proveeks_bilag.out The SAS System 22:27 Thursday, November 27, Source DF Squares Square F Value Pr > F

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "proveeks_bilag.out The SAS System 22:27 Thursday, November 27, 2003 1 Source DF Squares Square F Value Pr > F"

Transcription

1 The SAS System 22:27 Thursday, November 27, Model <.0001 Error Corrected Total Root MSE R-Square Dependent Adj R-Sq Coeff Var Intercept OMS <.0001 KONK NYPR Nypr_Oms The SAS System 22:27 Thursday, November 27, Dependent Variable: uhatsq Model <.0001 Error Corrected Total Root MSE R-Square Dependent Adj R-Sq Coeff Var Intercept OMS KONK NYPR Nypr_Oms Oms Oms_Konk Nypr_oms Nypr_Konk Nypr_Oms_Konk konk The SAS System 22:27 Thursday, November 27, Model <.0001 Corrected Total

2 Root MSE R-Square Dependent Adj R-Sq Coeff Var Intercept OMS <.0001 KONK NYPR Nypr_Oms The SAS System 22:27 Thursday, November 27, Model Root MSE R-Square Dependent Adj R-Sq Coeff Var Intercept <.0001 TOms TKonk TNypr NYPR The SAS System 22:27 Thursday, November 27, NOTE: No intercept in model. R-Square is redefined. Model <.0001 Error Uncorrected Total Root MSE R-Square Dependent Adj R-Sq Coeff Var OMS <.0001 KONK The SAS System 22:27 Thursday, November 27,

3 Model Error Root MSE R-Square Dependent Adj R-Sq Coeff Var Intercept <.0001 TKonk The SAS System 22:27 Thursday, November 27, Model <.0001 Corrected Total Root MSE R-Square Dependent Adj R-Sq Coeff Var Intercept OMS <.0001 KONK NYPR Nypr_Oms The SAS System 22:27 Thursday, November 27, Consistent Covariance of Estimates Variable Intercept OMS KONK NYPR Nypr_Oms Intercept OMS E KONK E NYPR Nypr_Oms The SAS System 22:27 Thursday, November 27, Model

4 Root MSE R-Square Dependent Adj R-Sq Coeff Var Intercept <.0001 TOms TKonk TNypr NYPR The SAS System 22:27 Thursday, November 27, Consistent Covariance of Estimates Variable Intercept TOms TKonk TNypr NYPR Intercept E TOms TKonk E TNypr NYPR The SAS System 22:27 Thursday, November 27, Model Error Root MSE R-Square Dependent Adj R-Sq Coeff Var Intercept <.0001 k_m k_m k_p k_p The SAS System 22:27 Thursday, November 27, Test 1 Results for Dependent Variable TPrmres Source DF Square F Value Pr > F Numerator Denominator The SAS System 22:27 Thursday, November 27, NOTE: No intercept in model. R-Square is redefined.

5 Model <.0001 Error Uncorrected Total Root MSE R-Square Dependent Adj R-Sq Coeff Var OMS <.0001 d_m d_m d_p d_p The SAS System 22:27 Thursday, November 27, Test 1 Results for Dependent Variable PRMRES Source DF Square F Value Pr > F Numerator Denominator

Source DF Squares Square F Value Pr > F Model 4 18106 4526.41616 54.70 <.0001 Error 245 20273 82.74845 Corrected Total 249 38379

Source DF Squares Square F Value Pr > F Model 4 18106 4526.41616 54.70 <.0001 Error 245 20273 82.74845 Corrected Total 249 38379 The SAS System 09:43 Thursday, April 28, 2005 1 The MEANS Procedure Variable N Minimum Maximum Std Dev Median ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ

More information

Outline. Topic 4 - Analysis of Variance Approach to Regression. Partitioning Sums of Squares. Total Sum of Squares. Partitioning sums of squares

Outline. Topic 4 - Analysis of Variance Approach to Regression. Partitioning Sums of Squares. Total Sum of Squares. Partitioning sums of squares Topic 4 - Analysis of Variance Approach to Regression Outline Partitioning sums of squares Degrees of freedom Expected mean squares General linear test - Fall 2013 R 2 and the coefficient of correlation

More information

Data Mining and Data Warehousing. Henryk Maciejewski. Data Mining Predictive modelling: regression

Data Mining and Data Warehousing. Henryk Maciejewski. Data Mining Predictive modelling: regression Data Mining and Data Warehousing Henryk Maciejewski Data Mining Predictive modelling: regression Algorithms for Predictive Modelling Contents Regression Classification Auxiliary topics: Estimation of prediction

More information

Random effects and nested models with SAS

Random effects and nested models with SAS Random effects and nested models with SAS /************* classical2.sas ********************* Three levels of factor A, four levels of B Both fixed Both random A fixed, B random B nested within A ***************************************************/

More information

California SCHIP Caregivers Perceptions of Dental Care

California SCHIP Caregivers Perceptions of Dental Care California SCHIP Caregivers Perceptions of Dental Care J.J. CRALL, C UCLA / MCHB National Oral Health Policy Center, LA, CA J. BROWN, RAND Survey Research Group, Santa Monica, CA L.U. BROWN, Managed Risk

More information

Getting Correct Results from PROC REG

Getting Correct Results from PROC REG Getting Correct Results from PROC REG Nathaniel Derby, Statis Pro Data Analytics, Seattle, WA ABSTRACT PROC REG, SAS s implementation of linear regression, is often used to fit a line without checking

More information

ARKANSAS PUBLIC SERVICE COMMISSYF cc7 DOCKET NO. 00-1 90-U IN THE MATTER OF ON THE DEVELOPMENT OF COMPETITION IF ANY, ON RETAIL CUSTOMERS

ARKANSAS PUBLIC SERVICE COMMISSYF cc7 DOCKET NO. 00-1 90-U IN THE MATTER OF ON THE DEVELOPMENT OF COMPETITION IF ANY, ON RETAIL CUSTOMERS ARKANSAS PUBLIC SERVICE COMMISSYF cc7 L I :b; -Ir '3, :I: 36 DOCKET NO. 00-1 90-U 1.. T -3. - " ~..-.ij IN THE MATTER OF A PROGRESS REPORT TO THE GENERAL ASSEMBLY ON THE DEVELOPMENT OF COMPETITION IN ELECTRIC

More information

1.1. Simple Regression in Excel (Excel 2010).

1.1. Simple Regression in Excel (Excel 2010). .. Simple Regression in Excel (Excel 200). To get the Data Analysis tool, first click on File > Options > Add-Ins > Go > Select Data Analysis Toolpack & Toolpack VBA. Data Analysis is now available under

More information

CORRELATION AND SIMPLE REGRESSION ANALYSIS USING SAS IN DAIRY SCIENCE

CORRELATION AND SIMPLE REGRESSION ANALYSIS USING SAS IN DAIRY SCIENCE CORRELATION AND SIMPLE REGRESSION ANALYSIS USING SAS IN DAIRY SCIENCE A. K. Gupta, Vipul Sharma and M. Manoj NDRI, Karnal-132001 When analyzing farm records, simple descriptive statistics can reveal a

More information

Regression of Systolic Blood Pressure on Age, Weight & Cholesterol

Regression of Systolic Blood Pressure on Age, Weight & Cholesterol Regression of Systolic Blood Pressure on Age, Weight & Cholesterol 1 * bp.sas; 2 options ls=120 ps=75 nocenter nodate; 3 title Regression of Systolic Blood Pressure on Age, Weight & Cholesterol ; 4 * BP

More information

6 Variables: PD MF MA K IAH SBS

6 Variables: PD MF MA K IAH SBS options pageno=min nodate formdlim='-'; title 'Canonical Correlation, Journal of Interpersonal Violence, 10: 354-366.'; data SunitaPatel; infile 'C:\Users\Vati\Documents\StatData\Sunita.dat'; input Group

More information

. g .,, . . , Applicability of

More information

An Introduction to Statistical Tests for the SAS Programmer Sara Beck, Fred Hutchinson Cancer Research Center, Seattle, WA

An Introduction to Statistical Tests for the SAS Programmer Sara Beck, Fred Hutchinson Cancer Research Center, Seattle, WA ABSTRACT An Introduction to Statistical Tests for the SAS Programmer Sara Beck, Fred Hutchinson Cancer Research Center, Seattle, WA Often SAS Programmers find themselves in situations where performing

More information

MEAN SEPARATION TESTS (LSD AND Tukey s Procedure) is rejected, we need a method to determine which means are significantly different from the others.

MEAN SEPARATION TESTS (LSD AND Tukey s Procedure) is rejected, we need a method to determine which means are significantly different from the others. MEAN SEPARATION TESTS (LSD AND Tukey s Procedure) If Ho 1 2... n is rejected, we need a method to determine which means are significantly different from the others. We ll look at three separation tests

More information

Topic 3. Chapter 5: Linear Regression in Matrix Form

Topic 3. Chapter 5: Linear Regression in Matrix Form Topic Overview Statistics 512: Applied Linear Models Topic 3 This topic will cover thinking in terms of matrices regression on multiple predictor variables case study: CS majors Text Example (NKNW 241)

More information

Detecting Email Spam. MGS 8040, Data Mining. Audrey Gies Matt Labbe Tatiana Restrepo

Detecting Email Spam. MGS 8040, Data Mining. Audrey Gies Matt Labbe Tatiana Restrepo Detecting Email Spam MGS 8040, Data Mining Audrey Gies Matt Labbe Tatiana Restrepo 5 December 2011 INTRODUCTION This report describes a model that may be used to improve likelihood of recognizing undesirable

More information

Permutation Tests with SAS

Permutation Tests with SAS Permutation Tests with SAS /* testmultest1.sas */ options linesize=79 noovp formdlim='_'; title 'Permutation test example from lecture: One-sided p = 0.10'; data scramble; input group Y; datalines; 1 1.3

More information

The Simple Linear Regression Model: Specification and Estimation

The Simple Linear Regression Model: Specification and Estimation Chapter 3 The Simple Linear Regression Model: Specification and Estimation 3.1 An Economic Model Suppose that we are interested in studying the relationship between household income and expenditure on

More information

Lecture Outline (week 13)

Lecture Outline (week 13) Lecture Outline (week 3) Analysis of Covariance in Randomized studies Mixed models: Randomized block models Repeated Measures models Pretest-posttest models Analysis of Covariance in Randomized studies

More information

Basic Statistical and Modeling Procedures Using SAS

Basic Statistical and Modeling Procedures Using SAS Basic Statistical and Modeling Procedures Using SAS One-Sample Tests The statistical procedures illustrated in this handout use two datasets. The first, Pulse, has information collected in a classroom

More information

data on Down's syndrome

data on Down's syndrome DATA a; INFILE 'downs.dat' ; INPUT AgeL AgeU BirthOrd Cases Births ; MidAge = (AgeL + AgeU)/2 ; Rate = 1000*Cases/Births; LogRate = Log( (Cases+0.5)/Births ); LogDenom = Log(Births); age_c = MidAge - 30;

More information

ORTHOGONAL POLYNOMIAL CONTRASTS INDIVIDUAL DF COMPARISONS: EQUALLY SPACED TREATMENTS

ORTHOGONAL POLYNOMIAL CONTRASTS INDIVIDUAL DF COMPARISONS: EQUALLY SPACED TREATMENTS ORTHOGONAL POLYNOMIAL CONTRASTS INDIVIDUAL DF COMPARISONS: EQUALLY SPACED TREATMENTS Many treatments are equally spaced (incremented). This provides us with the opportunity to look at the response curve

More information

ECON 142 SKETCH OF SOLUTIONS FOR APPLIED EXERCISE #2

ECON 142 SKETCH OF SOLUTIONS FOR APPLIED EXERCISE #2 University of California, Berkeley Prof. Ken Chay Department of Economics Fall Semester, 005 ECON 14 SKETCH OF SOLUTIONS FOR APPLIED EXERCISE # Question 1: a. Below are the scatter plots of hourly wages

More information

STA 4163 Lecture 10: Practice Problems

STA 4163 Lecture 10: Practice Problems STA 463 Lecture 0: Practice Problems Problem.0: A study was conducted to determine whether a student's final grade in STA406 is linearly related to his or her performance on the MATH ability test before

More information

ADVANCED FORECASTING MODELS USING SAS SOFTWARE

ADVANCED FORECASTING MODELS USING SAS SOFTWARE ADVANCED FORECASTING MODELS USING SAS SOFTWARE Girish Kumar Jha IARI, Pusa, New Delhi 110 012 gjha_eco@iari.res.in 1. Transfer Function Model Univariate ARIMA models are useful for analysis and forecasting

More information

This can dilute the significance of a departure from the null hypothesis. We can focus the test on departures of a particular form.

This can dilute the significance of a departure from the null hypothesis. We can focus the test on departures of a particular form. One-Degree-of-Freedom Tests Test for group occasion interactions has (number of groups 1) number of occasions 1) degrees of freedom. This can dilute the significance of a departure from the null hypothesis.

More information

ABSTRACT INTRODUCTION READING THE DATA SESUG 2012. Paper PO-14

ABSTRACT INTRODUCTION READING THE DATA SESUG 2012. Paper PO-14 SESUG 2012 ABSTRACT Paper PO-14 Spatial Analysis of Gastric Cancer in Costa Rica using SAS So Young Park, North Carolina State University, Raleigh, NC Marcela Alfaro-Cordoba, North Carolina State University,

More information

I n d i a n a U n i v e r s i t y U n i v e r s i t y I n f o r m a t i o n T e c h n o l o g y S e r v i c e s

I n d i a n a U n i v e r s i t y U n i v e r s i t y I n f o r m a t i o n T e c h n o l o g y S e r v i c e s I n d i a n a U n i v e r s i t y U n i v e r s i t y I n f o r m a t i o n T e c h n o l o g y S e r v i c e s Linear Regression Models for Panel Data Using SAS, Stata, LIMDEP, and SPSS * Hun Myoung Park,

More information

IAPRI Quantitative Analysis Capacity Building Series. Multiple regression analysis & interpreting results

IAPRI Quantitative Analysis Capacity Building Series. Multiple regression analysis & interpreting results IAPRI Quantitative Analysis Capacity Building Series Multiple regression analysis & interpreting results How important is R-squared? R-squared Published in Agricultural Economics 0.45 Best article of the

More information

Predicting the US Presidential Approval and Applying the Model to Foreign Countries.

Predicting the US Presidential Approval and Applying the Model to Foreign Countries. Predicting the US Presidential Approval and Applying the Model to Foreign Countries. Author: Daniel Mariani Date: May 01, 2013 Thesis Advisor: Dr. Samanta ABSTRACT Economic factors play a significant,

More information

Napster and its Effect on Music Industry: An Empirical Analysis. The global economic climate has experienced drastic changes over the last two

Napster and its Effect on Music Industry: An Empirical Analysis. The global economic climate has experienced drastic changes over the last two Napster and its Effect on Music Industry: An Empirical Analysis Patrick Mooney The College of New Jersey I. Introduction The global economic climate has experienced drastic changes over the last two decades

More information

Chapter 4 and 5 solutions

Chapter 4 and 5 solutions Chapter 4 and 5 solutions 4.4. Three different washing solutions are being compared to study their effectiveness in retarding bacteria growth in five gallon milk containers. The analysis is done in a laboratory,

More information

This section focuses on Chow Test and leaves general discussion on dummy variable models to other section.

This section focuses on Chow Test and leaves general discussion on dummy variable models to other section. Jeeshim and KUCC65 (3//008) Statistical Inferences in Linear Regression: 7 4. Tests of Structural Changes This section focuses on Chow Test and leaves general discussion on dummy variable models to other

More information

Lab 5 Linear Regression with Within-subject Correlation. Goals: Data: Use the pig data which is in wide format:

Lab 5 Linear Regression with Within-subject Correlation. Goals: Data: Use the pig data which is in wide format: Lab 5 Linear Regression with Within-subject Correlation Goals: Data: Fit linear regression models that account for within-subject correlation using Stata. Compare weighted least square, GEE, and random

More information

xtmixed & denominator degrees of freedom: myth or magic

xtmixed & denominator degrees of freedom: myth or magic xtmixed & denominator degrees of freedom: myth or magic 2011 Chicago Stata Conference Phil Ender UCLA Statistical Consulting Group July 2011 Phil Ender xtmixed & denominator degrees of freedom: myth or

More information

Department of Economics Session 2012/2013. EC352 Econometric Methods. Solutions to Exercises from Week 10 + 0.0077 (0.052)

Department of Economics Session 2012/2013. EC352 Econometric Methods. Solutions to Exercises from Week 10 + 0.0077 (0.052) Department of Economics Session 2012/2013 University of Essex Spring Term Dr Gordon Kemp EC352 Econometric Methods Solutions to Exercises from Week 10 1 Problem 13.7 This exercise refers back to Equation

More information

Evaluation of Correlation between Within-Barn Curing Environment and TSNA Accumulation in Dark Air-Cured Tobacco

Evaluation of Correlation between Within-Barn Curing Environment and TSNA Accumulation in Dark Air-Cured Tobacco Evaluation of Correlation between Within-Barn Curing Environment and TSNA Accumulation in Dark Air-Cured Tobacco Preliminary Study Grant Report CORESTA TSNA Sub-Group M.D. Richmond, W.A. Bailey, R.C. Pearce

More information

Assessing the Relationship Between Online Job Postings and Total Hires and Education Levels in Arizona Aruna Murthy Dan Bache Benjamin Fa anunu

Assessing the Relationship Between Online Job Postings and Total Hires and Education Levels in Arizona Aruna Murthy Dan Bache Benjamin Fa anunu Assessing the Relationship Between Online Job Postings and Total Hires and Education Levels in Arizona Aruna Murthy Dan Bache Benjamin Fa anunu Help Wanted Online (HWOL) HWOL data series from the Conference

More information

Outline. Session A: Various Definitions. 1. Basics of Path Diagrams and Path Analysis

Outline. Session A: Various Definitions. 1. Basics of Path Diagrams and Path Analysis Session A: Basics of Structural Equation Modeling and The Mplus Computer Program Kevin Grimm University of California, Davis June 9, 008 Outline Basics of Path Diagrams and Path Analysis Regression and

More information

12-1 Multiple Linear Regression Models

12-1 Multiple Linear Regression Models 12-1.1 Introduction Many applications of regression analysis involve situations in which there are more than one regressor variable. A regression model that contains more than one regressor variable is

More information

Experimental Design for Influential Factors of Rates on Massive Open Online Courses

Experimental Design for Influential Factors of Rates on Massive Open Online Courses Experimental Design for Influential Factors of Rates on Massive Open Online Courses December 12, 2014 Ning Li nli7@stevens.edu Qing Wei qwei1@stevens.edu Yating Lan ylan2@stevens.edu Yilin Wei ywei12@stevens.edu

More information

EXPECTED MEAN SQUARES AND MIXED MODEL ANALYSES. This will become more important later in the course when we discuss interactions.

EXPECTED MEAN SQUARES AND MIXED MODEL ANALYSES. This will become more important later in the course when we discuss interactions. EXPECTED MEN SQURES ND MIXED MODEL NLYSES Fixed vs. Random Effects The choice of labeling a factor as a fixed or random effect will affect how you will make the F-test. This will become more important

More information

Regression Analysis. Data Calculations Output

Regression Analysis. Data Calculations Output Regression Analysis In an attempt to find answers to questions such as those posed above, empirical labour economists use a useful tool called regression analysis. Regression analysis is essentially a

More information

Discrete with many values are often treated as continuous: Minnesota math scores, years of education.

Discrete with many values are often treated as continuous: Minnesota math scores, years of education. Lecture 9 1. Continuous and categorical predictors 2. Indicator variables 3. General linear model: child s IQ example 4. Proc GLM and Proc Reg 5. ANOVA example: Rat diets 6. Factorial design: 2 2 7. Interactions

More information

Probability Calculator

Probability Calculator Chapter 95 Introduction Most statisticians have a set of probability tables that they refer to in doing their statistical wor. This procedure provides you with a set of electronic statistical tables that

More information

Lecture 15. Endogeneity & Instrumental Variable Estimation

Lecture 15. Endogeneity & Instrumental Variable Estimation Lecture 15. Endogeneity & Instrumental Variable Estimation Saw that measurement error (on right hand side) means that OLS will be biased (biased toward zero) Potential solution to endogeneity instrumental

More information

A Tutorial on the Piecewise Regression Approach Applied to Bedload Transport Data

A Tutorial on the Piecewise Regression Approach Applied to Bedload Transport Data A Tutorial on the Piecewise Regression Approach Applied to Bedload Transport Data Sandra E. Ryan Laurie S. Porth United States Department of Agriculture Forest Service General Technical Report RMRS-GTR-189

More information

SAS Code to Select the Best Multiple Linear Regression Model for Multivariate Data Using Information Criteria

SAS Code to Select the Best Multiple Linear Regression Model for Multivariate Data Using Information Criteria Paper SA01_05 SAS Code to Select the Best Multiple Linear Regression Model for Multivariate Data Using Information Criteria Dennis J. Beal, Science Applications International Corporation, Oak Ridge, TN

More information

Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression

Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a

More information

NHTSA TIRE ROLLING RESISTANCE TEST DEVELOPMENT PROJECT PHASE I

NHTSA TIRE ROLLING RESISTANCE TEST DEVELOPMENT PROJECT PHASE I NHTSA TIRE ROLLING RESISTANCE TEST DEVELOPMENT PROJECT PHASE I Dr. M. Kamel Salaani, Larry R. Evans, John R. Harris Transportation Research Center Inc. James D. MacIsaac Jr. U.S. Department of Transportation

More information

5. Linear Regression

5. Linear Regression 5. Linear Regression Outline.................................................................... 2 Simple linear regression 3 Linear model............................................................. 4

More information

Addressing Alternative. Multiple Regression. 17.871 Spring 2012

Addressing Alternative. Multiple Regression. 17.871 Spring 2012 Addressing Alternative Explanations: Multiple Regression 17.871 Spring 2012 1 Did Clinton hurt Gore example Did Clinton hurt Gore in the 2000 election? Treatment is not liking Bill Clinton 2 Bivariate

More information

International Statistical Institute, 56th Session, 2007: Phil Everson

International Statistical Institute, 56th Session, 2007: Phil Everson Teaching Regression using American Football Scores Everson, Phil Swarthmore College Department of Mathematics and Statistics 5 College Avenue Swarthmore, PA198, USA E-mail: peverso1@swarthmore.edu 1. Introduction

More information

4.4. Further Analysis within ANOVA

4.4. Further Analysis within ANOVA 4.4. Further Analysis within ANOVA 1) Estimation of the effects Fixed effects model: α i = µ i µ is estimated by a i = ( x i x) if H 0 : µ 1 = µ 2 = = µ k is rejected. Random effects model: If H 0 : σa

More information

Introduction to PROC MIXED

Introduction to PROC MIXED Page 1 of 26 Introduction to PROC MIXED Table of Contents 1. Short description of methods of estimation used in PROC MIXED 2. Description of the syntax of PROC MIXED 3. References 4. Examples and comparisons

More information

Analysis of Longitudinal Data in Stata, Splus and SAS

Analysis of Longitudinal Data in Stata, Splus and SAS Analysis of Longitudinal Data in Stata, Splus and SAS Rino Bellocco, Sc.D. Department of Medical Epidemiology Karolinska Institutet Stockholm, Sweden rino@mep.ki.se March 12, 2001 NASUGS, 2001 OUTLINE

More information

Milk Data Analysis. 1. Objective Introduction to SAS PROC MIXED Analyzing protein milk data using STATA Refit protein milk data using PROC MIXED

Milk Data Analysis. 1. Objective Introduction to SAS PROC MIXED Analyzing protein milk data using STATA Refit protein milk data using PROC MIXED 1. Objective Introduction to SAS PROC MIXED Analyzing protein milk data using STATA Refit protein milk data using PROC MIXED 2. Introduction to SAS PROC MIXED The MIXED procedure provides you with flexibility

More information

Correlation and Regression

Correlation and Regression Correlation and Regression Scatterplots Correlation Explanatory and response variables Simple linear regression General Principles of Data Analysis First plot the data, then add numerical summaries Look

More information

ST 311 Evening Problem Session Solutions Week 11

ST 311 Evening Problem Session Solutions Week 11 1. p. 175, Question 32 (Modules 10.1-10.4) [Learning Objectives J1, J3, J9, J11-14, J17] Since 1980, average mortgage rates have fluctuated from a low of under 6% to a high of over 14%. Is there a relationship

More information

New SAS Procedures for Analysis of Sample Survey Data

New SAS Procedures for Analysis of Sample Survey Data New SAS Procedures for Analysis of Sample Survey Data Anthony An and Donna Watts, SAS Institute Inc, Cary, NC Abstract Researchers use sample surveys to obtain information on a wide variety of issues Many

More information

NCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )

NCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( ) Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates

More information

Demonstrating Systematic Sampling

Demonstrating Systematic Sampling Demonstrating Systematic Sampling Julie W. Pepe, University of Central Florida, Orlando, Florida Abstract A real data set involving the number of reference requests at a university library, will be used

More information

SPSS Guide: Regression Analysis

SPSS Guide: Regression Analysis SPSS Guide: Regression Analysis I put this together to give you a step-by-step guide for replicating what we did in the computer lab. It should help you run the tests we covered. The best way to get familiar

More information

Cloud Computing Business Framework. Dr Victor Chang 25 th November 2014, Paris, France

Cloud Computing Business Framework. Dr Victor Chang 25 th November 2014, Paris, France Cloud Computing Business Framework Dr Victor Chang 25 th November 2014, Paris, France 1 Overview Cloud Computing Overview Cloud Computing Business Framework Classification / Organizational Sustainability

More information

Paper DM05 A METHODOLOGICAL APPROACH TO PERFORMING CLUSTER ANALYSIS WITH SAS. William F. McCarthy. Maryland Medical Research Institute, Baltimore, MD

Paper DM05 A METHODOLOGICAL APPROACH TO PERFORMING CLUSTER ANALYSIS WITH SAS. William F. McCarthy. Maryland Medical Research Institute, Baltimore, MD Paper DM05 A METHODOLOGICAL APPROACH TO PERFORMING CLUSTER ANALYSIS WITH SAS William F. McCarthy Maryland Medical Research Institute, Baltimore, MD ABSTRACT The purpose of this paper is to present an outline

More information

Text Dependent Analysis: Exploring A New Construct (1.26.15)

Text Dependent Analysis: Exploring A New Construct (1.26.15) Text Dependent Analysis: Exploring A New Construct (1.26.15) Erika Hall, Center for Assessment Jeri Thompson, Center for Assessment Diane Simaska, Pennsylvania Department of Education Introduction A new

More information

Chapter 19 Split-Plot Designs

Chapter 19 Split-Plot Designs Chapter 19 Split-Plot Designs Split-plot designs are needed when the levels of some treatment factors are more difficult to change during the experiment than those of others. The designs have a nested

More information

A Cohort Study of Traffic-related Air Pollution and Mortality in Toronto, Canada: Online Appendix

A Cohort Study of Traffic-related Air Pollution and Mortality in Toronto, Canada: Online Appendix A Cohort Study of Traffic-related Air Pollution and Mortality in Toronto, Canada: Online Appendix Michael Jerrett, 1 Murray M. Finkelstein, 2 Jeff R. Brook, 3 M. Altaf Arain, 4 Palvos Kanaroglou, 4 Dave

More information

SELECTING THE BEST MODEL FOR MULTIPLE LINEAR REGRESSION

SELECTING THE BEST MODEL FOR MULTIPLE LINEAR REGRESSION SELECTING THE BEST MODEL FOR MULTIPLE LINEAR REGRESSION Introduction In multiple regression a common goal is to determine which independent variables contribute significantly to explaining the variability

More information

Comparing Regression Lines From Independent Samples

Comparing Regression Lines From Independent Samples Comparing Regression Lines From Independent Samples The analysis discussed in this document is appropriate when one wishes to determine whether the linear relationship between one continuously distributed

More information

Lesson 1: Comparison of Population Means Part c: Comparison of Two- Means

Lesson 1: Comparison of Population Means Part c: Comparison of Two- Means Lesson : Comparison of Population Means Part c: Comparison of Two- Means Welcome to lesson c. This third lesson of lesson will discuss hypothesis testing for two independent means. Steps in Hypothesis

More information

DETERMINANTS OF CAPITAL ADEQUACY RATIO IN SELECTED BOSNIAN BANKS

DETERMINANTS OF CAPITAL ADEQUACY RATIO IN SELECTED BOSNIAN BANKS DETERMINANTS OF CAPITAL ADEQUACY RATIO IN SELECTED BOSNIAN BANKS Nađa DRECA International University of Sarajevo nadja.dreca@students.ius.edu.ba Abstract The analysis of a data set of observation for 10

More information

In Chapter 2, we used linear regression to describe linear relationships. The setting for this is a

In Chapter 2, we used linear regression to describe linear relationships. The setting for this is a Math 143 Inference on Regression 1 Review of Linear Regression In Chapter 2, we used linear regression to describe linear relationships. The setting for this is a bivariate data set (i.e., a list of cases/subjects

More information

SAS 3: Comparing Means

SAS 3: Comparing Means SAS 3: Comparing Means University of Guelph Revised June 2011 Table of Contents SAS Availability... 2 Goals of the workshop... 2 Data for SAS sessions... 3 Statistical Background... 4 T-test... 8 1. Independent

More information

T Tests and Analysis of Variance

T Tests and Analysis of Variance T Tests and Analysis of Variance Pre course Assignment The purpose of this assignment is to give you practice with JMP software by performing ahead of time the analyses that will be done during the course.

More information

Survival analysis methods in Insurance Applications in car insurance contracts

Survival analysis methods in Insurance Applications in car insurance contracts Survival analysis methods in Insurance Applications in car insurance contracts Abder OULIDI 1-2 Jean-Marie MARION 1 Hérvé GANACHAUD 3 1 Institut de Mathématiques Appliquées (IMA) Angers France 2 Institut

More information

Module 10: Mixed model theory II: Tests and confidence intervals

Module 10: Mixed model theory II: Tests and confidence intervals St@tmaster 02429/MIXED LINEAR MODELS PREPARED BY THE STATISTICS GROUPS AT IMM, DTU AND KU-LIFE Module 10: Mixed model theory II: Tests and confidence intervals 10.1 Notes..................................

More information

MULTIPLE LINEAR REGRESSION ANALYSIS USING MICROSOFT EXCEL. by Michael L. Orlov Chemistry Department, Oregon State University (1996)

MULTIPLE LINEAR REGRESSION ANALYSIS USING MICROSOFT EXCEL. by Michael L. Orlov Chemistry Department, Oregon State University (1996) MULTIPLE LINEAR REGRESSION ANALYSIS USING MICROSOFT EXCEL by Michael L. Orlov Chemistry Department, Oregon State University (1996) INTRODUCTION In modern science, regression analysis is a necessary part

More information

Multiple Linear Regression

Multiple Linear Regression Multiple Linear Regression A regression with two or more explanatory variables is called a multiple regression. Rather than modeling the mean response as a straight line, as in simple regression, it is

More information

1 Simple Linear Regression I Least Squares Estimation

1 Simple Linear Regression I Least Squares Estimation Simple Linear Regression I Least Squares Estimation Textbook Sections: 8. 8.3 Previously, we have worked with a random variable x that comes from a population that is normally distributed with mean µ and

More information

Credit Scoring and Disparate Impact

Credit Scoring and Disparate Impact Credit Scoring and Disparate Impact Elaine Fortowsky Wells Fargo Home Mortgage & Michael LaCour-Little Wells Fargo Home Mortgage Conference Paper for Midyear AREUEA Meeting and Wharton/Philadelphia FRB

More information

Developing Risk Adjustment Techniques Using the SAS@ System for Assessing Health Care Quality in the lmsystem@

Developing Risk Adjustment Techniques Using the SAS@ System for Assessing Health Care Quality in the lmsystem@ Developing Risk Adjustment Techniques Using the SAS@ System for Assessing Health Care Quality in the lmsystem@ Yanchun Xu, Andrius Kubilius Joint Commission on Accreditation of Healthcare Organizations,

More information

VI. Introduction to Logistic Regression

VI. Introduction to Logistic Regression VI. Introduction to Logistic Regression We turn our attention now to the topic of modeling a categorical outcome as a function of (possibly) several factors. The framework of generalized linear models

More information

MULTIPLE REGRESSION EXAMPLE

MULTIPLE REGRESSION EXAMPLE MULTIPLE REGRESSION EXAMPLE For a sample of n = 166 college students, the following variables were measured: Y = height X 1 = mother s height ( momheight ) X 2 = father s height ( dadheight ) X 3 = 1 if

More information

25 Working with categorical data and factor variables

25 Working with categorical data and factor variables 25 Working with categorical data and factor variables Contents 25.1 Continuous, categorical, and indicator variables 25.1.1 Converting continuous variables to indicator variables 25.1.2 Converting continuous

More information

ECON Introductory Econometrics. Lecture 17: Experiments

ECON Introductory Econometrics. Lecture 17: Experiments ECON4150 - Introductory Econometrics Lecture 17: Experiments Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 13 Lecture outline 2 Why study experiments? The potential outcome framework.

More information

Multiple Regression YX1 YX2 X1X2 YX1.X2

Multiple Regression YX1 YX2 X1X2 YX1.X2 Multiple Regression Simple or total correlation: relationship between one dependent and one independent variable, Y versus X Coefficient of simple determination: r (or r, r ) YX YX XX Partial correlation:

More information

1. The parameters to be estimated in the simple linear regression model Y=α+βx+ε ε~n(0,σ) are: a) α, β, σ b) α, β, ε c) a, b, s d) ε, 0, σ

1. The parameters to be estimated in the simple linear regression model Y=α+βx+ε ε~n(0,σ) are: a) α, β, σ b) α, β, ε c) a, b, s d) ε, 0, σ STA 3024 Practice Problems Exam 2 NOTE: These are just Practice Problems. This is NOT meant to look just like the test, and it is NOT the only thing that you should study. Make sure you know all the material

More information

Statistical Modelling in Stata 5: Linear Models

Statistical Modelling in Stata 5: Linear Models Statistical Modelling in Stata 5: Linear Models Mark Lunt Arthritis Research UK Centre for Excellence in Epidemiology University of Manchester 08/11/2016 Structure This Week What is a linear model? How

More information

Introduction to Stata

Introduction to Stata Introduction to Stata September 23, 2014 Stata is one of a few statistical analysis programs that social scientists use. Stata is in the mid-range of how easy it is to use. Other options include SPSS,

More information

Domain of a Composition

Domain of a Composition Domain of a Composition Definition Given the function f and g, the composition of f with g is a function defined as (f g)() f(g()). The domain of f g is the set of all real numbers in the domain of g such

More information

Topic 9. Factorial Experiments [ST&D Chapter 15]

Topic 9. Factorial Experiments [ST&D Chapter 15] Topic 9. Factorial Experiments [ST&D Chapter 5] 9.. Introduction In earlier times factors were studied one at a time, with separate experiments devoted to each factor. In the factorial approach, the investigator

More information

Multicollinearity Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised January 13, 2015

Multicollinearity Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised January 13, 2015 Multicollinearity Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised January 13, 2015 Stata Example (See appendices for full example).. use http://www.nd.edu/~rwilliam/stats2/statafiles/multicoll.dta,

More information

Residuals. Residuals = ª Department of ISM, University of Alabama, ST 260, M23 Residuals & Minitab. ^ e i = y i - y i

Residuals. Residuals = ª Department of ISM, University of Alabama, ST 260, M23 Residuals & Minitab. ^ e i = y i - y i A continuation of regression analysis Lesson Objectives Continue to build on regression analysis. Learn how residual plots help identify problems with the analysis. M23-1 M23-2 Example 1: continued Case

More information

[This document contains corrections to a few typos that were found on the version available through the journal s web page]

[This document contains corrections to a few typos that were found on the version available through the journal s web page] Online supplement to Hayes, A. F., & Preacher, K. J. (2014). Statistical mediation analysis with a multicategorical independent variable. British Journal of Mathematical and Statistical Psychology, 67,

More information

Stat 5303 (Oehlert): Tukey One Degree of Freedom 1

Stat 5303 (Oehlert): Tukey One Degree of Freedom 1 Stat 5303 (Oehlert): Tukey One Degree of Freedom 1 > catch

More information

The Relationship Between Rodent Offspring Blood Lead Levels and Maternal Diet

The Relationship Between Rodent Offspring Blood Lead Levels and Maternal Diet The Relationship Between Rodent Offspring Blood Lead Levels and Maternal Diet Allison Crawford, Xiahong Li, Mira Shapiro 1, Ruitao Zhang Introduction A study was undertaken to understand the effect of

More information

Lecture 11: Confidence intervals and model comparison for linear regression; analysis of variance

Lecture 11: Confidence intervals and model comparison for linear regression; analysis of variance Lecture 11: Confidence intervals and model comparison for linear regression; analysis of variance 14 November 2007 1 Confidence intervals and hypothesis testing for linear regression Just as there was

More information