Residuals from regression on original data 1

Similar documents
Detecting and Assessing Data Outliers and Leverage Points

Answer Keys to Homework#10

Assignment 9 Answer Keys

3 Variables: Cyberloafing Conscientiousness Age

Chapter 11: Robust & Quantile regression

T-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

unadjusted model for baseline cholesterol 22:31 Monday, April 19,

Statistics 512: Solution to Homework#11. Problems 1-3 refer to the soybean sausage dataset of Problem 20.8 (ch21pr08.dat).

Assignment 6 Answer Keys

ANALYSES OF NCGS DATA FOR ALCOHOL STATUS CATEGORIES 1 22:46 Sunday, March 2, 2003

Handout 1: Predicting GPA from SAT

STA 302 H1F / 1001 HF Fall 2007 Test 1 October 24, 2007

data 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

Chapter 11: Robust & Quantile regression

STAT 3A03 Applied Regression Analysis With SAS Fall 2017

Multicollinearity Exercise

Chapter 8 (More on Assumptions for the Simple Linear Regression)

Topic 14: Inference in Multiple Regression

171:162 Design and Analysis of Biomedical Studies, Summer 2011 Exam #3, July 16th

Analysis of Variance. Source DF Squares Square F Value Pr > F. Model <.0001 Error Corrected Total

SAS Procedures Inference about the Line ffl model statement in proc reg has many options ffl To construct confidence intervals use alpha=, clm, cli, c

Topic 17 - Single Factor Analysis of Variance. Outline. One-way ANOVA. The Data / Notation. One way ANOVA Cell means model Factor effects model

Outline. Review regression diagnostics Remedial measures Weighted regression Ridge regression Robust regression Bootstrapping

Lecture 11: Simple Linear Regression

Chapter 1: Linear Regression with One Predictor Variable also known as: Simple Linear Regression Bivariate Linear Regression

STA 303 H1S / 1002 HS Winter 2011 Test March 7, ab 1cde 2abcde 2fghij 3

Lecture 13 Extra Sums of Squares

STAT 350: Summer Semester Midterm 1: Solutions

Data Set 8: Laysan Finch Beak Widths

STATISTICS 479 Exam II (100 points)

ST 512-Practice Exam I - Osborne Directions: Answer questions as directed. For true/false questions, circle either true or false.

Introduction to Linear regression analysis. Part 2. Model comparisons

Handling Categorical Predictors: ANOVA

COMPREHENSIVE WRITTEN EXAMINATION, PAPER III FRIDAY AUGUST 26, 2005, 9:00 A.M. 1:00 P.M. STATISTICS 174 QUESTION

Checking the Poisson assumption in the Poisson generalized linear model

Lecture 3: Inference in SLR

Booklet of Code and Output for STAC32 Final Exam

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

R Output for Linear Models using functions lm(), gls() & glm()

SAS Commands. General Plan. Output. Construct scatterplot / interaction plot. Run full model

General Linear Model (Chapter 4)

Homework 2: Simple Linear Regression

Week 7.1--IES 612-STA STA doc

In Class Review Exercises Vartanian: SW 540

Topic 20: Single Factor Analysis of Variance

Booklet of Code and Output for STAC32 Final Exam

Statistics for exp. medical researchers Regression and Correlation

A Short Introduction to Curve Fitting and Regression by Brad Morantz

STA 303H1F: Two-way Analysis of Variance Practice Problems

Topic 18: Model Selection and Diagnostics

STAT 350. Assignment 4

Regression: Main Ideas Setting: Quantitative outcome with a quantitative explanatory variable. Example, cont.

22S39: Class Notes / November 14, 2000 back to start 1

Lecture 3 Linear random intercept models

Econometrics 1. Lecture 8: Linear Regression (2) 黄嘉平

STAT 3A03 Applied Regression With SAS Fall 2017

Stat 302 Statistical Software and Its Applications SAS: Simple Linear Regression

Stat 5303 (Oehlert): Randomized Complete Blocks 1

One-sided and two-sided t-test

36-309/749 Experimental Design for Behavioral and Social Sciences. Sep. 22, 2015 Lecture 4: Linear Regression

IES 612/STA 4-573/STA Winter 2008 Week 1--IES 612-STA STA doc

Chapter 12: Multiple Regression

Failure Time of System due to the Hot Electron Effect

Section Least Squares Regression

Example: Four levels of herbicide strength in an experiment on dry weight of treated plants.

STAT 420: Methods of Applied Statistics

MULTILEVEL MODELS. Multilevel-analysis in SPSS - step by step

ANOVA Longitudinal Models for the Practice Effects Data: via GLM

Analysis of Covariance

K. Model Diagnostics. residuals ˆɛ ij = Y ij ˆµ i N = Y ij Ȳ i semi-studentized residuals ω ij = ˆɛ ij. studentized deleted residuals ɛ ij =

Lab # 11: Correlation and Model Fitting

STATISTICS 110/201 PRACTICE FINAL EXAM

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics

5.3 Three-Stage Nested Design Example

ANALYSIS OF VARIANCE OF BALANCED DAIRY SCIENCE DATA USING SAS

Stat 500 Midterm 2 12 November 2009 page 0 of 11

STAT 3900/4950 MIDTERM TWO Name: Spring, 2015 (print: first last ) Covered topics: Two-way ANOVA, ANCOVA, SLR, MLR and correlation analysis

Chapter 6 Multiple Regression

Lecture 10: 2 k Factorial Design Montgomery: Chapter 6

Variance. Standard deviation VAR = = value. Unbiased SD = SD = 10/23/2011. Functional Connectivity Correlation and Regression.

Booklet of Code and Output for STAC32 Final Exam

UNIVERSITY EXAMINATIONS NJORO CAMPUS SECOND SEMESTER 2011/2012

Repeated Measures Part 2: Cartoon data

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #6

The program for the following sections follows.

Verification of Continuous Forecasts

Comparing Nested Models

Oct Simple linear regression. Minimum mean square error prediction. Univariate. regression. Calculating intercept and slope

ST Correlation and Regression

STAT 512 MidTerm I (2/21/2013) Spring 2013 INSTRUCTIONS

11 Factors, ANOVA, and Regression: SAS versus Splus

UNIVERSITY OF TORONTO. Faculty of Arts and Science APRIL 2010 EXAMINATIONS STA 303 H1S / STA 1002 HS. Duration - 3 hours. Aids Allowed: Calculator

STOR 455 STATISTICAL METHODS I

Correlation and Simple Linear Regression

SMAM 319 Exam 1 Name. 1.Pick the best choice for the multiple choice questions below (10 points 2 each)

Correlation and the Analysis of Variance Approach to Simple Linear Regression

Regression Review. Statistics 149. Spring Copyright c 2006 by Mark E. Irwin

ssh tap sas913, sas

UPDATED STANDARDIZED CATCH RATES FOR BLUEFIN TUNA (Thunnus thynnus) FROM THE TRAP FISHERY IN THE STRAITS OF GIBRALTAR

Topic 25 - One-Way Random Effects Models. Outline. Random Effects vs Fixed Effects. Data for One-way Random Effects Model. One-way Random effects

Transcription:

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 3 3 12 13 2 2 3 1 13 14 2 2 3 2 14 15 2 2 3 3 15 16 2 3 3 1 20 17 2 3 3 2 21 18 2 3 3 3 22

Residuals from regression on original data 2 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 3 3 12 13 2 2 3 1 13 14 2 2 3 2 14 15 2 2 3 3 15 16 2 3 3 1 20 17 2 3 3 2 210 18 2 3 3 3 22

Residuals from regression on original data 3 The GLM Procedure Class Class Level Information Levels Values a 2 1 2 b 3 1 2 3 Number of Observations Read 18 Number of Observations Used 18

Residuals from regression on original data 4 The GLM Procedure Dependent Variable: y Source DF Sum of Squares Mean Square F Value Pr > F Model 5 692.5000000 138.5000000 138.50 <.0001 12 12.0000000 1.0000000 Corrected Total 17 704.5000000 R-Square Coeff Var Root MSE y Mean 0.982967 9.836066 1.000000 10.16667 Source DF Type I SS Mean Square F Value Pr > F a 1 480.5000000 480.5000000 480.50 <.0001 b 2 196.0000000 98.0000000 98.00 <.0001 a*b 2 16.0000000 8.0000000 8.00 0.0062 Source DF Type III SS Mean Square F Value Pr > F a 1 480.5000000 480.5000000 480.50 <.0001 b 2 196.0000000 98.0000000 98.00 <.0001 a*b 2 16.0000000 8.0000000 8.00 0.0062

Residuals from regression on original data 5 The GLM Procedure Class Class Level Information Levels Values a 2 1 2 b 3 1 2 3 Number of Observations Read 18 Number of Observations Used 18

Residuals from regression on original data 6 The GLM Procedure Dependent Variable: y Source DF Sum of Squares Mean Square F Value Pr > F Model 5 14710.00000 2942.00000 1.48 0.2665 12 23826.00000 1985.50000 Corrected Total 17 38536.00000 R-Square Coeff Var Root MSE y Mean 0.381721 215.6078 44.55895 20.66667 Source DF Type I SS Mean Square F Value Pr > F a 1 4418.000000 4418.000000 2.23 0.1616 b 2 5803.000000 2901.500000 1.46 0.2704 a*b 2 4489.000000 2244.500000 1.13 0.3550 Source DF Type III SS Mean Square F Value Pr > F a 1 4418.000000 4418.000000 2.23 0.1616 b 2 5803.000000 2901.500000 1.46 0.2704 a*b 2 4489.000000 2244.500000 1.13 0.3550

Residuals from regression on original data 7 The ROBUSTREG Procedure Model Information Data Set WORK.TABLE922 Dependent Variable y Number of Independent Variables 2 Number of Continuous Independent Variables 0 Number of Class Independent Variables 2 Number of Observations 18 Method M Estimation Number of Observations Read 18 Number of Observations Used 18 Name Class Level Information Levels Values a 2 1 2 b 3 1 2 3 Summary Statistics Variable Q1 Median Q3 Mean Deviation MAD y 5.0000 9.5000 14.0000 10.1667 6.4375 6.6717 Parameter Estimates Intercept 1 21.0000 0.6036 19.8170 22.1830 1210.56 <.0001 a 1 1-13.0000 0.8536-14.6730-11.3270 231.96 <.0001 a 2 0 0.0000..... b 1 1-10.0000 0.8536-11.6730-8.3270 137.25 <.0001 b 2 1-7.0000 0.8536-8.6730-5.3270 67.25 <.0001 b 3 0 0.0000..... a*b 1 1 1 4.0000 1.2071 1.6341 6.3659 10.98 0.0009 a*b 1 2 1 4.0000 1.2071 1.6341 6.3659 10.98 0.0009 a*b 1 3 0 0.0000..... a*b 2 1 0 0.0000..... a*b 2 2 0 0.0000.....

Residuals from regression on original data 8 The ROBUSTREG Procedure Parameter Estimates a*b 2 3 0 0.0000..... Scale 1 1.4826 Diagnostics Summary Observation Type Proportion Cutoff Outlier 0.0000 3.0000 Goodness-of-Fit Statistic Value R-Square 0.9373 AICR 8.9908 BICR 22.6891 Deviance 11.7530 Reduced Model Parameter Estimates for MAINA Intercept 1 21.0000 0.4741 20.0708 21.9292 1961.99 <.0001 a 1 0 0.0000..... a 2 0 0.0000..... b 1 1-10.0000 0.8212-11.6095-8.3905 148.30 <.0001 b 2 1-7.0000 0.8212-8.6095-5.3905 72.67 <.0001 b 3 0 0.0000..... a*b 1 1 1-9.0000 0.9482-10.8584-7.1416 90.09 <.0001 a*b 1 2 1-9.0000 0.9482-10.8584-7.1416 90.09 <.0001 a*b 1 3 0 0.0000..... a*b 2 1 0 0.0000..... a*b 2 2 0 0.0000..... a*b 2 3 0 0.0000..... Scale 0 1.4826

Residuals from regression on original data 9 The ROBUSTREG Procedure Robust Linear s MAINA Statistic Lambda DF Rho 21.0581 0.7977 1 26.40 <.0001 Rn2 231.9562 1 231.96 <.0001 Reduced Model Parameter Estimates for INTER Intercept 1 19.6394 0.7129 18.2422 21.0367 758.93 <.0001 a 1 1-10.2788 0.7129-11.6761-8.8816 207.89 <.0001 a 2 0 0.0000..... b 1 1-8.0000 0.8731-9.7113-6.2887 83.95 <.0001 b 2 1-5.0000 0.8731-6.7113-3.2887 32.79 <.0001 b 3 0 0.0000..... a*b 1 1 0 0.0000..... a*b 1 2 0 0.0000..... a*b 1 3 0 0.0000..... a*b 2 1 0 0.0000..... a*b 2 2 0 0.0000..... a*b 2 3 0 0.0000..... Scale 0 1.4826 Robust Linear s INTER Statistic Lambda DF Rho 3.2480 0.7977 2 4.07 0.1306 Rn2 14.6402 2 14.64 0.0007

Residuals from regression on original data 10 The ROBUSTREG Procedure Model Information Data Set WORK.BAD922 Dependent Variable y Number of Independent Variables 2 Number of Continuous Independent Variables 0 Number of Class Independent Variables 2 Number of Observations 18 Method M Estimation Number of Observations Read 18 Number of Observations Used 18 Name Class Level Information Levels Values a 2 1 2 b 3 1 2 3 Summary Statistics Variable Q1 Median Q3 Mean Deviation MAD y 5.0000 9.5000 14.0000 20.6667 47.6112 6.6717 Parameter Estimates Intercept 1 84.0000 0.7054 82.6175 85.3825 14181.7 <.0001 a 1 1-76.0000 0.9975-77.9551-74.0449 5804.51 <.0001 a 2 0 0.0000..... b 1 1-73.0000 0.9975-74.9551-71.0449 5355.30 <.0001 b 2 1-70.0000 0.9975-71.9551-68.0449 4924.19 <.0001 b 3 0 0.0000..... a*b 1 1 1 67.0000 1.4107 64.2350 69.7650 2255.58 <.0001 a*b 1 2 1 67.0000 1.4107 64.2350 69.7650 2255.58 <.0001 a*b 1 3 0 0.0000..... a*b 2 1 0 0.0000..... a*b 2 2 0 0.0000.....

Residuals from regression on original data 11 The ROBUSTREG Procedure Parameter Estimates a*b 2 3 0 0.0000..... Scale 1 1.4826 Obs Diagnostics ized Robust Residual Outlier 16-43.1673 * 17 84.9857 * 18-41.8184 * Diagnostics Summary Observation Type Proportion Cutoff Outlier 0.1667 3.0000 Goodness-of-Fit Statistic Value R-Square 0.6902 AICR 30.0488 BICR 43.7472 Deviance 58.0410 Reduced Model Parameter Estimates for MAINA Intercept 1 84.0000 1.1057 81.8330 86.1670 5771.88 <.0001 a 1 0 0.0000..... a 2 0 0.0000..... b 1 1-73.0000 1.9151-76.7534-69.2466 1453.06 <.0001 b 2 1-70.0000 1.9151-73.7534-66.2466 1336.08 <.0001 b 3 0 0.0000..... a*b 1 1 1 67.0000 2.2113 62.6659 71.3341 918.01 <.0001 a*b 1 2 1 67.0000 2.2113 62.6659 71.3341 918.01 <.0001 a*b 1 3 0 0.0000.....

Residuals from regression on original data 12 The ROBUSTREG Procedure Reduced Model Parameter Estimates for MAINA a*b 2 1 0 0.0000..... a*b 2 2 0 0.0000..... a*b 2 3 0 0.0000..... Scale 0 1.4826 Robust Linear s MAINA Statistic Lambda DF Rho 63.1742 0.7977 1 79.20 <.0001 Rn2 5804.511 1 5804.51 <.0001 Reduced Model Parameter Estimates for INTER Intercept 1 84.0000 0.7887 82.4541 85.5459 11342.6 <.0001 a 1 1-76.0000 0.7887-77.5459-74.4541 9284.98 <.0001 a 2 0 0.0000..... b 1 1-73.0000 0.9660-74.8933-71.1067 5710.95 <.0001 b 2 1-70.0000 0.9660-71.8933-68.1067 5251.20 <.0001 b 3 0 0.0000..... a*b 1 1 0 0.0000..... a*b 1 2 0 0.0000..... a*b 1 3 0 0.0000..... a*b 2 1 0 0.0000..... a*b 2 2 0 0.0000..... a*b 2 3 0 0.0000..... Scale 0 1.4826

Residuals from regression on original data 13 The ROBUSTREG Procedure Robust Linear s INTER Statistic Lambda DF Rho 21.0581 0.7977 2 26.40 <.0001 Rn2 3007.439 2 3007.44 <.0001

Residuals from regression on original data 14 y a b 1 1 1 2 1 1 3 1 1 4 1 2 5 1 2 6 1 2 7 1 3 8 1 3 9 1 3 10 2 1 11 2 1 12 2 1 13 2 2 14 2 2 15 2 2 20 2 3 210 2 3 22 2 3

2024 © sciencedocbox.com Privacy Policy | Terms of Service | Feedback