log: F:\stata_parthenope_01.smcl opened on: 17 Mar 2012, 18:21:56
|
|
- Flora Cannon
- 5 years ago
- Views:
Transcription
1 log: F:\stata_parthenope_01.smcl opened on: 17 Mar 2012, 18:21:56 (20 cities >100k pop). de obs: cities >100k pop vars: 13 size: 1,040 storage display value variable name type format label variable label city str16 %16s City state byte %8.0g slbl State code region byte %8.0g rlbl Geographical region divorce float %9.3f Divorces/1000 ages educ float %9.0g Median years education inequal float %9.0g Household inequality index change float %9.0g % population change pop float %9.1f Population in 1,000s poor float %9.2f Percent families below poverty homic float %9.2f Homicides/100,000 people count byte %8.0g Frequency poorrank float %9.0g Poverty rank homrank byte %8.0g Homicide rank Sorted by:. reg homic poor F( 1, 18) = 6.14 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = poor _cons reg homic poor, l(99) F( 1, 18) = 6.14 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = homic Coef. Std. Err. t P> t [99% Conf. Interval] poor _cons
2 . reg homic poor pop F( 2, 17) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = poor pop _cons vif Variable VIF 1/VIF poor pop Mean VIF pwcorr homic poor pop, star(.05) homic poor pop poor * pop * graph matrix homic poor pop, mlabel([city])
3 Homicides/100,000 people Columbus Rochester Rochester Honolulu Tulsa Portland Berkeley Columbus Rochester Berkeley Columbus Honolulu Portland Tulsa Albuquerque Salt Sunnyvale Concord Tempe Virginia Allentown Beach Peoria Erie Lake Salt Albuquerque Fullerton Allentown Sunnyvale Fullerton Concord Tempe Peoria Virginia Erie Lake Beach Columbus Rochester Berkeley Salt ErieLake Albuquerque Peoria Virginia Allentown Honolulu Tulsa Beach Portland Tempe Fullerton Concord 5.00Sunnyvale Percent families below poverty Berkeley Salt ErieLake Peoria Albuquerque AllentownPortland Virginia Honolulu Tulsa Beach Tempe Fullerton Concord Sunnyvale Albuquerque Portland Honolulu Tulsa Virginia Beach Rochester Sterling Sunnyvale Fullerton Concord Allentown Heights Tempe Peoria Salt Columbus Lake Erie Berkeley Honolulu Tulsa Portland Albuquerque Virginia Beach Rochester Sterling Sunnyvale Concord Fullerton Heights TempeAllentown Peoria Salt Lake Erie Berkeley Columbus Population in 1,000s pwcorr homic poor pop if city!="", star(.05) homic poor pop poor * pop * reg homic poor pop divorce educ F( 4, 15) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = poor pop divorce educ _cons test divorce educ
4 F( 2, 15) = 1.05 Prob > F = reg homic poor pop inequal F( 3, 16) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = poor pop inequal _cons vif Variable VIF 1/VIF inequal poor pop Mean VIF pwcorr homic poor pop inequal, star(0.05) homic poor pop inequal poor * pop * inequal * * test poor inequal ( 1) poor = 0 ( 2) inequal = 0 F( 2, 16) = 5.55 Prob > F = qui reg homic poor. est store mod1. qui reg homic poor pop. est store mod2. qui reg homic poor pop divorce educ. est store mod3. qui reg homic poor pop inequal. est store mod4. qui reg homic poor pop divorce educ inequal. est store mod5. est table mod1 mod2 mod3 mod4 mod5, b(%6.3f) se(%6.3f) p(%6.4f) stats(n rss
5 > df_r df_m r2 r2_a F) Variable mod1 mod2 mod3 mod4 mod poor pop divorce educ inequal _cons N rss df_r df_m r r2_a F legend: b/se/p. test divorce educ F( 2, 14) = 0.64 Prob > F = test divorce educ inequal ( 3) inequal = 0 F( 3, 14) = 0.65 Prob > F = log close log: F:\stata_parthenope_01.smcl closed on: 17 Mar 2012, 19:16:13
Week 11: Collinearity
Week 11: Collinearity Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Regression and holding other
More informationWeek 10: Heteroskedasticity
Week 10: Heteroskedasticity Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline The problem of (conditional)
More informationECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics May 2011
ECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics May 2011 Instructions: Answer all five (5) questions. Point totals for each question are given in parentheses. The parts within each
More informationThe Multivariate Regression Model
The Multivariate Regression Model Example Determinants of College GPA Sample of 4 Freshman Collect data on College GPA (4.0 scale) Look at importance of ACT Consider the following model CGPA ACT i 0 i
More information* STATA.OUTPUT -- Chapter 5
* STATA.OUTPUT -- Chapter 5.*bwt/confounder example.infile bwt smk gest using bwt.data.correlate (obs=754) bwt smk gest -------------+----- bwt 1.0000 smk -0.1381 1.0000 gest 0.3629 0.0000 1.0000.regress
More informationNotes on PS2
17.871 - Notes on PS2 Mike Sances MIT April 2, 2012 Mike Sances (MIT) 17.871 - Notes on PS2 April 2, 2012 1 / 9 Interpreting Regression: Coecient regress success_rate dist Source SS df MS Number of obs
More informationApplication: Effects of Job Training Program (Data are the Dehejia and Wahba (1999) version of Lalonde (1986).)
Application: Effects of Job Training Program (Data are the Dehejia and Wahba (1999) version of Lalonde (1986).) There are two data sets; each as the same treatment group of 185 men. JTRAIN2 includes 260
More informationECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics May 2014
ECONOMICS AND ECONOMIC METHODS PRELIM EXAM Statistics and Econometrics May 2014 Instructions: Answer all five (5) questions. Point totals for each question are given in parentheses. The parts within each
More informationExploring Functional Forms: NBA Shots. NBA Shots 2011: Success v. Distance. . bcuse nbashots11
NBA Shots 2011: Success v. Distance. bcuse nbashots11 Contains data from http://fmwww.bc.edu/ec-p/data/wooldridge/nbashots11.dta obs: 199,119 vars: 15 25 Oct 2012 09:08 size: 24,690,756 ------------- storage
More informationBios 312 Midterm: Appendix of Results March 1, Race of mother: Coded as 0==black, 1==Asian, 2==White. . table race white
Appendix. Use these results to answer 2012 Midterm questions Dataset Description Data on 526 infants with very low (
More information(R) / / / / / / / / / / / / Statistics/Data Analysis
Series de Tiempo FE-UNAM Thursday September 20 14:47:14 2012 Page 1 (R) / / / / / / / / / / / / Statistics/Data Analysis User: Prof. Juan Francisco Islas{space -4} Project: UNIDAD II ----------- name:
More informationROBUST ESTIMATION OF STANDARD ERRORS
ROBUST ESTIMATION OF STANDARD ERRORS -- log: Z:\LDA\DataLDA\sitka_Lab8.log log type: text opened on: 18 Feb 2004, 11:29:17. ****The observed mean responses in each of the 4 chambers; for 1988 and 1989.
More informationSoci Statistics for Sociologists
University of North Carolina Chapel Hill Soci708-001 Statistics for Sociologists Fall 2009 Professor François Nielsen Stata Commands for Module 11 Multiple Regression For further information on any command
More informationCategorical Data Analysis
Categorical Data Analysis Hsueh-Sheng Wu Center for Family and Demographic Research October 4, 200 Outline What are categorical variables? When do we need categorical data analysis? Some methods for categorical
More informationFlorida. Difference-in-Difference Models 8/23/2016
Florida Difference-in-Difference Models Bill Evans Health Economics 8/25/1997, State of Florida settles out of court in their suits against tobacco manufacturers Awarded $13 billion over 25 years Use $200m
More informationCompartmental Pharmacokinetic Analysis. Dr Julie Simpson
Compartmental Pharmacokinetic Analysis Dr Julie Simpson Email: julieas@unimelb.edu.au BACKGROUND Describes how the drug concentration changes over time using physiological parameters. Gut compartment Absorption,
More informationfor var trstprl trstlgl trstplc trstplt trstep: reg X trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty
for var trstprl trstlgl trstplc trstplt trstep: reg X trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty -> reg trstprl trust10 stfeco yrbrn hinctnt edulvl pltcare polint wrkprty Source SS df MS
More informationExample Analysis with STATA
Example Analysis with STATA Exploratory Data Analysis Means and Variance by Time and Group Correlation Individual Series Derived Variable Analysis Fitting a Line to Each Subject Summarizing Slopes by Group
More informationExample Analysis with STATA
Example Analysis with STATA Exploratory Data Analysis Means and Variance by Time and Group Correlation Individual Series Derived Variable Analysis Fitting a Line to Each Subject Summarizing Slopes by Group
More informationCOMPARING MODEL ESTIMATES: THE LINEAR PROBABILITY MODEL AND LOGISTIC REGRESSION
PLS 802 Spring 2018 Professor Jacoby COMPARING MODEL ESTIMATES: THE LINEAR PROBABILITY MODEL AND LOGISTIC REGRESSION This handout shows the log of a STATA session that compares alternative estimates of
More informationThe study obtains the following results: Homework #2 Basics of Logistic Regression Page 1. . version 13.1
Soc 73994, Homework #2: Basics of Logistic Regression Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 14, 2018 All answers should be typed and mailed to
More information(LDA lecture 4/15/08: Transition model for binary data. -- TL)
(LDA lecture 4/5/08: Transition model for binary data -- TL) (updated 4/24/2008) log: G:\public_html\courses\LDA2008\Data\CTQ2log log type: text opened on: 5 Apr 2008, 2:27:54 *** read in data ******************************************************
More informationIntroduction of STATA
Introduction of STATA News: There is an introductory course on STATA offered by CIS Description: Intro to STATA On Tue, Feb 13th from 4:00pm to 5:30pm in CIT 269 Seats left: 4 Windows, 7 Macintosh For
More informationİnsan Tunalı November 29, 2018 Econ 511: Econometrics I. ANSWERS TO ASSIGNMENT 10: Part II STATA Supplement
İnsan Tunalı November 29, 2018 Econ 511: Econometrics I STATA Exercise 1 ANSWERS TO ASSIGNMENT 10: Part II STATA Supplement TASK 1: --- name: log: g:\econ511\heter_housinglog log type: text opened
More informationCHECKING INFLUENCE DIAGNOSTICS IN THE OCCUPATIONAL PRESTIGE DATA
PLS 802 Spring 2018 Professor Jacoby CHECKING INFLUENCE DIAGNOSTICS IN THE OCCUPATIONAL PRESTIGE DATA This handout shows the log from a Stata session that examines the Duncan Occupational Prestige data
More informationSOCY7706: Longitudinal Data Analysis Instructor: Natasha Sarkisian Two Wave Panel Data Analysis
SOCY7706: Longitudinal Data Analysis Instructor: Natasha Sarkisian Two Wave Panel Data Analysis In any longitudinal analysis, we can distinguish between analyzing trends vs individual change that is, model
More informationECON Introductory Econometrics Seminar 6
ECON4150 - Introductory Econometrics Seminar 6 Stock and Watson EE10.1 April 28, 2015 Stock and Watson EE10.1 ECON4150 - Introductory Econometrics Seminar 6 April 28, 2015 1 / 21 Guns data set Some U.S.
More informationYou can find the consultant s raw data here:
Problem Set 1 Econ 475 Spring 2014 Arik Levinson, Georgetown University 1 [Travel Cost] A US city with a vibrant tourist industry has an industrial accident (a spill ) The mayor wants to sue the company
More informationMilk Data Analysis. 1. Objective: analyzing protein milk data using STATA.
1. Objective: analyzing protein milk data using STATA. 2. Dataset: Protein milk data set (in the class website) Data description: Percentage protein content of milk samples at weekly intervals from each
More informationMultilevel/ Mixed Effects Models: A Brief Overview
Multilevel/ Mixed Effects Models: A Brief Overview Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised March 27, 2018 These notes borrow very heavily, often/usually
More information(February draft)
For an International NGO Background statistics, cross tabs, summaries, graphs, t-tests and regression analysis for Nepal response survey data (February 2017 - draft) Contents Confidence level/statistical
More information. *increase the memory or there will problems. set memory 40m (40960k)
Exploratory Data Analysis on the Correlation Structure In longitudinal data analysis (and multi-level data analysis) we model two key components of the data: 1. Mean structure. Correlation structure (after
More informationThe Effect of Occupational Danger on Individuals Wage Rates. A fundamental problem confronting the implementation of many healthcare
The Effect of Occupational Danger on Individuals Wage Rates Jonathan Lee Econ 170-001 Spring 2003 PID: 703969503 A fundamental problem confronting the implementation of many healthcare policies is the
More information17.871: PS3 Key. Part I
17.871: PS3 Key Part I. use "cces12.dta", clear. reg CC424 CC334A [aweight=v103] if CC334A!= 8 & CC424 < 6 // Need to remove values that do not fit on the linear scale. This entails discarding all respondents
More informationPSC 508. Jim Battista. Dummies. Univ. at Buffalo, SUNY. Jim Battista PSC 508
PSC 508 Jim Battista Univ. at Buffalo, SUNY Dummies Dummy variables Sometimes we want to include categorical variables in our models Numerical variables that don t necessarily have any inherent order and
More informationTrunkierte Regression: simulierte Daten
Trunkierte Regression: simulierte Daten * Datengenerierung set seed 26091952 set obs 48 obs was 0, now 48 gen age=_n+17 gen yhat=2000+200*(age-18) gen wage = yhat + 2000*invnorm(uniform()) replace wage=max(0,wage)
More informationSociology 7704: Regression Models for Categorical Data Instructor: Natasha Sarkisian. Preliminary Data Screening
r's age when 1st child born 2 4 6 Density.2.4.6.8 Density.5.1 Sociology 774: Regression Models for Categorical Data Instructor: Natasha Sarkisian Preliminary Data Screening A. Examining Univariate Normality
More informationGuideline on evaluating the impact of policies -Quantitative approach-
Guideline on evaluating the impact of policies -Quantitative approach- 1 2 3 1 The term treatment derives from the medical sciences and has more meaning when is used in that context. However, this term
More informationEco311, Final Exam, Fall 2017 Prof. Bill Even. Your Name (Please print) Directions. Each question is worth 4 points unless indicated otherwise.
Your Name (Please print) Directions Each question is worth 4 points unless indicated otherwise. Place all answers in the space provided below or within each question. Round all numerical answers to the
More informationTable. XTMIXED Procedure in STATA with Output Systolic Blood Pressure, use "k:mydirectory,
Table XTMIXED Procedure in STATA with Output Systolic Blood Pressure, 2001. use "k:mydirectory,. xtmixed sbp nage20 nage30 nage40 nage50 nage70 nage80 nage90 winter male dept2 edu_bachelor median_household_income
More informationAnalyzing CHIS Data Using Stata
Analyzing CHIS Data Using Stata Christine Wells UCLA IDRE Statistical Consulting Group February 2014 Christine Wells Analyzing CHIS Data Using Stata 1/ 34 The variables bmi p: BMI povll2: Poverty level
More informationUnit 2 Regression and Correlation 2 of 2 - Practice Problems SOLUTIONS Stata Users
Unit 2 Regression and Correlation 2 of 2 - Practice Problems SOLUTIONS Stata Users Data Set for this Assignment: Download from the course website: Stata Users: framingham_1000.dta Source: Levy (1999) National
More informationApplied Econometrics
Applied Econometrics Lecture 3 Nathaniel Higgins ERS and JHU 20 September 2010 Outline of today s lecture Schedule and Due Dates Making OLS make sense Uncorrelated X s Correlated X s Omitted variable bias
More informationSurvey commands in STATA
Survey commands in STATA Carlo Azzarri DECRG Sample survey: Albania 2005 LSMS 4 strata (Central, Coastal, Mountain, Tirana) 455 Primary Sampling Units (PSU) 8 HHs by PSU * 455 = 3,640 HHs svy command:
More informationECON Introductory Econometrics Seminar 9
ECON4150 - Introductory Econometrics Seminar 9 Stock and Watson EE13.1 May 4, 2015 Stock and Watson EE13.1 ECON4150 - Introductory Econometrics Seminar 9 May 4, 2015 1 / 18 Empirical exercise E13.1: Data
More informationNumber of obs = R-squared = Root MSE = Adj R-squared =
Appendix for the details of statistical test results Statistical Package used:stata/se 11.1 1. ANOVA result with dependent variable: current level of happiness, independent variables: sexs, ages, and survey
More informationThis example demonstrates the use of the Stata 11.1 sgmediation command with survey correction and a subpopulation indicator.
Analysis Example-Stata 11.0 sgmediation Command with Survey Data Correction March 25, 2011 This example demonstrates the use of the Stata 11.1 sgmediation command with survey correction and a subpopulation
More informationLecture 2a: Model building I
Epidemiology/Biostats VHM 812/802 Course Winter 2015, Atlantic Veterinary College, PEI Javier Sanchez Lecture 2a: Model building I Index Page Predictors (X variables)...2 Categorical predictors...2 Indicator
More informationPREDICTIVE MODEL OF TOTAL INCOME FROM SALARIES/WAGES IN THE CONTEXT OF PASAY CITY
Page22 PREDICTIVE MODEL OF TOTAL INCOME FROM SALARIES/WAGES IN THE CONTEXT OF PASAY CITY Wilson Cordova wilson.cordova@cksc.edu.ph Chiang Kai Shek College, Philippines Abstract There are varied sources
More informationFinal Exam Spring Bread-and-Butter Edition
Final Exam Spring 1996 Bread-and-Butter Edition An advantage of the general linear model approach or the neoclassical approach used in Judd & McClelland (1989) is the ability to generate and test complex
More informationLongitudinal Data Analysis, p.12
Biostatistics 140624 2011 EXAM STATA LOG ( NEEDED TO ANSWER EXAM QUESTIONS) Multiple Linear Regression, p2 Longitudinal Data Analysis, p12 Multiple Logistic Regression, p20 Ordered Logistic Regression,
More informationWhy Are Electricity Prices in RTOs Increasingly Expensive?
ROBERT F. MCCULLOUGH, JR. MANAGING PARTNER Date: To: From: Subject: McCullough Research Clients Robert McCullough Heidi Schramm Why Are Electricity Prices in RTOs Increasingly Expensive? For the last two
More informationInteractions made easy
Interactions made easy André Charlett Neville Q Verlander Health Protection Agency Centre for Infections Motivation Scientific staff within institute using Stata to fit many types of regression models
More informationMultilevel Mixed-Effects Generalized Linear Models. Prof. Dr. Luiz Paulo Fávero Prof. Dr. Matheus Albergaria
Multilevel Mixed-Effects Generalized Linear Models in aaaa Prof. Dr. Luiz Paulo Fávero Prof. Dr. Matheus Albergaria SUMMARY - Theoretical Fundamentals of Multilevel Models. - Estimation of Multilevel Mixed-Effects
More informationGroup Comparisons: Using What If Scenarios to Decompose Differences Across Groups
Group Comparisons: Using What If Scenarios to Decompose Differences Across Groups Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised February 15, 2015 We saw that the
More informationElementary tests. proc ttest; title3 'Two-sample t-test: Does consumption depend on Damper Type?'; class damper; var dampin dampout diff ;
Elementary tests /********************** heat2.sas *****************************/ title2 'Standard elementary tests'; options pagesize=35; %include 'heatread.sas'; /* Basically the data step from heat1.sas
More informationSUGGESTED SOLUTIONS Winter Problem Set #1: The results are attached below.
450-2 Winter 2008 Problem Set #1: SUGGESTED SOLUTIONS The results are attached below. 1. The balanced panel contains larger firms (sales 120-130% bigger than the full sample on average), which are more
More informationA.O. Baranov, V.N. Pavlov, Yu.M. Slepenkova
25 th INFORUM World Conference Riga, Latvia, 28 August 1 September 2017 Construction of the Dynamic Input Output Model of Russian Economy with a Human Capital Block and Problems of Its Information Support
More informationExamples of Using Stata v11.0 with JRR replicate weights Provided in the NHANES data set
Examples of Using Stata v110 with JRR replicate weights Provided in the NHANES 1999-2000 data set This document is designed to illustrate comparisons of methods to use JRR replicate weights sometimes provided
More informationStata v 12 Illustration. One Way Analysis of Variance
Stata v 12 Illustration Page 1. Preliminary Download anovaplot.. 2. Descriptives Graphs. 3. Descriptives Numerical 4. Assessment of Normality.. 5. Analysis of Variance Model Estimation.. 6. Tests of Equality
More informationMAPPING CITIES/REGIONS IN KNOWLEDGE SPACE DAVID RIGBY GEOGRAPHY & STATISTICS
MAPPING CITIES/REGIONS IN KNOWLEDGE SPACE DAVID RIGBY GEOGRAPHY & STATISTICS OUTLINE Motivation Building knowledge spaces Example of Europe Example of Norway Mapping firms/cities/regions in knowledge space
More informationMidterm Exam. Friday the 29th of October, 2010
Midterm Exam Friday the 29th of October, 2010 Name: General Comments: This exam is closed book. However, you may use two pages, front and back, of notes and formulas. Write your answers on the exam sheets.
More informationFoley Retreat Research Methods Workshop: Introduction to Hierarchical Modeling
Foley Retreat Research Methods Workshop: Introduction to Hierarchical Modeling Amber Barnato MD MPH MS University of Pittsburgh Scott Halpern MD PhD University of Pennsylvania Learning objectives 1. List
More informationUnit 5 Logistic Regression Homework #7 Practice Problems. SOLUTIONS Stata version
Unit 5 Logistic Regression Homework #7 Practice Problems SOLUTIONS Stata version Before You Begin Download STATA data set illeetvilaine.dta from the course website page, ASSIGNMENTS (Homeworks and Exams)
More informationADVANCED ECONOMETRICS I
ADVANCED ECONOMETRICS I Practice Exercises (1/2) Instructor: Joaquim J. S. Ramalho E.mail: jjsro@iscte-iul.pt Personal Website: http://home.iscte-iul.pt/~jjsro Office: D5.10 Course Website: http://home.iscte-iul.pt/~jjsro/advancedeconometricsi.htm
More informationBiostatistics 208. Lecture 1: Overview & Linear Regression Intro.
Biostatistics 208 Lecture 1: Overview & Linear Regression Intro. Steve Shiboski Division of Biostatistics, UCSF January 8, 2019 1 Organization Office hours by appointment (Mission Hall 2540) E-mail to
More information3. The lab guide uses the data set cda_scireview3.dta. These data cannot be used to complete assignments.
Lab Guide Written by Trent Mize for ICPSRCDA14 [Last updated: 17 July 2017] 1. The Lab Guide is divided into sections corresponding to class lectures. Each section should be reviewed before starting the
More information!! NOTE: SAS Institute Inc., SAS Campus Drive, Cary, NC USA ! NOTE: The SAS System used:!
1 The SAS System NOTE: Copyright (c) 2002-2010 by SAS Institute Inc., Cary, NC, USA. NOTE: SAS (r) Proprietary Software 9.3 (TS1M0) Licensed to UNIVERSITY OF TORONTO/COMPUTING & COMMUNICATIONS, Site 70072784.
More informationUsing Stata 11 & higher for Logistic Regression Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised March 28, 2015
Using Stata 11 & higher for Logistic Regression Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised March 28, 2015 NOTE: The routines spost13, lrdrop1, and extremes
More informationInterpreting and Visualizing Regression models with Stata Margins and Marginsplot. Boriana Pratt May 2017
Interpreting and Visualizing Regression models with Stata Margins and Marginsplot Boriana Pratt May 2017 Interpreting regression models Often regression results are presented in a table format, which makes
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON4137 Applied Micro Econometrics Date of exam: Thursday, May 31, 2018 Grades are given: June 15, 2018 Time for exam: 09.00 to 12.00 The problem set covers
More informationProblem Points Score USE YOUR TIME WISELY SHOW YOUR WORK TO RECEIVE PARTIAL CREDIT
STAT 512 EXAM I STAT 512 Name (7 pts) Problem Points Score 1 40 2 25 3 28 USE YOUR TIME WISELY SHOW YOUR WORK TO RECEIVE PARTIAL CREDIT WRITE LEGIBLY. ANYTHING UNREADABLE WILL NOT BE GRADED GOOD LUCK!!!!
More informationDo not turn over until you are told to do so by the Invigilator.
UNIVERSITY OF EAST ANGLIA School of Economcs Man Seres PG Examnaton 016-17 FINANCIAL ECONOMETRICS ECO-7009A Tme allowed: HOURS Answer ALL FOUR questons. Queston 1 carres a weght of 5%; queston carres 0%;
More informationRead and Describe the SENIC Data
Read and Describe the SENIC Data If the data come in an Excel spreadsheet (very common), blanks are ideal for missing values. The spreadsheet must be.xls, not.xlsx. Beware of trying to read a.csv file
More informationWind Turbines and Coastal Recreation Demand
Wind Turbines and Coastal Recreation Demand Craig E. Landry, Tom Allen, Todd Cherry, and John Whitehead Appalachian Energy Center ECU Center for Sustainable Tourism Electric Power Generation Increasing
More informationThe relationship between innovation and economic growth in emerging economies
Mladen Vuckovic The relationship between innovation and economic growth in emerging economies 130 - Organizational Response To Globally Driven Institutional Changes Abstract This paper will investigate
More informationBIOSTATS 640 Spring 2016 At Your Request! Stata Lab #2 Basics & Logistic Regression. 1. Start a log Read in a dataset...
BIOSTATS 640 Spring 2016 At Your Request! Stata Lab #2 Basics & Logistic Regression 1. Start a log.... 2. Read in a dataset..... 3. Familiarize yourself with the data. 4. Create 1/2 Variables when you
More information********************************************************************************************** *******************************
1 /* Workshop of impact evaluation MEASURE Evaluation-INSP, 2015*/ ********************************************************************************************** ******************************* DEMO: Propensity
More informationExperiment Outcome &Literature Review. Presented by Fang Liyu
Experiment Outcome &Literature Review Presented by Fang Liyu Experiment outcome 1. Data from JD Sample size: 1) Data contains 3325 products in 8 days 2) There are 2000-3000 missing values in each data
More informationTechnical note The treatment effect: Comparing the ESR and PSM methods with an artificial example By: Araar, A.: April 2015:
Technical note The treatment effect: Comparing the ESR and PSM methods with an artificial example By: Araar, A.: April 2015: In this brief note, we propose to use an artificial example data in order to
More informationThe Servitization of Manufacturing: A Longitudinal Study of Global Trends. Professor Andy Neely Director, Cambridge Service Alliance
The Servitization of Manufacturing: A Longitudinal Study of Global Trends Professor Andy Neely Director, Cambridge Service Alliance The world of manufacturing is changing The shift to service based competitive
More informationChapter 2. Linear model. Put some concreteness on problem. The Bivariate Regression Model. Sample of n observations, labeled as i=1,2,..
Linear model Chapter 2 The Bivariate Regression Model Sample of n observations, labeled as i=1,2,..n y i = + x i 1 + i and 1 are population values represent the true relationship between x and y Unfortunately
More informationEFA in a CFA Framework
EFA in a CFA Framework 2012 San Diego Stata Conference Phil Ender UCLA Statistical Consulting Group Institute for Digital Research & Education July 26, 2012 Phil Ender EFA in a CFA Framework Disclaimer
More informationComputer Handout Two
Computer Handout Two /******* senic2.sas ***********/ %include 'senicdef.sas'; /* Effectively, Copy the file senicdef.sas to here */ title2 'Elementary statistical tests'; proc freq; title3 'Use proc freq
More informationAnnexe 1 : statistiques descriptives
Annexe 1 : statistiques descriptives The MEANS Procedure age 532 36.8533835 11.7263657 18.0000000 64.0000000 lnwage 532 2.0597883 0.5156435 0.5596000 3.2692000 ED 532 13.0187970 2.6195743 2.0000000 18.0000000
More informationX. Mixed Effects Analysis of Variance
X. Mixed Effects Analysis of Variance Analysis of variance with multiple observations per patient These analyses are complicated by the fact that multiple observations on the same patient are correlated
More informationAcaStat How To Guide. AcaStat. Software. Copyright 2016, AcaStat Software. All rights Reserved.
AcaStat How To Guide AcaStat Software Copyright 2016, AcaStat Software. All rights Reserved. http://www.acastat.com Table of Contents Frequencies... 3 List Variables... 4 Descriptives... 5 Explore Means...
More informationEFFECTS OF AUDIT COMMITTEE EXPERTISE AND MEETING ON AUDIT QUALITY OF LISTED CONSUMER-GOODS COMPANIES IN NIGERIA
EFFECTS OF AUDIT COMMITTEE EXPERTISE AND MEETING ON AUDIT QUALITY OF LISTED CONSUMER-GOODS COMPANIES IN NIGERIA Mohammed Omotosho Salawu 1, Joshua Okpanachi (PhD) 2, O. Adabenege Yahaya (PhD) 2 and C.
More informationDAY 2 Advanced comparison of methods of measurements
EVALUATION AND COMPARISON OF METHODS OF MEASUREMENTS DAY Advanced comparison of methods of measurements Niels Trolle Andersen and Mogens Erlandsen mogens@biostat.au.dk Department of Biostatistics DAY xtmixed:
More informationBENCHMARKING REPORT EDMONTON
BENCHMARKING REPORT EDMONTON I. INTRODUCTION We conducted an international benchmarking analysis for the members of the Consider Canada City Alliance Inc., consisting of 11 (C11) large Canadian cities
More informationMixed Mode Surveys in Business Research: A Natural Experiment. Dr Andrew Engeli March 14 th 2018
Mixed Mode Surveys in Business Research: A Natural Experiment Dr Andrew Engeli March 14 th 2018 Structure of todays presentation The general context The natural experiment Resources Conclusion Coverage
More informationBIOSTATS 640 Spring 2017 Stata v14 Unit 2: Regression & Correlation. Stata version 14
Stata version 14 Illustration Simple and Multiple Linear Regression February 2017 I- Simple Linear Regression.... 1. Introduction to Example... 2. Preliminaries: Descriptives.. 3. Model Fitting (Estimation)
More informationForgotten effects and heavy moving averages in exchange rate forecasting
Forgotten effects and heavy moving averages in exchange rate forecasting Ezequiel Aviles Ochoa and Ernesto Leon Castro Universidad de Occidente Blvd. Lola Beltrán s/n esq. Circuito Vial, Culiacán 80200,
More informationIBM ServicePac for Essential Support Maintenance Services
IBM Maintenance Services for Warranty and Maintenance Options IBM for Essential Support Maintenance Services Supported Products List January 24, 2012 IBM for Essential Support Maintenance Services Overview
More informationIBM ServicePac for Essential Support Warranty and Maintenance Options
IBM Maintenance Services ServicePac for Warranty and Maintenance Options IBM ServicePac for Essential Support Warranty and Maintenance Options Supported Products List July 27, 2010 IBM ServicePac for Essential
More informationF u = t n+1, t f = 1994, 2005
Forecasting an Electric Utility's Emissions Using SAS/AF and SAS/STAT Software: A Linear Analysis Md. Azharul Islam, The Ohio State University, Columbus, Ohio. David Wang, The Public Utilities Commission
More informationAppendix C: Lab Guide for Stata
Appendix C: Lab Guide for Stata 2011 1. The Lab Guide is divided into sections corresponding to class lectures. Each section includes both a review, which everyone should complete and an exercise, which
More informationApplying Regression Analysis
Applying Regression Analysis Jean-Philippe Gauvin Université de Montréal January 7 2016 Goals for Today What is regression? How do we do it? First hour: OLS Bivariate regression Multiple regression Interactions
More informationCHAPTER Activity Cost Behavior
3-1 CHAPTER Activity Cost Behavior Objectives 3-2 1. Define cost behavior After studying for fixed, this variable, and mixed costs. chapter, you should 2. Explain the role be of the able resource to: usage
More informationChecking the model. Linearity. Normality. Constant variance. Influential points. Covariate overlap
Checking the model Linearity Normality Constant variance Influential points Covariate overlap 1 Checking the model: linearity Average value of outcome initially assumed to be linear function of continuous
More information