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.
1 Page
hw4
Course: STAT 423, Winter 2008School: Washington
Rating:
Word Count: 398
Document Preview
STAT 423 Homework 4 Due Friday, January 30 This homework covers section 5.1 and 5.2 of your text. The first problem concerns an important theoretical property about simple linear regression. This will be reviewed in next week s lab session. For the second problem you will use simple linear regression and multiple linear regression to analyze real data. One goal for this homework set is for you to develop your...
Unformatted Document Excerpt
Coursehero >> Washington >> Washington >> STAT 423Course 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.
STAT 423 Homework 4 Due Friday, January 30 This homework covers section 5.1 and 5.2 of your text. The first problem concerns an important theoretical property about simple linear regression. This will be reviewed in next week s lab session. For the second problem you will use simple linear regression and multiple linear regression to analyze real data. One goal for this homework set is for you to develop your scientific writing skills by working to create concise and self-contained summaries of your findings. You will also be expected to follow the homework format guidelines described in Homework 2. 1. [20 points] Do Exercise 5.2 on pp. 96-97 of Fox (2nd edition). 2. [30 points] (a) Perform a least squares regression on a data set of your choosing. This can be the data set that you have located and studied for past homework sets, or a new data set of your choice. It is important that you perform this analysis on your own, and therefore each member of your group must independently choose their own set of data to analyze. Your data must have a continuous response variable and at least two independent variables (but I suggest you limit yourself to two or three). (i) Plot your data for each independent variable versus the response, and be sure to include the regression line in your plots for these simple linear regressions. (ii) Similarly plot the data for each independent variable versus each of the other independent variables, again including the regression line in your plots. (iii)Compute the standard error of the regression ( SE ) and the multiple correlation coefficient ( R ) and interpret each of these quantities. (b) Write a brief report that summarizes your analysis along with your interpretation and conclusions. Your report can be up to one page in length, and you are allowed to have one additional page for any relevant graphs that you would like to include with your report. Organize your report in ...
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:
Below is a small sample set of documents:
Washington - STAT - 423
Washington - STAT - 423
Some regression problems seen in the statistical consulting program in the past two years.Mechanical Engineering application: effect of time delays on tele-robotics performance Clients Problem Description: I would like to do ANOVA analysis I am doi
Washington - REGION - 7
Fitted trend (solid) vs Predicted (dashed): 320050004sq rt Ozone0.0 01/01/19890.201/01/199001/01/199101/01/1992 Date01/01/199301/01/1994Fitted trend (solid) vs Predicted (dashed): 320310016sq rt Ozone0.0 01/01/19890.201/01/199
Washington - STAT - 502
> 2 Treatments or Populations ANOVAApplied Statistics and Experimental Design Fritz Scholz Fall 2006Again we deal with circuit boards but this time investigate 3 types of uxes: X, Y, Z. We have 18 circuit boards, randomly assign each ux to 6 boa
Washington - STAT - 502
Chapter 3 Supplemental Text Material S3-1. The Definition of Factor Effects As noted in Sections 3-2 and 3-3, there are two ways to write the model for a singlefactor experiment, the means model and the effects model. We will generally use the effect
Washington - STAT - 502
ANOVA: A scientist wants to meet with you to discuss the analysis of a one-factor experiment they have run, using a CRD with an equal number of observations per treatment. They send you an email with the following computer output: Df Sum Sq Mean Sq F
Washington - STAT - 502
Stat 502 Concepts and Vocabulary: induction experimental units observational units Replication Randomization Blocking completely randomized design (CRD) randomized blocks design (RBD) randomized complete block design (RCBD) p-value randomization dist
Washington - STAT - 502
Stat 502 Homework 5 - Midterm Assigned 11/1/07 Due 11/8/07 Instructions: There are two problems on this exam. Make sure you do everything carefully and completely, and that your answers make sense. You are allowed to consult any book or written mater
Washington - STAT - 502
Homework #2 comments: #1. Sample size and precision (length) of confidence interval: Understand relationship between sample size and estimation (in addition to class focus on sample size and power). Note: "describe mathematically" means describe the
Washington - STAT - 502
Design and Analysis of ExperimentsDavid Yanez Department of Biostatistics University of WashingtonOutline Basic Ideas Definitions Structures of an Experimental Design DesignStructure Treatment Structure The Three Rs of Experimental Desi
Washington - STAT - 502
Stat 502 Homework 7 Assigned 11/15/07 Due 11/27/07 1. Hospital length of stay. The Study on the Efficacy of Nosocomial Infection Control (SENIC) was designed to determine whether infection surveillance and control programs in the U.S. have reduced th
Washington - STAT - 502
Statistics 502 Lecture NotesPeter D. Hoc December 6, 2006Contentsweek 11 Research Design Principles 1.1 Induction . . . . . . . . . . . . . . . . 1.2 Model of a process or system . . . . . . 1.3 Experiments and observational studies 1.4 Steps i
Washington - STAT - 502
The Distribution of FApplied Statistics and Experimental Design Fritz Scholz Fall 2006It can be shown thatSSTreat/(t 1) Ft1,Nt, F= SSE/(N t)na noncentral F -distributionwith t 1 and N t degrees of freedom and noncentrality parameteri
Washington - STAT - 502
Stat 502 Homework 6 Assigned 11/8/06 Due 11/15/06 1. Tomatoes: A horticulturist wants to do an experiment on the differences between two varieties of tomatoes, A and B, but only has a small strip of land running NorthSouth, and is worried that the So
Washington - STAT - 502
Stat 502 Final Homework Assigned 11/20/07 Due 12/12/07 (the scheduled date for the in-class final exam) As described and exemplified in the document Design.exercise.pdf (which is a copy of sect 5.17 of BHH (2nd ed), Learning by doing) you are asked t
Washington - STAT - 491
Stat/Math 491 Group problems. 1. Consider a time-reversible Markov chain with transition probabilities pij and stationary probabilities i. Now consider the chain truncated to the states {0,.,M}. (a) What are the transition probabilities for the trun
Washington - STAT - 549
INTRODUCTION TO ACTIVE PORTFOLIO MANAGEMENTFollows Grinold and Kahn (2000) Chap.4. See also Chaps. 5, 6Basic concepts and terms Information Ratios Expected Return Components Value Added Management5/24/06 Copyright R. Douglas Martin 1BASIC CONCE
Washington - STAT - 549
9. FACTOR MODELSINTRODUCTION TO FACTOR MODELS MACRO-ECONOMIC TIME SERIES MODELS FUNDAMENTAL CROSS-SECTION MODELS GLOBAL FACTOR MODELS ROBUST FACTOR MODEL FITTING NON-NORMAL DISTRIBUTION FACTOR MODELS STATISTICAL FACTOR MODELS6/1/2006 Copyright R. D
Washington - STAT - 549
Downloading the S-PLUS Software, Authors Scripts and Functions, and CRSP DataThe S-PLUS Software DownloadThe S-PLUS for Windows and S+NUOPT software being provided by Insightful for this book expires 150 days after install. As of the publication of
Washington - STAT - 549
1.1STATISTICAL METHODS FOR PORTFOLIOSSTAT 549 Spring 2006R. Douglas Martin B-319 Padelford doug@stat.washington.edu3/25/061.2Class Web Sitehttp:/www.stat.washington.edu/compfin/Courses/stat549/spring06/ Lecture slide sets Homework a
Washington - STAT - 549
6. CVAR OPTIMAL PORTFOLIOS6.1 CVaR Optimal Portfolios 6.2 A SIMPLE Model Function 6.3 A SIMPLE Portfolio Function 6.4 A SIMPLE CVAR Efficient Frontier Function 6.5 CVAR Versus Markowitz Portfolios 6.6 Choice of Tail Probability4/16/06Copyright R
Washington - STAT - 549
4. BETAS, MARGINAL CONTRIBUTION TO RISK AND IMPLIED RETURNS4.1 CAPM Betas 4.2 General Betas 4.3 Portfolio Risk and Covariances 4.4 Marginal Contribution to Risk 4.5 Implied Returns Appendix4A.1 Simple MMSE linear prediction 4A.2 Fitting a single fa
Washington - STAT - 549
3. PORTFOLIO OPTIMIZATION WITH NUOPT AND SIMPLENeed high-performance, scalable optimizer Maybe 1,000 or more assets in portfolio Quadratic and Linear Programming capabilityImpose real-world constraints 4/11/06Limits on short-selling (e.g.
Washington - STAT - 549
7. UTILITY FUNCTIONS, RISK AND PERFORMANCE MEASURES7.1 Expected Utility Framework 7.2 Risk Aversion 7.3 Some Utility Functions 7.4 Mean-Variance Rationales 7.5 Coherent Risk Measures 7.6 Performance Measures4/19/05 Copyright R. Douglas Martin 17.
Washington - STAT - 549
1. INTRODUCTION1.1 Investment Vehicles 1.2 Investment Management Styles 1.3 Prices and Returns 1.4 Return versus Classical Risk 1.5 Portfolio Returns and Classical Risk4/11/06Copyright R. Douglas Martin11.1 INVESTMENT VEHICLESEquities (stoc
Washington - STAT - 549
2. UNCONSTRAINED MV OPTIMAL PORTFOLIOS2.1 2.2 2.3 2.4 2.5 2.6 The Optimization Problem and the Efficient Frontier The Global Minimum Variance Portfolio Maximizing Quadratic Utility The Tangency Portfolio Optimal Risk Aversion Parameter Minimum Track
Washington - STAT - 549
8. BAYES METHODS FOR PORTFOLIOSBayes Modeling Paradigm Simple Bayes Models for Mean and Volatility Choice of Prior and Likelihood Markov Chain Monte Carlo (MCMC) and Gibbs Sampler Introduction to S+Bayes Fitting Mean and Volatility Models Working wi
Washington - STAT - 549
5. PORTFOLIO STATISTICS AND BOOTSTRAP RESAMPLING5.1 Asset Returns Distributions EDA 5.2 Sampling Distributions of Simple Estimates 5.3 Non-Parametric Bootstrap Applications 5.4 Parametric Portfolio Bootstrap Methods4/9/2006Copyright R. Douglas M
Washington - STAT - 547
2.12. Forwards and Futures2.1 Margins 2.2 Pricing forwards and futures 2.3 Hedging using futures9/29/052.2Forwards versus FuturesFORWARDS Private contract between 2 parties Non-standard contract Usually 1 specified delivery date Settled at
Washington - STAT - 593
BAYES METHODS FOR PORTFOLIOSBayes Modeling Paradigm Simple Bayes Models for Mean and Volatility Choice of Prior and Likelihood Markov Chain Monte Carlo (MCMC) and Gibbs Sampler Introduction to S+Bayes Fitting Mean and Volatility Models Working with
Washington - STAT - 593
7. INTRODUCTION TO ACTIVE PORTFOLIO MANAGEMENT7.1 Active Return and Risk 7.2 Unconstrained Optimal TEV Portfolios 7.3 Constrained Optimal TEV Portfolios 7.4 Information Ratios 7.5 Expected Return Components 7.6 Value Added Management4/29/05Copyr
Washington - STAT - 593
6. CVAR OPTIMAL PORTFOLIOS6.1 CVaR Optimal Portfolios* 6.2 A SIMPLE Model Function 6.3 A SIMPLE Portfolio Function 6.4 A SIMPLE CVAR Efficient Frontier Function 6.5 CVAR versus MV Efficient Frontiers 6.6 Choice of Tail Probability* Describes the fu
Washington - STAT - 593
5. UTILITY FUNCTIONS, RISK AND PERFORMANCE MEASURES5.1 Expected Utility Framework 5.2 Risk Aversion 5.3 Some Utility Functions 5.4 Why Mean-Variance 5.5 Risk Measures 5.6 Performance Measures4/19/05 Copyright R. Douglas Martin 15.1 EXPECTED UTILI
Washington - STAT - 592
0e 2{zx # 0! "(' &%$ "!' p x0 1 w w u ) # | } 0ir iYc {sy{e{ w w 'Yc ' p{sy{2hxR w y y w u sCykhsRvxs{zexvt y w ~ w w u ~ y ~ } w | } w
Washington - STAT - 592
Q q eo z q @ o 7 z go zo g I i 7 q cq1pHAFp9hjrllAlyA1I a H qo Pk zo g I z qo { { I @ i g zo g I i DpcmHj1DpDD1DHhWQ WW Q sDHs1 Fj1FDs2DvrWAHg n@ qG@ @ @ G @ 7 n Io e q Pn e { @ i @ z 8 @ 7 q I z @ z z I n |
Washington - STAT - 592
u mm n l m l { 0t|0ojltitX jqPtohdhXj|orgwtjqrt|todw p m m n l { { l p } } m { m l p m z { } q v w 6` r0n p p l m } m q p} q l p n { l p m m m n l m w jy Hh 7i gooj|tijthtjsti|Pq0oo u s qp l }p m { m
Washington - STAT - 592
} } } } ~{ o y j g m p jo t it i k j r m g j m k g |plhvlquvSqlYhvxhYn wp p j o w pg sg w mk s g r wplhtYnlvsYnmYYoxlHi l { WhfvpvvYYxv g k g wg o g y p $d dS $& f { { d { } h
Washington - STAT - 592
Note Title2/1/2004#means with k= 4 [1,] [2,] [3,] [4,] PopulationDependency.DG DeathRate.DG 0.6051061 3.132808 4.4235718 2.505056 7.4228040 2.765351 9.4325087 4.066870#variances with k = 4 PopulationDependency.DG DeathRate.DG PopulationDependen
Washington - STAT - 592
n m rj n po ~ r mn nn n ~o n m j ~on x x n po n po y i n ~o r n wYwqyvkzw}{}vp 0zkeeznk{}{v}vkwyvq2ykpev'wv j r yr o ~ ~o l ~o j l l ~o | r xo n n po or po ~ jn r p| or p Iww0Ejzk0vz{qzj~ vvqp}bwkqpevkysqej}wvwsEsko on x x y n '{e
Washington - STAT - 592
Homework 3 Stat 592/CSE 590 Winter 2004 Due on Friday February 20 1. Consider the Gaussian mixture model with J components. Restrict the variancecovariance j s associated to all J components in the mixture to be of the form j = j Id , where j R, j =
Washington - STAT - 592
| | e0 { | eae fs att0tysjxg s d i f ml f l f q g k m w | m s u m l s r f w m s s s q g r s l bG0`etoaaojjeeoebyo0TeGy}{n` m i m l s r f w m s s d kf q n s w e 7jeyGjjeeo
Washington - STAT - 390
x y time70 60 172 83 194 85 180 72 260 74 255 58 245 63 350 40 335 54 3
Washington - OC - 588
65 64 63 62 61 60 59 58 57 56 55 54 53 52 51 50 49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1LETTERS SCIENCE & SOCIETY POLICY FORUM BOOKS ET AL. PERSPECTIVE
Washington - ENVIR - 215
ENVIR215Spring 2005Lecture 15 Ocean Circulation (residual from last lecture) and Water Usage Ocean Circulation Slide 1-2 Recap of last lectures discussion on the distribution of water and the hydrological cycle Just like the atmosphere, circulat
Washington - ENVIR - 215
ENVIR215 Energy Unit Class DiscussionSpring 2005On Thursday, April 7; Tuesday, April 12; and perhaps Thursday April 14, we are going to use the lecture period for class discussions. Our goal is to explore whether oil supplies in the next few deca
Washington - OC - 588
THE GLOBAL CARBON CYCLE AND GREENHOUSE GASES The Program on Climate Change, Winter 2005 (OCE/ATMS/ESS 588) Meeting Time: MWF 1:30-2:20; Meeting Place: OTB 205 Course Web Page: http:/www.ocean.washington.edu/courses/oc588 Instructors: Steven Emerson,
Washington - OC - 400
KeyPaper discussion Assignment #1 Discussion of Sarmiento and Gruber (2002) Graded out of 15 points 1) Is the atmosphere presently at steady state with respect to CO2? (1pt) No. From figure 1 in the paper it is clear that the sources do not equal t
Washington - ENVIR - 215
Water - Its Properties and Role in the EnvironmentOutline: Role in early history and science Unique chemistry Hydrologic cycle - redistribution, climate effects Ocean circulation - basic wind & thermohalineChemically uniqueExists in all thr
Washington - ENVIR - 215
ENVIR215 Lecture 3 Energy Usage and BudgetsSpring 2005Our energy usage depends on our ability to store it, transport it and convert it to a useful form when we need it. Energy Storage Our utilization of energy is dependent on two properties. Fir
Washington - OC - 400
Ocean 400 Chemical Oceanography Winter 2007Your Name_Final ExamRead all questions carefully before you begin to answer. Use the back of the pages if necessary. Points are assigned to each question in (bold). Always explain your answers and show
Washington - ENVIR - 215
Eos,Vol. 84, No. 4, 28 January 2003VOLUME 84 PAGES 2936NUMBER 428 JANUARY 2003EOS,TRANSACTIONS, AMERICAN GEOPHYSICAL UNIONDam Removal in the United States: Emerging Needs for Science and Policyand a loss of economic viability have created a
Washington - AS - 222
Lecture 9: Global energy use by humansReview: global use of marketable energy: 4 x 1020 J/year divide by global population ~ 6 billion (and then divide by number of seconds in a year, about x 107 => 2 x 103 J/sec = 2 kilowatts for each person on E
Washington - OC - 512
Geophysical Fluid Dynamics I P.B. Rhines Notes on the Boussinesq Approximation - I. 27 ii 2004The basic vertical momentum balance in geophysical fluids is hydrostatic, if the aspect ratio of the flow is small, (H/L)2 < 1. The vertical forces invol
Washington - OC - 512
1Geophysical Fluid Dynamics I P.B. Rhines Problem Set 4 out: 13 Feb 2004 back: 20 Feb 2004 1. An east-west flow exists with the formu = U 0 cos(l0 y ) exp( z )The domain is H < z < 0, and the surface z = 0 is rigid. It is a geostrophic, hydrosta
Washington - ENVIR - 215
ENVIR215 Lecture 9 AtmosphereSpring 2005We start this lecture by finishing up the discussion of James Lovelocks Gaia hypothesis and his Daisyworld model. The notes for this are in lecture8.doc Vertical Structure The first three slides in the acc
Washington - OC - 201
One Man's Advice on the Determination of Dissolved Oxygen in SeawaterLou Codispoti April 1988Introduction With the exception of salinity determinations, dissolved oxygen is probably the most commonly measured chemical oceanographic variable. Usual
Washington - ENVIR - 215
ENVIR215 Energy Experiments: BackgroundA document prepared by Peter Rhines (the previous instructor of this class)Spring 2005Because our textbook relates to energy use, technology and impacts only, we need some more explanation of the science be
Washington - ENVIR - 215
ENVIR215Spring 2005Energy Experiments: Getting StartedThe first series of experiments will deal with energy. Energy plays the central role in the workings of our environment and it is the source of many of our environmental problems. An understa
Washington - KNORR - 2001
Continental Shelf Research 22 (2002) 14771492The role of mesoscale processes controlling biological variability in the Black Sea coastal waters: inferences from SeaWIFS-derived surface chlorophyll eldT. Oguza,*, A.G. Deshpandeb, P. Malanotte-Rizzo
Washington - OC - 400
Lecture 3 Trace Metals in SeawaterWhat are trace elements? Why are they important? Principal of Oceanographic Consistency. Profiles shapes as clues for controlling processes.A first look at spatial variationWhat are the different "types" of elem