WSUG M AY 2012 EViews, S-Plus and R Damian Staszek Bristol Water.

Slides:



Advertisements
Similar presentations
COINTEGRATION 1 The next topic is cointegration. Suppose that you have two nonstationary series X and Y and you hypothesize that Y is a linear function.
Advertisements

Augmented Dickey-Fuller Test Equation Dependent Variable: D(LGDPI) Method: Least Squares Sample (adjusted): Included observations: 44 after adjustments.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 13) Slideshow: tests of nonstationarity: example and further complications Original.
AUTOCORRELATION 1 The third Gauss-Markov condition is that the values of the disturbance term in the observations in the sample be generated independently.
Welcome to Econ 420 Applied Regression Analysis Study Guide Week Three Ending Tuesday, September 11 (Note: You must go over these slides and complete every.
Chapter 4 Using Regression to Estimate Trends Trend Models zLinear trend, zQuadratic trend zCubic trend zExponential trend.
1 TIME SERIES MODELS: STATIC MODELS AND MODELS WITH LAGS In this sequence we will make an initial exploration of the determinants of aggregate consumer.
LOGO Analysis of Unemployment Qi Li Trung Le David Petit Brian Weinberg Dwaraka Polakam Doug Skipper-Dotta Team #4.
NBA Statistical Analysis Econ 240A. Intro. to Econometrics. Fall Group 3 Lu Mao Ying Fan Matthew Koson Ryan Knefel Eric Johnson Tyler Nelson Grop.
Multiple Regression Predicting a response with multiple explanatory variables.
Angela Sordello Christopher Friedberg Can Shen Hui Lai Hui Wang Fang Guo.
Zinc Data SPH 247 Statistical Analysis of Laboratory Data.
Factors Determining the Price Of Used Mid- Compact Size Vehicles Team 4.
1 Lecture Twelve. 2 Outline Failure Time Analysis Linear Probability Model Poisson Distribution.
TAKE HOME PROJECT 2 Group C: Robert Matarazzo, Michael Stromberg, Yuxing Zhang, Yin Chu, Leslie Wei, and Kurtis Hollar.
Marietta College Week 14 1 Tuesday, April 12 2 Exam 3: Monday, April 25, 12- 2:30PM Bring your laptops to class on Thursday too.
x y z The data as seen in R [1,] population city manager compensation [2,] [3,] [4,]
1 Econ 240 C Lecture 3. 2 Part I Modeling Economic Time Series.
Is There a Difference?. How Should You Vote? Is “Big Government” better?Is “Big Government” better? –Republicans want less government involvement. –Democrats.
Global Warming: Is It True? Peter Fuller Odeliah Greene Amanda Smith May Zin.
Determents of Housing Prices. What & WHY Our goal was to discover the determents of rising home prices and to identify any anomies in historic housing.
NEW MODELS FOR HIGH AND LOW FREQUENCY VOLATILITY Robert Engle NYU Salomon Center Derivatives Research Project Derivatives Research Project.
Car Sales Analysis of monthly sales of light weight vehicles. Laura Pomella Karen Chang Heidi Braunger David Parker Derek Shum Mike Hu.
Why Can’t I Afford a Home? By: Philippe Bonnan Emelia Bragadottir Troy Dewitt Anders Graham S. Matthew Scott Lingli Tang.
1 Econ 240A Power 7. 2 This Week, So Far §Normal Distribution §Lab Three: Sampling Distributions §Interval Estimation and HypothesisTesting.
Lecture Week 3 Topics in Regression Analysis. Overview Multiple regression Dummy variables Tests of restrictions 2 nd hour: some issues in cost of capital.
California Expenditure VS. Immigration By: Daniel Jiang, Keith Cochran, Justin Adams, Hung Lam, Steven Carlson, Gregory Wiefel Fall 2003.
Nemours Biomedical Research Statistics April 2, 2009 Tim Bunnell, Ph.D. & Jobayer Hossain, Ph.D. Nemours Bioinformatics Core Facility.
1 Lecture One Econ 240C. 2 Outline Pooling Time Series and Cross- Section Review: Analysis of Variance –one-way ANOVA –two-way ANOVA Pooling Examples.
1 Lecture One Econ 240C. 2 Einstein’s blackboard, Theory of relativity, Oxford, 1931.
1 Power Fifteen Analysis of Variance (ANOVA). 2 Analysis of Variance w One-Way ANOVA Tabular Regression w Two-Way ANOVA Tabular Regression.
1 Econ 240A Power 7. 2 Last Week §Normal Distribution §Lab Three: Sampling Distributions §Interval Estimation and HypothesisTesting.
7/2/ Lecture 51 STATS 330: Lecture 5. 7/2/ Lecture 52 Tutorials  These will cover computing details  Held in basement floor tutorial lab,
1 Lab Five. 2 Lessons to be Learned “Look before you leap” “Look before you leap” Get a feel for the data using graphical techniques, i.e. exploratory.
1 Power Fifteen Analysis of Variance (ANOVA). 2 Analysis of Variance w One-Way ANOVA Tabular Regression w Two-Way ANOVA Tabular Regression.
Crime? FBI records violent crime, z x y z [1,] [2,] [3,] [4,] [5,]
Matt Mullens Gulsah Gunenc Alex Keyfes Gaoyuan Tian Andrew Booth.
EC220 - Introduction to econometrics (chapter 12)
DURBIN–WATSON TEST FOR AR(1) AUTOCORRELATION
Regression Transformations for Normality and to Simplify Relationships U.S. Coal Mine Production – 2011 Source:
How to plot x-y data and put statistics analysis on GLEON Fellowship Workshop January 14-18, 2013 Sunapee, NH Ari Santoso.
 Combines linear regression and ANOVA  Can be used to compare g treatments, after controlling for quantitative factor believed to be related to response.
What decides the price of used cars? Group 1 Jessica Aguirre Keith Cody Rui Feng Jennifer Griffeth Joonhee Lee Hans-Jakob Lothe Teng Wang.
Exercise 8.25 Stat 121 KJ Wang. Votes for Bush and Buchanan in all Florida Counties Palm Beach County (outlier)
Collaboration and Data Sharing What have I been doing that’s so bad, and how could it be better? August 1 st, 2010.
1 Economics 240A Power Eight. 2 Outline n Maximum Likelihood Estimation n The UC Budget Again n Regression Models n The Income Generating Process for.
Using R for Marketing Research Dan Toomey 2/23/2015
FACTORS AFFECTING HOUSING PRICES IN SYRACUSE Sample collected from Zillow in January, 2015 Urban Policy Class Exercise - Lecy.
Exercise 1 The standard deviation of measurements at low level for a method for detecting benzene in blood is 52 ng/L. What is the Critical Level if we.
NONPARAMETRIC MODELING OF THE CROSS- MARKET FEEDBACK EFFECT.
SPURIOUS REGRESSIONS 1 In a famous Monte Carlo experiment, Granger and Newbold fitted the model Y t =  1 +  2 X t + u t where Y t and X t were independently-generated.
Welcome to Econ 420 Applied Regression Analysis Study Guide Week Four Ending Wednesday, September 19 (Assignment 4 which is included in this study guide.
AUTOCORRELATION 1 Assumption C.5 states that the values of the disturbance term in the observations in the sample are generated independently of each other.
Tutorial 4 MBP 1010 Kevin Brown. Correlation Review Pearson’s correlation coefficient – Varies between – 1 (perfect negative linear correlation) and 1.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 4) Slideshow: exercise 4.5 Original citation: Dougherty, C. (2012) EC220 - Introduction.
2010, ECON Hypothesis Testing 1: Single Coefficient Review of hypothesis testing Testing single coefficient Interval estimation Objectives.
Lecture 6: Multiple Linear Regression Adjusted Variable Plots BMTRY 701 Biostatistical Methods II.
Linear Models Alan Lee Sample presentation for STATS 760.
FUNCTIONAL FORMS OF REGRESSION MODELS Application 5.
Air pollution is the introduction of chemicals and biological materials into the atmosphere that causes damage to the natural environment. We focused.
MEASURES OF GOODNESS OF FIT The sum of the squares of the actual values of Y (TSS: total sum of squares) could be decomposed into the sum of the squares.
Partial Equilibrium Framework Empirical Evidence for Argentina ( )
Page 0 Modelling Effective Office Rents by Matt Hall DTZ, 125 Old Broad Street, London, EC2N 2BQ Tel: +44 (0)
Tutorial 5 Thursday February 14 MBP 1010 Kevin Brown.
The Effect of Race on Wage by Region. To what extent were black males paid less than nonblack males in the same region with the same levels of education.
Résolution de l’ex 1 p40 t=c(2:12);N=c(55,90,135,245,403,665,1100,1810,3000,4450,7350) T=data.frame(t,N,y=log(N));T; > T t N y
Correlation and regression
Console Editeur : myProg.R 1
Introduction to Econometrics, 5th edition Chapter 12: Autocorrelation
Table 4. Regression Statistics for the Model
Presentation transcript:

WSUG M AY 2012 EViews, S-Plus and R Damian Staszek Bristol Water

EV IEWS : I NTRO & G RAPHS EViews (Econometric Views) is a statistical package for Windows, used mainly for the time-series oriented econometric analysis. Version 1.0 was released in March 1994 The current version of EViews is 7.2, released in Nov 2011

EV IEWS - R EGRESSION ANALYSIS Dependent Variable: BURSTS Method: Least Squares Date: 05/15/12 Time: 11:56 Sample: 2008M M03 Included observations: 51 BURSTS=C(1)+C(2)*FROST CoefficientStd. Errort-StatisticProb. C(1) C(2) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)

EV IEWS : O THER SETS OF ANALYSIS Time Series Econometrics Forecasting Cross-section data Panel data analysis

E VIEWS - W ORKFILE STRUCTURE

S-P LUS –I NTRO & G RAPHS S-PLUS is a commercial implementation of the S programming language sold by TIBCO Software Inc. 1988: S-PLUS is first produced by a Seattle- based start-up company called Statistical Sciences, Inc. 2004: Insightful purchases the S language 2007: S-PLUS 8 released. 2008: TIBCO acquires Insightful Corporation

S-P LUS –G RAPHS Nr Air Frost Days vs. Bursts

S-P LUS - R EGRESSION ANALYSIS *** Linear Model *** Call: lm(formula = Bursts ~ NrAirFrostDays, data = WSUG, na.action = na.exclude) Residuals: Min 1Q Median 3Q Max Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) NrAirFrostDays Residual standard error: on 49 degrees of freedom Multiple R-Squared: Adjusted R-squared: F-statistic: on 1 and 49 degrees of freedom, the p-value is e- 009

S-P LUS – 2 D G RAPHS Trellis Graph Scatterplot Matrix

R -I NTRO R is an open source programming language and software environment for statistical computing and graphics Appeared in 1993 Current version: Version – October 31, 2011 R is part of the GNU project (it is free) R is an implementation of the S programming language, much of the code written for S runs unaltered

R -G RAPH

R -R EGRESSION ANALYSIS Call: lm(formula = bursts ~ frost) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) < 2e-16 *** frost e-09 *** --- Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: on 49 degrees of freedom Multiple R-Squared: , Adjusted R-squared: F-statistic: on 1 and 49 DF, p-value: 2.186e-09

EV IEWS, S-P LUS AND R C OMPARISON EviewsS-PlusR Statistics Econometrics Panel Data Graphs Language Easy to use Help / Manual

EV IEWS - S OFTWARE POSITIVES EViews - econometrics software E-Views has an easy-to-learn graphical user interface (GUI) E-Views has the best workfile structure- easy to manage Panel Data Econometrics with E-Views

S-P LUS - S OFTWARE POSTIVES S-Plus and R - Statistical software S-Plus can be integrated with TIBCO Spotfire® Professional software - interactive analytic interface for accessing and manipulating data Vast documentation

R - S OFTWARE POSITIVES R - command driven language R is extended through user-created packages (collections of code + data + documentation) R has various Grapfical User Interfaces i.e. R Commander, Deducer, Weka, RStudio R is a freely-available programming language and software environment Vast documentation provided by R community: help pages, packages, manuals, R mailing lists Search engines (RSeek) and journals available for R