ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 1 22:46 Sunday, March 2, 2003
|
|
- Everett Hines
- 6 years ago
- Views:
Transcription
1 ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 1 22:46 Sunday, March 2, 2003 The MEANS Procedure DRINKING STATUS=1 Analysis Variable : TRIGL N Mean Std Dev Minimum Maximum DRINKING STATUS=2 Analysis Variable : TRIGL N Mean Std Dev Minimum Maximum DRINKING STATUS=3 Analysis Variable : TRIGL N Mean Std Dev Minimum Maximum
2 ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 2 unadjusted models with alcat 22:46 Sunday, March 2, 2003 The GLM Procedure Class Level Information Class Levels Values ALCAT Number of observations 305
3 Dependent Variable: TRIGL ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 3 unadjusted models with alcat 22:46 Sunday, March 2, 2003 The GLM Procedure Sum of Squares Mean Square F Value Pr > F Model Error Corrected Total R-Square Coeff Var Root MSE TRIGL Mean Type I SS Mean Square F Value Pr > F ALCAT Type III SS Mean Square F Value Pr > F ALCAT
4 ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 4 unadjusted models with alcat 22:46 Sunday, March 2, 2003 The GLM Procedure Least Squares Means ALCAT TRIGL LSMEAN
5 ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 5 unadjusted models with alcat 22:46 Sunday, March 2, 2003 The REG Procedure Model: MODEL1 Dependent Variable: TRIGL Analysis of Variance Sum of Squares Mean Square F Value Pr > F Model Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Variable Parameter Estimates Parameter Estimate Standard Error t Value Pr > t Intercept <.0001 EXDRK CRDRK
6 ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 6 unadjusted models with alcat 22:46 Sunday, March 2, 2003 The REG Procedure Model: MODEL1 Test ALCAT Results for Dependent Variable TRIGL Mean Square F Value Pr > F Numerator Denominator
7 ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 7 alcat by sex association 22:46 Sunday, March 2, 2003 The FREQ Procedure Frequency Row Pct Table of ALCAT by SEX ALCAT(DRINKING STATUS) SEX Total Total
8 ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 8 covariate adjusted models with alcat 22:46 Sunday, March 2, 2003 The GLM Procedure Class Level Information Class Levels Values ALCAT SEX Number of observations 305
9 Dependent Variable: TRIGL ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 9 covariate adjusted models with alcat 22:46 Sunday, March 2, 2003 The GLM Procedure Sum of Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE TRIGL Mean Type I SS Mean Square F Value Pr > F ALCAT SEX AGE WEIGHT CHOLEST <.0001 Type III SS Mean Square F Value Pr > F ALCAT SEX AGE WEIGHT CHOLEST <.0001
10 ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 10 covariate adjusted models with alcat 22:46 Sunday, March 2, 2003 The GLM Procedure Least Squares Means ALCAT TRIGL LSMEAN SEX TRIGL LSMEAN
11 ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 11 covariate adjusted models with alcat 22:46 Sunday, March 2, 2003 The REG Procedure Model: MODEL1 Dependent Variable: TRIGL Analysis of Variance Sum of Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Variable Parameter Estimates Parameter Estimate Standard Error t Value Pr > t Intercept EXDRK CRDRK SEX AGE WEIGHT CHOLEST <.0001
12 ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 12 covariate adjusted models with alcat 22:46 Sunday, March 2, 2003 The REG Procedure Model: MODEL1 Test ALCAT Results for Dependent Variable TRIGL Mean Square F Value Pr > F Numerator Denominator
13 ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 13 sex by age interaction model 22:46 Sunday, March 2, 2003 The REG Procedure Model: MODEL1 Dependent Variable: TRIGL Analysis of Variance Sum of Squares Mean Square F Value Pr > F Model Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Variable Parameter Estimates Parameter Estimate Standard Error t Value Pr > t Intercept SEX AGE sexage
14 ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 14 sex by age interaction model 22:46 Sunday, March 2, 2003 The REG Procedure Model: MODEL1 Test sex Results for Dependent Variable TRIGL Mean Square F Value Pr > F Numerator Denominator
unadjusted model for baseline cholesterol 22:31 Monday, April 19,
unadjusted model for baseline cholesterol 22:31 Monday, April 19, 2004 1 Class Level Information Class Levels Values TRETGRP 3 3 4 5 SEX 2 0 1 Number of observations 916 unadjusted model for baseline cholesterol
More informationT-test: means of Spock's judge versus all other judges 1 12:10 Wednesday, January 5, judge1 N Mean Std Dev Std Err Minimum Maximum
T-test: means of Spock's judge versus all other judges 1 The TTEST Procedure Variable: pcwomen judge1 N Mean Std Dev Std Err Minimum Maximum OTHER 37 29.4919 7.4308 1.2216 16.5000 48.9000 SPOCKS 9 14.6222
More informationdata proc sort proc corr run proc reg run proc glm run proc glm run proc glm run proc reg CONMAIN CONINT run proc reg DUMMAIN DUMINT run proc reg
data one; input id Y group X; I1=0;I2=0;I3=0;if group=1 then I1=1;if group=2 then I2=1;if group=3 then I3=1; IINT1=I1*X;IINT2=I2*X;IINT3=I3*X; *************************************************************************;
More informationGeneral Linear Model (Chapter 4)
General Linear Model (Chapter 4) Outcome variable is considered continuous Simple linear regression Scatterplots OLS is BLUE under basic assumptions MSE estimates residual variance testing regression coefficients
More information3 Variables: Cyberloafing Conscientiousness Age
title 'Cyberloafing, Mike Sage'; run; PROC CORR data=sage; var Cyberloafing Conscientiousness Age; run; quit; The CORR Procedure 3 Variables: Cyberloafing Conscientiousness Age Simple Statistics Variable
More informationSTA 303H1F: Two-way Analysis of Variance Practice Problems
STA 303H1F: Two-way Analysis of Variance Practice Problems 1. In the Pygmalion example from lecture, why are the average scores of the platoon used as the response variable, rather than the scores of the
More informationBooklet of Code and Output for STAC32 Final Exam
Booklet of Code and Output for STAC32 Final Exam December 7, 2017 Figure captions are below the Figures they refer to. LowCalorie LowFat LowCarbo Control 8 2 3 2 9 4 5 2 6 3 4-1 7 5 2 0 3 1 3 3 Figure
More informationLecture 11 Multiple Linear Regression
Lecture 11 Multiple Linear Regression STAT 512 Spring 2011 Background Reading KNNL: 6.1-6.5 11-1 Topic Overview Review: Multiple Linear Regression (MLR) Computer Science Case Study 11-2 Multiple Regression
More informationSection 9c. Propensity scores. Controlling for bias & confounding in observational studies
Section 9c Propensity scores Controlling for bias & confounding in observational studies 1 Logistic regression and propensity scores Consider comparing an outcome in two treatment groups: A vs B. In a
More informationResiduals from regression on original data 1
Residuals from regression on original data 1 Obs a b n i y 1 1 1 3 1 1 2 1 1 3 2 2 3 1 1 3 3 3 4 1 2 3 1 4 5 1 2 3 2 5 6 1 2 3 3 6 7 1 3 3 1 7 8 1 3 3 2 8 9 1 3 3 3 9 10 2 1 3 1 10 11 2 1 3 2 11 12 2 1
More informationEffect of Centering and Standardization in Moderation Analysis
Effect of Centering and Standardization in Moderation Analysis Raw Data The CORR Procedure 3 Variables: govact negemot Simple Statistics Variable N Mean Std Dev Sum Minimum Maximum Label govact 4.58699
More informationTopic 17 - Single Factor Analysis of Variance. Outline. One-way ANOVA. The Data / Notation. One way ANOVA Cell means model Factor effects model
Topic 17 - Single Factor Analysis of Variance - Fall 2013 One way ANOVA Cell means model Factor effects model Outline Topic 17 2 One-way ANOVA Response variable Y is continuous Explanatory variable is
More informationTopic 20: Single Factor Analysis of Variance
Topic 20: Single Factor Analysis of Variance Outline Single factor Analysis of Variance One set of treatments Cell means model Factor effects model Link to linear regression using indicator explanatory
More informationAnalysis of Variance. Source DF Squares Square F Value Pr > F. Model <.0001 Error Corrected Total
Math 221: Linear Regression and Prediction Intervals S. K. Hyde Chapter 23 (Moore, 5th Ed.) (Neter, Kutner, Nachsheim, and Wasserman) The Toluca Company manufactures refrigeration equipment as well as
More informationLecture 11: Simple Linear Regression
Lecture 11: Simple Linear Regression Readings: Sections 3.1-3.3, 11.1-11.3 Apr 17, 2009 In linear regression, we examine the association between two quantitative variables. Number of beers that you drink
More informationAnalysis of Covariance
Analysis of Covariance (ANCOVA) Bruce A Craig Department of Statistics Purdue University STAT 514 Topic 10 1 When to Use ANCOVA In experiment, there is a nuisance factor x that is 1 Correlated with y 2
More informationAnswer to exercise 'height vs. age' (Juul)
Answer to exercise 'height vs. age' (Juul) Question 1 Fitting a straight line to height for males in the age range 5-20 and making the corresponding illustration is performed by writing: proc reg data=juul;
More informationUse of Dummy (Indicator) Variables in Applied Econometrics
Chapter 5 Use of Dummy (Indicator) Variables in Applied Econometrics Section 5.1 Introduction Use of Dummy (Indicator) Variables Model specifications in applied econometrics often necessitate the use of
More informationRepeated Measures Part 2: Cartoon data
Repeated Measures Part 2: Cartoon data /*********************** cartoonglm.sas ******************/ options linesize=79 noovp formdlim='_'; title 'Cartoon Data: STA442/1008 F 2005'; proc format; /* value
More informationPLS205!! Lab 9!! March 6, Topic 13: Covariance Analysis
PLS205!! Lab 9!! March 6, 2014 Topic 13: Covariance Analysis Covariable as a tool for increasing precision Carrying out a full ANCOVA Testing ANOVA assumptions Happiness! Covariable as a Tool for Increasing
More informationStatistics 512: Solution to Homework#11. Problems 1-3 refer to the soybean sausage dataset of Problem 20.8 (ch21pr08.dat).
Statistics 512: Solution to Homework#11 Problems 1-3 refer to the soybean sausage dataset of Problem 20.8 (ch21pr08.dat). 1. Perform the two-way ANOVA without interaction for this model. Use the results
More informationStat 302 Statistical Software and Its Applications SAS: Simple Linear Regression
1 Stat 302 Statistical Software and Its Applications SAS: Simple Linear Regression Fritz Scholz Department of Statistics, University of Washington Winter Quarter 2015 February 16, 2015 2 The Spirit of
More informationThis is a Randomized Block Design (RBD) with a single factor treatment arrangement (2 levels) which are fixed.
EXST3201 Chapter 13c Geaghan Fall 2005: Page 1 Linear Models Y ij = µ + βi + τ j + βτij + εijk This is a Randomized Block Design (RBD) with a single factor treatment arrangement (2 levels) which are fixed.
More informationSTAT 3A03 Applied Regression Analysis With SAS Fall 2017
STAT 3A03 Applied Regression Analysis With SAS Fall 2017 Assignment 5 Solution Set Q. 1 a The code that I used and the output is as follows PROC GLM DataS3A3.Wool plotsnone; Class Amp Len Load; Model CyclesAmp
More informationAnalysis of variance and regression. November 22, 2007
Analysis of variance and regression November 22, 2007 Parametrisations: Choice of parameters Comparison of models Test for linearity Linear splines Lene Theil Skovgaard, Dept. of Biostatistics, Institute
More informationBiological Applications of ANOVA - Examples and Readings
BIO 575 Biological Applications of ANOVA - Winter Quarter 2010 Page 1 ANOVA Pac Biological Applications of ANOVA - Examples and Readings One-factor Model I (Fixed Effects) This is the same example for
More information171:162 Design and Analysis of Biomedical Studies, Summer 2011 Exam #3, July 16th
Name 171:162 Design and Analysis of Biomedical Studies, Summer 2011 Exam #3, July 16th Use the selected SAS output to help you answer the questions. The SAS output is all at the back of the exam on pages
More informationLecture 13 Extra Sums of Squares
Lecture 13 Extra Sums of Squares STAT 512 Spring 2011 Background Reading KNNL: 7.1-7.4 13-1 Topic Overview Extra Sums of Squares (Defined) Using and Interpreting R 2 and Partial-R 2 Getting ESS and Partial-R
More information5.3 Three-Stage Nested Design Example
5.3 Three-Stage Nested Design Example A researcher designs an experiment to study the of a metal alloy. A three-stage nested design was conducted that included Two alloy chemistry compositions. Three ovens
More informationAnswer Keys to Homework#10
Answer Keys to Homework#10 Problem 1 Use either restricted or unrestricted mixed models. Problem 2 (a) First, the respective means for the 8 level combinations are listed in the following table A B C Mean
More informationSTAT 3900/4950 MIDTERM TWO Name: Spring, 2015 (print: first last ) Covered topics: Two-way ANOVA, ANCOVA, SLR, MLR and correlation analysis
STAT 3900/4950 MIDTERM TWO Name: Spring, 205 (print: first last ) Covered topics: Two-way ANOVA, ANCOVA, SLR, MLR and correlation analysis Instructions: You may use your books, notes, and SPSS/SAS. NO
More informationChapter 8 (More on Assumptions for the Simple Linear Regression)
EXST3201 Chapter 8b Geaghan Fall 2005: Page 1 Chapter 8 (More on Assumptions for the Simple Linear Regression) Your textbook considers the following assumptions: Linearity This is not something I usually
More informationHandout 1: Predicting GPA from SAT
Handout 1: Predicting GPA from SAT appsrv01.srv.cquest.utoronto.ca> appsrv01.srv.cquest.utoronto.ca> ls Desktop grades.data grades.sas oldstuff sasuser.800 appsrv01.srv.cquest.utoronto.ca> cat grades.data
More informationUNIVERSITY OF TORONTO. Faculty of Arts and Science APRIL 2010 EXAMINATIONS STA 303 H1S / STA 1002 HS. Duration - 3 hours. Aids Allowed: Calculator
UNIVERSITY OF TORONTO Faculty of Arts and Science APRIL 2010 EXAMINATIONS STA 303 H1S / STA 1002 HS Duration - 3 hours Aids Allowed: Calculator LAST NAME: FIRST NAME: STUDENT NUMBER: There are 27 pages
More informationAssignment 9 Answer Keys
Assignment 9 Answer Keys Problem 1 (a) First, the respective means for the 8 level combinations are listed in the following table A B C Mean 26.00 + 34.67 + 39.67 + + 49.33 + 42.33 + + 37.67 + + 54.67
More informationusing the beginning of all regression models
Estimating using the beginning of all regression models 3 examples Note about shorthand Cavendish's 29 measurements of the earth's density Heights (inches) of 14 11 year-old males from Alberta study Half-life
More informationa. The least squares estimators of intercept and slope are (from JMP output): b 0 = 6.25 b 1 =
Stat 28 Fall 2004 Key to Homework Exercise.10 a. There is evidence of a linear trend: winning times appear to decrease with year. A straight-line model for predicting winning times based on year is: Winning
More informationMulticollinearity Exercise
Multicollinearity Exercise Use the attached SAS output to answer the questions. [OPTIONAL: Copy the SAS program below into the SAS editor window and run it.] You do not need to submit any output, so there
More informationUNIVERSITY EXAMINATIONS NJORO CAMPUS SECOND SEMESTER 2011/2012
UNIVERSITY EXAMINATIONS NJORO CAMPUS SECOND SEMESTER 2011/2012 THIRD YEAR EXAMINATION FOR THE AWARD BACHELOR OF SCIENCE IN AGRICULTURE AND BACHELOR OF SCIENCE IN FOOD TECHNOLOGY AGRO 391 AGRICULTURAL EXPERIMENTATION
More informationFailure Time of System due to the Hot Electron Effect
of System due to the Hot Electron Effect 1 * exresist; 2 option ls=120 ps=75 nocenter nodate; 3 title of System due to the Hot Electron Effect ; 4 * TIME = failure time (hours) of a system due to drift
More informationData 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 informationCOMPREHENSIVE WRITTEN EXAMINATION, PAPER III FRIDAY AUGUST 26, 2005, 9:00 A.M. 1:00 P.M. STATISTICS 174 QUESTION
COMPREHENSIVE WRITTEN EXAMINATION, PAPER III FRIDAY AUGUST 26, 2005, 9:00 A.M. 1:00 P.M. STATISTICS 174 QUESTION Answer all parts. Closed book, calculators allowed. It is important to show all working,
More informationChapter 8 Quantitative and Qualitative Predictors
STAT 525 FALL 2017 Chapter 8 Quantitative and Qualitative Predictors Professor Dabao Zhang Polynomial Regression Multiple regression using X 2 i, X3 i, etc as additional predictors Generates quadratic,
More information14.32 Final : Spring 2001
14.32 Final : Spring 2001 Please read the entire exam before you begin. You have 3 hours. No books or notes should be used. Calculators are allowed. There are 105 points. Good luck! A. True/False/Sometimes
More informationIn Class Review Exercises Vartanian: SW 540
In Class Review Exercises Vartanian: SW 540 1. Given the following output from an OLS model looking at income, what is the slope and intercept for those who are black and those who are not black? b SE
More informationSTOR 455 STATISTICAL METHODS I
STOR 455 STATISTICAL METHODS I Jan Hannig Mul9variate Regression Y=X β + ε X is a regression matrix, β is a vector of parameters and ε are independent N(0,σ) Es9mated parameters b=(x X) - 1 X Y Predicted
More informationStat 500 Midterm 2 12 November 2009 page 0 of 11
Stat 500 Midterm 2 12 November 2009 page 0 of 11 Please put your name on the back of your answer book. Do NOT put it on the front. Thanks. Do not start until I tell you to. The exam is closed book, closed
More informationChapter 1: Linear Regression with One Predictor Variable also known as: Simple Linear Regression Bivariate Linear Regression
BSTT523: Kutner et al., Chapter 1 1 Chapter 1: Linear Regression with One Predictor Variable also known as: Simple Linear Regression Bivariate Linear Regression Introduction: Functional relation between
More informationSTA 302 H1F / 1001 HF Fall 2007 Test 1 October 24, 2007
STA 302 H1F / 1001 HF Fall 2007 Test 1 October 24, 2007 LAST NAME: SOLUTIONS FIRST NAME: STUDENT NUMBER: ENROLLED IN: (circle one) STA 302 STA 1001 INSTRUCTIONS: Time: 90 minutes Aids allowed: calculator.
More informationLinear models Analysis of Covariance
Esben Budtz-Jørgensen April 22, 2008 Linear models Analysis of Covariance Confounding Interactions Parameterizations Analysis of Covariance group comparisons can become biased if an important predictor
More information4.8 Alternate Analysis as a Oneway ANOVA
4.8 Alternate Analysis as a Oneway ANOVA Suppose we have data from a two-factor factorial design. The following method can be used to perform a multiple comparison test to compare treatment means as well
More informationCategorical Predictor Variables
Categorical Predictor Variables We often wish to use categorical (or qualitative) variables as covariates in a regression model. For binary variables (taking on only 2 values, e.g. sex), it is relatively
More informationANOVA Longitudinal Models for the Practice Effects Data: via GLM
Psyc 943 Lecture 25 page 1 ANOVA Longitudinal Models for the Practice Effects Data: via GLM Model 1. Saturated Means Model for Session, E-only Variances Model (BP) Variances Model: NO correlation, EQUAL
More informationSAS Commands. General Plan. Output. Construct scatterplot / interaction plot. Run full model
Topic 23 - Unequal Replication Data Model Outline - Fall 2013 Parameter Estimates Inference Topic 23 2 Example Page 954 Data for Two Factor ANOVA Y is the response variable Factor A has levels i = 1, 2,...,
More informationLinear models Analysis of Covariance
Esben Budtz-Jørgensen November 20, 2007 Linear models Analysis of Covariance Confounding Interactions Parameterizations Analysis of Covariance group comparisons can become biased if an important predictor
More informationNC Births, ANOVA & F-tests
Math 158, Spring 2018 Jo Hardin Multiple Regression II R code Decomposition of Sums of Squares (and F-tests) NC Births, ANOVA & F-tests A description of the data is given at http://pages.pomona.edu/~jsh04747/courses/math58/
More informationModels for Clustered Data
Models for Clustered Data Edps/Psych/Soc 589 Carolyn J Anderson Department of Educational Psychology c Board of Trustees, University of Illinois Spring 2019 Outline Notation NELS88 data Fixed Effects ANOVA
More informationSTAT 350: Summer Semester Midterm 1: Solutions
Name: Student Number: STAT 350: Summer Semester 2008 Midterm 1: Solutions 9 June 2008 Instructor: Richard Lockhart Instructions: This is an open book test. You may use notes, text, other books and a calculator.
More informationSTATISTICS 479 Exam II (100 points)
Name STATISTICS 79 Exam II (1 points) 1. A SAS data set was created using the following input statement: Answer parts(a) to (e) below. input State $ City $ Pop199 Income Housing Electric; (a) () Give the
More informationChapter 6 Multiple Regression
STAT 525 FALL 2018 Chapter 6 Multiple Regression Professor Min Zhang The Data and Model Still have single response variable Y Now have multiple explanatory variables Examples: Blood Pressure vs Age, Weight,
More informationPaper: ST-161. Techniques for Evidence-Based Decision Making Using SAS Ian Stockwell, The Hilltop UMBC, Baltimore, MD
Paper: ST-161 Techniques for Evidence-Based Decision Making Using SAS Ian Stockwell, The Hilltop Institute @ UMBC, Baltimore, MD ABSTRACT SAS has many tools that can be used for data analysis. From Freqs
More informationModels for Clustered Data
Models for Clustered Data Edps/Psych/Stat 587 Carolyn J Anderson Department of Educational Psychology c Board of Trustees, University of Illinois Fall 2017 Outline Notation NELS88 data Fixed Effects ANOVA
More informationLecture 6 Multiple Linear Regression, cont.
Lecture 6 Multiple Linear Regression, cont. BIOST 515 January 22, 2004 BIOST 515, Lecture 6 Testing general linear hypotheses Suppose we are interested in testing linear combinations of the regression
More informationStatistics for exp. medical researchers Regression and Correlation
Faculty of Health Sciences Regression analysis Statistics for exp. medical researchers Regression and Correlation Lene Theil Skovgaard Sept. 28, 2015 Linear regression, Estimation and Testing Confidence
More informationIES 612/STA 4-573/STA Winter 2008 Week 1--IES 612-STA STA doc
IES 612/STA 4-573/STA 4-576 Winter 2008 Week 1--IES 612-STA 4-573-STA 4-576.doc Review Notes: [OL] = Ott & Longnecker Statistical Methods and Data Analysis, 5 th edition. [Handouts based on notes prepared
More information11 Factors, ANOVA, and Regression: SAS versus Splus
Adapted from P. Smith, and expanded 11 Factors, ANOVA, and Regression: SAS versus Splus Factors. A factor is a variable with finitely many values or levels which is treated as a predictor within regression-type
More informationOutline. Review regression diagnostics Remedial measures Weighted regression Ridge regression Robust regression Bootstrapping
Topic 19: Remedies Outline Review regression diagnostics Remedial measures Weighted regression Ridge regression Robust regression Bootstrapping Regression Diagnostics Summary Check normality of the residuals
More informationWITHIN-PARTICIPANT EXPERIMENTAL DESIGNS
1 WITHIN-PARTICIPANT EXPERIMENTAL DESIGNS I. Single-factor designs: the model is: yij i j ij ij where: yij score for person j under treatment level i (i = 1,..., I; j = 1,..., n) overall mean βi treatment
More information2-way analysis of variance
2-way analysis of variance We may be considering the effect of two factors (A and B) on our response variable, for instance fertilizer and variety on maize yield; or therapy and sex on cholesterol level.
More informationUnbalanced Data in Factorials Types I, II, III SS Part 2
Unbalanced Data in Factorials Types I, II, III SS Part 2 Chapter 10 in Oehlert STAT:5201 Week 9 - Lecture 2b 1 / 29 Types of sums of squares Type II SS The Type II SS relates to the extra variability explained
More informationOutline. Topic 19 - Inference. The Cell Means Model. Estimates. Inference for Means Differences in cell means Contrasts. STAT Fall 2013
Topic 19 - Inference - Fall 2013 Outline Inference for Means Differences in cell means Contrasts Multiplicity Topic 19 2 The Cell Means Model Expressed numerically Y ij = µ i + ε ij where µ i is the theoretical
More information****Lab 4, Feb 4: EDA and OLS and WLS
****Lab 4, Feb 4: EDA and OLS and WLS ------- log: C:\Documents and Settings\Default\Desktop\LDA\Data\cows_Lab4.log log type: text opened on: 4 Feb 2004, 09:26:19. use use "Z:\LDA\DataLDA\cowsP.dta", clear.
More informationBooklet of Code and Output for STAC32 Final Exam
Booklet of Code and Output for STAC32 Final Exam December 8, 2014 List of Figures in this document by page: List of Figures 1 Popcorn data............................. 2 2 MDs by city, with normal quantile
More informationSTAT 3A03 Applied Regression With SAS Fall 2017
STAT 3A03 Applied Regression With SAS Fall 2017 Assignment 2 Solution Set Q. 1 I will add subscripts relating to the question part to the parameters and their estimates as well as the errors and residuals.
More informationSuggested Answers Problem set 4 ECON 60303
Suggested Answers Problem set 4 ECON 60303 Bill Evans Spring 04. A program that answers part A) is on the web page and is named psid_iv_comparison.do. Below are some key results and a summary table is
More information6. Multiple regression - PROC GLM
Use of SAS - November 2016 6. Multiple regression - PROC GLM Karl Bang Christensen Department of Biostatistics, University of Copenhagen. http://biostat.ku.dk/~kach/sas2016/ kach@biostat.ku.dk, tel: 35327491
More informationStatistics 203 Introduction to Regression Models and ANOVA Practice Exam
Statistics 203 Introduction to Regression Models and ANOVA Practice Exam Prof. J. Taylor You may use your 4 single-sided pages of notes This exam is 7 pages long. There are 4 questions, first 3 worth 10
More informationChapter 1 Linear Regression with One Predictor
STAT 525 FALL 2018 Chapter 1 Linear Regression with One Predictor Professor Min Zhang Goals of Regression Analysis Serve three purposes Describes an association between X and Y In some applications, the
More informationSAS Procedures Inference about the Line ffl model statement in proc reg has many options ffl To construct confidence intervals use alpha=, clm, cli, c
Inference About the Slope ffl As with all estimates, ^fi1 subject to sampling var ffl Because Y jx _ Normal, the estimate ^fi1 _ Normal A linear combination of indep Normals is Normal Simple Linear Regression
More informationData Set 8: Laysan Finch Beak Widths
Data Set 8: Finch Beak Widths Statistical Setting This handout describes an analysis of covariance (ANCOVA) involving one categorical independent variable (with only two levels) and one quantitative covariate.
More informationOverview Scatter Plot Example
Overview Topic 22 - Linear Regression and Correlation STAT 5 Professor Bruce Craig Consider one population but two variables For each sampling unit observe X and Y Assume linear relationship between variables
More informationModel Building Chap 5 p251
Model Building Chap 5 p251 Models with one qualitative variable, 5.7 p277 Example 4 Colours : Blue, Green, Lemon Yellow and white Row Blue Green Lemon Insects trapped 1 0 0 1 45 2 0 0 1 59 3 0 0 1 48 4
More informationCourse Econometrics I
Course Econometrics I 3. Multiple Regression Analysis: Binary Variables Martin Halla Johannes Kepler University of Linz Department of Economics Last update: April 29, 2014 Martin Halla CS Econometrics
More information3. The F Test for Comparing Reduced vs. Full Models. opyright c 2018 Dan Nettleton (Iowa State University) 3. Statistics / 43
3. The F Test for Comparing Reduced vs. Full Models opyright c 2018 Dan Nettleton (Iowa State University) 3. Statistics 510 1 / 43 Assume the Gauss-Markov Model with normal errors: y = Xβ + ɛ, ɛ N(0, σ
More informationTopic 13. Analysis of Covariance (ANCOVA) [ST&D chapter 17] 13.1 Introduction Review of regression concepts
Topic 13. Analysis of Covariance (ANCOVA) [ST&D chapter 17] 13.1 Introduction The analysis of covariance (ANCOVA) is a technique that is occasionally useful for improving the precision of an experiment.
More informationssh tap sas913, sas
B. Kedem, STAT 430 SAS Examples SAS8 ===================== ssh xyz@glue.umd.edu, tap sas913, sas https://www.statlab.umd.edu/sasdoc/sashtml/onldoc.htm Multiple Regression ====================== 0. Show
More informationBooklet of Code and Output for STAC32 Final Exam
Booklet of Code and Output for STAC32 Final Exam December 12, 2015 List of Figures in this document by page: List of Figures 1 Time in days for students of different majors to find full-time employment..............................
More informationAnalysis of variance and regression. April 17, Contents Comparison of several groups One-way ANOVA. Two-way ANOVA Interaction Model checking
Analysis of variance and regression Contents Comparison of several groups One-way ANOVA April 7, 008 Two-way ANOVA Interaction Model checking ANOVA, April 008 Comparison of or more groups Julie Lyng Forman,
More informationSIMPLE LINEAR REGRESSION
SIMPLE LINEAR REGRESSION In linear regreion, we conider the frequency ditribution of one variable (Y) at each of everal level of a econd variable (). Y i known a the dependent variable. The variable for
More informationa. YOU MAY USE ONE 8.5 X11 TWO-SIDED CHEAT SHEET AND YOUR TEXTBOOK (OR COPY THEREOF).
STAT3503 Test 2 NOTE: a. YOU MAY USE ONE 8.5 X11 TWO-SIDED CHEAT SHEET AND YOUR TEXTBOOK (OR COPY THEREOF). b. YOU MAY USE ANY ELECTRONIC CALCULATOR. c. FOR FULL MARKS YOU MUST SHOW THE FORMULA YOU USE
More informationBE640 Intermediate Biostatistics 2. Regression and Correlation. Simple Linear Regression Software: SAS. Emergency Calls to the New York Auto Club
BE640 Intermediate Biostatistics 2. Regression and Correlation Simple Linear Regression Software: SAS Emergency Calls to the New York Auto Club Source: Chatterjee, S; Handcock MS and Simonoff JS A Casebook
More informationSTA441: Spring Multiple Regression. This slide show is a free open source document. See the last slide for copyright information.
STA441: Spring 2018 Multiple Regression This slide show is a free open source document. See the last slide for copyright information. 1 Least Squares Plane 2 Statistical MODEL There are p-1 explanatory
More informationR Output for Linear Models using functions lm(), gls() & glm()
LM 04 lm(), gls() &glm() 1 R Output for Linear Models using functions lm(), gls() & glm() Different kinds of output related to linear models can be obtained in R using function lm() {stats} in the base
More informationEXST7015: Estimating tree weights from other morphometric variables Raw data print
Simple Linear Regression SAS example Page 1 1 ********************************************; 2 *** Data from Freund & Wilson (1993) ***; 3 *** TABLE 8.24 : ESTIMATING TREE WEIGHTS ***; 4 ********************************************;
More informationPLS205 Lab 2 January 15, Laboratory Topic 3
PLS205 Lab 2 January 15, 2015 Laboratory Topic 3 General format of ANOVA in SAS Testing the assumption of homogeneity of variances by "/hovtest" by ANOVA of squared residuals Proc Power for ANOVA One-way
More informationAnalysis of variance. April 16, Contents Comparison of several groups
Contents Comparison of several groups Analysis of variance April 16, 2009 One-way ANOVA Two-way ANOVA Interaction Model checking Acknowledgement for use of presentation Julie Lyng Forman, Dept. of Biostatistics
More informationLeast Squares Analyses of Variance and Covariance
Least Squares Analyses of Variance and Covariance One-Way ANOVA Read Sections 1 and 2 in Chapter 16 of Howell. Run the program ANOVA1- LS.sas, which can be found on my SAS programs page. The data here
More informationAnalysis of variance. April 16, 2009
Analysis of variance April 16, 2009 Contents Comparison of several groups One-way ANOVA Two-way ANOVA Interaction Model checking Acknowledgement for use of presentation Julie Lyng Forman, Dept. of Biostatistics
More informationParametrisations, splines
/ 7 Parametrisations, splines Analysis of variance and regression course http://staff.pubhealth.ku.dk/~lts/regression_2 Marc Andersen, mja@statgroup.dk Analysis of variance and regression for health researchers,
More informationExample: Four levels of herbicide strength in an experiment on dry weight of treated plants.
The idea of ANOVA Reminders: A factor is a variable that can take one of several levels used to differentiate one group from another. An experiment has a one-way, or completely randomized, design if several
More information