Everything taken from (Hair, Hult et al. 2017) but some formulas taken elswere or created by Erik Mønness.
|
|
- Luke Rudolph Phillips
- 6 years ago
- Views:
Transcription
1 /Users/astacbf/Desktop/Assessing smartpls (engelsk).docx 1/8 Assessing smartpls Everything taken from (Hair, Hult et al. 017) but some formulas taken elswere or created by Erik Mønness. Run PLS algorithm, save picture and EXCEL diagnostics. Run Bootstrap to get significances and confidence intervals. Save EXCEL (as separate file) Run Blindfolding Run Important-Performant map. Save pictures and EXCEL every time if needed. Innhold Assessing smartpls... 1 Stage 5a: Reflective Measurement Models... Internal consistency... Converget Validity... Discriminant validity... 3 Rules of Thumb for evaluation Reflective Measurement Models... 4 Stage 5b: Formative Measurement Models... 5 Collinearity issues... 5 Significance and relevance... 6 Rules of Thumb for the Evaluation of Formative Measurement Indicators... 6 Stage 6: Evaluation of the Structural Model... 7 Rules of Thumb for Structural Model Evaluation... 7 Importance-Performance map References... 8
2 /Users/astacbf/Desktop/Assessing smartpls (engelsk).docx /8 P106 Systematic Evaluation of PLS-SEM Results Stage 5: Evaluation of the Measurement Models Stage 5a: Reflective Measurement Models Internal consistency (Cronbach's alpha, composite reliability) Convergent validity (indicator reliabiiity, average variance extracted) Discriminant validity Stage 6: Evaluation of the Structural Model Coefficients of determination (R ) Predictive relevance (Q ) Size and significance of path coefficients f effect sizes q effect sizes Stage 5b: Formative Measurement Models Convergent validity Collinearity between indicators Significance and relevance of outer weights Stage 5a: Reflective Measurement Models Internal consistency M æ ö si M ç å i 1 Cronbach a = ç 1- = M -1 ssum ç è ø composite reliability ö l ç i i c = è ø æ ö ç åli + å i i r è æ ø å var ( e ) Converget Validity Each loading should be significant and >=0.708 Thus variance explained >=0.5. See 4.4 Average variance extracted AVE M æ ö ç åli i= 1 AVE = ç If standardized. M ç è ø i «Sum of variances divided by variance of sum». If total independence, these are equal, making Cronbach alpha=0. NB Involved indicators must have non-negative correlations. Rescale if needed! l i is outer loading. Var(e i)= 1-l i : ok in exploratory analysis. How much variation in the measures are in the construct. Should be >=0.5
3 /Users/astacbf/Desktop/Assessing smartpls (engelsk).docx 3/8 Discriminant validity Is a construct distinct from others? Cross-loadings Fornell-Larcker Criterion Heterotrait-Monotrait ratio. HTMT. An estimate of the correlation between two constructs. Monotrait= correlations between indicators of same construct. Heterotrait= correlations between indicators from the two separate indicator groups. An indicator s outer loading with its construct should be greater than any of its cross-loadings (correlations) with other constructs. AVE( y ) ³ corr( y, y ) i i j The construct y i has more in common with its indicators than with other constructs. Considered best of the three measures
4 /Users/astacbf/Desktop/Assessing smartpls (engelsk).docx 4/8 IF HTMT> 0.9 the constructs are not distinct. If bootstrap interval includes 1 they are not distinct. Rules of Thumb for evaluation Reflective Measurement Models Internal consistency reliability: composite reliability should be higher than 0.70 (in exploratory research, 0.60 to 0.70 is considered acceptable). Consider Cronbach's alpha as the lower bound and composite reliability as the upper bound of internal consistency reliability. Indicator reliability: the indicator's outer loadings should be higher than Indicators with outer loadings between 0.40 and 0.70 should be considered for removal only if the deletion leads to an increase in composite reliability and AVE above the suggested threshold value. Convergent validity: the AVE should be higher than Discriminant validity: Use the HTMT criterion to assess discriminant validity in PLS-SEM. The confidence interval of the HTMT statistic should not include the value 1 for all combinations of constructs. According to the traditional discriminant validity assessment methods, an indicator's outer loadings on a construct should be higher than all its cross-loadings with other constructs. Furthermore, the square root of the AVE of each construct should be higher than its highest correlation with any other construct (Fornell-Larcker criterion)
5 /Users/astacbf/Desktop/Assessing smartpls (engelsk).docx 5/8 Stage 5b: Formative Measurement Models Formative Measurement Models Assessment Procedure 1 Assess convergent validity of formative measurement models Assess formative measurement models for collinearity issues 3 Assess the significance and relevance of the formative indicators Collinearity issues Variance Inflation Factor VIF =1/TOL is the correlation between one indicator TOLerance = - 1 Rx 1 R x 1 and the other indicators in the same construct. Measures collinearity. TOL<0. or VIF>5 indicate a problem If totally uncorrelated, max outer weight of an indicator is 1 n where n=number of indicators. Thus, when n large, some will appear insignificant. à0.7, 5à0.44, 10à0.31, 0à0.
6 /Users/astacbf/Desktop/Assessing smartpls (engelsk).docx 6/8 Significance and relevance Rules of Thumb for the Evaluation of Formative Measurement Indicators Assess the formative construct's convergent validity by examining its correlation with an alternative measure of the construct, using reflective measures or a global single item (redundancy analysis). The correlation between the constructs should be 0.70 or higher. Collinearity of indicators: Each indicator's VIF value should be lower than 5. Otherwise, consider eliminating indicators, merging indicators into a single index, or creating higherorder constructs to treat collinearity problems. Examine each indicator's outer weight (relative importance) and outer loading (absolute importance) and use bootstrapping to assess their significance. When an indicator's weight is significant, there is empirical support to retain the indicator. When an indicator's weight is not significant but the corresponding item loading is relatively high (i.e., >=0.50), or statistically significant, the indicator should generally be retained. If the outer weight is non-significant and the outer loading relatively low (i.e., <0.5), you should strongly consider to remove the formative indicator from the model.
7 /Users/astacbf/Desktop/Assessing smartpls (engelsk).docx 7/8 Stage 6: Evaluation of the Structural Model Structural Model Assessment Procedure Assess structural model VIF values of inner model (among constructs). VIF>5 indicate problems for collinearity issues Assess the significance Statistical significance vs. relevance of significant relations. and relevance of the structural model relationships Assess the level of R n -1 R How much is explained by explaining adj = 1-( 1-R ) n - k -1 constructs. Assess the f effect size Does contribute? y Excluded RIncluded - RExcluded f = 1- RIncluded Thumb rule: small=0.0, medium=0.15, large=0.35 Assess the predictive relevance Q Assess the q effect size Blindfolding procedure. Blindfolding procedure QIncluded - QExcluded q = 1- Q Included Rules of Thumb for Structural Model Evaluation Examine each set of predictors in the structural model for collinearity. Each predictor construct's tolerance (VIF) value should be higher than 0.0 (lower than 5). Otherwise, consider eliminating constructs, merging predictors into a single construct, or creating higher-order constructs to treat collinearity problems. Use bootstrapping to assess the significance of path coefficients. The minimum number of bootstrap samples must be at least as large as the number of valid observations but should be 5,000. Critical t values for a two-tailed test are 1.65 (significance level = 10%), 1.96 (significance level = 5%), and.57 (significance level = 1 %). Alternatively, examine the p value, which should be lower than 0.10 (significance level= 10%), 0.05 (significance level = 5%), or 0.01 (significance level = 1 %). In applications, you should usually assume a 5% significance level. Bootstrap confidence intervals provide additional information on the stability of path coefficient estimates. Use the percentile method for constructing confidence intervals. When models are not complex (i.e., fewer than four constructs) and sample size is small, use double bootstrapping. However, the running time can be extensive. PLS-SEM aims at maximizing the R values of the endogenous latent variable(s) in the path model. While the exact interpretation of the R value depends on the particular model and research discipline, in general R values of 0.75, 0.50, or 0.5 for the endogenous construct can be described as respectively substantial, moderate, and weak. Use the. when comparing models with different exogenous constructs and/or R adj different numbers of observations..
8 /Users/astacbf/Desktop/Assessing smartpls (engelsk).docx 8/8 The effect size f allows assessing an exogenous construct's contribution to an endogenous latent variable's R value. f values of 0.0, 0.15, and 0.35 indicate an exogenous construct's small, medium, or large effect, respectively, on an endogenous construct. Predictive relevance: Use blindfolding to obtain cross-validated redundancy measures for each endogenous construct. Make sure the number of observations used in the model estimation divided by the omission distance D is not an integer. Choose D values between 5 and 10. The resulting Q values larger than D indicate that the exogenous constructs have predictive relevance for the endogenous construct under consideration. The effect size q allows assessing an exogenous construct's contribution to an endogenous latent variable's Q value. As a relative measure of predictive relevance, q values of 0.0, 0.15, and 0.35, respectively, indicate that an exogenous construct has a small, medium, or large predictive relevance for a certain endogenous construct. For theory testing, consider using SRMR, RMS theta or the exact fit test. Apart from conceptual concerns, these measures' behaviors have not been researched in a PLS-SEM context in depth, and threshold values have not been derived yet. Following a conservative approach, an SRMR (RMS theta) value of less than 0.08 (0.1) indicates good fit. Do not use the GoF to determine model fit. Importance-Performance map. What constructs have a high impact on a target, and to what degree can it be improved?. References Hair, J. F., et al. (017). A primer on partial least squares structural equation modeling (PLS-Sem). Thousand Oaks, Calif, Sage.
CDAA No. 4 - Part Two - Multiple Regression - Initial Data Screening
CDAA No. 4 - Part Two - Multiple Regression - Initial Data Screening Variables Entered/Removed b Variables Entered GPA in other high school, test, Math test, GPA, High school math GPA a Variables Removed
More informationStudy Guide. Module 1. Key Terms
Study Guide Module 1 Key Terms general linear model dummy variable multiple regression model ANOVA model ANCOVA model confounding variable squared multiple correlation adjusted squared multiple correlation
More informationBasic Calculator Functions
Algebra I Common Graphing Calculator Directions Name Date Throughout our course, we have used the graphing calculator to help us graph functions and perform a variety of calculations. The graphing calculator
More informationBasics of Multivariate Modelling and Data Analysis
Basics of Multivariate Modelling and Data Analysis Kurt-Erik Häggblom 9. Linear regression with latent variables 9.1 Principal component regression (PCR) 9.2 Partial least-squares regression (PLS) [ mostly
More informationThe problem we have now is called variable selection or perhaps model selection. There are several objectives.
STAT-UB.0103 NOTES for Wednesday 01.APR.04 One of the clues on the library data comes through the VIF values. These VIFs tell you to what extent a predictor is linearly dependent on other predictors. We
More informationReals 1. Floating-point numbers and their properties. Pitfalls of numeric computation. Horner's method. Bisection. Newton's method.
Reals 1 13 Reals Floating-point numbers and their properties. Pitfalls of numeric computation. Horner's method. Bisection. Newton's method. 13.1 Floating-point numbers Real numbers, those declared to be
More informationConditional and Unconditional Regression with No Measurement Error
Conditional and with No Measurement Error /* reg2ways.sas */ %include 'readsenic.sas'; title2 ''; proc reg; title3 'Conditional Regression'; model infrisk = stay census; proc calis cov; /* Analyze the
More informationCHAPTER 3 AN OVERVIEW OF DESIGN OF EXPERIMENTS AND RESPONSE SURFACE METHODOLOGY
23 CHAPTER 3 AN OVERVIEW OF DESIGN OF EXPERIMENTS AND RESPONSE SURFACE METHODOLOGY 3.1 DESIGN OF EXPERIMENTS Design of experiments is a systematic approach for investigation of a system or process. A series
More informationPDF hosted at the Radboud Repository of the Radboud University Nijmegen
PDF hosted at the Radboud Repository of the Radboud University Nijmegen The following full text is an author's version which may differ from the publisher's version. For additional information about this
More informationMulticollinearity and Validation CIVL 7012/8012
Multicollinearity and Validation CIVL 7012/8012 2 In Today s Class Recap Multicollinearity Model Validation MULTICOLLINEARITY 1. Perfect Multicollinearity 2. Consequences of Perfect Multicollinearity 3.
More informationMeasures of Dispersion
Measures of Dispersion 6-3 I Will... Find measures of dispersion of sets of data. Find standard deviation and analyze normal distribution. Day 1: Dispersion Vocabulary Measures of Variation (Dispersion
More informationLatent variable transformation using monotonic B-splines in PLS Path Modeling
Latent variable transformation using monotonic B-splines in PLS Path Modeling E. Jakobowicz CEDRIC, Conservatoire National des Arts et Métiers, 9 rue Saint Martin, 754 Paris Cedex 3, France EDF R&D, avenue
More informationCREATING THE ANALYSIS
Chapter 14 Multiple Regression Chapter Table of Contents CREATING THE ANALYSIS...214 ModelInformation...217 SummaryofFit...217 AnalysisofVariance...217 TypeIIITests...218 ParameterEstimates...218 Residuals-by-PredictedPlot...219
More informationSerial Correlation and Heteroscedasticity in Time series Regressions. Econometric (EC3090) - Week 11 Agustín Bénétrix
Serial Correlation and Heteroscedasticity in Time series Regressions Econometric (EC3090) - Week 11 Agustín Bénétrix 1 Properties of OLS with serially correlated errors OLS still unbiased and consistent
More informationStatistical Pattern Recognition
Statistical Pattern Recognition Features and Feature Selection Hamid R. Rabiee Jafar Muhammadi Spring 2013 http://ce.sharif.edu/courses/91-92/2/ce725-1/ Agenda Features and Patterns The Curse of Size and
More informationLeveling Up as a Data Scientist. ds/2014/10/level-up-ds.jpg
Model Optimization Leveling Up as a Data Scientist http://shorelinechurch.org/wp-content/uploa ds/2014/10/level-up-ds.jpg Bias and Variance Error = (expected loss of accuracy) 2 + flexibility of model
More informationRegression. Dr. G. Bharadwaja Kumar VIT Chennai
Regression Dr. G. Bharadwaja Kumar VIT Chennai Introduction Statistical models normally specify how one set of variables, called dependent variables, functionally depend on another set of variables, called
More informationI. MODEL. Q3i: Check my . Q29s: I like to see films and TV programs from other countries. Q28e: I like to watch TV shows on a laptop/tablet/phone
1 Multiple Regression-FORCED-ENTRY HIERARCHICAL MODEL DORIS ACHEME COM 631/731, Spring 2017 Data: Film & TV Usage 2015 I. MODEL IV Block 1: Demographics Sex (female dummy):q30 Age: Q31 Income: Q34 Block
More informationAn Introduction to Growth Curve Analysis using Structural Equation Modeling
An Introduction to Growth Curve Analysis using Structural Equation Modeling James Jaccard New York University 1 Overview Will introduce the basics of growth curve analysis (GCA) and the fundamental questions
More informationCHAPTER 7 EXAMPLES: MIXTURE MODELING WITH CROSS- SECTIONAL DATA
Examples: Mixture Modeling With Cross-Sectional Data CHAPTER 7 EXAMPLES: MIXTURE MODELING WITH CROSS- SECTIONAL DATA Mixture modeling refers to modeling with categorical latent variables that represent
More informationData Analysis and Solver Plugins for KSpread USER S MANUAL. Tomasz Maliszewski
Data Analysis and Solver Plugins for KSpread USER S MANUAL Tomasz Maliszewski tmaliszewski@wp.pl Table of Content CHAPTER 1: INTRODUCTION... 3 1.1. ABOUT DATA ANALYSIS PLUGIN... 3 1.3. ABOUT SOLVER PLUGIN...
More informationPerformance Estimation and Regularization. Kasthuri Kannan, PhD. Machine Learning, Spring 2018
Performance Estimation and Regularization Kasthuri Kannan, PhD. Machine Learning, Spring 2018 Bias- Variance Tradeoff Fundamental to machine learning approaches Bias- Variance Tradeoff Error due to Bias:
More informationMinimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods
Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods Ned Kock Pierre Hadaya Full reference: Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in
More informationExcel 2010 with XLSTAT
Excel 2010 with XLSTAT J E N N I F E R LE W I S PR I E S T L E Y, PH.D. Introduction to Excel 2010 with XLSTAT The layout for Excel 2010 is slightly different from the layout for Excel 2007. However, with
More informationModule 1 Lecture Notes 2. Optimization Problem and Model Formulation
Optimization Methods: Introduction and Basic concepts 1 Module 1 Lecture Notes 2 Optimization Problem and Model Formulation Introduction In the previous lecture we studied the evolution of optimization
More informationFMA901F: Machine Learning Lecture 3: Linear Models for Regression. Cristian Sminchisescu
FMA901F: Machine Learning Lecture 3: Linear Models for Regression Cristian Sminchisescu Machine Learning: Frequentist vs. Bayesian In the frequentist setting, we seek a fixed parameter (vector), with value(s)
More informationApplied Regression Modeling: A Business Approach
i Applied Regression Modeling: A Business Approach Computer software help: SAS SAS (originally Statistical Analysis Software ) is a commercial statistical software package based on a powerful programming
More informationTilburg University. The use of canonical analysis Kuylen, A.A.A.; Verhallen, T.M.M. Published in: Journal of Economic Psychology
Tilburg University The use of canonical analysis Kuylen, A.A.A.; Verhallen, T.M.M. Published in: Journal of Economic Psychology Publication date: 1981 Link to publication Citation for published version
More informationStatistical Analysis of Metabolomics Data. Xiuxia Du Department of Bioinformatics & Genomics University of North Carolina at Charlotte
Statistical Analysis of Metabolomics Data Xiuxia Du Department of Bioinformatics & Genomics University of North Carolina at Charlotte Outline Introduction Data pre-treatment 1. Normalization 2. Centering,
More informationIndependent Variables
1 Stepwise Multiple Regression Olivia Cohen Com 631, Spring 2017 Data: Film & TV Usage 2015 I. MODEL Independent Variables Demographics Item: Age Item: Income Dummied Item: Gender (Female) Digital Media
More informationWeek 4: Simple Linear Regression II
Week 4: Simple Linear Regression II Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Algebraic properties
More informationData Mining By IK Unit 4. Unit 4
Unit 4 Data mining can be classified into two categories 1) Descriptive mining: describes concepts or task-relevant data sets in concise, summarative, informative, discriminative forms 2) Predictive mining:
More informationOutline. Topic 16 - Other Remedies. Ridge Regression. Ridge Regression. Ridge Regression. Robust Regression. Regression Trees. Piecewise Linear Model
Topic 16 - Other Remedies Ridge Regression Robust Regression Regression Trees Outline - Fall 2013 Piecewise Linear Model Bootstrapping Topic 16 2 Ridge Regression Modification of least squares that addresses
More informationDetecting and Circumventing Collinearity or Ill-Conditioning Problems
Chapter 8 Detecting and Circumventing Collinearity or Ill-Conditioning Problems Section 8.1 Introduction Multicollinearity/Collinearity/Ill-Conditioning The terms multicollinearity, collinearity, and ill-conditioning
More informationSTA 4273H: Statistical Machine Learning
STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.utstat.utoronto.ca/~rsalakhu/ Sidney Smith Hall, Room 6002 Lecture 12 Combining
More informationVoluntary State Curriculum Algebra II
Algebra II Goal 1: Integration into Broader Knowledge The student will develop, analyze, communicate, and apply models to real-world situations using the language of mathematics and appropriate technology.
More informationQuantitative Methods in Management
Quantitative Methods in Management MBA Glasgow University March 20-23, 2009 Luiz Moutinho, University of Glasgow Graeme Hutcheson, University of Manchester Exploratory Regression The lecture notes, exercises
More informationLecture on Modeling Tools for Clustering & Regression
Lecture on Modeling Tools for Clustering & Regression CS 590.21 Analysis and Modeling of Brain Networks Department of Computer Science University of Crete Data Clustering Overview Organizing data into
More informationWarpPLS 3.0 User Manual. Ned Kock
WarpPLS 3.0 User Manual Ned Kock WarpPLS 3.0 User Manual Ned Kock ScriptWarp Systems Laredo, Texas USA ii WarpPLS 3.0 User Manual, February 2012, Copyright by Ned Kock All rights reserved worldwide. No
More informationBig Data Methods. Chapter 5: Machine learning. Big Data Methods, Chapter 5, Slide 1
Big Data Methods Chapter 5: Machine learning Big Data Methods, Chapter 5, Slide 1 5.1 Introduction to machine learning What is machine learning? Concerned with the study and development of algorithms that
More information7. Collinearity and Model Selection
Sociology 740 John Fox Lecture Notes 7. Collinearity and Model Selection Copyright 2014 by John Fox Collinearity and Model Selection 1 1. Introduction I When there is a perfect linear relationship among
More informationIntroduction to Factor Analysis for Marketing
Introduction to Factor Analysis for Marketing SKIM/Sawtooth Software Conference 2016, Rome Chris Chapman, Google. April 2016. Special thanks to Josh Lewandowski at Google for helpful feedback (errors are
More informationSlides for Data Mining by I. H. Witten and E. Frank
Slides for Data Mining by I. H. Witten and E. Frank 7 Engineering the input and output Attribute selection Scheme-independent, scheme-specific Attribute discretization Unsupervised, supervised, error-
More informationHow to use FSBforecast Excel add in for regression analysis
How to use FSBforecast Excel add in for regression analysis FSBforecast is an Excel add in for data analysis and regression that was developed here at the Fuqua School of Business over the last 3 years
More informationConducting a Path Analysis With SPSS/AMOS
Conducting a Path Analysis With SPSS/AMOS Download the PATH-INGRAM.sav data file from my SPSS data page and then bring it into SPSS. The data are those from the research that led to this publication: Ingram,
More informationConfirmatory Factor Analysis on the Twin Data: Try One
Confirmatory Factor Analysis on the Twin Data: Try One /************ twinfac2.sas ********************/ TITLE2 'Confirmatory Factor Analysis'; %include 'twinread.sas'; proc calis corr; /* Analyze the correlation
More informationResources for statistical assistance. Quantitative covariates and regression analysis. Methods for predicting continuous outcomes.
Resources for statistical assistance Quantitative covariates and regression analysis Carolyn Taylor Applied Statistics and Data Science Group (ASDa) Department of Statistics, UBC January 24, 2017 Department
More informationMultiple Regression White paper
+44 (0) 333 666 7366 Multiple Regression White paper A tool to determine the impact in analysing the effectiveness of advertising spend. Multiple Regression In order to establish if the advertising mechanisms
More informationPROGRAM EFFICIENCY & COMPLEXITY ANALYSIS
Lecture 03-04 PROGRAM EFFICIENCY & COMPLEXITY ANALYSIS By: Dr. Zahoor Jan 1 ALGORITHM DEFINITION A finite set of statements that guarantees an optimal solution in finite interval of time 2 GOOD ALGORITHMS?
More informationSTA 570 Spring Lecture 5 Tuesday, Feb 1
STA 570 Spring 2011 Lecture 5 Tuesday, Feb 1 Descriptive Statistics Summarizing Univariate Data o Standard Deviation, Empirical Rule, IQR o Boxplots Summarizing Bivariate Data o Contingency Tables o Row
More informationMath 144 Activity #7 Trigonometric Identities
44 p Math 44 Activity #7 Trigonometric Identities What is a trigonometric identity? Trigonometric identities are equalities that involve trigonometric functions that are true for every single value of
More informationIntroduction to SAS proc calis
Introduction to SAS proc calis /* path1.sas */ %include 'SenicRead.sas'; title2 ''; /************************************************************************ * * * Cases are hospitals * * * * stay Average
More informationrange: [1,20] units: 1 unique values: 20 missing.: 0/20 percentiles: 10% 25% 50% 75% 90%
------------------ log: \Term 2\Lecture_2s\regression1a.log log type: text opened on: 22 Feb 2008, 03:29:09. cmdlog using " \Term 2\Lecture_2s\regression1a.do" (cmdlog \Term 2\Lecture_2s\regression1a.do
More informationThis file contains an excerpt from the character code tables and list of character names for The Unicode Standard, Version 3.0.
Range: This file contains an excerpt from the character code tables and list of character names for The Unicode Standard, Version.. isclaimer The shapes of the reference glyphs used in these code charts
More informationLAMPIRAN 1 : DATA HASIL PENELITIAN
LAMPIRAN 1 : DATA HASIL PENELITIAN SKPD SDM KOMUNIKASI SARANA KOMITMEN MOTIVASI RATA 43 15 74 42 64 78 52,6666667 47 14 66 40 50 80 49,5 55 15 61 40 56 87 52,3333333 49 12 50 41 58 87 49,5 44 12 49 30
More informationCHAPTER 5. BASIC STEPS FOR MODEL DEVELOPMENT
CHAPTER 5. BASIC STEPS FOR MODEL DEVELOPMENT This chapter provides step by step instructions on how to define and estimate each of the three types of LC models (Cluster, DFactor or Regression) and also
More informationSubset Selection in Multiple Regression
Chapter 307 Subset Selection in Multiple Regression Introduction Multiple regression analysis is documented in Chapter 305 Multiple Regression, so that information will not be repeated here. Refer to that
More informationSAS Structural Equation Modeling 1.3 for JMP
SAS Structural Equation Modeling 1.3 for JMP SAS Documentation The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2012. SAS Structural Equation Modeling 1.3 for JMP. Cary,
More informationStat 5100 Handout #14.a SAS: Logistic Regression
Stat 5100 Handout #14.a SAS: Logistic Regression Example: (Text Table 14.3) Individuals were randomly sampled within two sectors of a city, and checked for presence of disease (here, spread by mosquitoes).
More informationTwo-Stage Least Squares
Chapter 316 Two-Stage Least Squares Introduction This procedure calculates the two-stage least squares (2SLS) estimate. This method is used fit models that include instrumental variables. 2SLS includes
More informationSTAT 2607 REVIEW PROBLEMS Word problems must be answered in words of the problem.
STAT 2607 REVIEW PROBLEMS 1 REMINDER: On the final exam 1. Word problems must be answered in words of the problem. 2. "Test" means that you must carry out a formal hypothesis testing procedure with H0,
More informationWeek 4: Simple Linear Regression III
Week 4: Simple Linear Regression III Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Goodness of
More informationUNIT 1: NUMBER LINES, INTERVALS, AND SETS
ALGEBRA II CURRICULUM OUTLINE 2011-2012 OVERVIEW: 1. Numbers, Lines, Intervals and Sets 2. Algebraic Manipulation: Rational Expressions and Exponents 3. Radicals and Radical Equations 4. Function Basics
More informationLISA: Explore JMP Capabilities in Design of Experiments. Liaosa Xu June 21, 2012
LISA: Explore JMP Capabilities in Design of Experiments Liaosa Xu June 21, 2012 Course Outline Why We Need Custom Design The General Approach JMP Examples Potential Collinearity Issues Prior Design Evaluations
More informationAllen C. Johnston. Merrill Warkentin
RESEARCH ARTICLE AN ENHANCED FEAR APPEAL RHETORICAL FRAMEWORK: LEVERAGING THREATS TO THE HUMAN ASSET THROUGH SANCTIONING RHETORIC Allen C. Johnston Department of Management and Information Systems, Collat
More informationLecture 25: Review I
Lecture 25: Review I Reading: Up to chapter 5 in ISLR. STATS 202: Data mining and analysis Jonathan Taylor 1 / 18 Unsupervised learning In unsupervised learning, all the variables are on equal standing,
More informationLAMPIRAN. Sampel Penelitian
LAMPIRAN Lampiran 1 Daftar Perusahaan Sampel Penelitian No. Kode Kriteria Perusahaan 1 2 3 4 Sampel 1 ADES 1 2 AISA 2 3 ALTO 4 CEKA 5 DAVO 6 DLTA 3 7 ICBP 4 8 INDF 5 9 MLBI 6 10 MYOR 11 PSDN 7 12 ROTI
More informationMachine Learning Techniques for Data Mining
Machine Learning Techniques for Data Mining Eibe Frank University of Waikato New Zealand 10/25/2000 1 PART VII Moving on: Engineering the input and output 10/25/2000 2 Applying a learner is not all Already
More informationIntroduction to Mixed Models: Multivariate Regression
Introduction to Mixed Models: Multivariate Regression EPSY 905: Multivariate Analysis Spring 2016 Lecture #9 March 30, 2016 EPSY 905: Multivariate Regression via Path Analysis Today s Lecture Multivariate
More informationTHE UNIVERSITY OF BRITISH COLUMBIA FORESTRY 430 and 533. Time: 50 minutes 40 Marks FRST Marks FRST 533 (extra questions)
THE UNIVERSITY OF BRITISH COLUMBIA FORESTRY 430 and 533 MIDTERM EXAMINATION: October 14, 2005 Instructor: Val LeMay Time: 50 minutes 40 Marks FRST 430 50 Marks FRST 533 (extra questions) This examination
More informationBootstrapping Method for 14 June 2016 R. Russell Rhinehart. Bootstrapping
Bootstrapping Method for www.r3eda.com 14 June 2016 R. Russell Rhinehart Bootstrapping This is extracted from the book, Nonlinear Regression Modeling for Engineering Applications: Modeling, Model Validation,
More informationLinear Methods for Regression and Shrinkage Methods
Linear Methods for Regression and Shrinkage Methods Reference: The Elements of Statistical Learning, by T. Hastie, R. Tibshirani, J. Friedman, Springer 1 Linear Regression Models Least Squares Input vectors
More informationALTERNATIVE METHODS FOR CLUSTERING
ALTERNATIVE METHODS FOR CLUSTERING K-Means Algorithm Termination conditions Several possibilities, e.g., A fixed number of iterations Objects partition unchanged Centroid positions don t change Convergence
More informationWorkload Characterization Techniques
Workload Characterization Techniques Raj Jain Washington University in Saint Louis Saint Louis, MO 63130 Jain@cse.wustl.edu These slides are available on-line at: http://www.cse.wustl.edu/~jain/cse567-08/
More informationSPSS INSTRUCTION CHAPTER 9
SPSS INSTRUCTION CHAPTER 9 Chapter 9 does no more than introduce the repeated-measures ANOVA, the MANOVA, and the ANCOVA, and discriminant analysis. But, you can likely envision how complicated it can
More informationUsing the Deformable Part Model with Autoencoded Feature Descriptors for Object Detection
Using the Deformable Part Model with Autoencoded Feature Descriptors for Object Detection Hyunghoon Cho and David Wu December 10, 2010 1 Introduction Given its performance in recent years' PASCAL Visual
More informationPredict Outcomes and Reveal Relationships in Categorical Data
PASW Categories 18 Specifications Predict Outcomes and Reveal Relationships in Categorical Data Unleash the full potential of your data through predictive analysis, statistical learning, perceptual mapping,
More informationDI TRANSFORM. The regressive analyses. identify relationships
July 2, 2015 DI TRANSFORM MVstats TM Algorithm Overview Summary The DI Transform Multivariate Statistics (MVstats TM ) package includes five algorithm options that operate on most types of geologic, geophysical,
More information1. Answer: x or x. Explanation Set up the two equations, then solve each equation. x. Check
Thinkwell s Placement Test 5 Answer Key If you answered 7 or more Test 5 questions correctly, we recommend Thinkwell's Algebra. If you answered fewer than 7 Test 5 questions correctly, we recommend Thinkwell's
More informationDerivatives. Day 8 - Tangents and Linearizations
Derivatives Day 8 - Tangents and Linearizations Learning Objectives Write an equation for the tangent line to a graph Write an equation for the normal line to a graph Find the locations of horizontal and
More informationTechnical Report of ISO/IEC Test Program of the M-DISC Archival DVD Media June, 2013
Technical Report of ISO/IEC 10995 Test Program of the M-DISC Archival DVD Media June, 2013 With the introduction of the M-DISC family of inorganic optical media, Traxdata set the standard for permanent
More informationPackage matrixpls. September 5, 2013
Package matrixpls September 5, 2013 Encoding UTF-8 Type Package Title Matrix-based Partial Least Squares estimation Version 0.1.0 Date 2013-01-03 Author Mikko Rönkkö Maintainer Mikko Rönkkö
More informationDecision Trees Dr. G. Bharadwaja Kumar VIT Chennai
Decision Trees Decision Tree Decision Trees (DTs) are a nonparametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target
More informationIBM SPSS Categories 23
IBM SPSS Categories 23 Note Before using this information and the product it supports, read the information in Notices on page 55. Product Information This edition applies to version 23, release 0, modification
More informationModified multiblock partial least squares path modeling algorithm with backpropagation neural networks approach
Modified multiblock partial least squares path modeling algorithm with backpropagation neural networks approach Budi Yuniarto, and Robert Kurniawan Citation: AIP Conference Proceedings 1827, 020028 (2017);
More informationTransformations Review
Transformations Review 1. Plot the original figure then graph the image of Rotate 90 counterclockwise about the origin. 2. Plot the original figure then graph the image of Translate 3 units left and 4
More informationApplied Regression Modeling: A Business Approach
i Applied Regression Modeling: A Business Approach Computer software help: SPSS SPSS (originally Statistical Package for the Social Sciences ) is a commercial statistical software package with an easy-to-use
More informationPractical OmicsFusion
Practical OmicsFusion Introduction In this practical, we will analyse data, from an experiment which aim was to identify the most important metabolites that are related to potato flesh colour, from an
More informationMachine Learning: An Applied Econometric Approach Online Appendix
Machine Learning: An Applied Econometric Approach Online Appendix Sendhil Mullainathan mullain@fas.harvard.edu Jann Spiess jspiess@fas.harvard.edu April 2017 A How We Predict In this section, we detail
More informationAnd the benefits are immediate minimal changes to the interface allow you and your teams to access these
Find Out What s New >> With nearly 50 enhancements that increase functionality and ease-of-use, Minitab 15 has something for everyone. And the benefits are immediate minimal changes to the interface allow
More informationMinitab 17 commands Prepared by Jeffrey S. Simonoff
Minitab 17 commands Prepared by Jeffrey S. Simonoff Data entry and manipulation To enter data by hand, click on the Worksheet window, and enter the values in as you would in any spreadsheet. To then save
More informationStatistical Modelling for Social Scientists. Manchester University. January 20, 21 and 24, Exploratory regression and model selection
Statistical Modelling for Social Scientists Manchester University January 20, 21 and 24, 2011 Graeme Hutcheson, University of Manchester Exploratory regression and model selection The lecture notes, exercises
More informationBagging & System Combination for POS Tagging. Dan Jinguji Joshua T. Minor Ping Yu
Bagging & System Combination for POS Tagging Dan Jinguji Joshua T. Minor Ping Yu Bagging Bagging can gain substantially in accuracy The vital element is the instability of the learning algorithm Bagging
More informationAn Introduction to the Bootstrap
An Introduction to the Bootstrap Bradley Efron Department of Statistics Stanford University and Robert J. Tibshirani Department of Preventative Medicine and Biostatistics and Department of Statistics,
More information1. Determine the population mean of x denoted m x. Ans. 10 from bottom bell curve.
6. Using the regression line, determine a predicted value of y for x = 25. Does it look as though this prediction is a good one? Ans. The regression line at x = 25 is at height y = 45. This is right at
More informationLecture 7: Linear Regression (continued)
Lecture 7: Linear Regression (continued) Reading: Chapter 3 STATS 2: Data mining and analysis Jonathan Taylor, 10/8 Slide credits: Sergio Bacallado 1 / 14 Potential issues in linear regression 1. Interactions
More informationStatistical Models for Management. Instituto Superior de Ciências do Trabalho e da Empresa (ISCTE) Lisbon. February 24 26, 2010
Statistical Models for Management Instituto Superior de Ciências do Trabalho e da Empresa (ISCTE) Lisbon February 24 26, 2010 Graeme Hutcheson, University of Manchester Exploratory regression and model
More informationLinear Model Selection and Regularization. especially usefull in high dimensions p>>100.
Linear Model Selection and Regularization especially usefull in high dimensions p>>100. 1 Why Linear Model Regularization? Linear models are simple, BUT consider p>>n, we have more features than data records
More informationThemes in the Texas CCRS - Mathematics
1. Compare real numbers. a. Classify numbers as natural, whole, integers, rational, irrational, real, imaginary, &/or complex. b. Use and apply the relative magnitude of real numbers by using inequality
More informationCHAPTER 6 REAL-VALUED GENETIC ALGORITHMS
CHAPTER 6 REAL-VALUED GENETIC ALGORITHMS 6.1 Introduction Gradient-based algorithms have some weaknesses relative to engineering optimization. Specifically, it is difficult to use gradient-based algorithms
More information