Regression. Notes. Page 1. Output Created Comments 25-JAN :29:55

Similar documents
*************NO YOGA!!!!!!!************************************.

Correlations. Notes. Output Created Comments 04-OCT :34:52

Advanced Quantitative Data Analysis

EDF 7405 Advanced Quantitative Methods in Educational Research. Data are available on IQ of the child and seven potential predictors.

Research Design - - Topic 19 Multiple regression: Applications 2009 R.C. Gardner, Ph.D.

Univariate Analysis of Variance

Sociology 593 Exam 2 March 28, 2002

SAVE OUTFILE='C:\Documents and Settings\ddelgad1\Desktop\FactorAnalysis.sav' /COMPRESSED.

Regression ( Kemampuan Individu, Lingkungan kerja dan Motivasi)

SPSS LAB FILE 1

2 Prediction and Analysis of Variance

Mediation question: Does executive functioning mediate the relation between shyness and vocabulary? Plot data, descriptives, etc. Check for outliers

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

Item-Total Statistics. Corrected Item- Cronbach's Item Deleted. Total

General Linear Model

Interactions and Centering in Regression: MRC09 Salaries for graduate faculty in psychology

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

Sociology 593 Exam 2 Answer Key March 28, 2002

EDF 7405 Advanced Quantitative Methods in Educational Research MULTR.SAS

Dependent Variable Q83: Attended meetings of your town or city council (0=no, 1=yes)

The inductive effect in nitridosilicates and oxysilicates and its effects on 5d energy levels of Ce 3+

Research Design - - Topic 17 Multiple Regression & Multiple Correlation: Two Predictors 2009 R.C. Gardner, Ph.D.

Simple Linear Regression: One Quantitative IV

Simple Linear Regression: One Qualitative IV

Chapter 4 Regression with Categorical Predictor Variables Page 1. Overview of regression with categorical predictors

Topic 1. Definitions

SPSS Output. ANOVA a b Residual Coefficients a Standardized Coefficients

Multiple OLS Regression

Area1 Scaled Score (NAPLEX) .535 ** **.000 N. Sig. (2-tailed)

Interactions between Binary & Quantitative Predictors

ESP 178 Applied Research Methods. 2/23: Quantitative Analysis

General Linear Model. Notes Output Created Comments Input. 19-Dec :09:44

Entering and recoding variables

4:3 LEC - PLANNED COMPARISONS AND REGRESSION ANALYSES

Multiple Regression. More Hypothesis Testing. More Hypothesis Testing The big question: What we really want to know: What we actually know: We know:

McGill University. Faculty of Science MATH 204 PRINCIPLES OF STATISTICS II. Final Examination

Sociology 593 Exam 1 February 17, 1995

ECONOMETRIC ANALYSIS OF THE COMPANY ON STOCK EXCHANGE

Simple Linear Regression

y response variable x 1, x 2,, x k -- a set of explanatory variables

Sociology Research Statistics I Final Exam Answer Key December 15, 1993

Investigating Models with Two or Three Categories

VARIANCE ANALYSIS OF WOOL WOVEN FABRICS TENSILE STRENGTH USING ANCOVA MODEL

(rather than just guessing numbers); these are:

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

Sociology 593 Exam 1 February 14, 1997

CHAPTER6 LINEAR REGRESSION

( ), which of the coefficients would end

Review of Multiple Regression

Multicollinearity Richard Williams, University of Notre Dame, Last revised January 13, 2015

Self-Assessment Weeks 6 and 7: Multiple Regression with a Qualitative Predictor; Multiple Comparisons

SPSS Guide For MMI 409

1 A Review of Correlation and Regression

: The model hypothesizes a relationship between the variables. The simplest probabilistic model: or.

Three Factor Completely Randomized Design with One Continuous Factor: Using SPSS GLM UNIVARIATE R. C. Gardner Department of Psychology

Example. Multiple Regression. Review of ANOVA & Simple Regression /749 Experimental Design for Behavioral and Social Sciences

Multivariate Correlational Analysis: An Introduction

Washington State Test

Ordinary Least Squares Regression Explained: Vartanian

Multiple linear regression S6

Chapter 9 - Correlation and Regression

Multiple Regression and Model Building (cont d) + GIS Lecture 21 3 May 2006 R. Ryznar

In Class Review Exercises Vartanian: SW 540

WORKSHOP 3 Measuring Association

( ) *

9. Linear Regression and Correlation

Univariate analysis. Simple and Multiple Regression. Univariate analysis. Simple Regression How best to summarise the data?

ANOVA Situation The F Statistic Multiple Comparisons. 1-Way ANOVA MATH 143. Department of Mathematics and Statistics Calvin College

Lecture 3: Inference in SLR

FIN822 project 2 Project 2 contains part I and part II. (Due on November 10, 2008)

Scenario 5: Internet Usage Solution. θ j

TOPIC 9 SIMPLE REGRESSION & CORRELATION

A Re-Introduction to General Linear Models (GLM)

1 Correlation and Inference from Regression

Self-Assessment Weeks 8: Multiple Regression with Qualitative Predictors; Multiple Comparisons

LAB 3 INSTRUCTIONS SIMPLE LINEAR REGRESSION

Chapter 7: Correlation

The 1905 Einstein equation in a general mathematical analysis model of Quasars

Perpustakaan Unika LAMPIRAN

Lampiran 1. Hasil Determinasi Tanaman Umbi Singkong

4/22/2010. Test 3 Review ANOVA

Structural Equation Modeling Lab 5 In Class Modification Indices Example

Practical Biostatistics

Inter Item Correlation Matrix (R )

A discussion on multiple regression models

CAMPBELL COLLABORATION

Extensions of One-Way ANOVA.

General Linear Model (Chapter 4)

Introduction to Regression

Linear Regression Measurement & Evaluation of HCC Systems

Multiple linear regression

Chapter 19: Logistic regression

QUANTITATIVE STATISTICAL METHODS: REGRESSION AND FORECASTING JOHANNES LEDOLTER VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS ADMINISTRATION SPRING 2013

Extensions of One-Way ANOVA.

Multiple Regression and Model Building Lecture 20 1 May 2006 R. Ryznar

Bivariate Regression Analysis. The most useful means of discerning causality and significance of variables

2/26/2017. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2

R Demonstration ANCOVA

/STATISTICS=MEAN STDDEV VARIANCE RANGE MIN MAX SEMEAN. Descriptive Statistics CR

Confidence Interval for the mean response

Transcription:

REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) BCOV R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT favorability /METHOD=ENTER Zcontemp ZAnxious6 zallcontact. Regression Notes Output Created Comments Input Missing Value Handling Data Active Dataset Filter Weight Split File N of Rows in Working Data File Definition of Missing Cases Used 25-JAN-2017 06:29:55 /Users/bettencourta/Docu ments/mollie's Seems newst for Anxiety intergroup Generalization Data/2017_No_HR_No_le ss4minutes_new_super_ Merged_3_datasets.sav DataSet2 User-defined missing values are treated as missing. Statistics are based on cases with no missing values for any variable used. 379 Page 1

Syntax Resources Notes Processor Time Elapsed Time Memory Required Additional Memory Required for Residual Plots REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) BCOV R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT favorability /METHOD=ENTER Zcontemp ZAnxious6 zallcontact. 10832 bytes 0 bytes 00:00:00.02 00:00:00.00 Variables Entered/Removed a Variables Entered 1 (allcontact), (Contemp), IntContempAn x6, (Anxious6) b Variables Removed Method. Enter a. Dependent Variable: Favorability b. All requested variables entered. R R Square Summary Adjusted R Square Std. Error of the Estimate 1.733 a.537.532.74078 a. Predictors: (Constant), (allcontact),,, Page 2

ANOVA a 1 Regression Residual Total Sum of Squares df Mean Square F Sig. 237.024 4 59.256 107.983.000 b 204.136 372.549 441.160 376 a. Dependent Variable: Favorability b. Predictors: (Constant), (allcontact),,, Coefficients a 1 (Constant) (allcontact) Unstandardized Coefficients Standardized Coefficients B Std. Error Beta t Sig. 4.594.038 119.651.000 -.028.042 -.024 -.666.506 -.540.043 -.495-12.428.000.060.036.059 1.653.099.389.043.360 9.038.000 Coefficients a 1 (Constant) (allcontact) 95.0% Confidence Interval for B Lower Bound Upper Bound 4.518 4.669 -.110.054 -.626 -.455 -.011.130.304.473 a. Dependent Variable: Favorability Page 3

Coefficient Correlations a 1 Correlations (allcontact) Covariances (allcontact) (allcontact) (Contemp) IntContempAnx 6 1.000 -.073.053 -.073 1.000 -.152.053 -.152 1.000.460 -.082 -.021.002.000 8.267E-5.000.002.000 8.267E-5.000.001.001.000-3.346E-5 Coefficient Correlations a 1 Correlations (allcontact) Covariances (allcontact) (Anxious6).460 -.082 -.021 1.000.001.000-3.346E-5.002 a. Dependent Variable: Favorability REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) BCOV R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT POSITIVETRAITS /METHOD=ENTER Zcontemp ZAnxious6 zallcontact. Regression Page 4

Output Created Comments Input Missing Value Handling Syntax Resources Data Notes Active Dataset Filter Weight Split File N of Rows in Working Data File Definition of Missing Cases Used Processor Time Elapsed Time Memory Required Additional Memory Required for Residual Plots 25-JAN-2017 06:30:38 /Users/bettencourta/Docu ments/mollie's Seems newst for Anxiety intergroup Generalization Data/2017_No_HR_No_le ss4minutes_new_super_ Merged_3_datasets.sav DataSet2 User-defined missing values are treated as missing. Statistics are based on cases with no missing values for any variable used. REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) BCOV R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT POSITIVETRAITS /METHOD=ENTER Zcontemp ZAnxious6 zallcontact. 10832 bytes 0 bytes 379 00:00:00.01 00:00:00.00 Page 5

Variables Entered/Removed a Variables Entered 1 (allcontact), (Contemp), IntContempAn x6, (Anxious6) b Variables Removed Method. Enter a. Dependent Variable: positivetraits b. All requested variables entered. R R Square Summary Adjusted R Square Std. Error of the Estimate 1.588 a.346.339.64097 a. Predictors: (Constant), (allcontact),,, ANOVA a 1 Regression Residual Total Sum of Squares df Mean Square F Sig. 80.756 4 20.189 49.140.000 b 152.834 372.411 233.590 376 a. Dependent Variable: positivetraits b. Predictors: (Constant), (allcontact),,, Page 6

Coefficients a 1 (Constant) (allcontact) Unstandardized Coefficients Standardized Coefficients B Std. Error Beta t Sig. 3.704.033 111.502.000 -.027.036 -.032 -.740.460 -.287.038 -.362-7.633.000.046.031.063 1.471.142.256.037.326 6.890.000 Coefficients a 1 (Constant) (allcontact) 95.0% Confidence Interval for B Lower Bound Upper Bound 3.639 3.770 -.097.044 -.361 -.213 -.015.107.183.329 a. Dependent Variable: positivetraits Coefficient Correlations a 1 Correlations (allcontact) Covariances (allcontact) (allcontact) (Contemp) IntContempAnx 6 1.000 -.073.053 -.073 1.000 -.152.053 -.152 1.000.460 -.082 -.021.001-9.744E-5 6.189E-5-9.744E-5.001.000 6.189E-5.000.001.001.000-2.505E-5 Page 7

Coefficient Correlations a 1 Correlations (allcontact) Covariances (allcontact) (Anxious6).460 -.082 -.021 1.000.001.000-2.505E-5.001 a. Dependent Variable: positivetraits Page 8