Dealing with Missing Data
|
|
- Rikke Bech
- 3 år siden
- Visninger:
Transkript
1 Dealing with Missing Data Challenges and Solutions Nicole Erler Department of Biostatistics, Erasmus Medical Center N_Erler wwwnerlercom NErler 13 January 2020
2 Handling Missing Values is Easy! Functions automatically exclude missing values: ## [] ## Residual standard error: 2305 on 69 degrees of freedom ## (25 observations deleted due to missingness) ## Multiple R-squared: , Adjusted R-squared: ## F-statistic: 1407 on 5 and 69 DF, p-value:
3 Handling Missing Values is Easy! Functions automatically exclude missing values: ## [] ## Residual standard error: 2305 on 69 degrees of freedom ## (25 observations deleted due to missingness) ## Multiple R-squared: , Adjusted R-squared: ## F-statistic: 1407 on 5 and 69 DF, p-value: Imputation is super easy: library("mice") imp <- mice(mydata) However 1
4 Handling Missing Values Correctly is Not So Easy! Complete case analysis is usually biased 2
5 Handling Missing Values Correctly is Not So Easy! Complete case analysis is usually biased (Imputation) methods make certain assumptions, eg: missingness is M(C)AR 2
6 Handling Missing Values Correctly is Not So Easy! Complete case analysis is usually biased (Imputation) methods make certain assumptions, eg: missingness is M(C)AR the incomplete variable has a certain conditional distribution (eg normal) 2
7 Handling Missing Values Correctly is Not So Easy! Complete case analysis is usually biased (Imputation) methods make certain assumptions, eg: missingness is M(C)AR the incomplete variable has a certain conditional distribution (eg normal) all associations are linear 2
8 Handling Missing Values Correctly is Not So Easy! Complete case analysis is usually biased (Imputation) methods make certain assumptions, eg: missingness is M(C)AR the incomplete variable has a certain conditional distribution (eg normal) all associations are linear compatibility and congeniality 2
9 Handling Missing Values Correctly is Not So Easy! Complete case analysis is usually biased (Imputation) methods make certain assumptions, eg: missingness is M(C)AR the incomplete variable has a certain conditional distribution (eg normal) all associations are linear compatibility and congeniality violation bias 2
10 Imputation??? Remind me, how did that imputation thing work again??? 3
11 Imputation Imputation filling in missing values with (good) "guesses" 4
12 Imputation Imputation filling in missing values with (good) "guesses" Important: Missing values uncertainty This needs to be taken into account!!! 4
13 Imputation Imputation filling in missing values with (good) "guesses" Important: Missing values uncertainty This needs to be taken into account!!! Donald Rubin (in the 1970s): Represent each missing value with multiple imputed values Multiple Imputation Note: Imputation is not the only approach to handle missing values (Also: maximum likelihood, inverse probability weighting, ) 4
14 Multiple Imputation incomplete data multiple imputed datasets analysis results pooled results 1 Imputation: impute multiple times multiple completed datasets 2 Analysis: analyse each of the datasets 3 Pooling: combine results, taking into account additional uncertainty 5
15 Imputation Step Two main approaches Joint Model Multiple Imputation the "original" approach often using a multivariate normal distribution 6
16 Imputation Step Two main approaches Joint Model Multiple Imputation the "original" approach often using a multivariate normal distribution Multiple Imputation with Chained Equations (MICE) also: Fully Conditional Specification (FCS) now often considered the gold standard 6
17 Multiple Imputation with Chained Equations (MICE) For each incomplete variable, specify a model using all other variables: }{{} full conditionals x 1 x 2 x 3 x 4 NA NA NA NA NA NA 7
18 Multiple Imputation with Chained Equations (MICE) For each incomplete variable, specify a model using all other variables: }{{} full conditionals x 1 x 2 x 3 x 4 NA NA NA NA NA NA x 1 x 2 + x 3 + x 4 + x 2 x 1 + x 3 + x 4 + x 3 x 1 + x 2 + x 4 + x 4 x 1 + x 2 + x 3 + 7
19 Multiple Imputation with Chained Equations (MICE) For each incomplete variable, specify a model using all other variables: }{{} full conditionals For example: x 1 x 2 x 3 x 4 NA NA NA NA NA NA x 1 x 2 + x 3 + x 4 + x 2 x 1 + x 3 + x 4 + x 3 x 1 + x 2 + x 4 + x 4 x 1 + x 2 + x 3 + linear regression logistic regression 7
20 Multiple Imputation with Chained Equations (MICE) MICE is an iterative algorithm: start with initial guess x 1 x 2 x 3 x 4 NA NA NA NA NA NA 8
21 Multiple Imputation with Chained Equations (MICE) MICE is an iterative algorithm: start with initial guess update x 1 based on initial values of x 2, x 3, x 4, x 1 x 2 x 3 x 4 NA NA NA NA NA NA 8
22 Multiple Imputation with Chained Equations (MICE) MICE is an iterative algorithm: start with initial guess update x 1 based on initial values of x 2, x 3, x 4, update x 2 based on new x 1 and initial values of x 3, x 4, x 1 x 2 x 3 x 4 NA NA NA NA NA NA 8
23 Multiple Imputation with Chained Equations (MICE) MICE is an iterative algorithm: start with initial guess update x 1 based on initial values of x 2, x 3, x 4, update x 2 based on new x 1 and initial values of x 3, x 4, update x 1 again, based on updated x 2, x 3, x 4, x 1 x 2 x 3 x 4 NA NA NA NA NA NA 8
24 Multiple Imputation with Chained Equations (MICE) MICE is an iterative algorithm: start with initial guess update x 1 based on initial values of x 2, x 3, x 4, update x 2 based on new x 1 and initial values of x 3, x 4, update x 1 again, based on updated x 2, x 3, x 4, until convergence x 1 x 2 x 3 x 4 NA NA NA NA NA NA 8
25 Multiple Imputation with Chained Equations (MICE) MICE is an iterative algorithm: start with initial guess update x 1 based on initial values of x 2, x 3, x 4, update x 2 based on new x 1 and initial values of x 3, x 4, update x 1 again, based on updated x 2, x 3, x 4, until convergence x 1 x 2 x 3 x 4 NA NA NA NA NA NA Values from last iteration one imputed dataset 8
26 MICE Makes Assumptions (Imputation) methods make certain assumptions, eg: missingness is M(C)AR the incomplete variable has a certain conditional distribution (eg normal) all associations are linear compatibility and congeniality 9
27 Missing Data Mechanisms Missing Completely At Random (MCAR) Missing At Random (MAR) Missing Not At Random (MNAR) 10
28 Missing Data Mechanisms Missing Completely At Random (MCAR) p(r X obs, X mis ) = p(r) Missingness is independent of all data questionnaire got lost in mail Missing At Random (MAR) Missing Not At Random (MNAR) 10
29 Missing Data Mechanisms Missing Completely At Random (MCAR) p(r X obs, X mis ) = p(r) Missingness is independent of all data Missing At Random (MAR) p(r X obs, X mis ) = p(r X obs ) Missingness depends only on observed data questionnaire got lost in mail overweight participants are less likely to report their chocolate consumption (and we know their weight) Missing Not At Random (MNAR) 10
30 Missing Data Mechanisms Missing Completely At Random (MCAR) p(r X obs, X mis ) = p(r) Missingness is independent of all data Missing At Random (MAR) p(r X obs, X mis ) = p(r X obs ) Missingness depends only on observed data questionnaire got lost in mail overweight participants are less likely to report their chocolate consumption (and we know their weight) Missing Not At Random (MNAR) p(r X obs, X mis ) p(r X obs ) Missingness depends (also) on unobserved data overweight participants are less likely to report their weight 10
31 MICE Makes Assumptions (Imputation) methods make certain assumptions, eg: missingness is M(C)AR the incomplete variable has a certain conditional distribution (eg normal) all associations are linear compatibility and congeniality In case of MNAR: MICE bias 11
32 MICE Makes Assumptions (Imputation) methods make certain assumptions, eg: missingness is M(C)AR the incomplete variable has a certain conditional distribution (eg normal) all associations are linear compatibility and congeniality 12
33 Imputation Model Misspecification x 1 x 2 + x 3 + x 4 + x 2 x 1 + x 3 + x 4 + x 3 x 1 + x 2 + x 4 + x 4 x 1 + x 2 + x 3 + For example: linear regression logistic regression 13
34 Imputation Model Misspecification x 1 x 2 + x 3 + x 4 + x 2 x 1 + x 3 + x 4 + x 3 x 1 + x 2 + x 4 + x 4 x 1 + x 2 + x 3 + For example: linear regression logistic regression count count incomplete covariate incomplete covariate incomplete covariate covariate 13
35 Imputation Model Misspecification count count incomplete covariate incomplete covariate incomplete covariate covariate misspecification of the residual distribution misspecification of the association structure 14
36 Imputation Model Misspecification count count incomplete covariate incomplete covariate incomplete covariate covariate misspecification of the residual distribution misspecification of the association structure Partial solutions: Predictive Mean Matching Passive imputation 14
37 Imputation Model Misspecification count count incomplete covariate incomplete covariate incomplete covariate covariate misspecification of the residual distribution misspecification of the association structure Partial solutions: Predictive Mean Matching Passive imputation But can get tedious requires knowledge (about data & methods) users often inexperienced and/or lazy 14
38 MICE Makes Assumptions (Imputation) methods make certain assumptions, eg: missingness is M(C)AR the incomplete variable has a certain conditional distribution (eg normal) all associations are linear compatibility and congeniality Model misspecification bias 15
39 MICE Makes Assumptions (Imputation) methods make certain assumptions, eg: missingness is M(C)AR the incomplete variable has a certain conditional distribution (eg normal) all associations are linear compatibility and congeniality 16
40 Compatibility & Congeniality Compatibility A joint distribution exists, that has the full conditionals (imputation models) as its conditional distributions 17
41 Compatibility & Congeniality Compatibility A joint distribution exists, that has the full conditionals (imputation models) as its conditional distributions Congeniality The imputation model is compatible with the analysis model 17
42 Compatibility & Congeniality in MICE MICE is based on the idea of Gibbs sampling Exploits the fact that a joint distribution is fully determined by its full conditional distributions 18
43 Compatibility & Congeniality in MICE MICE is based on the idea of Gibbs sampling Exploits the fact that a joint distribution is fully determined by its full conditional distributions But: In MICE Imputation models are specified directly no guarantee that a corresponding joint distribution exists 18
44 Compatibility & Congeniality in MICE joint distribution Gibbs MICE full conditionals 19
45 Compatibility & Congeniality in MICE joint distribution Gibbs MICE full conditionals Is this a problem? often not but it can be when imputation/analysis models contradict each other different assumptions are made during analysis and imputation the outcome cannot easily be included in the imputation models 19
46 Example 1: Contradicting Models Analysis model with a quadratic association: y = β 0 + β 1 x + β 2 x 2 + x y observed missing 20
47 Example 1: Contradicting Models Imputation model for x (when using MICE naively): x = θ 10 + θ 11 y +, ie, a linear relation between x and y is assumed x y observed missing imputed 21
48 Example 1: Contradicting Models Imputation model for x (when using MICE naively): x = θ 10 + θ 11 y +, ie, a linear relation between x and y is assumed x y fit on complete fit on imputed observed missing imputed 21
49 Example 2: Contradicting Models Analysis model with interaction term: y = β 0 + β x x + β z z + β xz xz +, ie, y again has a non-linear relationship with x x y missing (z = 0) missing (z = 1) observed (z = 0) observed (z = 1) 22
50 Example 2: Contradicting Models Imputation model for x (when using MICE naively): x = θ 10 + θ 11 y + θ 12 z +, x y missing (z = 0) missing (z = 1) observed (z = 0) observed (z = 1) imputed (z = 0) imputed (z = 1) 23
51 Example 2: Contradicting Models Imputation model for x (when using MICE naively): x = θ 10 + θ 11 y + θ 12 z +, y missing (z = 0) missing (z = 1) observed (z = 0) observed (z = 1) imputed (z = 0) imputed (z = 1) true imputed x 23
52 Example 3: Longitudinal / Multi-level Data id time y x NA NA NA NA 24
53 Example 3: Longitudinal / Multi-level Data Imputation in long format rows are treated as independent imputations in baseline covariates will vary over time bias Can we use data in wide format (one row per subject)? can be very inefficient not always possible id time y x NA NA NA NA 25
54 Example 3: Longitudinal / Multi-level Data y y time time y y time time 26
55 Compatibility & Congeniality in MICE Lack of compatibility / congeniality can become a problem for MICE in settings with Non-linear associations non-linear effects interaction terms complex outcomes multi-level settings time-to-event outcomes What can we do in these settings? 27
56 Imputation in Complex Settings Remember, the problem is joint distribution Gibbs MICE full conditionals 28
57 Imputation in Complex Settings Remember, the problem is joint distribution Gibbs MICE full conditionals Solution: Start with the joint distribution! 28
58 Imputation in Complex Settings Remember, the problem is joint distribution Gibbs MICE full conditionals Solution: Start with the joint distribution! New problem: What is the multivariate distribution of multiple variables of different types? 28
59 Imputation in Complex Settings Remember, the problem is joint distribution Gibbs MICE full conditionals Solution: Start with the joint distribution! New problem: What is the multivariate distribution of multiple variables of different types? Usually, the joint distribution is not of any known form 28
60 Joint Model Imputation Multivariate Normal Model Approximate the joint distribution by a known multivariate (usually normal) distribution this is Joint Model Multiple Imputation assures compatibility & congeniality can t handle non-linear associations 29
61 Joint Model Imputation Multivariate Normal Model Approximate the joint distribution by a known multivariate (usually normal) distribution this is Joint Model Multiple Imputation assures compatibility & congeniality can t handle non-linear associations Sequential Factorization Factorize the joint distribution into (a sequence of) conditional distributions assures compatibility & congeniality can handle non-linear associations 29
62 Sequential Factorization A joint distribution p(y, x) can be written as the product of conditional distributions: p(y, x) = p(y x) p(x) (or alternatively p(y, x) = p(x y) p(y)) 30
63 Sequential Factorization A joint distribution p(y, x) can be written as the product of conditional distributions: p(y, x) = p(y x) p(x) (or alternatively p(y, x) = p(x y) p(y)) This can be extended for more variables: p(y, x 1,, x p ) = p(y x 1,, x p ) p(x 1 x 2,, x p ) p(x 2 x 3,, x p ) p(x p ) 30
64 Sequential Factorization in the Bayesian Framework Joint Distribution p(y, X, θ) = p(y X, θ) }{{} analysis model p(x θ) }{{} imputation part θ contains regr coefficients, variance parameters, p(θ) }{{} priors 31
65 Sequential Factorization in the Bayesian Framework Joint Distribution p(y, X, θ) = p(y X, θ) }{{} analysis model p(x θ) }{{} imputation part θ contains regr coefficients, variance parameters, Imputation part p(θ) }{{} priors X {}}{ p( x 1,, x p, X compl θ) = p(x 1 X compl, θ) p(x 2 X compl, x 1, θ) p(x 3 X compl, x 1, x 2, θ) 31
66 Sequential Factorization in the Bayesian Framework Extension for a Multi-level Setting p(y X, b, θ) p(x θ) }{{}}{{} analysis imputation model part p(b θ) }{{} random effects p(θ) }{{} priors 32
67 Sequential Factorization in the Bayesian Framework Extension for a Multi-level Setting p(y X, b, θ) p(x θ) }{{}}{{} analysis imputation model part p(b θ) }{{} random effects p(θ) }{{} priors Extension for a Time-to-Event Outcome p(t, D X, θ) }{{} analysis model p(x θ) }{{} imputation part p(θ) }{{} priors 32
68 Sequential Factorization in the Bayesian Framework Extension for a Multi-level Setting p(y X, b, θ) p(x θ) }{{}}{{} analysis imputation model part p(b θ) }{{} random effects p(θ) }{{} priors Extension for a Time-to-Event Outcome p(t, D X, θ) }{{} analysis model Extension for a Multivariate Outcome p(y 1, y 2 X, θ) } {{ } analysis model p(x θ) }{{} imputation part p(x θ) }{{} imputation part p(θ) }{{} priors p(θ) }{{} priors 32
69 MICE vs Sequential Factorization Imputation in MICE p(x 1 y, X compl, x 2, x 3, x 4,, θ) p(x 2 y, X compl, x 1, x 3, x 4,, θ) p(x 3 y, X compl, x 1, x 2, x 4,, θ) Sequential Factorization p(y X compl, x 1, x 2, x 3,, θ) p(x 1 X compl, θ) p(x 2 X compl, x 1, θ) p(x 3 X compl, x 1, x 2, θ) 33
70 MICE vs Sequential Factorization Imputation in MICE p(x 1 y, X compl, x 2, x 3, x 4,, θ) p(x 2 y, X compl, x 1, x 3, x 4,, θ) p(x 3 y, X compl, x 1, x 2, x 4,, θ) Sequential Factorization p(y X compl, x 1, x 2, x 3,, θ) p(x 1 X compl, θ) p(x 2 X compl, x 1, θ) p(x 3 X compl, x 1, x 2, θ) No issues with complex outcomes, eg: multi-level survival non-linear effects congeniality compatibility 33
71 MICE vs Sequential Factorization Imputation in MICE p(x 1 y, X compl, x 2, x 3, x 4,, θ) p(x 2 y, X compl, x 1, x 3, x 4,, θ) p(x 3 y, X compl, x 1, x 2, x 4,, θ) Sequential Factorization p(y X compl, x 1, x 2, x 3,, θ) p(x 1 X compl, θ) p(x 2 X compl, x 1, θ) p(x 3 X compl, x 1, x 2, θ) Analysis model part of specification parameters of interest directly available no need for pooling simultaneous analysis and imputation 34
72 Joint Analysis and Imputation in Sequential Factorization is implemented in the package JointAI 35
73 Joint Analysis and Imputation in Sequential Factorization is implemented in the package JointAI Bayesian analysis of incomplete data using (generalized) linear regression (generalized) linear mixed models ordinal (mixed) models parametric (Weibull) time-to-event models Cox proportional hazards models 35
74 Joint Analysis and Imputation in Sequential Factorization is implemented in the package JointAI Bayesian analysis of incomplete data using (generalized) linear regression (generalized) linear mixed models ordinal (mixed) models parametric (Weibull) time-to-event models Cox proportional hazards models on CRAN: GitHub: website: 35
75 Joint Analysis and Imputation in standard regression mixed model type outcome covariate outcome covariate normal lognormal (soon) (soon) Gamma beta (soon) (soon) (soon) binomial poisson (soon) ordinal multinomial (soon) (soon) (soon) Available soon: Joint models (of longitudinal & time-to-event data) Multivariate models 36
76 JointAI: How does it work? Requirements: ( JAGS (Just Another Gibbs Sampler; 37
77 JointAI: How does it work? Requirements: ( JAGS (Just Another Gibbs Sampler; Installation: installpackages("jointai") 37
78 JointAI: How does it work? Requirements: ( JAGS (Just Another Gibbs Sampler; Installation: installpackages("jointai") Usage: library("jointai") res <- lm_imp(sbp ~ age + gender + smoke + occup, data = NHANES, niter = 300) 37
79 JointAI: How does it work? traceplot(res) 38
80 JointAI: How does it work? summary(res) ## ## Linear model fitted with JointAI ## ## Call: ## lm_imp(formula = SBP ~ age + gender + smoke + occup, data = NHANES, ## niter = 300) ## ## Posterior summary: ## Mean SD 25% 975% tail-prob GR-crit ## (Intercept) ## age ## genderfemale ## smokeformer ## smokecurrent ## occuplooking for work ## occupnot working ## ## Posterior summary of residual std deviation: ## Mean SD 25% 975% GR-crit ## sigma_sbp ## ## ## MCMC settings ## [] 39
81 What is left to do? (Imputation) methods make certain assumptions, eg: missingness is M(C)AR the incomplete variable has a certain conditional distribution all associations are linear compatibility and congeniality 40
82 What is left to do? (Imputation) methods make certain assumptions, eg: missingness is M(C)AR the incomplete variable has a certain conditional distribution all associations are linear compatibility and congeniality extension to MNAR using pattern mixture model 40
83 What is left to do? (Imputation) methods make certain assumptions, eg: missingness is M(C)AR the incomplete variable has a certain conditional distribution all associations are linear compatibility and congeniality extension to MNAR using pattern mixture model non-parametric Bayesian methods 40
84 What is left to do? (Imputation) methods make certain assumptions, eg: missingness is M(C)AR the incomplete variable has a certain conditional distribution all associations are linear compatibility and congeniality extension to MNAR using pattern mixture model non-parametric Bayesian methods semi-parametric methods 40
85 Take-Home Message handling missing values correctly: not that easy all methods have assumptions violation bias 41
86 Take-Home Message handling missing values correctly: not that easy all methods have assumptions violation bias good use of (imputation) methods requires knowledge of the data knowledge of the methods knowledge of the software time & patience! 41
87 Take-Home Message handling missing values correctly: not that easy all methods have assumptions violation bias good use of (imputation) methods requires knowledge of the data knowledge of the methods knowledge of the software time & patience! JointAI aims to facilitate correct handling of missing values by assuring compatibility & congeniality simultaneous analysis & imputation especially for complex settings 41
88 Take-Home Message handling missing values correctly: not that easy all methods have assumptions violation bias good use of (imputation) methods requires knowledge of the data knowledge of the methods knowledge of the software time & patience! JointAI aims to facilitate correct handling of missing values by assuring compatibility & congeniality simultaneous analysis & imputation especially for complex settings There is no magical solution that will always work in all settings 41
89 Thank you for your attention N_Erler NErler wwwnerlercom
Vina Nguyen HSSP July 13, 2008
Vina Nguyen HSSP July 13, 2008 1 What does it mean if sets A, B, C are a partition of set D? 2 How do you calculate P(A B) using the formula for conditional probability? 3 What is the difference between
Læs mereX M Y. What is mediation? Mediation analysis an introduction. Definition
What is mediation? an introduction Ulla Hvidtfeldt Section of Social Medicine - Investigate underlying mechanisms of an association Opening the black box - Strengthen/support the main effect hypothesis
Læs mereBasic statistics for experimental medical researchers
Basic statistics for experimental medical researchers Sample size calculations September 15th 2016 Christian Pipper Department of public health (IFSV) Faculty of Health and Medicinal Science (SUND) E-mail:
Læs mereDoodleBUGS (Hands-on)
DoodleBUGS (Hands-on) Simple example: Program: bino_ave_sim_doodle.odc A simulation example Generate a sample from F=(r1+r2)/2 where r1~bin(0.5,200) and r2~bin(0.25,100) Note that E(F)=(100+25)/2=62.5
Læs mereGeneralized Probit Model in Design of Dose Finding Experiments. Yuehui Wu Valerii V. Fedorov RSU, GlaxoSmithKline, US
Generalized Probit Model in Design of Dose Finding Experiments Yuehui Wu Valerii V. Fedorov RSU, GlaxoSmithKline, US Outline Motivation Generalized probit model Utility function Locally optimal designs
Læs mereLinear Programming ١ C H A P T E R 2
Linear Programming ١ C H A P T E R 2 Problem Formulation Problem formulation or modeling is the process of translating a verbal statement of a problem into a mathematical statement. The Guidelines of formulation
Læs mereProject Step 7. Behavioral modeling of a dual ported register set. 1/8/ L11 Project Step 5 Copyright Joanne DeGroat, ECE, OSU 1
Project Step 7 Behavioral modeling of a dual ported register set. Copyright 2006 - Joanne DeGroat, ECE, OSU 1 The register set Register set specifications 16 dual ported registers each with 16- bit words
Læs mereSkriftlig Eksamen Kombinatorik, Sandsynlighed og Randomiserede Algoritmer (DM528)
Skriftlig Eksamen Kombinatorik, Sandsynlighed og Randomiserede Algoritmer (DM58) Institut for Matematik og Datalogi Syddansk Universitet, Odense Torsdag den 1. januar 01 kl. 9 13 Alle sædvanlige hjælpemidler
Læs mereBlack Jack --- Review. Spring 2012
Black Jack --- Review Spring 2012 Simulation Simulation can solve real-world problems by modeling realworld processes to provide otherwise unobtainable information. Computer simulation is used to predict
Læs mereIBM Network Station Manager. esuite 1.5 / NSM Integration. IBM Network Computer Division. tdc - 02/08/99 lotusnsm.prz Page 1
IBM Network Station Manager esuite 1.5 / NSM Integration IBM Network Computer Division tdc - 02/08/99 lotusnsm.prz Page 1 New esuite Settings in NSM The Lotus esuite Workplace administration option is
Læs merePrivat-, statslig- eller regional institution m.v. Andet Added Bekaempelsesudfoerende: string No Label: Bekæmpelsesudførende
Changes for Rottedatabasen Web Service The coming version of Rottedatabasen Web Service will have several changes some of them breaking for the exposed methods. These changes and the business logic behind
Læs mereDen nye Eurocode EC Geotenikerdagen Morten S. Rasmussen
Den nye Eurocode EC1997-1 Geotenikerdagen Morten S. Rasmussen UDFORDRINGER VED EC 1997-1 HVAD SKAL VI RUNDE - OPBYGNINGEN AF DE NYE EUROCODES - DE STØRSTE UDFORDRINGER - ER DER NOGET POSITIVT? 2 OPBYGNING
Læs merePARALLELIZATION OF ATTILA SIMULATOR WITH OPENMP MIGUEL ÁNGEL MARTÍNEZ DEL AMOR MINIPROJECT OF TDT24 NTNU
PARALLELIZATION OF ATTILA SIMULATOR WITH OPENMP MIGUEL ÁNGEL MARTÍNEZ DEL AMOR MINIPROJECT OF TDT24 NTNU OUTLINE INEFFICIENCY OF ATTILA WAYS TO PARALLELIZE LOW COMPATIBILITY IN THE COMPILATION A SOLUTION
Læs mereA multimodel data assimilation framework for hydrology
A multimodel data assimilation framework for hydrology Antoine Thiboult, François Anctil Université Laval June 27 th 2017 What is Data Assimilation? Use observations to improve simulation 2 of 8 What is
Læs mereStatistik for MPH: 7
Statistik for MPH: 7 3. november 2011 www.biostat.ku.dk/~pka/mph11 Attributable risk, bestemmelse af stikprøvestørrelse (Silva: 333-365, 381-383) Per Kragh Andersen 1 Fra den 6. uges statistikundervisning:
Læs mereStatistik for MPH: oktober Attributable risk, bestemmelse af stikprøvestørrelse (Silva: , )
Statistik for MPH: 7 29. oktober 2015 www.biostat.ku.dk/~pka/mph15 Attributable risk, bestemmelse af stikprøvestørrelse (Silva: 333-365, 381-383) Per Kragh Andersen 1 Fra den 6. uges statistikundervisning:
Læs mereThe X Factor. Målgruppe. Læringsmål. Introduktion til læreren klasse & ungdomsuddannelser Engelskundervisningen
The X Factor Målgruppe 7-10 klasse & ungdomsuddannelser Engelskundervisningen Læringsmål Eleven kan give sammenhængende fremstillinger på basis af indhentede informationer Eleven har viden om at søge og
Læs merePortal Registration. Check Junk Mail for activation . 1 Click the hyperlink to take you back to the portal to confirm your registration
Portal Registration Step 1 Provide the necessary information to create your user. Note: First Name, Last Name and Email have to match exactly to your profile in the Membership system. Step 2 Click on the
Læs mereEngelsk. Niveau D. De Merkantile Erhvervsuddannelser September Casebaseret eksamen. og
052431_EngelskD 08/09/05 13:29 Side 1 De Merkantile Erhvervsuddannelser September 2005 Side 1 af 4 sider Casebaseret eksamen Engelsk Niveau D www.jysk.dk og www.jysk.com Indhold: Opgave 1 Presentation
Læs mereSubject to terms and conditions. WEEK Type Price EUR WEEK Type Price EUR WEEK Type Price EUR WEEK Type Price EUR
ITSO SERVICE OFFICE Weeks for Sale 31/05/2015 m: +34 636 277 307 w: clublasanta-timeshare.com e: roger@clublasanta.com See colour key sheet news: rogercls.blogspot.com Subject to terms and conditions THURSDAY
Læs mereHvor er mine runde hjørner?
Hvor er mine runde hjørner? Ofte møder vi fortvivlelse blandt kunder, når de ser deres nye flotte site i deres browser og indser, at det ser anderledes ud, i forhold til det design, de godkendte i starten
Læs mereResource types R 1 1, R 2 2,..., R m CPU cycles, memory space, files, I/O devices Each resource type R i has W i instances.
System Model Resource types R 1 1, R 2 2,..., R m CPU cycles, memory space, files, I/O devices Each resource type R i has W i instances. Each process utilizes a resource as follows: request use e.g., request
Læs mereSkriftlig Eksamen Diskret matematik med anvendelser (DM72)
Skriftlig Eksamen Diskret matematik med anvendelser (DM72) Institut for Matematik & Datalogi Syddansk Universitet, Odense Onsdag den 18. januar 2006 Alle sædvanlige hjælpemidler (lærebøger, notater etc.),
Læs mereKvant Eksamen December 2010 3 timer med hjælpemidler. 1 Hvad er en continuous variable? Giv 2 illustrationer.
Kvant Eksamen December 2010 3 timer med hjælpemidler 1 Hvad er en continuous variable? Giv 2 illustrationer. What is a continuous variable? Give two illustrations. 2 Hvorfor kan man bedre drage konklusioner
Læs mereShooting tethered med Canon EOS-D i Capture One Pro. Shooting tethered i Capture One Pro 6.4 & 7.0 på MAC OS-X 10.7.5 & 10.8
Shooting tethered med Canon EOS-D i Capture One Pro Shooting tethered i Capture One Pro 6.4 & 7.0 på MAC OS-X 10.7.5 & 10.8 For Canon EOS-D ejere der fotograferer Shooting tethered med EOS-Utility eller
Læs mereSOFTWARE PROCESSES. Dorte, Ida, Janne, Nikolaj, Alexander og Erla
SOFTWARE PROCESSES Dorte, Ida, Janne, Nikolaj, Alexander og Erla Hvad er en software proces? Et struktureret sæt af AKTIVITETER, hvis mål er udvikling af software. En software proces model er en abstrakt
Læs mereLESSON NOTES Extensive Reading in Danish for Intermediate Learners #8 How to Interview
LESSON NOTES Extensive Reading in Danish for Intermediate Learners #8 How to Interview CONTENTS 2 Danish 5 English # 8 COPYRIGHT 2019 INNOVATIVE LANGUAGE LEARNING. ALL RIGHTS RESERVED. DANISH 1. SÅDAN
Læs mereAgenda. The need to embrace our complex health care system and learning to do so. Christian von Plessen Contributors to healthcare services in Denmark
Agenda The need to embrace our complex health care system and learning to do so. Christian von Plessen Contributors to healthcare services in Denmark Colitis and Crohn s association Denmark. Charlotte
Læs mereEngelsk. Niveau C. De Merkantile Erhvervsuddannelser September 2005. Casebaseret eksamen. www.jysk.dk og www.jysk.com.
052430_EngelskC 08/09/05 13:29 Side 1 De Merkantile Erhvervsuddannelser September 2005 Side 1 af 4 sider Casebaseret eksamen Engelsk Niveau C www.jysk.dk og www.jysk.com Indhold: Opgave 1 Presentation
Læs mereDepartment of Public Health. Case-control design. Katrine Strandberg-Larsen Department of Public Health, Section of Social Medicine
Department of Public Health Case-control design Katrine Strandberg-Larsen Department of Public Health, Section of Social Medicine Case-control design Brief summary: Comparison of cases vs. controls with
Læs mereParticle-based T-Spline Level Set Evolution for 3D Object Reconstruction with Range and Volume Constraints
Particle-based T-Spline Level Set for 3D Object Reconstruction with Range and Volume Constraints Robert Feichtinger (joint work with Huaiping Yang, Bert Jüttler) Institute of Applied Geometry, JKU Linz
Læs mereHow Long Is an Hour? Family Note HOME LINK 8 2
8 2 How Long Is an Hour? The concept of passing time is difficult for young children. Hours, minutes, and seconds are confusing; children usually do not have a good sense of how long each time interval
Læs mereModule I: Statistical Background on Multi-level Models
Module I: Statistical Background on Multi-level Models Francesca Dominici Michael Griswold The Johns Hopkins University Bloomberg School of Public Health 2005 Hopkins Epi-Biostat Summer Institute 1 Statistical
Læs mereKA 4.2 Kvantitative Forskningsmetoder Forår 2010
KA 4.2 Kvantitative Forskningsmetoder Forår 2010 Besvar alle spørgsmål. Brug ikke mere end én side af tekst på de åbne spørgsmål som er markeret * Answer all questions. Do not write more than one page
Læs mereUser Manual for LTC IGNOU
User Manual for LTC IGNOU 1 LTC (Leave Travel Concession) Navigation: Portal Launch HCM Application Self Service LTC Self Service 1. LTC Advance/Intimation Navigation: Launch HCM Application Self Service
Læs mereForslag til implementering af ResearcherID og ORCID på SCIENCE
SCIENCE Forskningsdokumentation Forslag til implementering af ResearcherID og ORCID på SCIENCE SFU 12.03.14 Forslag til implementering af ResearcherID og ORCID på SCIENCE Hvad er WoS s ResearcherID? Hvad
Læs mereMolio specifications, development and challenges. ICIS DA 2019 Portland, Kim Streuli, Molio,
Molio specifications, development and challenges ICIS DA 2019 Portland, Kim Streuli, Molio, 2019-06-04 Introduction The current structure is challenged by different factors. These are for example : Complex
Læs mereBrug sømbrættet til at lave sjove figurer. Lav fx: Få de andre til at gætte, hvad du har lavet. Use the nail board to make funny shapes.
Brug sømbrættet til at lave sjove figurer. Lav f: Et dannebrogsflag Et hus med tag, vinduer og dør En fugl En bil En blomst Få de andre til at gætte, hvad du har lavet. Use the nail board to make funn
Læs mereDK - Quick Text Translation. HEYYER Net Promoter System Magento extension
DK - Quick Text Translation HEYYER Net Promoter System Magento extension Version 1.0 15-11-2013 HEYYER / Email Templates Invitation Email Template Invitation Email English Dansk Title Invitation Email
Læs mereIntroduction Ronny Bismark
Introduction 1 Outline Motivation / Problem Statement Tool holder Sensor calibration Motion primitive Concatenation of clouds Segmentation Next possible pose Problems and Challenges Future Work 2 Motivation
Læs mereTrolling Master Bornholm 2013
Trolling Master Bornholm 2013 (English version further down) Tilmeldingen åbner om to uger Mandag den 3. december kl. 8.00 åbner tilmeldingen til Trolling Master Bornholm 2013. Vi har flere tilmeldinger
Læs mereOn the complexity of drawing trees nicely: corrigendum
Acta Informatica 40, 603 607 (2004) Digital Object Identifier (DOI) 10.1007/s00236-004-0138-y On the complexity of drawing trees nicely: corrigendum Thorsten Akkerman, Christoph Buchheim, Michael Jünger,
Læs merewhat is this all about? Introduction three-phase diode bridge rectifier input voltages input voltages, waveforms normalization of voltages voltages?
what is this all about? v A Introduction three-phase diode bridge rectifier D1 D D D4 D5 D6 i OUT + v OUT v B i 1 i i + + + v 1 v v input voltages input voltages, waveforms v 1 = V m cos ω 0 t v = V m
Læs mereUserguide. NN Markedsdata. for. Microsoft Dynamics CRM 2011. v. 1.0
Userguide NN Markedsdata for Microsoft Dynamics CRM 2011 v. 1.0 NN Markedsdata www. Introduction Navne & Numre Web Services for Microsoft Dynamics CRM hereafter termed NN-DynCRM enable integration to Microsoft
Læs mereBesvarelser til Lineær Algebra Reeksamen Februar 2017
Besvarelser til Lineær Algebra Reeksamen - 7. Februar 207 Mikkel Findinge Bemærk, at der kan være sneget sig fejl ind. Kontakt mig endelig, hvis du skulle falde over en sådan. Dette dokument har udelukkende
Læs mereOverlevelse efter AMI. Hvilken betydning har følgende faktorer for risikoen for ikke at overleve: Køn og alder betragtes som confoundere.
Overlevelse efter AMI Hvilken betydning har følgende faktorer for risikoen for ikke at overleve: Diabetes VF (Venticular fibrillation) WMI (Wall motion index) CHF (Cardiac Heart Failure) Køn og alder betragtes
Læs mereRemember the Ship, Additional Work
51 (104) Remember the Ship, Additional Work Remember the Ship Crosswords Across 3 A prejudiced person who is intolerant of any opinions differing from his own (5) 4 Another word for language (6) 6 The
Læs mereBilag. Resume. Side 1 af 12
Bilag Resume I denne opgave, lægges der fokus på unge og ensomhed gennem sociale medier. Vi har i denne opgave valgt at benytte Facebook som det sociale medie vi ligger fokus på, da det er det største
Læs mereECE 551: Digital System * Design & Synthesis Lecture Set 5
ECE 551: Digital System * Design & Synthesis Lecture Set 5 5.1: Verilog Behavioral Model for Finite State Machines (FSMs) 5.2: Verilog Simulation I/O and 2001 Standard (In Separate File) 3/4/2003 1 ECE
Læs mereFinancial Literacy among 5-7 years old children
Financial Literacy among 5-7 years old children -based on a market research survey among the parents in Denmark, Sweden, Norway, Finland, Northern Ireland and Republic of Ireland Page 1 Purpose of the
Læs mereGUIDE TIL BREVSKRIVNING
GUIDE TIL BREVSKRIVNING APPELBREVE Formålet med at skrive et appelbrev er at få modtageren til at overholde menneskerettighederne. Det er en god idé at lægge vægt på modtagerens forpligtelser over for
Læs mereBusiness Opening. Very formal, recipient has a special title that must be used in place of their name
- Opening English Danish Dear Mr. President, Kære Hr. Direktør, Very formal, recipient has a special title that must be used in place of their name Dear Sir, Formal, male recipient, name unknown Dear Madam,
Læs mereBusiness Opening. Very formal, recipient has a special title that must be used in place of their name
- Opening Danish English Kære Hr. Direktør, Dear Mr. President, Very formal, recipient has a special title that must be used in place of their name Kære Hr., Formal, male recipient, name unknown Kære Fru.,
Læs mereRoE timestamp and presentation time in past
RoE timestamp and presentation time in past Jouni Korhonen Broadcom Ltd. 5/26/2016 9 June 2016 IEEE 1904 Access Networks Working Group, Hørsholm, Denmark 1 Background RoE 2:24:6 timestamp was recently
Læs mereIkke-parametriske tests
Ikke-parametriske tests 2 Dagens menu t testen Hvordan var det nu lige det var? Wilcoxson Mann Whitney U Kruskall Wallis Friedman Kendalls og Spearmans correlation 3 t-testen Patient Drug Placebo difference
Læs mereE-PAD Bluetooth hængelås E-PAD Bluetooth padlock E-PAD Bluetooth Vorhängeschloss
E-PAD Bluetooth hængelås E-PAD Bluetooth padlock E-PAD Bluetooth Vorhängeschloss Brugervejledning (side 2-6) Userguide (page 7-11) Bedienungsanleitung 1 - Hvordan forbinder du din E-PAD hængelås med din
Læs mereUnitel EDI MT940 June 2010. Based on: SWIFT Standards - Category 9 MT940 Customer Statement Message (January 2004)
Unitel EDI MT940 June 2010 Based on: SWIFT Standards - Category 9 MT940 Customer Statement Message (January 2004) Contents 1. Introduction...3 2. General...3 3. Description of the MT940 message...3 3.1.
Læs mereTrolling Master Bornholm 2016 Nyhedsbrev nr. 6
Trolling Master Bornholm 2016 Nyhedsbrev nr. 6 English version further down Johnny Nielsen med 8,6 kg laks Laksen blev fanget seks sømil ud for Tejn. Det var faktisk dobbelthug, så et kig ned i køletasken
Læs mereCHAPTER 8: USING OBJECTS
Ruby: Philosophy & Implementation CHAPTER 8: USING OBJECTS Introduction to Computer Science Using Ruby Ruby is the latest in the family of Object Oriented Programming Languages As such, its designer studied
Læs mereVores mange brugere på musskema.dk er rigtig gode til at komme med kvalificerede ønsker og behov.
På dansk/in Danish: Aarhus d. 10. januar 2013/ the 10 th of January 2013 Kære alle Chefer i MUS-regi! Vores mange brugere på musskema.dk er rigtig gode til at komme med kvalificerede ønsker og behov. Og
Læs mereMust I be a registered company in Denmark? That is not required. Both Danish and foreign companies can trade at Gaspoint Nordic.
General Questions What kind of information do you need before I can start trading? Please visit our webpage www.gaspointnordic.com under the heading How to become a participant Is it possible to trade
Læs mereTrolling Master Bornholm 2016 Nyhedsbrev nr. 5
Trolling Master Bornholm 2016 Nyhedsbrev nr. 5 English version further down Kim Finne med 11 kg laks Laksen blev fanget i denne uge øst for Bornholm ud for Nexø. Et andet eksempel er her to laks taget
Læs mereAktivering af Survey funktionalitet
Surveys i REDCap REDCap gør det muligt at eksponere ét eller flere instrumenter som et survey (spørgeskema) som derefter kan udfyldes direkte af patienten eller forsøgspersonen over internettet. Dette
Læs mereMandara. PebbleCreek. Tradition Series. 1,884 sq. ft robson.com. Exterior Design A. Exterior Design B.
Mandara 1,884 sq. ft. Tradition Series Exterior Design A Exterior Design B Exterior Design C Exterior Design D 623.935.6700 robson.com Tradition OPTIONS Series Exterior Design A w/opt. Golf Cart Garage
Læs mereIPv6 Application Trial Services. 2003/08/07 Tomohide Nagashima Japan Telecom Co., Ltd.
IPv6 Application Trial Services 2003/08/07 Tomohide Nagashima Japan Telecom Co., Ltd. Outline Our Trial Service & Technology Details Activity & Future Plan 2 Outline Our Trial Service & Technology Details
Læs mereObservation Processes:
Observation Processes: Preparing for lesson observations, Observing lessons Providing formative feedback Gerry Davies Faculty of Education Preparing for Observation: Task 1 How can we help student-teachers
Læs mereNyhedsmail, december 2013 (scroll down for English version)
Nyhedsmail, december 2013 (scroll down for English version) Kære Omdeler Julen venter rundt om hjørnet. Og netop julen er årsagen til, at NORDJYSKE Distributions mange omdelere har ekstra travlt med at
Læs mereManaging stakeholders on major projects. - Learnings from Odense Letbane. Benthe Vestergård Communication director Odense Letbane P/S
Managing stakeholders on major projects - Learnings from Odense Letbane Benthe Vestergård Communication director Odense Letbane P/S Light Rail Day, Bergen 15 November 2016 Slide om Odense Nedenstående
Læs mereATEX direktivet. Vedligeholdelse af ATEX certifikater mv. Steen Christensen stec@teknologisk.dk www.atexdirektivet.
ATEX direktivet Vedligeholdelse af ATEX certifikater mv. Steen Christensen stec@teknologisk.dk www.atexdirektivet.dk tlf: 7220 2693 Vedligeholdelse af Certifikater / tekniske dossier / overensstemmelseserklæringen.
Læs mereErik Parner Sektion for Biostatistik. Biostatistisk metode et par eksempler
Erik Parner Sektion for Biostatistik Biostatistisk metode et par eksempler 1 Percent Kvantitativ data 60 50 40 30 20 10 1960 1970 1980 1990 2000 Year Er der en trend i proportionerne? 2 Mere informations
Læs mereReexam questions in Statistics and Evidence-based medicine, august sem. Medis/Medicin, Modul 2.4.
Reexam questions in Statistics and Evidence-based medicine, august 2013 2. sem. Medis/Medicin, Modul 2.4. Statistics : ESSAY-TYPE QUESTION 1. Intelligence tests are constructed such that the average score
Læs mereDENCON ARBEJDSBORDE DENCON DESKS
DENCON ARBEJDSBORDE Mennesket i centrum betyder, at vi tager hensyn til kroppen og kroppens funktioner. Fordi vi ved, at det er vigtigt og sundt jævnligt at skifte stilling, når man arbejder. Bevægelse
Læs mereMandara. PebbleCreek. Tradition Series. 1,884 sq. ft robson.com. Exterior Design A. Exterior Design B.
Mandara 1,884 sq. ft. Tradition Series Exterior Design A Exterior Design B Exterior Design C Exterior Design D 623.935.6700 robson.com Tradition Series Exterior Design A w/opt. Golf Cart Garage Exterior
Læs mereDANSK INSTALLATIONSVEJLEDNING VLMT500 ADVARSEL!
DANSK INSTALLATIONSVEJLEDNING VLMT500 Udpakningsinstruktioner Åben indpakningen forsigtigt og læg indholdet på et stykke pap eller en anden beskyttende overflade for at undgå beskadigelse. Kontroller at
Læs mereVelkommen til IFF QA erfa møde d. 15. marts Erfaringer med miljømonitorering og tolkning af nyt anneks 1.
Velkommen til IFF QA erfa møde d. 15. marts 2018 Erfaringer med miljømonitorering og tolkning af nyt anneks 1. 1 Fast agenda kl.16.30-18.00 1. Nyt fra kurser, seminarer, myndighedsinspektioner, audit som
Læs mereThe GAssist Pittsburgh Learning Classifier System. Dr. J. Bacardit, N. Krasnogor G53BIO - Bioinformatics
The GAssist Pittsburgh Learning Classifier System Dr. J. Bacardit, N. Krasnogor G53BIO - Outline bioinformatics Summary and future directions Objectives of GAssist GAssist [Bacardit, 04] is a Pittsburgh
Læs mereUnited Nations Secretariat Procurement Division
United Nations Secretariat Procurement Division Vendor Registration Overview Higher Standards, Better Solutions The United Nations Global Marketplace (UNGM) Why Register? On-line registration Free of charge
Læs mereCross-Sectorial Collaboration between the Primary Sector, the Secondary Sector and the Research Communities
Cross-Sectorial Collaboration between the Primary Sector, the Secondary Sector and the Research Communities B I R G I T T E M A D S E N, P S Y C H O L O G I S T Agenda Early Discovery How? Skills, framework,
Læs mereImplementing SNOMED CT in a Danish region. Making sharable and comparable nursing documentation
Implementing SNOMED CT in a Danish region Making sharable and comparable nursing documentation INTRODUCTION Co-operation pilot project between: The Region of Zealand Their EHR vendor - CSC Scandihealth
Læs mereDifferential Evolution (DE) "Biologically-inspired computing", T. Krink, EVALife Group, Univ. of Aarhus, Denmark
Differential Evolution (DE) Differential Evolution (DE) (Storn and Price, 199) Step 1 - Initialize and evaluate Generate a random start population and evaluate the individuals x 2 search space x 1 Differential
Læs mereAdvanced Statistical Computing Week 5: EM Algorithm
Advanced Statistical Computing Week 5: EM Algorithm Aad van der Vaart Fall 2012 Contents EM Algorithm Mixtures Hidden Markov models 2 EM Algorithm EM-algorithm SETTING: Observation X, likelihood θ p θ
Læs mereDen uddannede har viden om: Den uddannede kan:
Den uddannede har viden om: Den uddannede kan: Den uddannede kan: Den studerende har udviklingsbaseret viden om og forståelse for Den studerende kan Den studerende kan Den studerende har udviklingsbaseret
Læs mereEngineering of Chemical Register Machines
Prague International Workshop on Membrane Computing 2008 R. Fassler, T. Hinze, T. Lenser and P. Dittrich {raf,hinze,thlenser,dittrich}@minet.uni-jena.de 2. June 2008 Outline 1 Motivation Goal Realization
Læs mereSikkerhedsvejledning
11-01-2018 2 Sikkerhedsvejledning VIGTIGT! Venligst læs disse instruktioner inden sengen samles og tages i brug Tjek at alle dele og komponenter er til stede som angivet i vejledningen Fjern alle beslagsdele
Læs mereCS 4390/5387 SOFTWARE V&V LECTURE 5 BLACK-BOX TESTING - 2
1 CS 4390/5387 SOFTWARE V&V LECTURE 5 BLACK-BOX TESTING - 2 Outline 2 HW Solution Exercise (Equivalence Class Testing) Exercise (Decision Table Testing) Pairwise Testing Exercise (Pairwise Testing) 1 Homework
Læs mereSom mentalt og moralsk problem
Rasmus Vincentz 'Klimaproblemerne - hvad rager det mig?' Rasmus Vincentz - November 2010 - Som mentalt og moralsk problem Som problem for vores videnskablige verdensbillede Som problem med økonomisk system
Læs mereLED STAR PIN G4 BASIC INFORMATION: Series circuit. Parallel circuit. www.osram.com 1. HOW CAN I UNDERSTAND THE FOLLOWING SHEETS?
BASIC INFORMATION: 1. HOW CAN I UNDERSTAND THE FOLLOWING SHES? Compatibility to OSRAM s: -Series Circuit... Page 2 -Parallel Circuit... Page 3 Compatibility to OTHER s : -Series Circuit... Page 4 -Parallel
Læs mereSign variation, the Grassmannian, and total positivity
Sign variation, the Grassmannian, and total positivity arxiv:1503.05622 Slides available at math.berkeley.edu/~skarp Steven N. Karp, UC Berkeley FPSAC 2015 KAIST, Daejeon Steven N. Karp (UC Berkeley) Sign
Læs mereTitel: Barry s Bespoke Bakery
Titel: Tema: Kærlighed, kager, relationer Fag: Engelsk Målgruppe: 8.-10.kl. Data om læremidlet: Tv-udsendelse: SVT2, 03-08-2014, 10 min. Denne pædagogiske vejledning indeholder ideer til arbejdet med tema
Læs mereOur activities. Dry sales market. The assortment
First we like to start to introduce our activities. Kébol B.V., based in the heart of the bulb district since 1989, specialises in importing and exporting bulbs world-wide. Bulbs suitable for dry sale,
Læs mereApplications. Computational Linguistics: Jordan Boyd-Graber University of Maryland RL FOR MACHINE TRANSLATION. Slides adapted from Phillip Koehn
Applications Slides adapted from Phillip Koehn Computational Linguistics: Jordan Boyd-Graber University of Maryland RL FOR MACHINE TRANSLATION Computational Linguistics: Jordan Boyd-Graber UMD Applications
Læs mereGenerelle lineære modeller
Generelle lineære modeller Regressionsmodeller med én uafhængig intervalskala variabel: Y en eller flere uafhængige variable: X 1,..,X k Den betingede fordeling af Y givet X 1,..,X k antages at være normal
Læs mereStatus på det trådløse netværk
Status på det trådløse netværk Der er stadig problemer med det trådløse netværk, se status her: http://driftstatus.sdu.dk/?f=&antal=200&driftid=1671#1671 IT-service arbejder stadig med at løse problemerne
Læs mereForventer du at afslutte uddannelsen/har du afsluttet/ denne sommer?
Kandidatuddannelsen i Informationsarkitektur - Aalborg 3 respondenter 10 spørgeskemamodtagere Svarprocent: 30% Forventer du at afslutte uddannelsen/har du afsluttet/ denne sommer? I hvilken grad har uddannelsen
Læs mereThe River Underground, Additional Work
39 (104) The River Underground, Additional Work The River Underground Crosswords Across 1 Another word for "hard to cope with", "unendurable", "insufferable" (10) 5 Another word for "think", "believe",
Læs mereHow consumers attributions of firm motives for engaging in CSR affects their willingness to pay
Bachelor thesis Institute for management Author: Jesper Andersen Drescher Bscb(sustainability) Student ID: 300545 Supervisor: Mai Skjøtt Linneberg Appendix for: How consumers attributions of firm motives
Læs mereBarnets navn: Børnehave: Kommune: Barnets modersmål (kan være mere end et)
Forældreskema Barnets navn: Børnehave: Kommune: Barnets modersmål (kan være mere end et) Barnets alder: år og måneder Barnet begyndte at lære dansk da det var år Søg at besvare disse spørgsmål så godt
Læs mereThe effects of occupant behaviour on energy consumption in buildings
The effects of occupant behaviour on energy consumption in buildings Rune Vinther Andersen, Ph.D. International Centre for Indoor Environment and Energy Baggrund 40 % af USA's samlede energiforbrug sker
Læs mereLogistisk Regression - fortsat
Logistisk Regression - fortsat Likelihood Ratio test Generel hypotese test Modelanalyse Indtil nu har vi set på to slags modeller: 1) Generelle Lineære Modeller Kvantitav afhængig variabel. Kvantitative
Læs mereRichter 2013 Presentation Mentor: Professor Evans Philosophy Department Taylor Henderson May 31, 2013
Richter 2013 Presentation Mentor: Professor Evans Philosophy Department Taylor Henderson May 31, 2013 OVERVIEW I m working with Professor Evans in the Philosophy Department on his own edition of W.E.B.
Læs mere