Regression ( Kemampuan Individu, Lingkungan kerja dan Motivasi)
|
|
|
- Marylou Berry
- 5 years ago
- Views:
Transcription
1 Regression (, Lingkungan kerja dan ) Descriptive Statistics Mean Std. Deviation N s Pearson Sig. (-tailed) N Kemampuan Lingkungan Individu Kerja Variables Entered/Removed b Variables Entered Variables Removed Kemampuan Individu, Lingkungan Kerja a. Enter a. All requested variables entered. b. Dependent Variable: Method Summary b R a.572 Adjusted R Std. Error of R Square Square the Estimate Durbin-Watson a. Predictors: (Constant),, b. Dependent Variable:
2 Regression Residual Total ANOVA b Sum of Squares df Mean Square F Sig..000 a a. Predictors: (Constant),, b. Dependent Variable: Coefficients a (Constant) a. Dependent Variable: Unstandardized Standardized Coefficients Coefficients Collinearity Statistics B Std. Error Beta t Sig. Tolerance VIF Dimension 2 3 a. Dependent Variable: Collinearity Diagnostics a Variance Proportions Kemampuan Lingkungan Eigenvalue Condition Index (Constant) Individu Kerja Re siduals Statis tics a Minimum Maximum Mean Std. Deviation N Predicted Value Residual Std. Predicted Val ue Std. Residual a. Dependent Variable:
3 Regression (,,, dan Kepuasan Kerja) Descriptive Statistics Kepuasan Kerja Kemampuan individu Lingkungan kerja Mean Std. Deviation N Variables Entered/Removed b Variables Entered Kemampuan individu, Lingkungan kerja, a. All requested variables entered. a Variables Removed b. Dependent Variable: Kepuasan Kerja. Enter Method Summary b Adjusted R Std. Error of R R Square Square the Estimate Durbin-Watson.84 a a. Predictors: (Constant), Kemampuan individu, Lingkungan kerja, b. Dependent Variable: Kepuasan Kerja Regression Residual Total ANOVA b Sum of Squares df Mean Square 2.46 F Sig..000 a a. Predictors: (Constant), Kemampuan individu, Lingkungan kerja, b. Dependent Variable: Kepuasan Kerja
4 (Constant) Kemampuan individu Lingkungan kerja a. Dependent Variable: Kepuasan Kerja Coefficients a Unstandardized Standardized Coefficients Coefficients Collinearity Statistics B Std. Error Beta t Sig. Tolerance VIF Collinearity Diagnostics a Dimension a. Dependent Variable: Kepuasan Kerja Variance Proportions Kemampuan Lingkungan Eigenvalue Condition Index (Constant) individu kerja Re siduals Statis tics a Minimum Maximum Mean Std. Deviation N Predicted Value Residual Std. Predicted Val ue Std. Residual a. Dependent Variable: Kepuasan Kerja
5 Normal P-P Plot of Regression Standardized Residual Dependent Variable: Kepuasan Kerja Charts Expected Cum Prob Observed Cum Prob
6 Kepuasan (Validitas) Item-Total Statistics Scale Mean if Scale Variance if Corrected Item- Squared Multiple Alpha if Item Total Deleted Q Q Q Q Q Q Q Q Q Q Kepuasan (Reliabilitas) Reliability Statistics Alpha Alpha Based on Standardized Items N of Items
7 (Validitas) Item-Total Statistics Scale Mean if Scale Variance if Corrected Item- Squared Multiple Alpha if Item Total Deleted Q Q Q Q Q Q Q (Reliabilitas) Reliability Statistics Alpha Alpha Based on Standardized Items N of Items
8 (Validitas) Item-Total Statistics Scale Mean if Scale Variance if Corrected Item- Squared Multiple Alpha if Item Total Deleted Q Q Q Q Q Q Q Q Q Q (Reliabilitas) Reliability Statistics Alpha Alpha Based on Standardized Items N of Items
9 (Validitas) Item-Total Statistics Scale Mean if Scale Variance if Corrected Item- Squared Multiple Alpha if Item Total Deleted Q Q Q Q Q Q Q Q Q (Reliabilitas) Reliability Statistics Alpha Alpha Based on Standardized Items N of Items
Item-Total Statistics. Corrected Item- Cronbach's Item Deleted. Total
45 Lampiran 3 : Uji Validitas dan Reliabilitas Reliability Case Processing Summary N % Valid 75 00.0 Cases Excluded a 0.0 Total 75 00.0 a. Listwise deletion based on all variables in the procedure. Reliability
Area1 Scaled Score (NAPLEX) .535 ** **.000 N. Sig. (2-tailed)
Institutional Assessment Report Texas Southern University College of Pharmacy and Health Sciences "An Analysis of 2013 NAPLEX, P4-Comp. Exams and P3 courses The following analysis illustrates relationships
Inter Item Correlation Matrix (R )
7 1. I have the ability to influence my child s well-being. 2. Whether my child avoids injury is just a matter of luck. 3. Luck plays a big part in determining how healthy my child is. 4. I can do a lot
Sociology 593 Exam 1 February 14, 1997
Sociology 9 Exam February, 997 I. True-False. ( points) Indicate whether the following statements are true or false. If false, briefly explain why.. There are IVs in a multiple regression model. If the
EDF 7405 Advanced Quantitative Methods in Educational Research MULTR.SAS
EDF 7405 Advanced Quantitative Methods in Educational Research MULTR.SAS The data used in this example describe teacher and student behavior in 8 classrooms. The variables are: Y percentage of interventions
Regression. Notes. Page 1. Output Created Comments 25-JAN :29:55
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
Multiple Regression. More Hypothesis Testing. More Hypothesis Testing The big question: What we really want to know: What we actually know: We know:
Multiple Regression Ψ320 Ainsworth More Hypothesis Testing What we really want to know: Is the relationship in the population we have selected between X & Y strong enough that we can use the relationship
Regression: Main Ideas Setting: Quantitative outcome with a quantitative explanatory variable. Example, cont.
TCELL 9/4/205 36-309/749 Experimental Design for Behavioral and Social Sciences Simple Regression Example Male black wheatear birds carry stones to the nest as a form of sexual display. Soler et al. wanted
36-309/749 Experimental Design for Behavioral and Social Sciences. Sep. 22, 2015 Lecture 4: Linear Regression
36-309/749 Experimental Design for Behavioral and Social Sciences Sep. 22, 2015 Lecture 4: Linear Regression TCELL Simple Regression Example Male black wheatear birds carry stones to the nest as a form
Parametric Test. Multiple Linear Regression Spatial Application I: State Homicide Rates Equations taken from Zar, 1984.
Multiple Linear Regression Spatial Application I: State Homicide Rates Equations taken from Zar, 984. y ˆ = a + b x + b 2 x 2K + b n x n where n is the number of variables Example: In an earlier bivariate
VARIANCE ANALYSIS OF WOOL WOVEN FABRICS TENSILE STRENGTH USING ANCOVA MODEL
ANNALS OF THE UNIVERSITY OF ORADEA FASCICLE OF TEXTILES, LEATHERWORK VARIANCE ANALYSIS OF WOOL WOVEN FABRICS TENSILE STRENGTH USING ANCOVA MODEL VÎLCU Adrian 1, HRISTIAN Liliana 2, BORDEIANU Demetra Lăcrămioara
*************NO YOGA!!!!!!!************************************.
*************NO YOGA!!!!!!!************************************. temporary. select if human gt 1 and Q_TotalDuration gt 239 and subjectnum ne 672 and subj ectnum ne 115 and subjectnum ne 104 and subjectnum
Sociology 593 Exam 1 February 17, 1995
Sociology 593 Exam 1 February 17, 1995 I. True-False. (25 points) Indicate whether the following statements are true or false. If false, briefly explain why. 1. A researcher regressed Y on. When he plotted
MLBI
Lampiran 1 : Pengolahan Data ROA Variabel bebas Variabel terikat : VAIC, Ukuran, dan Leverage : ROA EMITEN TAHUN ROA (%) VAIC (%) UKURAN CEKA DLTA INDF MYOR LEVERAGE (Rp) LnROA LnVAIC LnLEVERAGE 2007 0.04
Multiple Regression Analysis
Multiple Regression Analysis Where as simple linear regression has 2 variables (1 dependent, 1 independent): y ˆ = a + bx Multiple linear regression has >2 variables (1 dependent, many independent): ˆ
Multiple linear regression S6
Basic medical statistics for clinical and experimental research Multiple linear regression S6 Katarzyna Jóźwiak k.jozwiak@nki.nl November 15, 2017 1/42 Introduction Two main motivations for doing multiple
Multiple OLS Regression
Multiple OLS Regression Ronet Bachman, Ph.D. Presented by Justice Research and Statistics Association 12/8/2016 Justice Research and Statistics Association 720 7 th Street, NW, Third Floor Washington,
Descriptive Statistics
Lampiran STATISTIK DESKRIPTIF MODEL REGRESI Descriptive Statistics IR Valid N (listwise) N Minimum Maximum Mean Std. Dev iation 30,02222,98000,2595358,2555370 30,050 6,5334,354584,2806794 30-3,44268 6,38392,8378998,87505435
Multiple Regression and Model Building (cont d) + GIS Lecture 21 3 May 2006 R. Ryznar
Multiple Regression and Model Building (cont d) + GIS 11.220 Lecture 21 3 May 2006 R. Ryznar Model Summary b 1-[(SSE/n-k+1)/(SST/n-1)] Model 1 Adjusted Std. Error of R R Square R Square the Estimate.991
ESP 178 Applied Research Methods. 2/23: Quantitative Analysis
ESP 178 Applied Research Methods 2/23: Quantitative Analysis Data Preparation Data coding create codebook that defines each variable, its response scale, how it was coded Data entry for mail surveys and
Multiple Regression and Model Building Lecture 20 1 May 2006 R. Ryznar
Multiple Regression and Model Building 11.220 Lecture 20 1 May 2006 R. Ryznar Building Models: Making Sure the Assumptions Hold 1. There is a linear relationship between the explanatory (independent) variable(s)
x3,..., Multiple Regression β q α, β 1, β 2, β 3,..., β q in the model can all be estimated by least square estimators
Multiple Regression Relating a response (dependent, input) y to a set of explanatory (independent, output, predictor) variables x, x 2, x 3,, x q. A technique for modeling the relationship between variables.
SPSS LAB FILE 1
SPSS LAB FILE www.mcdtu.wordpress.com 1 www.mcdtu.wordpress.com 2 www.mcdtu.wordpress.com 3 OBJECTIVE 1: Transporation of Data Set to SPSS Editor INPUTS: Files: group1.xlsx, group1.txt PROCEDURE FOLLOWED:
Multicollinearity Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 13, 2015
Multicollinearity Richard Williams, University of Notre Dame, https://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,
Multivariate Correlational Analysis: An Introduction
Assignment. Multivariate Correlational Analysis: An Introduction Mertler & Vanetta, Chapter 7 Kachigan, Chapter 4, pps 180-193 Terms you should know. Multiple Regression Linear Equations Least Squares
Simple Linear Regression
Simple Linear Regression 1 Correlation indicates the magnitude and direction of the linear relationship between two variables. Linear Regression: variable Y (criterion) is predicted by variable X (predictor)
Correlations. Notes. Output Created Comments 04-OCT :34:52
Correlations Output Created Comments Input Missing Value Handling Syntax Resources Notes Data Active Dataset Filter Weight Split File N of Rows in Working Data File Definition of Missing Cases Used Processor
LAMPIRAN. NO Kode Perusahaan Nama Perusahaan. 1 ADRO Adaro Energy Tbk. 2 BSSR Baramulti Suksessarana Tbk. 3 GEMS Golden Energy Mines Tbk
110 LAMPIRAN Lampiran 1 Daftar Perusahaan Sub Sektor Batubara NO Kode Perusahaan Nama Perusahaan 1 ADRO Adaro Energy Tbk 2 BSSR Baramulti Suksessarana Tbk 3 GEMS Golden Energy Mines Tbk 4 KKGI Resource
SPSS Output. ANOVA a b Residual Coefficients a Standardized Coefficients
SPSS Output Homework 1-1e ANOVA a Sum of Squares df Mean Square F Sig. 1 Regression 351.056 1 351.056 11.295.002 b Residual 932.412 30 31.080 Total 1283.469 31 a. Dependent Variable: Sexual Harassment
Perpustakaan Unika LAMPIRAN
LAMPIRAN LAMPIRAN DATA VARIABEL PENELITIAN No Kode Tahun ROA DER LOG_ASSET AGE OPAD KULAD OUTSIDER EXTRA OPERA AUDIT DELAY ADES 2004 -.26 2.40.03 0 0.694 0 90 2 AISA 2004 0.00 2.60.53 7 0 0 0.2736 0 88
The inductive effect in nitridosilicates and oxysilicates and its effects on 5d energy levels of Ce 3+
The inductive effect in nitridosilicates and oxysilicates and its effects on 5d energy levels of Ce 3+ Yuwei Kong, Zhen Song, Shuxin Wang, Zhiguo Xia and Quanlin Liu* The Beijing Municipal Key Laboratory
Introduction to Regression
Regression Introduction to Regression If two variables covary, we should be able to predict the value of one variable from another. Correlation only tells us how much two variables covary. In regression,
Chapter 9 - Correlation and Regression
Chapter 9 - Correlation and Regression 9. Scatter diagram of percentage of LBW infants (Y) and high-risk fertility rate (X ) in Vermont Health Planning Districts. 9.3 Correlation between percentage of
Daniel Boduszek University of Huddersfield
Daniel Boduszek University of Huddersfield d.boduszek@hud.ac.uk Introduction to moderator effects Hierarchical Regression analysis with continuous moderator Hierarchical Regression analysis with categorical
Prepared by: Prof. Dr Bahaman Abu Samah Department of Professional Development and Continuing Education Faculty of Educational Studies Universiti
Prepared by: Prof Dr Bahaman Abu Samah Department of Professional Development and Continuing Education Faculty of Educational Studies Universiti Putra Malaysia Serdang M L Regression is an extension to
Washington State Test
Technical Report # 1101 easycbm Reading Criterion Related Validity Evidence: Washington State Test 2009-2010 Daniel Anderson Julie Alonzo Gerald Tindal University of Oregon Published by Behavioral Research
Advanced Quantitative Data Analysis
Chapter 24 Advanced Quantitative Data Analysis Daniel Muijs Doing Regression Analysis in SPSS When we want to do regression analysis in SPSS, we have to go through the following steps: 1 As usual, we choose
TOPIC 9 SIMPLE REGRESSION & CORRELATION
TOPIC 9 SIMPLE REGRESSION & CORRELATION Basic Linear Relationships Mathematical representation: Y = a + bx X is the independent variable [the variable whose value we can choose, or the input variable].
QUANTITATIVE STATISTICAL METHODS: REGRESSION AND FORECASTING JOHANNES LEDOLTER VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS ADMINISTRATION SPRING 2013
QUANTITATIVE STATISTICAL METHODS: REGRESSION AND FORECASTING JOHANNES LEDOLTER VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS ADMINISTRATION SPRING 3 Introduction Objectives of course: Regression and Forecasting
STAT 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
Self-Assessment Weeks 6 and 7: Multiple Regression with a Qualitative Predictor; Multiple Comparisons
Self-Assessment Weeks 6 and 7: Multiple Regression with a Qualitative Predictor; Multiple Comparisons 1. Suppose we wish to assess the impact of five treatments on an outcome Y. How would these five treatments
A discussion on multiple regression models
A discussion on multiple regression models In our previous discussion of simple linear regression, we focused on a model in which one independent or explanatory variable X was used to predict the value
Research Design - - Topic 19 Multiple regression: Applications 2009 R.C. Gardner, Ph.D.
Research Design - - Topic 19 Multiple regression: Applications 2009 R.C. Gardner, Ph.D. Curve Fitting Mediation analysis Moderation Analysis 1 Curve Fitting The investigation of non-linear functions using
Chapter 4 Regression with Categorical Predictor Variables Page 1. Overview of regression with categorical predictors
Chapter 4 Regression with Categorical Predictor Variables Page. Overview of regression with categorical predictors 4-. Dummy coding 4-3 4-5 A. Karpinski Regression with Categorical Predictor Variables.
Correlation and simple linear regression S5
Basic medical statistics for clinical and eperimental research Correlation and simple linear regression S5 Katarzyna Jóźwiak k.jozwiak@nki.nl November 15, 2017 1/41 Introduction Eample: Brain size and
WORKSHOP 3 Measuring Association
WORKSHOP 3 Measuring Association Concepts Analysing Categorical Data o Testing of Proportions o Contingency Tables & Tests o Odds Ratios Linear Association Measures o Correlation o Simple Linear Regression
LAB 3 INSTRUCTIONS SIMPLE LINEAR REGRESSION
LAB 3 INSTRUCTIONS SIMPLE LINEAR REGRESSION In this lab you will first learn how to display the relationship between two quantitative variables with a scatterplot and also how to measure the strength of
The 1905 Einstein equation in a general mathematical analysis model of Quasars
DePaul University From the SelectedWorks of Byron E. Bell Spring May 3, 2010 The 1905 Einstein equation in a general mathematical analysis model of Quasars Byron E. Bell Available at: https://works.bepress.com/byron_bell/2/
( ), which of the coefficients would end
Discussion Sheet 29.7.9 Qualitative Variables We have devoted most of our attention in multiple regression to quantitative or numerical variables. MR models can become more useful and complex when we consider
MORE ON SIMPLE REGRESSION: OVERVIEW
FI=NOT0106 NOTICE. Unless otherwise indicated, all materials on this page and linked pages at the blue.temple.edu address and at the astro.temple.edu address are the sole property of Ralph B. Taylor and
EDF 7405 Advanced Quantitative Methods in Educational Research. Data are available on IQ of the child and seven potential predictors.
EDF 7405 Advanced Quantitative Methods in Educational Research Data are available on IQ of the child and seven potential predictors. Four are medical variables available at the birth of the child: Birthweight
Practical Biostatistics
Practical Biostatistics Clinical Epidemiology, Biostatistics and Bioinformatics AMC Multivariable regression Day 5 Recap Describing association: Correlation Parametric technique: Pearson (PMCC) Non-parametric:
Sociology 593 Exam 1 Answer Key February 17, 1995
Sociology 593 Exam 1 Answer Key February 17, 1995 I. True-False. (5 points) Indicate whether the following statements are true or false. If false, briefly explain why. 1. A researcher regressed Y on. When
Correlation and Regression Bangkok, 14-18, Sept. 2015
Analysing and Understanding Learning Assessment for Evidence-based Policy Making Correlation and Regression Bangkok, 14-18, Sept. 2015 Australian Council for Educational Research Correlation The strength
4:3 LEC - PLANNED COMPARISONS AND REGRESSION ANALYSES
4:3 LEC - PLANNED COMPARISONS AND REGRESSION ANALYSES FOR SINGLE FACTOR BETWEEN-S DESIGNS Planned or A Priori Comparisons We previously showed various ways to test all possible pairwise comparisons for
Interactions between Binary & Quantitative Predictors
Interactions between Binary & Quantitative Predictors The purpose of the study was to examine the possible joint effects of the difficulty of the practice task and the amount of practice, upon the performance
Ridge Regression. Summary. Sample StatFolio: ridge reg.sgp. STATGRAPHICS Rev. 10/1/2014
Ridge Regression Summary... 1 Data Input... 4 Analysis Summary... 5 Analysis Options... 6 Ridge Trace... 7 Regression Coefficients... 8 Standardized Regression Coefficients... 9 Observed versus Predicted...
Topic 1. Definitions
S Topic. Definitions. Scalar A scalar is a number. 2. Vector A vector is a column of numbers. 3. Linear combination A scalar times a vector plus a scalar times a vector, plus a scalar times a vector...
Statistics 5100 Spring 2018 Exam 1
Statistics 5100 Spring 2018 Exam 1 Directions: You have 60 minutes to complete the exam. Be sure to answer every question, and do not spend too much time on any part of any question. Be concise with all
Univariate analysis. Simple and Multiple Regression. Univariate analysis. Simple Regression How best to summarise the data?
Univariate analysis Example - linear regression equation: y = ax + c Least squares criteria ( yobs ycalc ) = yobs ( ax + c) = minimum Simple and + = xa xc xy xa + nc = y Solve for a and c Univariate analysis
Self-Assessment Weeks 8: Multiple Regression with Qualitative Predictors; Multiple Comparisons
Self-Assessment Weeks 8: Multiple Regression with Qualitative Predictors; Multiple Comparisons 1. Suppose we wish to assess the impact of five treatments while blocking for study participant race (Black,
Assoc.Prof.Dr. Wolfgang Feilmayr Multivariate Methods in Regional Science: Regression and Correlation Analysis REGRESSION ANALYSIS
REGRESSION ANALYSIS Regression Analysis can be broadly defined as the analysis of statistical relationships between one dependent and one or more independent variables. Although the terms dependent and
ECON 497 Midterm Spring
ECON 497 Midterm Spring 2009 1 ECON 497: Economic Research and Forecasting Name: Spring 2009 Bellas Midterm You have three hours and twenty minutes to complete this exam. Answer all questions and explain
ECONOMETRIC ANALYSIS OF THE COMPANY ON STOCK EXCHANGE
ECONOMETRIC ANALYSIS OF THE COMPANY ON STOCK EXCHANGE Macovei Anamaria Geanina Ştefan cel Mare of University Suceava,Faculty of Economics and Public Administration street Universității, no. 3, city Suceava,
KUESIONER PENELITIAN. No. Responden: Kepada Yth. Saudara/i di tempat. Dengan hormat,
LAMPIRAN 50 No. Atribut 4 5 9. Pegawai di counter penjualan Starbucks Coffee PVJ memahami kebutuhan Saya secara spesifik 10. Merek Starbucks Coffee PVJ dapat dipercaya 11. Starbucks Coffee PVJ dapat diandalkan
1 A Review of Correlation and Regression
1 A Review of Correlation and Regression SW, Chapter 12 Suppose we select n = 10 persons from the population of college seniors who plan to take the MCAT exam. Each takes the test, is coached, and then
Analysis of Covariance
Analysis of Covariance Using categorical and continuous predictor variables Example An experiment is set up to look at the effects of watering on Oak Seedling establishment Three levels of watering: (no
SPSS Guide For MMI 409
SPSS Guide For MMI 409 by John Wong March 2012 Preface Hopefully, this document can provide some guidance to MMI 409 students on how to use SPSS to solve many of the problems covered in the D Agostino
Bivariate Regression Analysis. The most useful means of discerning causality and significance of variables
Bivariate Regression Analysis The most useful means of discerning causality and significance of variables Purpose of Regression Analysis Test causal hypotheses Make predictions from samples of data Derive
Multicollinearity 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
Introduction to Regression
Introduction to Regression Using Mult Lin Regression Derived variables Many alternative models Which model to choose? Model Criticism Modelling Objective Model Details Data and Residuals Assumptions 1
Ordinary Least Squares Regression Explained: Vartanian
Ordinary Least Squares Regression Eplained: Vartanian When to Use Ordinary Least Squares Regression Analysis A. Variable types. When you have an interval/ratio scale dependent variable.. When your independent
ANEXO C. ****** Method 1 (space saver) will be used for this analysis ****** R E L I A B I L I T Y A N A L Y S I S S C A L E (A L P H A)
ANEXO C Alfa del factor de motivación general ****** Method (space saver) will be used for this analysis ****** R E L I A B I L I T Y A N A L Y S I S S C A L E (A L P H A) Item total Statistics Scale Scale
The strain field model and its modification of semi-rigid asphalt pavement with graded granular interlayer
Available online at www.sciencedirect.com ScienceDirect Procedia - Social and Behavioral Scienc es 96 ( 2013 ) 933 944 13th COTA International Conference of Transportation Professionals (CICTP 2013) The
Multiple linear regression
Multiple linear regression Course MF 930: Introduction to statistics June 0 Tron Anders Moger Department of biostatistics, IMB University of Oslo Aims for this lecture: Continue where we left off. Repeat
( ) *
: * ** : 189.. (r.m.moghadam@gmail.com) ( ) * ** (kemami@hotmail.com) 1391/1/20 : 1390/6/27 :.189-216 1391 . ( ) ( ).... :. 1391 4 190 : 191...... (1)... . (2).. 43 41 34 1379. :. (3). 38/7 28 50 15 8206
16.400/453J Human Factors Engineering. Design of Experiments II
J Human Factors Engineering Design of Experiments II Review Experiment Design and Descriptive Statistics Research question, independent and dependent variables, histograms, box plots, etc. Inferential
y response variable x 1, x 2,, x k -- a set of explanatory variables
11. Multiple Regression and Correlation y response variable x 1, x 2,, x k -- a set of explanatory variables In this chapter, all variables are assumed to be quantitative. Chapters 12-14 show how to incorporate
Interactions and Centering in Regression: MRC09 Salaries for graduate faculty in psychology
Psychology 308c Dale Berger Interactions and Centering in Regression: MRC09 Salaries for graduate faculty in psychology This example illustrates modeling an interaction with centering and transformations.
Multicollinearity : Estimation and Elimination
Multicollinearity : Estimation and Elimination S.S.Shantha Kumari 1 Abstract Multiple regression fits a model to predict a dependent (Y) variable from two or more independent (X) variables. If the model
Using the Regression Model in multivariate data analysis
Bulletin of the Transilvania University of Braşov Series V: Economic Sciences Vol. 10 (59) No. 1-2017 Using the Regression Model in multivariate data analysis Cristinel CONSTANTIN 1 Abstract: This paper
Ref.: Spring SOS3003 Applied data analysis for social science Lecture note
SOS3003 Applied data analysis for social science Lecture note 05-2010 Erling Berge Department of sociology and political science NTNU Spring 2010 Erling Berge 2010 1 Literature Regression criticism I Hamilton
McGill University. Faculty of Science MATH 204 PRINCIPLES OF STATISTICS II. Final Examination
McGill University Faculty of Science MATH 204 PRINCIPLES OF STATISTICS II Final Examination Date: 20th April 2009 Time: 9am-2pm Examiner: Dr David A Stephens Associate Examiner: Dr Russell Steele Please
Available online at (Elixir International Journal) Statistics. Elixir Statistics 49 (2012)
10108 Available online at www.elixirpublishers.com (Elixir International Journal) Statistics Elixir Statistics 49 (2012) 10108-10112 The detention and correction of multicollinearity effects in a multiple
: The model hypothesizes a relationship between the variables. The simplest probabilistic model: or.
Chapter Simple Linear Regression : comparing means across groups : presenting relationships among numeric variables. Probabilistic Model : The model hypothesizes an relationship between the variables.
Review of Multiple Regression
Ronald H. Heck 1 Let s begin with a little review of multiple regression this week. Linear models [e.g., correlation, t-tests, analysis of variance (ANOVA), multiple regression, path analysis, multivariate
REVIEW 8/2/2017 陈芳华东师大英语系
REVIEW Hypothesis testing starts with a null hypothesis and a null distribution. We compare what we have to the null distribution, if the result is too extreme to belong to the null distribution (p
Applied Econometrics. Applied Econometrics Second edition. Dimitrios Asteriou and Stephen G. Hall
Applied Econometrics Second edition Dimitrios Asteriou and Stephen G. Hall MULTICOLLINEARITY 1. Perfect Multicollinearity 2. Consequences of Perfect Multicollinearity 3. Imperfect Multicollinearity 4.
Chapter 15 - Multiple Regression
15.1 Predicting Quality of Life: Chapter 15 - Multiple Regression a. All other variables held constant, a difference of +1 degree in Temperature is associated with a difference of.01 in perceived Quality
Sociology Research Statistics I Final Exam Answer Key December 15, 1993
Sociology 592 - Research Statistics I Final Exam Answer Key December 15, 1993 Where appropriate, show your work - partial credit may be given. (On the other hand, don't waste a lot of time on excess verbiage.)
COMPREHENSIVE 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,
3 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
Example. Multiple Regression. Review of ANOVA & Simple Regression /749 Experimental Design for Behavioral and Social Sciences
36-309/749 Experimental Design for Behavioral and Social Sciences Sep. 29, 2015 Lecture 5: Multiple Regression Review of ANOVA & Simple Regression Both Quantitative outcome Independent, Gaussian errors
Dr. Maddah ENMG 617 EM Statistics 11/28/12. Multiple Regression (3) (Chapter 15, Hines)
Dr. Maddah ENMG 617 EM Statistics 11/28/12 Multiple Regression (3) (Chapter 15, Hines) Problems in multiple regression: Multicollinearity This arises when the independent variables x 1, x 2,, x k, are
School of Mathematical Sciences. Question 1. Best Subsets Regression
School of Mathematical Sciences MTH5120 Statistical Modelling I Practical 9 and Assignment 8 Solutions Question 1 Best Subsets Regression Response is Crime I n W c e I P a n A E P U U l e Mallows g E P
1. How will an increase in the sample size affect the width of the confidence interval?
Study Guide Concept Questions 1. How will an increase in the sample size affect the width of the confidence interval? 2. How will an increase in the sample size affect the power of a statistical test?
LI EAR REGRESSIO A D CORRELATIO
CHAPTER 6 LI EAR REGRESSIO A D CORRELATIO Page Contents 6.1 Introduction 10 6. Curve Fitting 10 6.3 Fitting a Simple Linear Regression Line 103 6.4 Linear Correlation Analysis 107 6.5 Spearman s Rank Correlation
Project Report for STAT571 Statistical Methods Instructor: Dr. Ramon V. Leon. Wage Data Analysis. Yuanlei Zhang
Project Report for STAT7 Statistical Methods Instructor: Dr. Ramon V. Leon Wage Data Analysis Yuanlei Zhang 77--7 November, Part : Introduction Data Set The data set contains a random sample of observations
1 Correlation and Inference from Regression
1 Correlation and Inference from Regression Reading: Kennedy (1998) A Guide to Econometrics, Chapters 4 and 6 Maddala, G.S. (1992) Introduction to Econometrics p. 170-177 Moore and McCabe, chapter 12 is
4/22/2010. Test 3 Review ANOVA
Test 3 Review ANOVA 1 School recruiter wants to examine if there are difference between students at different class ranks in their reported intensity of school spirit. What is the factor? How many levels