Free-Standing Office/Retail Newport News, Virginia
|
|
- Albert Dawson
- 5 years ago
- Views:
Transcription
1 Free-Standng Offce/Reta Newport News, Vrgna Locaton: 645 J Cyde Morrs Bvd, Newport News, Vrgna 2361 Descrpton: Stuated at the "Far Rght" corner of the sgnazed ntersecton of Woods Road and J Cyde Morrs Bvd, the free-standng Medca/ Reta budng offers exceent vsbty and access to one of the Pennsuas most traveed corrdors Sze: Land: Parkng: 9,72 Sq Ft 18 Acre 39 Parkng Stas Area Tenancy: Rversde Regona Hospta, Cty Center, Oyster Pont Traffc Counts: J Cyde Morrs Bvd: 42, VPD Demographc Summary 217 Estmated Popuaton 217 Est Medan ncome 217 Est Day Tme Popuaton 1 Mes 2 Mes 3 Mes 12,35 43,9 79,16 $56,574 $58,763 $69,41 14,25 36,518 51,116 Reta Advsors, nc, Yorktown, VA, Rob Heavner rob@retaadvsorsus P O Box 1327 Yorktown, Vrgna Offce: Fax: Ths report was produced usng prvate and govt sources deemed to be reabe The nformaton heren s provded wthout representaton or warranty
2
3
4 fon 4 WDE CONCRETE GUTTER DETAL q :a: PF SHRUBS SHRs E-:;+rJE, _j N _ -f 12 BSB[ - ccwc -DJC 2Q--TRANS+QNAk--BR { _ " " - --, " L : --,62 :, 2 2X2X7" ; "\" :" : : " PAD _ : :- 11, ) DUMPSTER > /, - - : N F ",, " ; : : " 1KC HOUJNGS LLC 5 R,,, : : :-,ap,6 /c; s1(p[},eo) _ : > ::::: 1 _z_ ONED_ c 1 f " : " " " DRY EXTEND/ED, " Jm4 ARP, ; " " " " " ",," " DETENT ON : " 47,64 SF : " " " " " " BASN " 18 AC " " " " " ; " """ """ 1---,, CONCRETE : : " : ",," "" " " BUMPER 18>: " " 4" " " BLOCK (TYP) "" "" " "" "" " f J s1d " " t " " )(" " "5;R)( )( Jt - : )( >c )( )( >c >c )( )( )( >c J4 () O " >c ">c )(,()( 1)( >c <,1 " 9?"o sf BULDNG, ::_ _ " " " " "" " " " " " " " " " 1 " " " ", :"" _z < " t f :,_ """" """ "" "" "" "" " "" "" "" ",," "" r,,",,"{ :: ;:/ ff O)( )( )r )( )( )( " )( )( " 1 - <( - Jr)( )( )( ">c J(, 1::?n:: - - _@-"()( )( )( )( )( )()( )( )(" )()( )( )( )( )( )( : <- J",,1, orc- )( )( )( )( )( )( )( )( )( - c :d: (_) JJ CJ) ">r )(,, [C )(, _, "->c>c )( ::" "", 5 R, :::: r:,, " " " " " Pf L " " =t < ; w "" " " 1e; -_ - - : -,--- - _::: Jc )( - )( R7,8 H/C SGN " " " W / ALL REOD " " " " " " " : : ADD SGNAGE ",,",," " ",," " 1 ;: TAC(-fLE" " : LAND : b/ AAMR " YARD ff);p r "",,:r:: :,-,r7, t )( )( )( "" ":::, " " 1-_,,--1-t 5, 5 SW " " )( " x <", )( )( )( )()f )( )(O 1c: ; "" )()( )( cc; 5R "" "><fr - _,-: )(" Jc" >c >c Jc" :::_::: 5 R ",,,," " " ;ff_ j,_,, ;=, - )()( )()()( " "G-1 A " " " " " d O Jc" >c )( re e >< >< r ----,;, )(>c )( >c>c JcJc : < < < A,<*"o" rl )( )( )( < < )( >c )( )( < " )( )( "t(" V\ )( )( )( )( )( " " " " " " "u" ff " " " " " " " " " " )( >cr )( )("" >c)( >c --; )( )( )(" )(" )( )( Jr r )( )( )( ) < " - f " " " "5 >< " S " " " " >< " " " " " " co s_ RFT _, 117 """",,,," "",," ",," 1133,Js - _:_:/,:_:_:/: ;,, rjns;jr SGN c < «_J L , - c" c, "«_< < - J " m T9, EX CONCRETE >= ", " 3 f a) <", \ Q, N - "- " <:,;, ---n , CQNC c ;c 1" acj>- [1 EX 1 WOE c < < < m,,/ CONC WALK < c,c 3 D < < > f," u - -< [ 1 1,, - - <, :" J - - <<"" <:,,, < - rn < c <, " c: ",<- j <, -< - < _;< < < r -: < - "(- <"/ <, -, < ( " <", < < - A, < "" :-,,, < c" : ; ;- E J 1 1sss f : / T Dfv< / <, 28± OF r? OF CG-1 MATCH E, 111- <:, NEW 5 SD BEHND EX ;< 11,, < < < 11- f <, < c TE NEW SW TO EX $W r-1 vns: unddco """"" ::UADft
5 r - " -- : - m a r,, - H -;;; r,, M -,,, - - > z -J < df> e 11 /( ;;:,, M 7 " \ "" 1,- ), 1u" 1\ r>< j " E, v- :: ;@ ]O -+"", _,,_, --- c,;--- _, < "",,-,; - 1 : ;(_( < 1 ; w C) -q ;r 1 d ) 1 ;-f -:: 1 :: r,_, H M,_,, ; h :, 1\ ; t 7:- y; J m 3" ;; ; : ; " t-,, ::=: d : : ;, c - - L ---- j t 81 " ( 8, ( 1 L -;@ ( [@\ :1 - :,,- ", 1 < C)\,_, )f \ j 11 MT "2 c sh 9 r L{f; [ F \ - <e 1 > Pe (@ " ", (,_ " M 1 : < 1:,_ 1 - f;: \ B - - < feo )> FLOOR PLAN " REVOOt< -- -,_,_ ro(")m J_ 11 m2vurgery CENTER ;;no 4-r m- u -- NEWPORT NEWS VRGNA ";: " " -, 3
6 SUMMARY PROFLE 2-21 Census, 217 Estmates wth 222 Projectons Cacuated usng Weghted Bock Centrod from Bock Groups Reta Advsors, nc Lat/Lon: 37758/ J Cyde Morrs Bvd Newport News, VA m radus 2 m radus 3 m radus RS1 POPULATON HOUSEHOLDS RACE AND ETHNCTY NCOME EDUCATON (AGE 25+) 217 Estmated Popuaton 12,35 43,9 79, Projected Popuaton 11,946 42,716 78, Census Popuaton 11,589 42,621 78,325 2 Census Popuaton 9,987 39,186 74,358 Projected Annua Growth 217 to 222-1% -1% -2% Hstorca Annua Growth 2 to % 6% 4% 217 Medan Age Estmated Househods 5,679 18,348 33, Projected Househods 5,868 18,967 34,12 21 Census Househods 5,269 17,44 31,495 2 Census Househods 4,551 16,59 3,138 Projected Annua Growth 217 to 222 7% 7% 6% Hstorca Annua Growth 2 to % 6% 6% 217 Estmated Whte 554% 561% 585% 217 Estmated Back or Afrcan Amercan 29% 329% 297% 217 Estmated Asan or Pacfc sander 36% 3% 42% 217 Estmated Amercan ndan or Natve Aaskan 4% 4% 4% 217 Estmated Other Races 116% 76% 71% 217 Estmated Hspanc 133% 86% 77% 217 Estmated Average Househod ncome $67,1 $7,898 $85, Estmated Medan Househod ncome $56,574 $58,763 $69, Estmated Per Capta ncome $31,682 $3,476 $36, Estmated Eementary (Grade Leve to 8) 6% 4% 31% 217 Estmated Some Hgh Schoo (Grade Leve 9 to 11) 74% 69% 52% 217 Estmated Hgh Schoo Graduate 234% 252% 225% 217 Estmated Some Coege 275% 273% 255% 217 Estmated Assocates Degree Ony 11% 95% 99% 217 Estmated Bacheors Degree Ony 158% 163% 198% Ths report was produced usng data from prvate and government sources deemed to be reabe The nformaton heren s provded wthout representaton or warranty 217 Estmated Graduate Degree 99% 17% 14% BUSNESS 217 Estmated Tota Busnesses 1,189 2,588 3, Estmated Tota Empoyees 14,25 36,518 51, Estmated Empoyee Popuaton per Busness Estmated Resdenta Popuaton per Busness , Stes USA, Chander, Arzona, page 1 of 1 Demographc Source: Apped Geographc Soutons 1/217, TGER Geography
7
Spatial Statistics and Analysis Methods (for GEOG 104 class).
Spatal Statstcs and Analyss Methods (for GEOG 104 class). Provded by Dr. An L, San Dego State Unversty. 1 Ponts Types of spatal data Pont pattern analyss (PPA; such as nearest neghbor dstance, quadrat
More informationUniversity of California at Berkeley Fall Introductory Applied Econometrics Final examination
SID: EEP 118 / IAS 118 Elsabeth Sadoulet and Daley Kutzman Unversty of Calforna at Berkeley Fall 01 Introductory Appled Econometrcs Fnal examnaton Scores add up to 10 ponts Your name: SID: 1. (15 ponts)
More information"ll,' I. \~', l LI\) ',,I "~ ~ PROP.OSED -,.J PROJECT'', FIGURE 1 Proposed Project Location and Vicinity Map Tolsona Exploration Project
5 a Vicinity Map Legend \~', l L\) ',, "~ ~ ~ ' ~~," 13. ' o ~ PROP.OSED -,.J PROJECT'', ~ Tolsona Well Pad e Mile Post (MP) -- Tolsona Access Route - Glenn Highay c:j Survey Boundary 1 - _ -_ PLS Section
More informationCentral tendency. mean for metric data. The mean. "I say what I means and I means what I say!."
Central tendency "I say hat I means and I means hat I say!." Popeye Normal dstrbuton vdeo clp To ve an unedted verson vst: http://.learner.org/resources/seres65.html# mean for metrc data mportant propertes
More informationSupplemental Instruction sessions next week
Homework #4 Wrtten homework due now Onlne homework due on Tue Mar 3 by 8 am Exam 1 Answer keys and scores wll be posted by end of the week Supplemental Instructon sessons next week Wednesday 8:45 10:00
More informationMidterm Examination. Regression and Forecasting Models
IOMS Department Regresson and Forecastng Models Professor Wllam Greene Phone: 22.998.0876 Offce: KMC 7-90 Home page: people.stern.nyu.edu/wgreene Emal: wgreene@stern.nyu.edu Course web page: people.stern.nyu.edu/wgreene/regresson/outlne.htm
More informationLecture 6: Introduction to Linear Regression
Lecture 6: Introducton to Lnear Regresson An Manchakul amancha@jhsph.edu 24 Aprl 27 Lnear regresson: man dea Lnear regresson can be used to study an outcome as a lnear functon of a predctor Example: 6
More informationStatistics MINITAB - Lab 2
Statstcs 20080 MINITAB - Lab 2 1. Smple Lnear Regresson In smple lnear regresson we attempt to model a lnear relatonshp between two varables wth a straght lne and make statstcal nferences concernng that
More informationLecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding
Recall: man dea of lnear regresson Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 8 Lnear regresson can be used to study an
More informationLecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding
Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 008 Recall: man dea of lnear regresson Lnear regresson can be used to study
More informationChapter 3 Describing Data Using Numerical Measures
Chapter 3 Student Lecture Notes 3-1 Chapter 3 Descrbng Data Usng Numercal Measures Fall 2006 Fundamentals of Busness Statstcs 1 Chapter Goals To establsh the usefulness of summary measures of data. The
More informationCathy Walker March 5, 2010
Cathy Walker March 5, 010 Part : Problem Set 1. What s the level of measurement for the followng varables? a) SAT scores b) Number of tests or quzzes n statstcal course c) Acres of land devoted to corn
More informationRegression Analysis. Regression Analysis
Regresson Analyss Smple Regresson Multvarate Regresson Stepwse Regresson Replcaton and Predcton Error 1 Regresson Analyss In general, we "ft" a model by mnmzng a metrc that represents the error. n mn (y
More informationDefinition. Measures of Dispersion. Measures of Dispersion. Definition. The Range. Measures of Dispersion 3/24/2014
Measures of Dsperson Defenton Range Interquartle Range Varance and Standard Devaton Defnton Measures of dsperson are descrptve statstcs that descrbe how smlar a set of scores are to each other The more
More informationORIGIN 1. PTC_CE_BSD_3.2_us_mp.mcdx. Mathcad Enabled Content 2011 Knovel Corp.
Clck to Vew Mathcad Document 2011 Knovel Corp. Buldng Structural Desgn. homas P. Magner, P.E. 2011 Parametrc echnology Corp. Chapter 3: Renforced Concrete Slabs and Beams 3.2 Renforced Concrete Beams -
More informationModel and program for the prediction of the indoor air temperature and the indoor air relative humidity
Insttute of Buldng Clmatology, Faculty of Archtecture, Dresden Unversty of Technology, Germany Model and program for the predcton of the ndoor ar temperature and the ndoor ar relatve humdty Dr.-Ing. A.
More informationChapter 9: Statistical Inference and the Relationship between Two Variables
Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,
More informationPBAF 528 Week Theory Is the variable s place in the equation certain and theoretically sound? Most important! 2. T-test
PBAF 528 Week 6 How do we choose our model? How do you decde whch ndependent varables? If you want to read more about ths, try Studenmund, A.H. Usng Econometrcs Chapter 7. (ether 3 rd or 4 th Edtons) 1.
More informationPopulation element: 1 2 N. 1.1 Sampling with Replacement: Hansen-Hurwitz Estimator(HH)
Chapter 1 Samplng wth Unequal Probabltes Notaton: Populaton element: 1 2 N varable of nterest Y : y1 y2 y N Let s be a sample of elements drawn by a gven samplng method. In other words, s s a subset of
More informationDid suburbanization cause residential segregation? Evidence from U.S. metropolitan areas
Revew of Appled Soco- Economc Research (Volume 9, Issue 1/ 2015), pp. 25 e-mal: edtors@reaser.eu Dd suburbanzaton cause resdental segregaton? Evdence from U.S. metropoltan areas Boshampayan Chatterjee
More informationThe young are not forever young:
The young are not forever young: the assmlaton of young persons n the labour market n France Stephen Bazen and Khald Maman Wazr Paper presented at AMSE-Banque de France Labour Economcs Conference December
More informationC001 SCALE: 1" = 20'-0"
GV W NORTH BREESE TERRE LTTLE STREET 6 MNERAL PONT ROAD MADSON, W 70-9 79' - " CRAZY LEGS LANE SDE ' - " PROPERTY LNE RALNG ENTRY PLAZA PLAZA RALNG EXSTNG BULDNG FOOTPRNT FOUR-STORY MASONRY BULDNG #0 9,
More informationI -- MATCH LINE 1/C1.4
CONCRETE PAVEMENT SDEWALK PAVEMENT, SDEWALK PAVERS FRE LANE STRPNG, ==,_ FELD LOCATE EX UNDERGROUND ELECTRC PROR TO DRT WORK & TRENCHNG OPERATONS & :f d CURB & GUTTER 39 LF THS VEW RE: DETAL 82 5 CURB
More informationPHYS 1441 Section 002 Lecture #15
PHYS 1441 Secton 00 Lecture #15 Monday, March 18, 013 Work wth rcton Potental Energy Gravtatonal Potental Energy Elastc Potental Energy Mechancal Energy Conservaton Announcements Mdterm comprehensve exam
More informationContinuous vs. Discrete Goods
CE 651 Transportaton Economcs Charsma Choudhury Lecture 3-4 Analyss of Demand Contnuous vs. Dscrete Goods Contnuous Goods Dscrete Goods x auto 1 Indfference u curves 3 u u 1 x 1 0 1 bus Outlne Data Modelng
More informationPlease review the following statement: I certify that I have not given unauthorized aid nor have I received aid in the completion of this exam.
ME 270 Sprng 2017 Exam 1 NAME (Last, Frst): Please revew the followng statement: I certfy that I have not gven unauthorzed ad nor have I receved ad n the completon of ths exam. Sgnature: Instructor s Name
More informationColumbian Exchange/Conquistador Web Activity
NAME: Assgnment # DATE: Columban ExchangeConqustador Web Actvty Drectons: Please follow the drectons to each webste and answer the questons for each completely usng COMPLETESENTENCESThs assgnment wll help
More informationAssociative Memories
Assocatve Memores We consder now modes for unsupervsed earnng probems, caed auto-assocaton probems. Assocaton s the task of mappng patterns to patterns. In an assocatve memory the stmuus of an ncompete
More informationSoc 3811 Basic Social Statistics Third Midterm Exam Spring 2010
Soc 3811 Basc Socal Statstcs Thrd Mdterm Exam Sprng 2010 Your Name [50 ponts]: ID #: Your TA: Kyungmn Baek Meghan Zacher Frank Zhang INSTRUCTIONS: (A) Wrte your name on the lne at top front of every sheet.
More informationLAU CONSULTANTS TERRYLEDGER. ..nm nun. nun KEY PLAN IMAGEDERIVEDFROM MUSKOKAWEB MAPS GROUND FLOOR AREABOATHOUSE: 364 SO FT
:3 E E Eu -1»? 0. a 2?. Ea D; vi =. =..nm nun. nun TERRYLEDGER CONSULTANTS SS.. -xi.9 ".., 9....3..,,. m o m at, 2 0 W2.,- an = -=3 ".u. of "n.- COTTAGE sumrrvrow LFor A FRN mow 217.4. JALFED,AN THE EGORES.
More informationMASS HAUL DIAGRAM. Determination of eartworks volumes From all cross-sections (including the typical crosssection)
MASS HAUL DIAGRAM Determnaton of eartworks volumes From all cross-sectons (ncludng the typcal crosssecton) Fnd total area of flls and cuts n each cross-secton usng CAD area functon or by the trapezod method
More informationComparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method
Appled Mathematcal Scences, Vol. 7, 0, no. 47, 07-0 HIARI Ltd, www.m-hkar.com Comparson of the Populaton Varance Estmators of -Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method
More informationVIKING INSPECTION PROPERTY 4921 U.S. Hwy. 85, Williston, ND 58801
SALE PRICE: $799,000 LOT SIZE: +/-2.49 Acres BUILDING SIZE: +/-3,800 SF DRIVE-IN DOORS 2 CEILING HEIGHT: 16' YEAR BUILT: 2007 ZONING: Commercial PROPERTY OVERVIEW Highly visible, hard-to-find small shop/office/apartment
More informationTutorial 2. COMP4134 Biometrics Authentication. February 9, Jun Xu, Teaching Asistant
Tutoral 2 COMP434 ometrcs uthentcaton Jun Xu, Teachng sstant csjunxu@comp.polyu.edu.hk February 9, 207 Table of Contents Problems Problem : nswer the questons Problem 2: Power law functon Problem 3: Convoluton
More informationUNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours
UNIVERSITY OF TORONTO Faculty of Arts and Scence December 005 Examnatons STA47HF/STA005HF Duraton - hours AIDS ALLOWED: (to be suppled by the student) Non-programmable calculator One handwrtten 8.5'' x
More informationreviewed paper Explore the Effect of Urban Flood with the Integration of Spatial Analysis Technique Hsueh-Sheng Chang, Chin-Hsien Liao
revewed paper Explore the Effect of Urban Flood wth the Integraton of Spatal Analyss Technque Hsueh-Sheng Chang, Chn-Hsen Lao (Hsueh-Sheng Chang, Assstant Professor, Department of Urban Plannng, Natonal
More information')Q. ~Hl ~~~ ' L-I 3~n1n.:J l'ef S3/\ll"i/N}:l31 l'ef ... A 'ef MIX'i/ 1 Ol31.:l-SSQ}:l8 D.""~ ' ' I j. .r:. t;~t.
SAW212157 Piedont Triad Airport AuthorityPTAA) Phase Northest Site Deveopt and CrossFied Taiay Sheet 1 of 7 i t;t D H
More informationWord Count: 6850(text) + 7 (tables/figures) x 250 = 8600 equivalent words
ESTIMATING SURVEY WEIGHTS WITH MULTIPLE CONSTRAINTS USING ENTROPY OPTIMIZATION METHODS Hllel Bar-Gera Department of Industral Engneerng and Management Ben-Guron Unversty of the Negev P O Box 653, Beer-Sheva,
More informationChapter 13: Multiple Regression
Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to
More informationFORT KING RANCH MPUD REZONING SECRET PROMISE, LP PASCO COUNTY L-700-COVER. Applicant:
LEGAL DESCRPTON DESCRPTON EAST PARCEL THE EAST 1/2 OF SECTON 32, TOWNSHP 24 SOUTH, 19 EAST; AND ALL OF SECTON 33, TOWNSHP 24 SOUTH, RANGE 19 EAST; AND ALL OF SECTON 34, TOWNSHP 24 SOUTH, RANGE 19 EAST;
More informationChapter 14 Simple Linear Regression
Chapter 4 Smple Lnear Regresson Chapter 4 - Smple Lnear Regresson Manageral decsons often are based on the relatonshp between two or more varables. Regresson analss can be used to develop an equaton showng
More informationLecture Notes 11 Econ 20150, Principles of Statistics Kevin R Foster, CCNY Spring 2012
Lecture Notes 11 Econ 20150, Prncples of Statstcs Kevn R Foster, CCNY Sprng 2012 Multple Regresson more than one X varable Regressng just one varable on another can be helpful and useful (and provdes a
More information18. SIMPLE LINEAR REGRESSION III
8. SIMPLE LINEAR REGRESSION III US Domestc Beers: Calores vs. % Alcohol Ftted Values and Resduals To each observed x, there corresponds a y-value on the ftted lne, y ˆ ˆ = α + x. The are called ftted values.
More informationAdiabatic Sorption of Ammonia-Water System and Depicting in p-t-x Diagram
Adabatc Sorpton of Ammona-Water System and Depctng n p-t-x Dagram J. POSPISIL, Z. SKALA Faculty of Mechancal Engneerng Brno Unversty of Technology Techncka 2, Brno 61669 CZECH REPUBLIC Abstract: - Absorpton
More information28. SIMPLE LINEAR REGRESSION III
8. SIMPLE LINEAR REGRESSION III Ftted Values and Resduals US Domestc Beers: Calores vs. % Alcohol To each observed x, there corresponds a y-value on the ftted lne, y ˆ = βˆ + βˆ x. The are called ftted
More informationFor Sublease. Turn-Key Restaurant 1303 South 72nd Street Suites 101 & 102 Omaha, NE 68124
For Sublease Turn-Key Restaurant 0 South nd Street Suites 0 & 0 Omaha, NE Turn-key restaurant located in one of Omaha s hottest retail corridors, this mixed-use retail center has a former endcap restaurant
More informationAS-Level Maths: Statistics 1 for Edexcel
1 of 6 AS-Level Maths: Statstcs 1 for Edecel S1. Calculatng means and standard devatons Ths con ndcates the slde contans actvtes created n Flash. These actvtes are not edtable. For more detaled nstructons,
More informationso that you can see that it explains some, but certainly not nearly all!, of the variation.
Lecture Notes 9 Econ 20150, Prncples of Statstcs Kevn R Foster, CCNY Fall 2012 Multple Regresson more than one X varable Regressng just one varable on another can be helpful and useful (and provdes a great
More informationY = β 0 + β 1 X 1 + β 2 X β k X k + ε
Chapter 3 Secton 3.1 Model Assumptons: Multple Regresson Model Predcton Equaton Std. Devaton of Error Correlaton Matrx Smple Lnear Regresson: 1.) Lnearty.) Constant Varance 3.) Independent Errors 4.) Normalty
More information/ n ) are compared. The logic is: if the two
STAT C141, Sprng 2005 Lecture 13 Two sample tests One sample tests: examples of goodness of ft tests, where we are testng whether our data supports predctons. Two sample tests: called as tests of ndependence
More informationtrawhmmry ffimmf,f;wnt
r nsr rwry fff,f;wn My 26, $51 Swe, k "Te Srwberry Cp f e Vr,, c) [ re ers 6 (, r " * f rn ff e # s S,r,* )er*,3n*,.\ ) x 8 2 n v c e 6 r D r, } e ;s 1 :n..< Z r : 66 3 X f; 1r_ X r { j r Z r 1r 3r B s
More informationBIKING ON MILWAUKEE S WEST SIDE. a historical analysis of bicycle shops near Washington Park. Spring. 2013t
BIKING ON MILWAUKEE S WEST SIDE a hstorcal analyss of bcycle shops near Washngton Sprng S Graduate School of Urban Plannng I G C a p s t o n e P r o j e c 2013t MUnversty of Wsconsnlwaukee The followng
More informationDistributions /06. G.Serazzi 05/06 Dimensionamento degli Impianti Informatici distrib - 1
Dstrbutons 8/03/06 /06 G.Serazz 05/06 Dmensonamento degl Impant Informatc dstrb - outlne densty, dstrbuton, moments unform dstrbuton Posson process, eponental dstrbuton Pareto functon densty and dstrbuton
More informationIf the solution does not follow a logical thought process, it will be assumed in error.
Group # Please revew the followng statement: I certfy that I have not gven unauthorzed ad nor have I receved ad n the completon of ths exam. Sgnature: INSTRUCTIONS Begn each problem n the space provded
More information1-FACTOR ANOVA (MOTIVATION) [DEVORE 10.1]
1-FACTOR ANOVA (MOTIVATION) [DEVORE 10.1] Hgh varance between groups Low varance wthn groups s 2 between/s 2 wthn 1 Factor A clearly has a sgnfcant effect!! Low varance between groups Hgh varance wthn
More informationMultiple Regression more than one X variable
Class Oct 24 Kevn R Foster, CCNY, ECO B2000 Fall 2013 Multple Regresson more than one X varable Regressng just one varable on another can be helpful and useful (and provdes a great graphcal ntuton) but
More informationWeighted Estimating Equations with Response Propensities in Terms of Covariates Observed only for Responders
Weghted Estmatng Equatons wth Response Propenstes n Terms of Covarates Observed only for Responders Erc V. Slud, U.S. Census Bureau, CSRM Unv. of Maryland, Mathematcs Dept. NISS Mssng Data Workshop, November
More informationLinear regression. Regression Models. Chapter 11 Student Lecture Notes Regression Analysis is the
Chapter 11 Student Lecture Notes 11-1 Lnear regresson Wenl lu Dept. Health statstcs School of publc health Tanjn medcal unversty 1 Regresson Models 1. Answer What Is the Relatonshp Between the Varables?.
More information- Prefix 'audi', 'photo' and 'phobia' - What's striped and bouncy? A zebra on a trampoline!
- Pf '', '' '' - Nm: Ws 11 D: W's s y? A m! A m f s s f ws. Ts ws. T ws y ( ss), y ( w) y (fm ). W y f, w. s m y m y m w q y q s q m w s k s w q w s y m s m m m y s s y y www.s..k s.s 2013 s www.sss.m
More informationEXAMINATION. N0028N Econometrics. Luleå University of Technology. Date: (A1016) Time: Aid: Calculator and dictionary
EXAMINATION Luleå Unversty of Technology N008N Econometrcs Date: 011-05-16 (A1016) Tme: 09.00-13.00 Ad: Calculator and dctonary Teacher on duty (complete telephone number) Robert Lundmark (070-1735788)
More informationK E L LY T H O M P S O N
K E L LY T H O M P S O N S E A O LO G Y C R E ATO R, F O U N D E R, A N D PA R T N E R K e l l y T h o m p s o n i s t h e c r e a t o r, f o u n d e r, a n d p a r t n e r o f S e a o l o g y, a n e x
More informationCorrelation and Regression. Correlation 9.1. Correlation. Chapter 9
Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,
More information~---~ ~----~ ~~
t =n :! ::::t C) 7(...; f J t h r==n : t::r,,.! 7 m 7 m {J) :AT rn rn L. "; j i =t :;;;: t.. :, h ). ")?J.. r;..., X h U,< r Q.!. i: :J; :!"")EYJ },_. c ". " :( (;. ). " t? / t e t!r J t j "! t)! (j) N
More informationDO NOT OPEN THE QUESTION PAPER UNTIL INSTRUCTED TO DO SO BY THE CHIEF INVIGILATOR. Introductory Econometrics 1 hour 30 minutes
25/6 Canddates Only January Examnatons 26 Student Number: Desk Number:...... DO NOT OPEN THE QUESTION PAPER UNTIL INSTRUCTED TO DO SO BY THE CHIEF INVIGILATOR Department Module Code Module Ttle Exam Duraton
More informationPhase I Monitoring of Nonlinear Profiles
Phase I Montorng of Nonlnear Profles James D. Wllams Wllam H. Woodall Jeffrey B. Brch May, 003 J.D. Wllams, Bll Woodall, Jeff Brch, Vrgna Tech 003 Qualty & Productvty Research Conference, Yorktown Heghts,
More informationWeek 11: Chapter 11. The Vector Product. The Vector Product Defined. The Vector Product and Torque. More About the Vector Product
The Vector Product Week 11: Chapter 11 Angular Momentum There are nstances where the product of two vectors s another vector Earler we saw where the product of two vectors was a scalar Ths was called the
More informationSome basic statistics and curve fitting techniques
Some basc statstcs and curve fttng technques Statstcs s the dscplne concerned wth the study of varablty, wth the study of uncertanty, and wth the study of decsonmakng n the face of uncertanty (Lndsay et
More informationThe Governing Equations
The Governng Equatons L. Goodman General Physcal Oceanography MAR 555 School for Marne Scences and Technology Umass-Dartmouth Dynamcs of Oceanography The Governng Equatons- (IPO-7) Mass Conservaton and
More informationSupporting information
Supportng nformaton S1. Sample characterzaton S1.1. Hall coeffcent and resstvty measurements Hall and resstvty measurements on sample B have been performed wth a PPMS (Physcal Property Measurement System)
More informationCE 311. Elementary & Higher Surveying
CE 311 Elementary & Higher Surveying Surveying Concept What do surveyors do? What do surveyors measure? What distances do surveyors measure? What angles do surveyors measure? What positions do surveyors
More informationAn (almost) unbiased estimator for the S-Gini index
An (almost unbased estmator for the S-Gn ndex Thomas Demuynck February 25, 2009 Abstract Ths note provdes an unbased estmator for the absolute S-Gn and an almost unbased estmator for the relatve S-Gn for
More informationDIVISION OF MiNERAi... RESOURCES OFFICE ADDRESS, JA MES L. CALVER, COMMiSSIONER WELL COMPLETION REPORT. n'-"~'--_pl-''''..:.:...
j p MAl NG ADDRESS Box 3667 Charlottesville VA 22903 Uct ('h' Lli \ J~: EHV/:\'i ' ion /\ il) t \J) UrJd~ UEV ELOPMENT DVSON OF MiNERAi RESOURCES OFFCE ADDRESS JA MES L CALVER COMMiSSONER WELL COMPLETON
More informationCopyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor
Taylor Enterprses, Inc. Control Lmts for P Charts Copyrght 2017 by Taylor Enterprses, Inc., All Rghts Reserved. Control Lmts for P Charts Dr. Wayne A. Taylor Abstract: P charts are used for count data
More informationUncertainty as the Overlap of Alternate Conditional Distributions
Uncertanty as the Overlap of Alternate Condtonal Dstrbutons Olena Babak and Clayton V. Deutsch Centre for Computatonal Geostatstcs Department of Cvl & Envronmental Engneerng Unversty of Alberta An mportant
More informationSection 8.1 Exercises
Secton 8.1 Non-rght Trangles: Law of Snes and Cosnes 519 Secton 8.1 Exercses Solve for the unknown sdes and angles of the trangles shown. 10 70 50 1.. 18 40 110 45 5 6 3. 10 4. 75 15 5 6 90 70 65 5. 6.
More informationSolutions Problem Set 1 Social Welfare
s Problem Set 1 Socal Welfare Professor: Marcelo Ner TA: Tago Bonomo March 9, 017 Exercse 1 1.1 A populaton s dvded nto four groups, each one wth four ndvduals. The ndvdual ncomes are: x 1 = [1, 1,, ]
More information~ "'~ ,~ ~.., 0,(",,- ':. C I \..," "'471S Of. Thomas C. Walsh Chief, Demographic Surveys Division " '-
.. ("-..y "' ':. C.. f. \.." "'471S Of UNTED STATES DEPARTMENT OF COMMERCE Bureau of the Census Washngton. D.C 2233 December 18 1987 MEMORANDUM FOR From: Thomas C. Walsh Chef Demographc Surveys Dvson "
More information- Q ... _... o:;~ .,, '>< ~ .., ': -... ,:J es 8 '.Ea '.. '.< <( a= -:' -., ,..,. p z. ~ r-1 ~ ~ ~ ~ :... ,.. ,,, 8 ll!l. Pot 8. l=q ' ?-4 '\.
,, -, z >< - Q z c,: t < UJ w o:;,:j es 8 Ea r- z! >< ;,Z
More informationEcon107 Applied Econometrics Topic 9: Heteroskedasticity (Studenmund, Chapter 10)
I. Defnton and Problems Econ7 Appled Econometrcs Topc 9: Heteroskedastcty (Studenmund, Chapter ) We now relax another classcal assumpton. Ths s a problem that arses often wth cross sectons of ndvduals,
More informationLow default modelling: a comparison of techniques based on a real Brazilian corporate portfolio
Low default modellng: a comparson of technques based on a real Brazlan corporate portfolo MSc Gulherme Fernandes and MSc Carlos Rocha Credt Scorng and Credt Control Conference XII August 2011 Analytcs
More informationHAYS County MINUTE ORDER Page 1 of 1. AUSTIN District
TEXAS TRANSPORTATON COMMSSON HAYS County MNUTE ORDER Page 1 of 1 AUSTN District n the City of San Marcos, HAYS COUNTY, on FARM TO MARKET ROAD 2439, the state of Texas acquired certain land and drainage
More informationChap 10: Diagnostics, p384
Chap 10: Dagnostcs, p384 Multcollnearty 10.5 p406 Defnton Multcollnearty exsts when two or more ndependent varables used n regresson are moderately or hghly correlated. - when multcollnearty exsts, regresson
More informationI certify that I have not given unauthorized aid nor have I received aid in the completion of this exam.
ME 270 Fall 2012 Fnal Exam Please revew the followng statement: I certfy that I have not gven unauthorzed ad nor have I receved ad n the completon of ths exam. Sgnature: INSTRUCTIONS Begn each problem
More informationUsing the estimated penetrances to determine the range of the underlying genetic model in casecontrol
Georgetown Unversty From the SelectedWorks of Mark J Meyer 8 Usng the estmated penetrances to determne the range of the underlyng genetc model n casecontrol desgn Mark J Meyer Neal Jeffres Gang Zheng Avalable
More informationInterpreting Slope Coefficients in Multiple Linear Regression Models: An Example
CONOMICS 5* -- Introducton to NOT CON 5* -- Introducton to NOT : Multple Lnear Regresson Models Interpretng Slope Coeffcents n Multple Lnear Regresson Models: An xample Consder the followng smple lnear
More informationPhysics 2A Chapter 3 HW Solutions
Phscs A Chapter 3 HW Solutons Chapter 3 Conceptual Queston: 4, 6, 8, Problems: 5,, 8, 7, 3, 44, 46, 69, 70, 73 Q3.4. Reason: (a) C = A+ B onl A and B are n the same drecton. Sze does not matter. (b) C
More informationPHYS 1101 Practice problem set 12, Chapter 32: 21, 22, 24, 57, 61, 83 Chapter 33: 7, 12, 32, 38, 44, 49, 76
PHYS 1101 Practce problem set 1, Chapter 3: 1,, 4, 57, 61, 83 Chapter 33: 7, 1, 3, 38, 44, 49, 76 3.1. Vsualze: Please reer to Fgure Ex3.1. Solve: Because B s n the same drecton as the ntegraton path s
More informationParametric fractional imputation for missing data analysis. Jae Kwang Kim Survey Working Group Seminar March 29, 2010
Parametrc fractonal mputaton for mssng data analyss Jae Kwang Km Survey Workng Group Semnar March 29, 2010 1 Outlne Introducton Proposed method Fractonal mputaton Approxmaton Varance estmaton Multple mputaton
More informationBenchmarking in pig production
Benchmarkng n pg producton Thomas Algot Søllested Egeberg Internatonal A/S Agenda Who am I? Benchmarkng usng Data Envelopment Analyss Focus-Fnder an example of benchmarkng n pg producton 1 Who am I? M.Sc.
More informationChapter 14: Logit and Probit Models for Categorical Response Variables
Chapter 4: Logt and Probt Models for Categorcal Response Varables Sect 4. Models for Dchotomous Data We wll dscuss only ths secton of Chap 4, whch s manly about Logstc Regresson, a specal case of the famly
More informationGEL 446: Applied Environmental Geology
GE 446: ppled Envronmental Geology Watershed Delneaton and Geomorphology Watershed Geomorphology Watersheds are fundamental geospatal unts that provde a physal and oneptual framewor wdely used by sentsts,
More informationIV. Performance Optimization
IV. Performance Optmzaton A. Steepest descent algorthm defnton how to set up bounds on learnng rate mnmzaton n a lne (varyng learnng rate) momentum learnng examples B. Newton s method defnton Gauss-Newton
More informationTHE ROYAL STATISTICAL SOCIETY 2006 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE
THE ROYAL STATISTICAL SOCIETY 6 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE PAPER I STATISTICAL THEORY The Socety provdes these solutons to assst canddates preparng for the eamnatons n future years and for
More informationSummary with Examples for Root finding Methods -Bisection -Newton Raphson -Secant
Summary wth Eamples or Root ndng Methods -Bsecton -Newton Raphson -Secant Nonlnear Equaton Solvers Bracketng Graphcal Open Methods Bsecton False Poston (Regula-Fals) Newton Raphson Secant All Iteratve
More informationAP Physics 1 & 2 Summer Assignment
AP Physcs 1 & 2 Summer Assgnment AP Physcs 1 requres an exceptonal profcency n algebra, trgonometry, and geometry. It was desgned by a select group of college professors and hgh school scence teachers
More informationPhysics 1202: Lecture 11 Today s Agenda
Physcs 122: Lecture 11 Today s Agenda Announcements: Team problems start ths Thursday Team 1: Hend Ouda, Mke Glnsk, Stephane Auger Team 2: Analese Bruder, Krsten Dean, Alson Smth Offce hours: Monday 2:3-3:3
More informationSTATISTICS QUESTIONS. Step by Step Solutions.
STATISTICS QUESTIONS Step by Step Solutons www.mathcracker.com 9//016 Problem 1: A researcher s nterested n the effects of famly sze on delnquency for a group of offenders and examnes famles wth one to
More informationProperty Prospectus Colonial Avenue
Prperty Prspectus Clnal Avenue 27 N. Clnal Ave. Laytn, UT 84041 Presented by: WhlesaleUtahRE@gmal.cm (801) 4104056 Ntes Ths prperty s lcated n a great famly neghbrhd wth cnvenent freeway access ff the
More informationConstructing socio-demographic indicators for National Statistical Institutes using mobile phone data: estimating literacy rates in Senegal
Constructng soco-demographc ndcators for Natonal Statstcal Insttutes usng moble phone data: estmatng lteracy rates n Senegal Tmo Schmd Faban Bruckschen Ncola Salvat Tll Zbransk School of Busness & Economcs
More informationADORO TE DEVOTE (Godhead Here in Hiding) te, stus bat mas, la te. in so non mor Je nunc. la in. tis. ne, su a. tum. tas: tur: tas: or: ni, ne, o:
R TE EVTE (dhd H Hdg) L / Mld Kbrd gú s v l m sl c m qu gs v nns V n P P rs l mul m d lud 7 súb Fí cón ví f f dó, cru gs,, j l f c r s m l qum t pr qud ct, us: ns,,,, cs, cut r l sns m / m fí hó sn sí
More information