squares as an incomplete factorial design instead Though his example seems to have at least one block (batch) Latin squares hav
Los Angeles Southwest College - STAT - 706
Chapter coverage Part A 1: Practical tools 2: Consulting 3: Design Principles Part B (4-6) One-way ANOVA Part C (7-9) Factorial CRD Part D (10-12) Unbalanced CRDChapter coverage Part E (13-15) Questioning Assumptions Part F (16-18) ANC
Los Angeles Southwest College - QARTOD - 3
Quality Assurance of Real-Time Oceanographic Data (QARTOD III)Scripps Institution of Oceanography, La Jolla, CA November 2-4, 2005Day 1 (Wednesday, November 2, 2005)Time 7:30 - 8:30am 8:30 8:45 8:45 9:15 Topic Continental Breakfast/Registration
Los Angeles Southwest College - QARTOD - 4
QARTOD IV AgendaFourth Workshop on the QC/QA of Real-Time Oceanographic Data June 21 - 23, 2006Woods Hole Oceanographic Institution Clark 507/Quissett Campus WEDNESDAY - JUNE 21, 20067:30 - 8:30 8:30-9:20 REGISTRATION & CONTINENTAL BREAKFAST INTR
Los Angeles Southwest College - QARTOD - 4
QARTOD IV June 21-23, 2006QARTOD IV: Breakout Group DescriptionJune 21 23, 2006 Woods Hole Oceanographic Institute, Woods Hole, MA QARTOD IV breakout sessions will address quality assurance (QA) procedures for the following four data parameters:
Los Angeles Southwest College - QARTOD - 3
Los Angeles Southwest College - QARTOD - 3
QARTOD-IIIWednesday, November 2, 2005, 8:30 AM NOONJohn OrcuttScripps Welcome/ORION SCCOOS Welcome Ocean.US status Eric TerrillGood time to discuss QAQC 38% of buoy data to NDBC now is from IOOS partners QAQC is an issue for NDBC SCCOOS Overv
Los Angeles Southwest College - CSCE - 790
Intrusion ControlFall 2001 CSCE 590CSCE 790 - Internet Security1ReadingsRequired: Maximum security: Ch 12 Recommended: Some of the links on http:/www.cse.sc.edu/ research/isl/CSCE 790 - Internet Security2Historical Research Prevention
Los Angeles Southwest College - CSCE - 548
CSCE 548 Code ReviewReading Thislecture: McGraw: Chapter 4 Recommended:Best Practices for Peer Code Review, http:/www.smartbear.com/docs/BestPracticesForPeerCodeReview.pdfWorst Practices in Developing Secure Software, http:/www.infosec
Los Angeles Southwest College - CSCE - 824
An Example of Economical Modeling Using AgentsRecommender Systems: A Marked-Based DesignYan Zheng Wei, Luc Moreau, Nicholas R. Jennings Proc. 2nd International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS03)Presented by Be
Los Angeles Southwest College - CSCE - 548
CSCE 548 Secure Software DevelopmentRisk-Based Security TestingReading This Nextlecture: lecture: Risk-Based Security Testing, McGraw: Chapter 7 Security Operations, McGraw: Chapter 9CSCE 548 - Farkas2Application of TouchpointsExtern
Los Angeles Southwest College - CSCE - 145
Career Fair Blitz, September 20, 2006, 11-2:00 @ Carolina ColiseumCompanies interested in Engineering & IT MajorsAs of 9/5/06Company Name Position(s)Major(s)CompE, CS, CIS CompE, CS, CIS CS, CIS CompE, CS, CIS, EE, ME IT Majors, ME IT Majors,
Los Angeles Southwest College - CSCE - 204
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Los Angeles Southwest College - CSCE - 204
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Los Angeles Southwest College - CSCE - 204
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Los Angeles Southwest College - CSCE - 145
CSCE 145: Test 1 Answers 1. RobotR2D2AttributesheadDirec t b tionbodyDirectionboxMeth odsR2D2findObiWanplay R f M Message2.1. public R2D2(City city, Direction dir) 2.2 arguments 3. 3 attributes 4. public void findObiWan(City aCity) 5. R2D2 arto
Los Angeles Southwest College - CSE - 352
EECE 352Problem Set #1Due: Tuesday September 1, 1998Fall 1998 Problem: In a cost-cutting frenzy, the University has decided to eliminate all their accounting staff and hired you to develop a system for keeping track of all pertinent information a
Los Angeles Southwest College - CSE - 352
EECE 352Problem Set # 10Due: November 17Fall 1998 Task 1 (50%): You will implement your own versions of both Shell sort and Quick Sort. Their signatures are: template <class Etype> void ShellSort( Etype A[ ], int N )/A is array template <class Et
Los Angeles Southwest College - CSE - 352
EECE 352Problem Set # 6Due: October 6Fall 1998 Problem: Like most people, you have become extremely annoyed with the MS Word spellchecker-especially the fact that it takes forever to spell check your favorite Russian novels. With your complete ma
Los Angeles Southwest College - CSE - 352
EECE 352Problem Set #2Due: September 8, 1998Fall 1998 Problem 1 (25%): Indicate for each pair of expressions (A,B) in the table below, whether A is O, , or of B. Assume that k 1, d > 0, m > 0, and b > 1 are constants. Your answer should fill ea
Los Angeles Southwest College - CSCE - 790
An example demonstrating:Creation and Initialization of an object from a file, using the CoGetInstanceFromFile API
Los Angeles Southwest College - CSCE - 790
=NOTE: Build these projects in the order shown below.===Inner (A component that implements an inner, reusable object.)= This component uses the [.\.\IDL\ocr.idl] file, so you need to build and register the OCRps.dll in that directory in
Los Angeles Southwest College - CSCE - 790
=NOTE: Build these projects in the order shown below.===IDL= This is the IDL file that supports the IThesaurus interface, an interface that is implemented by an outer object. The outer object (CoThesaurus) reuses the inner object, w
Los Angeles Southwest College - CSE - 822
Router is runningRouter is runningRegister Request from Vidal-Jose acceptedIPRecvThread createdServer loginConnection RequestedConnection Request from Vidal-JoseVidal-Jose startedjava.io.EOFExceptionNew file will be generated, error thrownV
Los Angeles Southwest College - CSE - 352
C+.mkr Page 1 Wednesday, December 9, 1998 7:27 AMC+ Review for COP-3530This material is excerpted from Data Structures and Algorithm Analysis in C+ (Second Edition) by Mark Allen Weiss and is copyrighted. All rights are reserved.1C+ ClassesI
Los Angeles Southwest College - CSE - 352
__3 ___A Tour of the Standard LibraryWhy waste time learning when ignorance is instantaneous? HobbesStandard libraries output strings input vectors range checking lists maps container overview algorithms iterators I/O iterators
Los Angeles Southwest College - CSE - 352
EECE 352Problem Set # 9Due: November 10Fall 1998 Task 1 (20%): Exercise 7.1 from the Weiss book. Task 2 (20%): Exercise 7.2 Task 3 (20%): Exercise 7.3, show your work. Task 4 (20%): Exercise 7.4, show the result for each k Task 5 (20%): Exercise
Los Angeles Southwest College - CSE - 352
EECE 352Problem Set #5Due: September 29, 1998Fall 1998 Task 1 (30%): Exercise 4.23 from the book. Task 2 (30%): Exercise 4.30 from the book. Task 3 (40%): Exercise 4.35 from the book. Note: For the last two problems you do not have to type in and
Los Angeles Southwest College - CSE - 352
EECE 352Timer UsageFall 1998/ This is how you can use the Timers function: #include "Timers.h" CCpuTimer cTimer; cTimer.Start( ); / do stuff cTimer.Stop( ) cout < "elapsed CPU time: " < cTimer.Report( ) < endl;Department of Electrical and Comput
Los Angeles Southwest College - CHAP - 07
*fig7_12.txt*/* 1*/template <class Etype>/* 2*/void/* 3*/Quick_Sort( Etype A[ ], const unsigned int N )/* 4*/{/* 5*/ const unsigned int One = 1;/* 6*/ Q_Sort( A, One, N );/* 7*/ Insertion_Sort( A, N );/* 8*/}/* 9*/templ
Los Angeles Southwest College - CHAP - 352
*fig7_12.txt*/* 1*/template <class Etype>/* 2*/void/* 3*/Quick_Sort( Etype A[ ], const unsigned int N )/* 4*/{/* 5*/ const unsigned int One = 1;/* 6*/ Q_Sort( A, One, N );/* 7*/ Insertion_Sort( A, N );/* 8*/}/* 9*/templ