# 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.

11 Pages

### Bailey&Gatrell(Chapter 5)

Course: GEO 6938, Summer 2011
School: University of Florida
Rating:

Word Count: 1502

#### Document Preview

Data Continuous Analysis Analysis of Spatially Continuous Data Bailey and Gatrell Chapter 5 Focus is on patterns in the attribute values not locations as in the analysis of point patterns The locations are simply sites at which attribute values have been recorded within a region Attributes are conceptually spatially continuous. Examples include observations on rainfall, temperature, salinity, air quality...

Register Now

#### Unformatted Document Excerpt

Coursehero >> Florida >> University of Florida >> GEO 6938

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.
Data Continuous Analysis Analysis of Spatially Continuous Data Bailey and Gatrell Chapter 5 Focus is on patterns in the attribute values not locations as in the analysis of point patterns The locations are simply sites at which attribute values have been recorded within a region Attributes are conceptually spatially continuous. Examples include observations on rainfall, temperature, salinity, air quality variables such as ozone, soil variables such as permeability, conductivity, pH, Lecture 13 October 20, 2009 1 2 The Data Observations on a spatially stochastic process Y s , s R that varies continuously over a region R and has been sampled at fixed point locations si. Referred to as y ( si ) for random variable Y ( si ) Shortened to y or Y(si) for the vector Y = (Y(s1) ,...Y(sn)) for i the random variable Y Often referred to as geostatistical data Analysis Objectives: Infer the nature of spatial variation in an attribute over the whole of a region R based on sampled point values. Examine first order effects variations in the mean value of surface (large scale), and second order effects (spatial dependence between values at any 2 locations) locations). Model the pattern of variability of an attribute and determine factors that might relate to it Obtain predictions of a value at un-sampled locations 3 4 1 Continuous Data Analysis Develop descriptions that capture global trends as well as local variability Consider first and second order effects Visualizing Spatially Continuous Data Use symbols that will represent the information on the data values Proportional circles or rectangles are often used The size of the circle is proportional to the data value e.g. radius equal to the square root of data values Or height of the rectangle is proportional to the data value Colors can be used to reinforce the same data value or add a different variable 5 6 E Y ( s ) s COV Y ( si ), Y ( s j ) Spatial dependence between Y (si )and Y (s j ) (s (s Propose models consisting of two components First order component representing large (coarse) scale variation Second order component representing fine scale spatial dependence 7 8 2 Visualizing Spatially Continuous Data When mapping symbols to classes the number of classes and the type of class interval "influence the message" Larger numbers of data values typically require more classes to cover the range Rule of thumb Number of classes equal to 1+ 3.3 log n where n is the number of observations Start by first examining the distribution of values before selecting class intervals For data with very skewed distributions it is useful to transform the data values first. 9 10 Visualizing Spatially Continuous Data Equal Intervals The equal interval method divides the range of attribute values into equal sized sub-ranges. Good if data values are uniformly distributed over their range If data are skewed there will be large number of values in a few classes, and classes with few, if any map features Trimmed Equal Intervals Assign top and bottom ten percent to separate classes and equally divide remainder 11 Equal Intervals 12 3 Trimmed Equal Interval Visualizing Spatially Continuous Data Percentiles of the distribution each class contains the same number of features, a good method of classification for evenly distributed data, features with greatly different values can be placed in a single class Standard deviates Class breaks are set above and below the mean at intervals of either 1/4, 1/2, or 1 standard deviations until all the data values are contained within the classes. Natural break intervals Natural breaks find groupings and patterns inherent in the data by minimizing the sum of the variance within each of the classes 13 14 Quantiles Standard Deviates 15 16 4 Natural Breaks Exploring Spatially Continuous Data First consider approaches to investigate variation in the mean value E Y ( s ) s over the region Spatial moving averages Interpolation based on tessellations Kernel estimation Next consider approaches to investigate second order effect or spatial dependence among values within the region Covariogram, variogram 17 18 Spatial Moving Averages Estimate mean by averaging values at nearby data points Unweighted average Average n data values nearest to location s Weighted W i ht d average Methods Based on Tessellations Estimate (s) from a tiling of the observed sample locations si Common tessellation is the Delauney Triangulation n locations in the plane can be assigned a territory closer to the point than to any other Dirchelet Tessellation, Voronoi, or Theissen polygons Lines joining contiguous locations form a triangulation ^ ( s) wi ( s ) yi i 1 n w ( s) 1 i wi ( s) hi wi ( s ) e hi , parameter to change the 19 degree of smoothing 20 hi is the distance from s to si 5 Methods Based on Tessellations Each triangular face is associated a with function that is used to interpolate values at unknown points within the triangle Interpolated values are then used to construct isolines TIN and constructed Isolines 21 22 Methods Based on Tessellations The resulting contours are an exploratory device to examine variation in the mean value Natural neighborhood interpolation Kernel Estimation ^ ( s) n 2 i 1 1 s si k We are now interested in an estimate of the mean value for the attribute yi rather than intensity of events ^ ( s ) wi ( s ) yi i 1 n si s i 1 n 1 s si k yi 2 Weights are now based on areas of Vornoi polygon around si "stolen" by the tile around s Represents the amount of the attribute per unit area To find the average we need to divide by number of observations per unit area 23 24 6 Kernel Estimation s si k y i ^ ( s ) i 1n s s k i i 1 n The effect of increasing the bandwidth is to increase the degree of smoothing Kernel Estimate for mean August temperature Variation on the weighted moving average w ( s) y i 1 i n i s si k wi ( s ) n s s k j j 1 25 26 Covariogram and Variogram Used to explore the spatial dependence of deviations in attribute values from their mean The covariance function is analogous to the K function for analyzing second order properties in point patterns In the continuous data case we are interested in the way the deviations of observations from their mean values covary over the region In most cases we expect positive covariance or correlation at short distances for spatially continuous phenomena 27 Covariogram and Variogram Assume we have a spatial stochastic process Y s , s R E Y ( s ) as s VAR(Y ( s )) as 2 ( s ) The covariance of the process at any two points si and sj C si , s j E ((Y ( si ) ( si ))(Y ( s j ) ( s j ))) Correlation is: Variance si , s j C ( si , s j ) ( si ) ( s j ) C ( s, s ) 2 ( s ) 28 7 Covariogram and Variogram The process is stationary if: (s ) Covariogram and Variogram (s) 2 2 Mean and variance are independent of location and constant throughout the region and The process is isotropic if the dependence is only a function of distance and not direction That is dependent only on the length of the vector h Then C ( si , s j ) C ( si s j ) C (h) Referred to as the covariogram or covariance function of the process C ( si , s j ) C (h) Covariance depends only on the vector difference h and ( si , s j ) (h) C (h) (h) Referred to as the correlogram C (0) 2 29 30 Covariogram and Variogram Intrinsic Stationarity A weaker assumption of stationarity There is a constant mean and constant variance in the differences between values at locations separated by a given distance and direction Covariogram and Variogram For stationary processes the covariogram, correlogram and variogram are related ( h) C ( h) 2 E (Y ( s h) Y ( s )) 0 VAR(Y ( s h) Y ( s )) 2 (h) (h) 2 C (h) (h) is strictly the semivariogram but commonly referred to as the variogram 31 32 8 Covariogram and Variogram 2 C ( 0) covariogram correlogram C (h) (h) 2 h h (h) ( h) variogram C ( h) 2 Variogram stops increasing beyond a certain distance and becomes stable at a limit value () called Sill () Var{Y ( s )} C (0) Range distance at which this occurs - transition from state in which spatial correlation exists to absence of correlation Nugget value of variation ( h ) 2 C ( h) h 33 (h) at h = 0 due to measurement error or micro-scale 34 Exploratory Analysis Steps Estimate an isotropic variogram or covariogram Estimate directional variograms in 2 or 3 broad directions to examine directional effects - anisotrophy A general exploratory technique is the variogram cloud Estimation of the Variogram 2^ (h) 1 2 hyi y j n ( h ) si s j Sum squared differences over all pairs less than or equal to a distance h Assuming isotropy, variogram is estimated over all directions for a given separation distance h Estimation of the Covariogram 1 ^ C (h) yi y y j y n ( h ) si s j h 35 36 9 Estimation of the Variogram 3000 2500 se emivariance 363677 2000 1500 1000 126191 500 0 0 5 10 15 q1b 0 Nug(0) + 3100 Sph(12) 20 25 distance 30 35 40 108521 1063443 1066870 74336777295 8 930802037260 554407 1 n(h) varies as h increases so the reliability of estimates vary 37 38 Anisotrophic Variograms Variogram Cloud The variogram cloud is the distribution of the variance between all pairs of points at all possible distances (h). Plot of or Plot of ( yi y j ) 2 against h (si sj) (| yi y j |) / 2 against h 39 40 10 2*10^6 Variogram Cloud Variogram Cloud 8000 1.5*10^6 ga amma 10^6 gam mma 5*10^5 0 0 0 2000 40 000 6000 1000 2000 distance 3000 0 20 40 60 distance 80 100 120 41 140 42 Radon Data 600 g gamma 0 0 100 200 3 300 400 500 50 100 distance 150 200 43 11
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:

University of Florida - GEO - 6938
Exploratory temporal visualization of Massachusetts breast cancer data archives Alex Brown, Toxics Use Reduction Institute (TURI) and UMass-Lowell Dept of Environmental, Earth &amp; Atmospheric Sciences (Corresponding author: Alexander_Brown@uml.edu) Dr Rich
University of Florida - GEO - 6938
Regression Models for Count Data in RAchim ZeileisUniversitt Innsbruck aChristian KleiberUniversitt Basel aSimon JackmanStanford UniversityAbstract The classical Poisson, geometric and negative binomial regression models for count data belong to th
University of Florida - GEO - 6938
Appendix D:Negative Binomial Regression Models and Estimation MethodsBy Dominique Lord Texas A&amp;M University Byung-Jung Park Korea Transport Institute This appendix presents the characteristics of Negative Binomial regression models and discusses their e
University of Florida - GEO - 6938
Appendix COrdinary Least Squares and Poisson Regression Modelsby Luc Anselin University of Illinois Champaign-Urbana, IL This note provides a brief description of the statistical background, estimators and model characteristics for a regression specific
University of Florida - GEO - 6938
SaTScan User GuideTMfor version 9.0By Martin Kulldorff July, 2010 http:/www.satscan.org/ContentsIntroduction . 4 The SaTScan Software . 4 Download and Installation. 5 Test Run . 5 Sample Data Sets . 6 Statistical Methodology . 9
University of Florida - GEO - 6938
EXTREME VALUE THEORYRichard L. Smith Department of Statistics and Operations Research University of North Carolina Chapel Hill, NC 27599-3260 rls@email.unc.edu AMS Committee on Probability and Statistics Short Course on Statistics of Extreme Events Phoen
University of Florida - GEO - 6938
An Application of Extreme Value Theory for Measuring RiskManfred Gilli, Evis Kllezi eDepartment of Econometrics, University of Geneva and FAME CH1211 Geneva 4, SwitzerlandAbstract Many fields of modern science and engineering have to deal with events w
University of Florida - GEO - 6938
1WHY EXTREME VALUE THEORY?1.1 A Simple Extreme Value ProblemMany statistical tools are available in order to draw information concerning specific measures in a statistical distribution. In this textbook, we focus on the behaviour of the extreme values
University of Florida - GEO - 6938
Spatial analysis of density dependent pattern in coniferous forest stands*Janet Franklin 1, Joel Michaelsen 1 &amp; Alan H. Strahler2*, *1Department of Geography, University of California, Santa Barbara, California, 93106; 2Department of Geology and Geograp
University of Florida - GEO - 6938
University of Florida - GEO - 6938
GeoDa: An Introduction to Spatial Data AnalysisLuc Anselin, Ibnu Syabri and Youngihn Kho Spatial Analysis Laboratory Department of Agricultural and Consumer Economics University of Illinois, Urbana-Champaign Urbana, IL 61801 USAanselin@uiuc.edu, syabri@
University of Florida - GEO - 6938
Geographical Analysis ISSN 0016-7363GeoDa: An Introduction to Spatial Data AnalysisLuc Anselin1, Ibnu Syabri2, Youngihn Kho11Spatial Analysis Laboratory, Department of Geography, University of Illinois, Urbana, IL, 2Laboratory for Spatial Computing an
University of Florida - GEO - 6938
Exploring Spatial Data with GeoDaTM : A WorkbookLuc AnselinSpatial Analysis Laboratory Department of Geography University of Illinois, Urbana-Champaign Urbana, IL 61801http:/sal.agecon.uiuc.edu/Center for Spatially Integrated Social Sciencehttp:/www.
University of Florida - GEO - 6938
Available online at www.sciencedirect.comEconomics Letters 99 (2008) 585 590 www.elsevier.com/locate/econbaseFunctional forms for the negative binomial model for count dataWilliam Greene Department of Economics, Stern School of Business, New York Univ
University of Florida - GEO - 6938
Geographical Processes and the Analysis of Point Patterns: Testing Models of Diffusion by Quadrat Sampling Author(s): D. W. Harvey Source: Transactions of the Institute of British Geographers, No. 40 (Dec., 1966), pp. 81-95 Published by: Blackwell Publish
University of Florida - GEO - 6938
Some Methodological Problems in the Use of the Neyman Type A and the Negative Binomial Probability Distributions for the Analysis of Spatial Point Patterns Author(s): David Harvey Source: Transactions of the Institute of British Geographers, No. 44 (May,
University of Florida - GEO - 6938
Landscape Ecology 15: 467478, 2000. 2000 Kluwer Academic Publishers. Printed in the Netherlands.467Lacunarity analysis of spatial pattern: A comparisonM.R.T. DaleDepartment of Biological Sciences, University of Alberta, Edmonton, Alberta, T6G 2E9, Can
University of Florida - GEO - 6938
GEO 6938 Advanced Quantitative Methods for Spatial Analysis Spring 2012 Timothy J. Fik, Ph.D. Associate Professor Department of Geography University of Florida e-mail: &quot;fik@ufl.edu&quot;Selected Topics include. Point-Pattern/Pattern Analysis &amp; Modeling Cluste
University of Florida - GEO - 6938
Lab #1. Point Pattern Analysis using Quadrat counts Carry out a point pattern analysis using quadrat counts based on grid cells superimposed on a given study (a two dimensional surface) containing a spatial distribution of points that represent the locati
University of Florida - GEO - 6938
Poisson Regression. continuedIn the PR model, the mean and variance V are assumed/restricted to be equal.something that rarely occurs in practice (as real data almost always rejects this restriction when tested).Typically, the variance is greater than t
University of Florida - GEO - 6938
Spatial Diffusion &amp; Pattern AnalysisFive general types of spatial diffusion processes.3 2 11. Expansion Diffusion a simple outward expansion from the source (covering a larger, more extensive area over time).Study area2. Relocation Diffusion the move
University of Florida - GEO - 6938
Nearest-Neighbor MethodsDefining &quot;connectivity&quot; between points Point data can be used in various ways to measure the degree to which the point pattern exhibits spatial autocorrelation.But first, care must be taken in describing the nature of connectivit
University of Florida - GEO - 6938
Analysis of Pattern Measuresat the Local/Regional Scale9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.Local Moran's I The Score Statistic Also Getis G-statistic Tango's CF (which we will review later) 2 Cumulative test Maximum 2 test Local Quadrat Test
University of Florida - GEO - 6938
Identification of Local Clusters for Count Data: A Model-Based Moran's I TestTonglin Zhang and Ge LinPurdue University and West Virginia University February 14, 2007Department of Statistics, Purdue University, 250 North University Street,West Lafayette
University of Florida - GEO - 6938
Chapter 4 Modelling Counts - The Poisson and Negative Binomial RegressionIn this chapter, we discuss methods that model counts. In a longitudinal setting, these counts typically result from the collapsing repeated binary events on subjects measured over
University of Florida - GEO - 6938
Notes on the Negative Binomial DistributionJohn D. Cook October 28, 2009Abstract These notes give several properties of the negative binomial distribution. 1. Parameterizations 2. The connection between the negative binomial distribution and the binomia
University of Florida - GEO - 6938
On Model Fitting Procedures for Inhomogeneous Neyman-Scott ProcessesYongtao GuanJuly 31, 2006ABSTRACTIn this paper we study computationally efficient procedures to estimate the second-order parameters for a class of inhomogeneous Neyman-Scott processe
University of Florida - GEO - 6938
Spatial AutocorrelationGeography 683 - Introduction to Geographic AnalysisSpatial AutocorrelationGuoxiang Ding Department of Geography1155 Derby Hall Phone: 292-2704 Email: ding.45@osu.edu First law of geography: &quot;everything is related to everything
University of Florida - GEO - 6938
Overdispersion and Poisson RegressionRichard Berk John MacDonald Department of Statistics Department of Criminology University of Pennsylvania November 19, 2007Abstract This article discusses the use of regression models for count data. A claim is often
University of Florida - GEO - 6938
Spatial AutocorrelationMoran's I Geary's C Arthur J. Lembo, Jr. Salisbury UniversitySpatial Autocorrelation First law of geography: &quot;everything is related to everything else, but near things are more related than distant things&quot; Waldo Tobler Many geog
University of Florida - GEO - 6938
Analysing spatial point patterns in RAdrian Baddeley CSIRO and University of Western Australia Adrian.Baddeley@csiro.au adrian@maths.uwa.edu.au Workshop Notes Version 3 October 2008 Copyright c CSIRO 2008Abstract This is a detailed set of notes for a wo
University of Florida - GEO - 6938
136Poisson Regression Analysis13. Poisson Regression AnalysisWe have so far considered situations where the outcome variable is numeric and Normally distributed, or binary. In clinical work one often encounters situations where the outcome variable is
University of Florida - GEO - 6938
Parametric Test Quadrat AnalysisEquations taken from Rogerson, 2001.i=m i =1s2 = (xs2 xi- x )2m -1 m -1 (VMR - 1) z= 2 m is the number of quadrats, x is the mean of the number of points per quadrat, s2 is the variance of the number of points per
University of Florida - GEO - 6938
AN INTRODUCTION TO QUADRAT ANALYSISR.W.ThomasISSN 0306-6142ISBN 0 902246 66 6 1977 R.W. ThomasCONCEPTS AND TECHNIQUES IN MODERN GEOGRAPHY No. 12CATMOG(Concepts and Techniques in Modern Geography) CATMOG has been created to fill a teaching need in th
University of Florida - GEO - 6938
Rate Transformations and SmoothingLuc Anselin Nancy Lozano Julia KoschinskySpatial Analysis Laboratory Department of Geography University of Illinois, Urbana-Champaign Urbana, IL 61801 http:/sal.uiuc.edu/Revised Version, January 31, 2006Copyright c 20
University of Florida - GEO - 6938
Change Detection Thresholds: Alternative Statistical Approaches to Detecting Temporal Change in Spatial PatternsPeter A. Rogerson Daikwon Han Ikuho Yamada Department of Geography National Center for Geographic Information and Analysis University at Buffa
University of Florida - GEO - 6938
The author(s) shown below used Federal funds provided by the U.S. Department of Justice and prepared the following final report: Document Title: Author(s): Document No.: Date Received: Award Number: Crime Analysis Geographic Information System Services: A
University of Florida - GEO - 6938
Spatial AutocorrelationMorans I Gearys C Arthur J. Lembo, Jr. Salisbury UniversitySpatial Autocorrelation First law of geography: everything is related to everything else, but near things are more related than distant things Waldo Tobler Many geograph
University of Florida - GEO - 6938
SaTScan User GuideTMfor version 8.0By Martin Kulldorff February, 2009 http:/www.satscan.org/ContentsIntroduction . 4 The SaTScan Software . 4 Download and Installation . 5 Test Run . 5 Sample Data Sets. 5 Statistical Methodology.
University of Florida - GEO - 6938
Further Methods for Point Pattern AnalysisBailey and Gatrell Chapter 4Variations in Populationn Certain types of events will exhibit clustering due to heterogeneity in the underlying distribution e.g disease cases or crimes will tend to cluster where t
University of Florida - GEO - 6938
Non-technical Overview of Geospatial Statistical MethodsGIS/Mapping and Census Data Second Annual Census Workshop Series Workshop 3: Spatial Statistics, Spatial Research &amp; Confidential Census DataNew York Census Research Data Center (CRDC) Baruch Colleg
University of Florida - GEO - 6938
Package `spatstat'December 21, 2011Version 1.25-1 Date 2011-12-21 Title Spatial Point Pattern analysis, model-fitting, simulation, tests Author Adrian Baddeley &lt;Adrian.Baddeley@csiro.au&gt; and Rolf Turner &lt;r.turner@auckland.ac.nz&gt; with substantial contrib
University of Florida - GEO - 6938
Andrei Rogers and Norbert G. GomarStatistical inference in Quadrat AnalysisThe growing recognition of the need for establishing a systematic and quantitative means for describing and analyzing, the spatial dispersion of activities in urban areas has gen
University of Florida - GEO - 6938
Biometrical Journal 50 (2008) 1, 4357 DOI: 10.1002/bimj.20061033943Parameter Estimation and Model Selection for Neyman-Scott Point ProcessesUshio Tanaka1, Yosihiko Ogata*, 1, 2, and Dietrich Stoyan31 2 3The Graduate University for Advanced Studies, M
University of Florida - GEO - 4167
Review of Matrix AlgebraMatrices A matrix is a rectangular or square array of values arranged in rows and columns. An m n matrix A, has m rows and n columns, and has a general form of a11 a = 21 . am1 a12 a22 . am 2 . a1n . a2 n . . . amn mn mn1Exa
University of Florida - GEO - 4167
University of Florida - GEO - 4167
Geographically Weighted RegressionA Tutorial on using GWR in ArcGIS 9.3Martin Charlton A Stewart FotheringhamNational Centre for Geocomputation National University of Ireland Maynooth Maynooth, County Kildare, Ireland http:/ncg.nuim.ieThe authors grat
University of Florida - GEO - 4167
GEOGRAPHICALLY WEIGHTED REGRESSIONWHITE PAPERMARTIN CHARLTON A STEWART FOTHERINGHAMNational Centre for Geocomputation National University of Ireland Maynooth Maynooth, Co Kildare, IRELANDMarch 3 2009The authors gratefully acknowledge support from a S
University of Florida - GEO - 4167
Lab#1, Spring 2012 (25 points) GEO 4167/GEO 6161 Intermediate Quantitative Methods (Fik) Name: _ Score: _ Instructions: Complete this lab to the best of your abilities. Attach your work sheets, relevant computer output, results, and write-up to this cover
University of Florida - GEO - 4167
Polynomial regressionDaniel Borcard, Dpartement de sciences biologiques, Universit de Montral Reference: Legendre and Legendre (1998) p. 526A variant form of multiple regression can be used to fit a nonlinear model of an explanatory variable x (or sever
University of Florida - GEO - 4167
Board of the Foundation of the Scandinavian Journal of Statistics 2004. Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA Vol 31: 515534, 2004Functional Coefficient Regression Mode
University of Florida - GEO - 4167
AN INTRODUCTION TO TREND SURFACE ANALYSISD.UnwinISSN 0305-6142 ISBN 0 902246 51 8 1978 David J. UnwinCONCEPTS AND TECHNIQUES IN MODERN GEOGRAPHY No. 5CATMOG(Concepts and Techniques in Modern Geography) CATMOG has been created to fill a teaching need
University of Florida - GEO - 4167
Intermediate Quantitative MethodsTimothy J. Fik Associate Professor GEO 4167 section #6647 (undergraduate) GEO 6161 section #8377 (graduate)Credit hours: 3Thursdays (periods 2-4): 8:30-11:30AM Location: TUR 3012 SPRING 2012Intermediate Quantitative Me
University of Florida - GEO - 4167
More on the Reliability, Precision, and Performance of the regression model and its estimated parameters. As the least-squares coefficient/parameter estimates ( j's) and the SRF's ability to explain variation in the dependent variable (Y) can vary from sa
University of Florida - GEO - 4167
II. Testing for Multicollinearity When two or more independent variables in a regression model are highly correlated with one another (or collinear), they will contribute &quot;redundant&quot; explanatory information. Hence, not all of those independent variables
University of Florida - GEO - 4167
Recall our recent Reading Assignments. Read and review: (a) the technical appendix in your textbook on Matrix approach to LS regression. Basic Econometrics by D. Gujarati, 2007, 4th edition. and/or (b) the posted Matrix Algebra review and the Matrix Appro
University of Florida - GEO - 4167
Extending Linear Regression: Weighted Least Squares, Heteroskedasticity, Local Polynomial Regression36-350, Data Mining 23 October 2009Contents1 Weighted Least Squares 2 Heteroskedasticity 2.1 Weighted Least Squares as a Solution to Heteroskedasticity
University of Florida - GEO - 4167
OLS Under Heteroskedasticity Testing for HeteroskedasticityHeteroskedasticity and Weighted Least SquaresWalter Sosa-EscuderoEcon 507. Econometric Analysis. Spring 2009April 14, 2009Walter Sosa-EscuderoHeteroskedasticity and Weighted Least SquaresOL
University of Florida - GEO - 4167
Regression Analysis Tutorial183LECTURE / DISCUSSION Weighted Least SquaresEconometrics Laboratory C University of California at Berkeley C 22-26 March 1999Regression Analysis Tutorial184IntroductionIn a regression problem with time series data (whe
University of Florida - GEO - 4167
Intermediate Quantitative MethodsTimothy J. Fik Associate Professor GEO 4167 section #6647 (undergraduate) GEO 6161 section #8377 (graduate)Credit hours: 3Thursdays (periods 2-4): 8:30-11:30AM Location: TUR 3012 SPRING 2012Intermediate Quantitative Me
University of Florida - AST - 1002
UFIDQ1 9.2 8.25 4.5 8 9.85 5.5 9.1 10 7.5 4.5 9.85 6 3.5 7 6.35 10 9 7.5 9.5 5.25 6.75 5 5.75 2.5 5.25 3.25 6.1 7 6.5 9.1 5 3.25 6.5 8.75 9 3.5 10 5 4.1 5.1 4.5 6.7501713653 03291993 03891805 05193165 09669612 11156163 11161338 11314038 11334031 1139879