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Course: ESE 415, Spring 2011School: Washington University...
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Assignment Homework 4 From Chapter 3 of Text Assigned Date: March 3, 2010 Due Date: March 15 in class or hand into the HW bin by midnight. 1. 2. 3. 4. 5. 6. 7. 8. 9. Problem 3.43 Problem 3.33 Problem 3.44 Problem 3.48 Problem 3.51 Problem 3.52 Problem 3.56 Problem 3.58 Problem 3.61
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Assignment Homework 4
From Chapter 3 of Text
Assigned Date: March 3, 2010
Due Date: March 15 in class or hand into the HW bin by midnight.
1.
2.
3.
4.
5.
6.
7.
8.
9.
Problem 3.43
Problem 3.33
Problem 3.44
Problem 3.48
Problem 3.51
Problem 3.52
Problem 3.56
Problem 3.58
Problem 3.61
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Washington University in St. Louis - ESE - 415
Homework Assignment 5From Chapter 4 of TextAssigned Date: March 29, 2010Due Date: April 5, 200910Turn in your work during the class or in the HW bin outside the ESE Office (Bryan 201)by midnight on the due date.1.2.3.4.5.6.7.Problem 4.3Probl
Washington University in St. Louis - ESE - 415
Homework Assignment 5From Chapter 4 of TextAssigned Date: March 29, 2010Due Date: April 5, 200910Turn in your work during the class or in the HW bin outside the ESE Office (Bryan 201)by midnight on the due date.1.2.3.4.5.6.7.Problem 4.3Probl
Washington University in St. Louis - ESE - 415
Homework Assignment 6From Chapter 4 of TextAssigned Date: April 7, 2010Due Date: April 19, 2010Turn in your work during the class or in the HW bin outside the ESE Office (Bryan 201)by midnight on the due date.1.2.3.4.5.6.7.Problem 4.21Proble
Washington University in St. Louis - ESE - 415
Homework Assignment 7From Chapter 5 of TextAssigned Date: April 21, 2010Due Date: Not to be turned in1. Problem 5.52. Problem 5.83. Problem 5.114. Problem 5.135. Problem 5.226. Problem 5.277. Problem 5.368. Problem 5.439. Problem 5.5010. Prob
Washington University in St. Louis - ESE - 415
ESE 471 Project Spring 2010 Assigned Date: April 19, 2010 Due Date: May 3 in class. Throughout the semester, we learned many different aspects of communication theory. For example, we learned about the entropy and channel capacity for determining requirem
UPenn - MEAN - 147
function analyze(ballMovieFilename)% ANALYZE% *% This function loads the specified avi movie to measure the vertical% trajectory of an object that moves across the frame. The object should% be light-colored on a black background. The function generat
UPenn - MEAN - 147
Windows Registry Editor Version 5.00 Compatible by SenTech camera driver[HKEY_CURRENT_USER\SOFTWARE\Sentech\AxCapturePreference\0305\lab03]"SDK Version"=hex:07,00,03,00,00,00,00,00,07,00,03,00,00,00,00,00"Filter Version"=hex:01,00,00,00,00,00,03,00"Cl
UPenn - MEAN - 147
function calibrate()% CALIBRATE% *% This function asks the user for and stores the height and width of the% camera's view, in centimeters, so that examine.m can output data in% real-world units.cclc% Print a banner.banner = sprintf('\nMEAM 147: I
UPenn - MEAN - 147
function calibrate()% CALIBRATE% *% This function asks the user for and stores the height and width of the% camera's view, in centimeters, so that examine.m can output data in% real-world units.cclc% Print a banner.banner = sprintf('\nMEAM 147: I
UPenn - MEAN - 147
function examine(ballMovieFilename)% EXAMINE% *% This function loads the specified avi movie to measure the trajectory of an% object that moves across the frame. The object should be light-colored on ablack background.% The function outputs a text f
UPenn - MEAN - 147
function examine(ballMovieFilename)% EXAMINE% *% This function loads the specified avi movie to measure the trajectory of an% object that moves across the frame. The object should be light-colored on ablack background.% The function outputs a text f
UPenn - MEAN - 147
% Code for MEAM 147% Lab 4: Force Equilibrium and Static Equilibrium: Snap-through Buckling% Clear the workspace.clear% Initial values: measured from system.% FIX THESE TO MATCH YOUR SYSTEM.bo = 0.05; % mdo = 0.08; % mthetao = 30; % degrees% More
UPenn - MEAN - 147
%%%%%%MEAM 147 Lab 5Moment Equilibrium and Rotational Stability:Tipping and Moment-Induced BucklingProfessor K. J. KuchenbeckerOctober 9, 2007Edited on October 15, 2009%%%%%%%%%%Description-This script plots the theoretical relation
UPenn - MEAN - 147
%%%%%MEAM 147 Lab 6Stress, Strain, and Hooke's Law:Stretching Rods of Varying Material and GeometryProfessor K. J. KuchenbeckerOctober 14, 2007%%%%%%%%%%%%%Description-This script performs error analysis on measurements of the mass
UPenn - MEAN - 147
%%%%%%%%%afm.m*Original Author: Katherine J. KuchenbeckerInteraction Forces at a Distance: Model of an Atomic Force Microscope (AFM)This script provides the basic structure for the activities of the modelAFM lab in MEAM 147 at the University
UPenn - MEAN - 147
function calibrate_evaluate()% CALIBRATE_EVALUATE% *% This function asks the user for and stores the names of the students on% the team, the width of the camera's view, the proportion of the video to% clip off the top and bottom of the frame, and the
UPenn - MEAN - 147
function evaluate(carMovieFilename)% EVALUATE% *% This function loads the specified avi movie to measure the trajectory of% two red objects that move across the frame. The function generates% plots and writes a .mat file of the captured horizontal mo
UPenn - ESE - 502
NOTEBOOK FOR SPATIAL DATA ANALYSISPart I. Spatial Point Pattern Analysis_2. Models of Spatial RandomnessAs with most all statistical analyses, cluster analysis of point patterns begins by asking:What would point patterns look like if points were rand
UPenn - ESE - 502
NOTEBOOK FOR SPATIAL DATA ANALYSISPart I. Spatial Point Pattern Analysis_3. Testing Spatial RandomnessThere are at least three approaches to testing the CSR Hypothesis: the quadrat method,the nearest-neighbor method, and the method of K-functions. We
UPenn - ESE - 502
NOTEBOOK FOR SPATIAL DATA ANALYSISPart I. Spatial Point Pattern Analysis_4. K-Function Analysis of Point PatternsIn the Bodmin Tors example above, notice from Figure 14a (p.20) that the clusteringstructure is actually quite different from that of the
UPenn - ESE - 502
ASYMPTOTIC COVARIANCE1. SAR Modely = X + u , u = Wu + , N (0, 2 I n ) u = ( I n W ) 1 = B 1 y N ( X , 2 ( B B ) 1 )If G = WB 1 , then the asymptotic covariance matrix is given by: 2 X ' B B X cov( , 2 , ) = 4 00n/2 2tr (G )24 tr (G ) tr (
UPenn - ESE - 502
ESE502Tony E. SmithBATCHFILE FOR USING SHP2BNDThe following is a procedure for using SHP2BND in the MUSA Lab (thanks to DarrylDepencier and Ankit Jain). This procedure allows SHP2BND to be run directly from abatch file, rather than using the DOS prom
UPenn - ESE - 502
BOUNDARY FILES1. IntroductionThis file describes the formatting procedures for converting between ARCMAP boundaryshapefiles and MATLAB boundary files.2. Converting ARCMAP Boundary Shapefiles to MATLAB Boundary FilesThe program for doing this is a DOS
UPenn - ESE - 502
Comparison of OLS and SAR(y = 0 + 1 x + u , u = Wu + , ~ N 0, 2True Model:)[ 0 = 10, 1 = 2, = 5, = .9, W = contiguities]OLS Estimation:R-square= 0.674*VARCOEFFt-VALP-VALconst3.9574640.6767370.253588x2.9795156.1034390.000005SAR Estima
UPenn - ESE - 502
Extreme Example of OLS vs SAR(y = 0 + 1 x + u , u = Wu + , ~ N 0, 2True Model:)[ 0 = 10, 1 = 2, = 5, = .9, W = contiguities]OLS Estimation:R-square= 0.305*VARCOEFFt-VALP-VALconst70.2929663.3882950.001638x-4.864506-2.8088860.005807SAR
UPenn - ESE - 502
ESE 502Tony E. SmithDACEYS COUNTY SEAT MODEL1. IntroductionThe following note specializes the general modified Poisson model of Dacey (1964) tothe case of county seats. His model is designed to capture the frequency distribution ofplaces across coun
UPenn - ESE - 502
ESE 502Tony E. SmithDETERMINANTS AS VOLUMESa c For n = 2 let A = b d , so thatdet( A) = ad bcNow look at the image of the positive unit square:acbdNow observe that the area is not changed bysliding the right side down to the axis:acbxdES
UPenn - ESE - 502
ESE 502Tony E. Smith_DISCRETE SPATIAL AUTOREGRESSIVE MODELSThe standard logistic, binomial and Poisson regression models of discrete counting datahave natural spatial generalizations in a manner similar to the conditional autoregressive(CAR) model.
UPenn - ESE - 502
ESE 502Tony E. SmithEMPIRICAL VARIOGRAMS1. Basic Idea. A typical plot of all distinct squared-difference pairs, y ( si ) y ( s j ) , is2shown in Figure 4.20 below. This plot, called the variogram cloud, typically containsthousands of points. [For ex
UPenn - ESE - 502
SPATIAL DATAANALYSISTony E. SmithUniversity of Pennsylvania Point Pattern Analysis Spatial Regression Analysis Continuous Pattern AnalysisPOINT PATTERN ANALYSISExample Application Areas Housing Sales Crime Incidents Infectious DiseasesPhiladel
UPenn - ESE - 502
Directory IndexName1_Examples_of_Point_Patterns.pdf2_Models_of_Spatial_ Randomness.pdf3_Testing_Spatial_Randomness.pdf4_K_Functions.pdfASYMPTOTIC_COVARIANCE.pdfBATCHFILE for SHP2BND.pdfBIAS OF RHO ESTIMATE.pdfBID_paper.pdfBND2SHP.EXEBOUNDARY_FI
UPenn - ESE - 502
16.11615.915.815.715.615.515.415.315.2415420425430435440445Discontinuity due to Nugget16.11615.915.815.715.615.515.415.315.2415420425430435440Smoothing with Zeroed Nugget445
UPenn - ESE - 502
1615.915.815.715.615.515.415.315.2415420425430435440Slice Through Kriged SurfaceKriged Error at (422,380) is given by i = cCHere the nugget is .20 andc = 0.029527C = -0.370190.0462770.0302380.0483880.26734-1.9345-0.722260.56385
UPenn - ESE - 502
ESE 502Tony E. SmithMULTIVARIATE NORMAL DISTRIBUTION1. Univariate Density.If X is distributed univariate normal with mean, , and variance, 2 , i.e., ifX N ( , 2 ) , then the probability density function, ( x; , 2 ) , of X is given by ( x; , ) =21
UPenn - ESE - 502
ESE 502 Tony E. Smith _POINT-EVENT REGRESSION MODELS Logistic Regression Models:Point Events:1 if i is diseased (etc.) si = 0 otherwise Probability Model:( x ) Pr(s = 1) = 1 + exp ( x ) expq i j =1 q j ij j =1 j ij Poisson Regression Models:Poin
UPenn - ESE - 502
A SCALE-SENSITIVE TEST OFATTRACTION AND REPULSIONBETWEENSPATIAL POINT PATTERNSTony E. SmithUniversity of Pennsylvania Diggle-Cox Test Lotwick-Hartwick Test A Combined Test Selected ApplicationsPoint Patterns:SX 1 = cfw_x1i : i = 1,., n1 SX 2
UPenn - ESE - 502
REGRESSION EXAMPLE Given data points xi = i , i =1,.,20 Consider the regression modelYi = 0 + 1 xi + i , i = 1,.,20 u1i = i1 + ui, i =1, i = 2,.,20ui iid N (0, 2 ) , i = 1,.,20:with parameters 0 = 1 , 1 = 0.8 , = 2302520151050-5-1002
UPenn - ESE - 502
REGRESSION SIMULATIONSY = 0 + 1 x1 + 2 x2 + u, [ 0 = 1, 1 = .04, 2 = .08]where u N (0, ) with :ij = s (hij | r , a, s ), hij = dist (i, j ),[r = 5, a = 0, s = 1]Simulation Summary (average values for 100 samples):GLS Est GLS Std Errconst 0.92840.48
UPenn - ESE - 502
T0T0T1T0T1T2 22 2 2T0T1 2T2T3 32 23 3 23 3 3T0T1 2T2 3T3T4 42 23 34 4 23 36 4 34 4 4
UPenn - ESE - 502
EXAMPLES OF SMOOTHERS FOR SPATIAL INTERPOLATION IN GEOSTATISTICAL ANALYSTESE 502 Tony E. SmithLocal Polynomial FitRadial Basis FitSpline Function FitOrdinary Krige FitLOCAL LINEAR POLYNOMIAL FITRADIAL BASIS FUNCTIONSGiven data ( si , yi ) , i = 1,
UPenn - ESE - 502
Spatial Autocorrelation Problem One-Dimensional ExampleAssume x values increasing along a roadwayTRUE TRENDy Correlated ErrorsxTRUE TRENDy REGRESSION LINEx
UPenn - ESE - 502
SPATIAL DIFFUSIONANALYSISExample Application Areas Diffusion of Information Diffusion of Toxic Wastes Spread of Infectious DiseasesProduct Adoption ExampleTony E. Smith and Sanyoung Songhttp:/www.seas.upenn.edu~tesmith Basic Model Steady State A
UPenn - ESE - 502
VECTOR COSINESAND ORTHOGONALITYxyy2min x y = ( x y )( x y )= xx 2 xy + 2 yy x y0=2= 2 xy + 2 yyxy xy ==2yyyxycos( ) =yx=yyx=xy yy2x=xyyx
UPenn - ESE - 502
MATLAB 5.0 MAT-file, Platform: PCWIN, Created on: Sat Feb 12 16:11:50 2005#IM#c##hgS_050200#7#type#handle#properties#children#special#@##f#i#g#u#r#e#0##Color#Colormap#InvertHardcopy#PaperPosition#Position#ResizeFcn#ApplicationData#DefaultaxesCreate
UPenn - ESE - 502
USC - BUAD - 310
M ul tiple Regression ProjectBUAD 31010:00 T, ThProfessor Gabrys4/28/11Team Members:Nicholaus JohnsonBrett KanA aron KimEugene KimJason K imJeremy K lifDavid KoRoy Kwon1 a) Examine the var iables and thei r relationships to each other:Profi
USC - BUAD - 310
M ul tiple Regression ProjectBUAD 31010:00 T, ThProfessor Gabrys4/28/11Team Members:Nicholaus JohnsonBrett KanA aron KimEugene KimJason K imJeremy K lifDavid KoRoy Kwon1 a)E xamine the var iables and thei r relationships to each other:Prof
USC - BUAD - 310
Jeremy K lifBUAD 310ID: 9128653432STATISTICS PROJECT1)a)Commission has no pattern of skeweness. Profit is very roughly normal butleft skewed. Area is heavily r ight skewed because the peaks are on the leftand it is unimodal. Outlet is roughly norm
USC - BUAD - 310
1)a)Pagecost and circulation seem to be pretty r ight skewed. Percentage of male and mediani ncome seem to be bimodal and do not really have any pattern of skewness.b) y=bo + b1x1 + b2x2 + b3x3 + EpsilononB) Page cost and circulation seem to have a m
USC - BUAD - 310
Nicholaus Johnson1. Examine the variables and their relationships to each other:a)Profit is roughly normal with no pattern of skeweness (peaks are in the middle of thegraph). Area is unimodal (has one peak) and right skewed (peak is to the left of the
USC - BUAD - 310
ProjectDue Friday, April 29The marketing managers of an office products company have some difficulty inevaluating the field sales representatives performance. The representatives travel among theoutlets that carry companys products, create displays, t
USC - BUAD - 310
DIST1234567891011121314151617181920212223242526272829303132333435363738394041424344PROFIT AREAPOPNOUTLETS COMMIS101116.963.88213113187.313.14158115567.813.77203115217.314.59170197919.84
USC - BUAD - 310
Nicholas HuangProjectBUAD-310, Section: 14880Instructor: Robertas GabrysApril 29, 2011BUAD-310Project1. Examine the variables and their relationships to each other:a. First look at how each variable behaves on its own by creating histograms ofeac
USC - BUAD - 310
USC - BUAD - 310
Use the following to answer question 1.The researchers conducting this study wish to estimate the winnings when the averagenumber of putts per hole is 1.75. The following results were obtained from software.Predicted winningsStandard error95.0% C.I.
USC - BUAD - 310
BUAD 310: Final ExamFirst name: . Last name:.1. You are doing a one sided, greater than, hypothesis test for the population proportion using a sample of 25, and youget a test statistic of 1.7. This means:A)B)C)D)E)you cannot reject the null hypot