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SEM in AMOS when you have incomplete data (new, 2018)
 
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This video provides an overview of SEM (using path analysis) in AMOS when you have missing data. It demonstrates how to estimate the basic model using FIML estimation (enacted by clicking on Estimate Means and Intercepts under Analysis Properties). It also demonstrates the use of the Regression Imputation approach to generate a complete dataset (which will allow you to use other options in AMOS such as Modification indices and Bootstrapping). The video does not cover the theory behind missing data mechanisms or the approaches recommended with particular patterns of missingness. The viewer is encouraged to read more on those topics. A copy of the SPSS data file used in the video can be downloaded here: https://drive.google.com/open?id=18azDsnEoPGBozyF4dMBrw3dh4QKcS4R1 A copy of the .amw file containing the path model generated for the video can be downloaded here: https://drive.google.com/open?id=10ryZkcsNacq_60BWaao7f-ftqkOtqYad A pdf copy of the page referenced in the video on interpreting fit statistics can be obtained here: https://drive.google.com/open?id=14zM-5fZUpN2drO3ZwTfm18ET2ybZBVOF You can also access another video on dealing with missing data by going here: https://youtube.com/watch?v=N-v_PFI98MI&feature=youtu.be For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below: Introductory statistics: https://sites.google.com/view/statisticsfortherealworldagent/home Multivariate statistics: https://sites.google.com/view/statistics-for-the-real-world/home
Views: 2690 Mike Crowson
How to Use SPSS-Replacing Missing Data Using Multiple Imputation (Regression Method)
 
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Technique for replacing missing data using the regression method. Appropriate for data that may be missing randomly or non-randomly. Also appropriate for data that will be used in inferential analysis. Determining randomness of missing data can be confirmed with Little's MCAR Test (http://youtu.be/6ybgVTabJ6s). Resources: FAQ- http://sites.stat.psu.edu/~jls/mifaq.html Schafer, Joseph L. "Multiple imputation: a primer." Statistical methods in medical research 8.1 (1999): 3-15. Sterne, Jonathan AC, et al. "Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls." BMJ: British Medical Journal 338 (2009). McKnight, Patrick E., Katherine M. McKnight, and Aurelio Jose Figueredo. Missing data: A gentle introduction. Guilford Press, 2007. Haukoos, Jason S., and Craig D. Newgard. "Advanced statistics: missing data in clinical research—part 1: an introduction and conceptual framework." Academic Emergency Medicine 14.7 (2007): 662-668. Newgard, Craig D., and Jason S. Haukoos. "Advanced statistics: missing data in clinical research—part 2: multiple imputation." Academic Emergency Medicine 14.7 (2007): 669-678.
Overview of Missing Data Causes _ Treatments in Clinical Trials
 
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Presented by Andrew Grannell Senior Statistician at Statistical Solutions. Here are the links for the reports mentioned in the webinar: 1) "Note for Guidance on Statistical Principles for Clinical Trials" - ICH, 1998 http://www.emea.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/WC500002928.pdf 2) "Guidance on Important Considerations for when Participation of Human subjects in Research is Discontinued" - OHRP, 2008 http://www.primr.org/uploadedFiles/PRIMR_Site_Home/Public_Policy/Recently_Files_Comments/Draft_Guidance_on_Discontinued_Participation.pdf 3) "The Prevention and Treatment of Missing Data in Clinical Trials" - NRC, 2010 http://www.nap.edu/catalog.php?record_id=12955 4) "Guidance for Sponsors, Clinical Investigators and IRB's; Data Retention when Subjects withdraw from FDA-Regulated Clinical Trials - FDA, 2008 http://www.fda.gov/downloads/RegulatoryInformation/Guidances/ucm126489.pdf 5) "Guideline on Missing Data in Confirmatory Clinical Trials" - EMA, 2010 http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2010/09/WC500096793.pdf
Views: 663 StatSolSoftware
Missing Data Analysis : Multiple Imputation in R
 
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Paper: Advanced Data Analysis Module: Missing Data Analysis : Multiple Imputation in R Content Writer: Souvik Bandyopadhyay
Views: 21938 Vidya-mitra
The RevMan Caculator: Calculating missing standard deviations
 
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Entering Data with the RevMan Calculator: Calculating missing standard deviations. Part 3 in a video series produced by Cochrane UK.
Views: 3316 Cochrane UK
Little's Missing Completely at Random (MCAR) Test - SPSS
 
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Learn how to perform and interpret Little's MCAR test in SPSS. Little's test tests the hypothesis that one's data are missing completely at random, which is an assumption that must be satisfied prior to replacing missing values with various imputation techniques. Missing value analysis
Views: 103730 how2stats
StatQuest: Maximum Likelihood, clearly explained!!!
 
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If you hang out around statisticians long enough, sooner or later someone is going to mumble "maximum likelihood" and everyone will knowingly nod. After this video, so can you! Also, some viewers asked for a worked out example that includes the math. Here it is! (you may need to click on the "Show More" button below to see the link) https://youtu.be/cDlNsHUBmw4 For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 995474 David Langer
Quartile - Decile - Percentile
 
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partitional values... quartile, decile and percentile... follow me on instagram : Yasser.98555159292
Views: 277596 Yasser Khan
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 389161 APMonitor.com
Missing value analysis in SPSS - part 1
 
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This video demonstrates missing value analysis in SPSS
Views: 59722 Murtaza Haider
R Stats: Data Prep and Imputation of Missing Values
 
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This video demonstrates how to prepare data for use with the Naive Bayes classifier and its cross-validation. It focuses primarily on the selection of suitable variables from a large data set and imputation of missing values. The video also explains the use of Spearman rank correlation for ordinal variables, where the traditional Pearson correlation is not applicable. The lesson is quite informal and avoids more complex statistical concepts. The data for this lesson can be obtained from the UCI Machine Learning Repository: * https://archive.ics.uci.edu/ml/datasets/wiki4he The R source code for this video can be found (some small discrepancies are possible): * http://visanalytics.org/youtube-rsrc/r-stats/Demo-B3-Imputing-Missing-Values.r Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.
Views: 17876 ironfrown
Statistical Rethinking - Lecture 20
 
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Lecture 20 - Measurement error, missing data imputation, false-positive science - Statistical Rethinking: A Bayesian Course with R Examples
Views: 2292 Richard McElreath
Forecasting - Simple moving average - Example 1
 
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In this video, you will learn how to find out the 3 month and 4 monthly moving average for demand forecasting.
Views: 208603 maxus knowledge
Robust or Clustered Errors and Post-Regression Statistics - R for Economists Moderate 2
 
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This series of videos will serve as an introduction to the R statistics language, targeted at economists. In this video we cover what to do once you've already run your regression! We pull out the predicted values and residuals with predict() and residuals(), we use Breusch-Pagan (bptest()) to check for heteroskedasticity, we calculate heteroskedasticity- or cluster-robust standard errors with coeftest() in the sandwich package, and we perform F-tests of regression coefficients with linearHypothesis() in the AER package. These commands and tests work with all kinds of regression commands, not just OLS (lm()). The code and script for this video can be found at https://www.dropbox.com/s/py6v5rnshyxsbfc/Moderate%202%20Robust%20Errors%20and%20Post%20Regression%20Tests.R?dl=1 Download the code from all my R videos at once at http://nickchk.com/R%20for%20Economists%20Code.zip You can find links to every video in the series here: http://nickchk.com/videos.html#rstats There are videos on: [BASIC] Getting Started, Getting Help, Objectives and Variables, Vectors and Matrices, Data Frames, Packages, Summary Statistics (of One and Two Variables), Plots and Graphs, and Linear Regression (OLS), [MODERATE] Regression Formulas, Robust or Clustered Standard Errors and Post-Regression Stats, Regression Plots, Instrumental Variables (IV Regression), Time Series, ARIMA and ARMA, Probit and Logit, Tobit and Heckman, Panel Data, and Missing Data, and [ADVANCED] Simulations, The Tidyverse, Reshape and Join/Merge, dplyr (Introduction, Piping, and Grouping), ggplot (Introduction, Geometries, Overlaid and Grouped Plots, and Titles and Labels), and vtable
Data cleaning in SPSS
 
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How to find and correct obvious errors using the software SPSS. More information is available on: http://science-network.tv/clean-data-file/
Views: 72314 Science Network TV
Replacing Missing Values in SPSS with the Series Mean
 
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This video demonstrates how to replace missing values with the series mean in SPSS. Recoding missing values using the “Recode into Same Variables” function is reviewed.
Views: 56901 Dr. Todd Grande
Vincent D  Warmerdam - The Duct Tape of Heroes  Bayesian statistics
 
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PyData London 2016 In this talk I will give many examples of when Bayes rule will help you in your day to day work. I'll quickly show many examples of bayesian statistical thinking in action; the pleasure of inference, probabilistic graphs, model selection, feature generation, even operations research! I'll finish with a dataset from Heroes of the Storm and I'll show why Bayesian models can outperform randomforests. My talk is made up of the following examples; -basic disease example: what is the value of adding an extra test to a patient -give an example of an inference task that is very hard to do properly without bayesian thinking -creating simple probibalistic models with pandas and showing how they are robust against missing data -demo the daft, corner and pomegrenate library -show how you can use bayes rule to pick models -demo a bayesian probablistic approach to finding overpowered characters in the Heroes of the Storm video game. Slides available here: http://koaning.io/theme/notebooks/bayes.pdf​​
Views: 2458 PyData
[#1]Assignment Problem|Hungarian Method[Solved Problem using Simple Algorithm] in OR: kauserwise
 
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NOTE: After row and column scanning, If you stuck with more than one zero in the matrix, please do the row scanning and column scanning (REPEATEDLY) as much as possible to cover that zeros with lines, based on algorithm If you still find some zeros without covered by lines, then we need to go for [DIAGONAL selection RULE ]for that I have uploaded a separate video to understand that method easily., please watch this link [ [#2]Assignment Problem||Hungarian Method[DIAGONAL RULE] When we Find More than one Zero ] https://youtu.be/-0DEQmp7B9o Here is the video about assignment problem - Hungarian method on Operations research, In this video we discussed what is assignment problem and how to solve using Hungarian method with step by step procedure of algorithm, hope this will help you to get the subject knowledge at the end. Thanks and All the best. To watch more tutorials pls visit: www.youtube.com/c/kauserwise * Financial Accounts * Corporate accounts * Cost and Management accounts * Operations Research * Statistics ▓▓▓▓░░░░───CONTRIBUTION ───░░░▓▓▓▓ If you like this video and wish to support this kauserwise channel, please contribute via, * Paytm a/c : 6383617203 * Western Union / MoneyGram [ Name: Kauser, Country: India & Email: [email protected] ] [Every contribution is helpful] Thanks & All the Best!!! ───────────────────────────
Views: 1502213 Kauser Wise
Expected Value and Variance of Discrete Random Variables
 
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An introduction to the concept of the expected value of a discrete random variable. I also look at the variance of a discrete random variable. The formulas are introduced, explained, and an example is worked through.
Views: 406104 jbstatistics
Post-hoc power analysis in SmartPLS and AMOS
 
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In this video I explain and demonstrate how to do a post-hoc power analysis in SmartPLS and AMOS. I now have an article published that cites this video. Paul Benjamin Lowry and James Gaskin (2014). "Partial least squares (PLS)structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it," IEEE Transactions on Professional Communication (57:2), pp. 123-146. http://www.kolobkreations.com/PLSIEEETPC2014.pdf
Views: 7243 James Gaskin
How to Clean SPSS Data
 
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This video will teach you valuable skills to prepare your data for analysis in SPSS by describing the process of running frequencies, replacing missing data, and recoding items for reverse coding.
Views: 124186 CPG Orlando
Multiple Linear Regression using Excel Data Analysis Toolpak
 
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LearnAnalytics demonstrates use of Multiple Linear Regression on Excel 2010. (Data Analysis Toolpak). Data set referenced in video can be downloaded at www.learnanalytics.in/blog/wp-content/uploads/2014/02/car_sales.xlsx
Views: 65214 Learn Analytics
Compare Two Excel Lists to Spot the Differences
 
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Over time you will collect many lists of Excel data. It can be a challenge to compare the contents of one list with the contents in another list. For example, to find out which customers do not exist in another list. In this lesson I demonstrate three techniques that you can use to compare Customer lists: 1) The =MATCH() Function 2) The VLookup() Function 3) Pivot Tables I invite you to visit my website - www.thecompanyrocks.com - to view all of my video lessons.
Views: 656448 Danny Rocks
Introductory - Advanced factor analysis and SEM - Mplus Short Courses, Topic 1.
 
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Topic 1. Introductory - advanced factor analysis and structural equation modeling with continuous outcomes. Recorded presentation at Johns Hopkins University, August 20, 2009. Link to handouts associated with this segment: http://www.statmodel.com/download/Topic%201.pdf NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.
Views: 2788 Mplus
Gradient Boost Part 1: Regression Main Ideas
 
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Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it's not that complicated! This video is the first part in a series that walks through it one step at a time. This video focuses on the main ideas behind using Gradient Boost to predict a continuous value, like someone's weight. We call this, "using Gradient Boost for Regression". In the next video, we'll work through the math to prove that Gradient Boost for Regression really is this simple. In part 3, we'll walk though how Gradient Boost classifies samples into two different categories, and in part 4, we'll go through the math again, this time focusing on classification. This StatQuest assumes that you already understand.... Decision Trees: https://youtu.be/7VeUPuFGJHk AdaBoost: https://youtu.be/LsK-xG1cLYA ...and the tradeoff between Bias and Variance that plagues Machine Learning: https://youtu.be/EuBBz3bI-aA For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ This StatQuest is based on the following sources: A 1999 manuscript by Jerome Friedman that introduced Stochastic Gradient Boost: https://statweb.stanford.edu/~jhf/ftp/stobst.pdf The Wikipedia article on Gradient Boosting: https://en.wikipedia.org/wiki/Gradient_boosting The scikit-learn implementation of Gradient Boosting: https://scikit-learn.org/stable/modules/ensemble.html#gradient-boosting If you'd like to support StatQuest, please consider a cool StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer
Less than more than ogive for cumulative frequency distribution ll CBSE class 10 maths statistics
 
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Less than more than ogive for cumulative frequency distribution ll CBSE class 10 maths statistics Statistics | Cumulative Frequency Distribution | Less Than Type Ogive | Grade 10 Class 10 Maths Statistics | Cumulative Frequency Distribution | More Than Type Ogive | Grade X Class 10 Maths Ogive or Cumulative Frequency Curve How To Draw An Ogive How to draw Cumulative From graph, find Median from it Oswaal CBSE Sample Question Papers Class 10 Mathematics http://amzn.to/2gxJkSN Oswaal CBSE Sample Question Papers Class 10 Science http://amzn.to/2gSMPQX Oswaal CBSE Sample Question Papers Class 10 English Communicative http://amzn.to/2l02NNn Oswaal CBSE Sample Question Paper for Class 10 English Communicative, Hindi B, Science, Social Science and Maths (Set A 10SP) http://amzn.to/2xSw2b9 Shiv Das CBSE Past Years Board Papers Pack of 4 for Class 10 Maths Science Social Science English Communicative (2018 Board Exam Edition) http://amzn.to/2yv2akC Super 20 Mathematics Sample Papers Class 10th CBSE 2017-18 http://amzn.to/2ytokn9 Super 20 Science Sample Papers Class 10th CBSE 2017-18 http://amzn.to/2x99les Parker Frontier Matte Black CT Fountain Pen http://amzn.to/2f6ZvG0 Parker Ambient Laque Black GT Ball Pen http://amzn.to/2wbPqyW Paperkraft Premium Notebook and Pen Gift Set http://amzn.to/2wboMGu Parker Galaxy Gold Trim Ball Pen http://amzn.to/2y19HUk Lenovo Yoga Tab 3 8 Tablet (8 inch, 16GB, Wi-Fi + 4G LTE + Voice Calling), Slate Black http://amzn.to/2f8KWSu buy Redmi 4 (Gold, 64GB) http://amzn.to/2eAriya buy Cables Kart Omnidirectional 3.5mm Microphone with Stand for Laptop, PC - (Black) http://amzn.to/2wy7ekx buy Blue Microphones Snowball iCE Condenser Microphone (White) http://amzn.to/2wvD1Eu buy Photron Tripod Stedy 450 with 4.5 Feet Pan Head http://amzn.to/2gDHyiF buy Canon EOS 1200D 18MP Digital SLR Camera (Black) http://amzn.to/2eA0gY6 buy Sony Cybershot DSC-WX350/B 18.2MP Digital Camera (Black) http://amzn.to/2gveJRU If you like our videos, subscribe to our channel https://www.youtube.com/channel/UCEVG-1G2sP_CCvRUp3i_fyg Please Like Our Facebook Page. https://www.facebook.com/galaxycoachingclasses/ Or At https://www.facebook.com/galaxymathstricks/ https://www.facebook.com/cbsemaths.8.9.10/ Please Follow Me On Instagram https://www.instagram.com/chetanptl12/ Please Follow me on Twitter. https://twitter.com/chetan21385 Have fun, while you learn. Thanks for watching
Views: 442463 galaxy coaching classes
RClimTool (Tutorial Video) In English
 
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RClimtool has been designed with the objective to facilitate the performance of statistical analysis, quality control, filling missing data, homogeneity analysis and calculation of indicators for daily weather series of maximum temperature, minimum temperature and precipitation. User manual available here: http://www.aclimatecolombia.org/download/Investigacion%20Uno/RClimTool_UserManual.pdf Join us: https://groups.google.com/forum/?hl=es#!forum/rclimtool
Univariate Analysis and Bivariate Analysis
 
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Subject: Social Work Education Paper:Research Methods and Statistics Module: Univariate Analysis & Bivariate Analysis Content Writer: Dr. Graciella Tavares
Views: 34809 Vidya-mitra
The FBI crime statistics website shows ZERO murders in Newtown Ct in 2012. Look it up
 
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UPDATE i said & or typed 36 instead of 38 , oops! 146 Connecticut murders happened in 2012 & 110 were assigned to specific local jurisdictions on FBI . gov BUT ZERO in newtown for sandy hook so thats 38 not assigned to a specific city BUTT that could not possibly have been the "missing" 27 Sandy Hook massacre victims & 11 others the agency with primary jurisdiction for a crime at a school & tasked with investigation of the Sandy Hook incident was State Police & NOT the local 50 person Newton police or the FBI. the 26 victims at the school, & Lanza's mother were excluded from Newton local police statistics this number was then lumped into the state police statistics & auto reported by CONNECTICUT to the FBI who auto data listed them under state-gathered statistics & not under city or FBI gathered statistics The FBI web site raw data auto generated pages is a statistical tool not a report most resident trooper towns are not listed on that particular FBI page but they are in in the UCR fbi.gov Table 8 Data Declaration data will not be included in the table, & a discrepancy will be explained in a footnote ONLY if the FBI determines an agency does not comply with national UCR guidelines The FBI collects these data through the Uniform Crime Reporting (UCR) Program. for Offenses Known to Law Enforcement, by State by City, 2012 & This table provides the volume of violent crime as reported by city and town law enforcement agencies that contributed data to the UCR Program & The data used in creating this table were from all city and town law enforcement agencies submitting 12 months of complete offense data for 2012. BUT for example = Arson is not included but i still happens https://archive.fo/7E31N 2012 fbi.gov Table 08 Offenses Known to Law Enforcement listed 00 Murders if sorted by Citys in CONNECTICUT https://archive.fo/PQL31 https://archive.fo/M3I1N 2012 FBI.gov Table 11 Offenses Known to Law Enforcement listed 36 Murders if sorted by State / Tribal / & "Other Agencies" in Connecticut under "State Police Listings' http://archive.fo/13NZa http://archive.fo/eN8VD the State of Connecticut 's 2012 Annual Report of , the Uniform Crime Reporting Program, Department of Emergency Services & Public Protection, Crimes Analysis Unit page 415 & page 11, 12 ,14, 25, "Includes 27 victims of Newtown mass shooting." BUT page 33, & 245 "Does NOT include 27 victims of Newtown mass shooting, see page 415 State Police Misc. https://archive.fo/6Eauc http://web.archive.org/web/*/https://www.dpsdata.ct.gov/dps/ucr/data/2012/Crime%20In%20Connecticut%20COMPLETE%202012.pdf SEO FBI data sheet interpretation as a case against the “official” narrative BUT the FBI report is proof positive Sandy Hook was staged,, by the FBI!? , i was told by conspiracy theorist truthers, never trust or cite data sourced from FED gov & why did the FBI says no one was killed at sandy hook & then also say they were killed in "A Study of Active Shooter Incidents in the United States Between 2000 and 2013" https://www.fbi.gov/file-repository/active-shooter-study-2000-2013-1.pdf http://metabunk.org/debunked-fbi-says-no-one-killed-at-sandy-hook-included-in-ct-state-total.t4570/ http://archive.fo/LuUhG up next 2013/02/03 high rez photo of Sandy Hook School’s 25 member 4th grade choir sang America the Beautiful at Super Bowl XLVII in New Orleans February 3rd, that deniers refuse to use http://www.crisisactorsguild.com/img/choir_circled_001.jpg are the same children were in front of their schoolmates 2012/12/12 & 2012/12/13 for the SHES 4th Grade Winter Concert http://crisisactorsguild.com/2018/08/27/did-any-of-the-children-killed-at-sandy-hook-appear-at-super-bowl-xlvii/ http://www.crisisactorsguild.com/2016/11/01/an-actual-expert-weighs-in-on-the-sandy-hook-super-bowl-choir-conspiracy-theory/ https://imgur.com/a/AcvXR5i why on Earth are you addressing this now that you know damn well "they" have deleted a ton of information that many of us had researched long ago YouTube & other websites have deleted many videos that expose this Did you look at the links I posted on your video from this morning I have been waiting for you to look at the information & try to debunk it Wake up man , You want evidence & you're getting it. Look it up Your moronic idea of reality is memorizing & repeating whatever you are told reality is You think these people are real, but we are the stupid ones, right? The FBI crime statistics website shows ZERO murders in Newtown Ct in 2012. "How is it that all of these "victims" were alive & well & singing at the Super Bowl " Son just murdered, not one tear. https://archive.fo/lCptZ https://archive.fo/FJHLw https://archive.fo/YOk2m https://archive.fo/puksp https://archive.fo/1Wj4r https://archive.fo/UkXVe https://archive.fo/z3qmY https://youtu.be/-0_1RFU51i0 https://youtu.be/xFhfmsoBMcc https://youtu.be/pdGXCJSgIcU https://youtu.be/ynq9dVvbdKw
Views: 61 waptek
How to make pdf of multiple graphs in R studio using for loop
 
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Hello researchers, This video is very useful when you want to make multiple graphs on a single PDF. It looks awesome friends.
Views: 3953 Sarveshwar Inani
JASP/Excel - One-Way Between Subjects ANOVA Example
 
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Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2017 This video covers one-way between subjects ANOVA. Excel: we cover data screening: accuracy, missing, outliers, normality, linearity, homogeneity, homoscedasticity JASP: Levene's test, how to run the ANOVA, how to analyze post hocs, effect size, write ups Excel: Graphs with error bars GPower: sample size estimation for future studies Lecture materials and assignment available at statstools.com. http://statstools.com/learn/advanced-statistics/
Views: 950 Statistics of DOOM
Conducting a Binomial Test in SPSS
 
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This video demonstrates how to conduct a Binomial Test in SPSS. A Binomial Test compares an observed proportion of a dichotomous variable to a specified test proportion.
Views: 17354 Dr. Todd Grande
Discovering Statistics Using IBM SPSS Statistics, 5th Edition
 
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Conquer IBM SPSS Statistics with Andy Field's new 5th edition of his bestseller. Find out more at www.sagepub.co.uk/catistics
Views: 482 SAGE Students
SPSS Tutorial.1
 
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SPSS EPIDEMIOLOGY PREVENTIVE MEDICINE MEDICINE BIO-STATISTICS STATISTICS DATA EPIDEMIOLOGICAL STUDIES
Data Manipulation: Origin: Setting Column Values Part 1
 
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Part1 to part4 of this tutorial go together to show you how to set column values using values from other columns as well as from built-in functions, values contained in specific worksheet cell, or metadata stored in workbook.
Views: 46250 OriginLab Corp.
Preview: Latent class analysis (LCA) in Stata 15
 
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You can now perform latent class analysis (LCA) with Stata's -gsem- command. Discover and understand unobserved groups in your data, such a groups consumers with different buying preferences or groups of healthy and unhealthy individuals. You can estimate the proportion of the population in each group, and and you can evaluate how observed variables differ across groups. This video provides a quick overview of using Stata's -gsem- command to fit latent class models. For more information, see http://www.stata.com/new-in-stata/latent-class-analysis/. Copyright 2017 StataCorp LLC. All rights reserved.
Views: 8566 StataCorp LLC
2013POL242HW4, Conservative Vote Intention (POL242Geneva2013)
 
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2013POL242HW4 *Reliability Analysis using the 2011 CES Data*. *Add weight* WEIGHT by WGTSAMP *Recode indicators of DV (vote intention for Conservative Party of Canada)* missing values CPS11_18 CPS11_23 (996 thru 999) missing values MBS11_D1 (-99) compute RECPS_18 = CPS11_18 / 10 compute RECPS_23 = CPS11_23 / 10 fre var=RECPS_18/. fre var=RECPS_23/. fre var=MBS11_D1/. *Checking that DV indicators go together* reliability /variables=CPS11_18 CPS11_23 MBS11_D1 /scale(all)=all /summary=All. reliability /variables=RECPS_18 RECPS_23 MBS11_D1 /scale(all)=all /summary=All. *Combining DV indicators into an index*. compute VoteIntC=RECPS_18+RECPS_23+MBS11_D1 recode VoteIntC (0 thru 10=1) (10.40 thru 20=2) (20.40 thru 30=3) into CVoteInt value labels CVoteInt 1'low' 2'med' 3'hi' *Checking the distribution of DV index*. fre var=VoteIntC /statistics mean median mode stddev variance skew kurtosis. fre var=CVoteInt /statistics mean median mode stddev variance skew kurtosis. *Correlation Analysis using the 2011 CES Data*. *Add weight* WEIGHT by WGTSAMP *Recode indicators of DV (vote intention for Conservative Party of Canada)* missing values CPS11_18 CPS11_23 (996 thru 999) missing values MBS11_D1 (-99) compute RECPS_18 = CPS11_18 / 10 compute RECPS_23 = CPS11_23 / 10 fre var=RECPS_18/. fre var=RECPS_23/. fre var=MBS11_D1/. *Combining DV indicators into an index*. compute VoteIntC=RECPS_18+RECPS_23+MBS11_D1 recode VoteIntC (0 thru 10=1) (10.40 thru 20=2) (20.40 thru 30=3) into CVoteInt value labels CVoteInt 1'low' 2'med' 3'hi' *Recode Attitudes towards the Gun Registry* missing values PES11_27 (8, 9) recode PES11_27 (7=1) (5=2) (3=3) (1=4) into AtSGuReg value labels AtSGuReg 1'strongly disagree' 2'somewhat disagree' 3'somewhat agree' 4'strongly agree' fre var=PES11_27 AtSGuReg/. *Recode Perceptions about the Performance of the Economy* missing values CPS11_40 (8, 9) recode CPS11_40 (3=1) (5=2) (1=3) into EconPerc value labels EconPerc 1'worse' 2'not made much difference' 3'better' fre var= CPS11_40 EconPerc/. *Recode Support for same-sex marriage* missing values PES11_29 (9) recode PES11_29 (1=3) (5=1) (8=2) into SSamSexM value labels SSamSexM 1'oppose' 2'no opinion' 3'favour' fre var=PES11_29 SSamSexM/. *Running Correlations between the DV and the 3 IVs*. Correlations VoteIntC AtSGuReg EconPerc SSamSexM *Running Correlations among DV and IVS. Correlation VoteIntC AtSGuReg EconPerc SSamSexM *Multivariate Regression Syntax using the 2011 CES Data.* *Add weight* WEIGHT by WGTSAMP *Recode indicators of DV (vote intention for Conservative Party of Canada)* missing values CPS11_18 CPS11_23 (996 thru 999) missing values MBS11_D1 (-99) compute RECPS_18 = CPS11_18 / 10 compute RECPS_23 = CPS11_23 / 10 fre var=RECPS_18/. fre var=RECPS_23/. fre var=MBS11_D1/. *Combining DV indicators into an index*. compute VoteIntC=RECPS_18+RECPS_23+MBS11_D1 recode VoteIntC (0 thru 10=1) (10.40 thru 20=2) (20.40 thru 30=3) into CVoteInt value labels CVoteInt 1'low' 2'med' 3'hi' *Recode Attitudes towards the Gun Registry* missing values PES11_27 (8, 9) recode PES11_27 (7=1) (5=2) (3=3) (1=4) into AtSGuReg value labels AtSGuReg 1'strongly disagree' 2'somewhat disagree' 3'somewhat agree' 4'strongly agree' fre var=PES11_27 AtSGuReg/. *Recode Perceptions about the Performance of the Economy* missing values CPS11_40 (8, 9) recode CPS11_40 (3=1) (5=2) (1=3) into EconPerc value labels RECPS_40 1'worse' 2'not made much difference' 3'better' fre var= CPS11_40 EconPerc/. *Recode Support for same-sex marriage* missing values PES11_29 (9) recode PES11_29 (1=3) (5=1) (8=2) into SSamSexM value labels REPES_29 1'oppose' 2'no opinion' 3'favour' fre var=PES11_29 SSamSexM/. *Running Multivariate Regression with the DV predicted by 3 Ivs*. Regression variables VoteIntC AtSGuReg EconPerc SSamSexM /statistics coeff r tol /descriptive=n /dependent=VoteIntC /method=enter. *Running the same Multivariate Regression in Stages* Regression variables VoteIntC AtSGuReg EconPerc SSamSexM /statistics coeff r tol /descriptives=n /dependent=VoteIntC /method=enter AtSGuReg /method=enter EconPerc /method=enter SSamSexM a. As a learning experience: 10 b. As a means of assessing student performance: 9 c. As preparation for future study and work life: 8.5 REFERENCE: 1. Anderson, Cameron D. 2008. "Economic Voting, Multilevel Governance and Information in Canada." Canadian Journal of Political Science Vol. 41, No. 2: 329-354. 2. Belanger, Eric and Bonnie M. Meguid. 2008. "Issus Salience, Issue Ownership, and Issue-based Vote Choice." Electoral Studies Vol. 27: 477-491. 3. Conservative Party of Canada. 2011. "Here for Canada." Last modified April 18. http://www.conservative.ca/media/2012/06/ConservativePlatform2011_ENs.pdf. 4. LeDuc, Lawrence. 2013. "The federal election in Canada, May 2011." Electoral Studies Vol. 31: 222-242. doi: 10.1016/j.electstud.2011.12.002
Views: 48 Pol242Geneva2013
Vincent Warmerdam - The Duct Tape of Heroes: Bayes Rule
 
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PyData Amsterdam 2016 In this talk I will give many examples of when Bayes rule will help you in your day to day work. I'll quickly show many examples of bayesian statistical thinking in action; the pleasure of inference, probabilistic graphs, model selection, feature generation, even operations research! I'll finish with a dataset from Heroes of the Storm and I'll show why Bayesian models can outperform randomforests. My talk is made up of the following examples; basic disease example: what is the value of adding an extra test to a patient give an example of an inference task that is very hard to do properly without bayesian thinking creating simple probibalistic models with pandas and showing how they are robust against missing data. I will also demo pomegranate, a new probabilistic programming tool for python. show how you can use bayes rule to pick models demo a bayesian probablistic approach to finding overpowered characters in the Heroes of the Storm video game. Slides available here: http://koaning.io/theme/notebooks/bayes.pdf.
Views: 2782 PyData
03 SPSS for Beginners - Descriptive Statistics
 
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How to Use SPSS In this third video about SPSS for Beginners, Dr. Daniel shows you three ways to approach descriptive statistics in SPSS. If you want quick and basic descriptives, use the Descriptives command to get the most commonly used statistics. The Frequencies command gives you a wide range of possibilities with the most flexibility to choose exactly what output that you want. When you want maximum output with lots of graphs – or if you want to split the descriptive statistics by a categorical variable (like gender), then use the Explore command. Link to a Google Drive folder with all of the files that I use in the videos including the Bear Handout and StatsClass.sav. As I add new files, they will appear here, as well. https://drive.google.com/drive/folders/1n9aCsq5j4dQ6m_sv62ohDI69aol3rW6Q?usp=sharing
Views: 128058 Research By Design
NIPS 2013 Tutorial - Causes and Counterfactuals: Concepts, Principles and Tools
 
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Judea Pearl and Elias Bareinboim Slides: http://www.cs.ucla.edu/~eb/nips-dec2013-pearl-bareinboim-tutorial-full.pdf The traditional aim of machine learning methods is to infer meaningful features of an underlying probability distribution from samples drawn of that distribution. With the help of such features, one can infer associations of interest and predict or classify yet unobserved samples. Causal analysis goes one step further; it aims at inferring features of the data-generating process, that is, of the invariant strategy by which Nature assigns values to the variables in the distribution. Process features enable us to predict, not merely relationships governed by the underlying distribution, but also how that distribution would CHANGE when conditions are altered, say, by deliberate interventions or by spontaneous transformations. We will review concepts, principles, and mathematical tools that were found useful in reasoning about causal and counterfactual relations, and will demonstrate their applications in several data-intensive sciences. These include questions of confounding control, policy analysis, misspecification tests, mediation, heterogeneity, selection bias, missing data, and the integration of findings from diverse studies. The following topics will be emphasized: 1. The 3-layer causal hierarchy: association, intervention and counterfactuals. http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf 2. What mathematics can tell us about "transfer learning" or "generalizing across domains" http://ftp.cs.ucla.edu/pub/stat_ser/r372.pdf http://ftp.cs.ucla.edu/pub/stat_ser/r387.pdf 3. What causal analysis tells us about recovery from selection bias and missing data. http://ftp.cs.ucla.edu/pub/stat_ser/r381.pdf http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf 4. The Mediation Formula, and what it tells us about "How nature works" http://ftp.cs.ucla.edu/pub/stat_ser/r379.pdf
Views: 2862 NIPS
SPSS for Beginners 1: Introduction
 
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Updated video 2018: SPSS for Beginners - Introduction https://youtu.be/_zFBUfZEBWQ This video provides an introduction to SPSS/PASW. It shows how to navigate between Data View and Variable View, and shows how to modify properties of variables.
Views: 1536209 Research By Design
Analysis of Data from a Clinical Trial on Wheelchair Seat Cushions
 
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EPUAP 2015 - Satellite Symposium David Brienza, Ph.D.
The submission process from the publisher's point of view
 
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Jonathan Patience, Senior Editor at Taylor & Francis, outlines the pathway to publication; encompassing the initial decision, peer review, revision and acceptance. Recorded 13 June 2018 at a MedComms Networking event in Oxford. Produced by NetworkPharma.tv = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = ABSTRACT: Taylor & Francis Group, an Informa business, publishes more than 2,500 journals and over 5,000 new books each year, offering a range of commercial services, including accelerated publishing, supplements, sponsored focus and reprints. The company recently acquired Dove Medical Press, which specialises in the publication of open access journals across the broad spectrum of science, technology and especially medicine. This talk aims to de-mystify the process of manuscript submission to medical journals. From a publisher’s perspective, it covers considerations prior to submission, pre-submission inquiries, the stages your manuscript undergoes after submission, and how to improve your chances of manuscript acceptance. Prior to submission, it is important to check the journal’s author guidelines and aims and scope. The manuscript should be on scope for the journal, have a clear objective explaining what it adds to the literature, and include all requested sections and funding information. At peer review, the most common criticisms we receive for industry-funded papers are that they have a marketing tone, so it is important to be careful of the language used, letting data speak for itself. Other common criticisms include missing references, flawed statistical analysis, lack of transparency or results being over or under-emphasised, and missing p-values in support of claims of significance. To speed up the decision and avoid rejection post-peer review, make sure to provide clear, point-by-point responses to all reviewers’ comments, or rationale where you feel no change is needed. Adding new data, long delays during revision, unclear responses and unaddressed comments can all delay the editorial decision. Written by Jonathan Patience, Senior Editor at Taylor & Francis = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = We are building a library of free webcasts, like this one, for the global MedComms Community and others at http://www.networkpharma.tv and we’d welcome your suggestions for new topics and speakers. Full details of this MedComms Networking event are at http://medcommsnetworking.com/event_130618.html Jonathan’s presentation (PDF format) is at http://medcommsnetworking.com/presentations/patience_130618.pdf Jonathan’s Linkedin page is at https://www.linkedin.com/in/jonathan-patience-679b1224/ More about Taylor & Francis can be found at http://taylorandfrancis.com/ Filming and technical direction by Mario Crispino, Freelance Cameraman & Editor Editorial support by Penny Gray, Freelance Medical Writer [For the avoidance of doubt: this video is intended to be freely accessible to all. Please feel free to share and use however you like. Cheers, Peter Llewellyn, Director NetworkPharma Ltd and Founder of the MedComms Networking Community activity at http://www.medcommsnetworking.com]
Views: 284 MedComms
SmartPLS New Project, Load and Troubleshoot Data
 
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A demo for how to start a new SmartPLS project, load data, and troubleshoot it. I now have an article published that cites this video. Paul Benjamin Lowry and James Gaskin (2014). "Partial least squares (PLS)structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it," IEEE Transactions on Professional Communication (57:2), pp. 123-146. http://www.kolobkreations.com/PLSIEEETPC2014.pdf
Views: 27324 James Gaskin
IDEA Workshop: Journal Entry Testing
 
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In this video, I will walk through how to perform journal entry testing using IDEA. In this video, you learn the following: 1) How to reconcile journal entries to the trial balance 2) How to perform risk profiling including: a) Entries containing key words b) Entries posted by certain individuals c) Entries posted within 5 business days of quarter end
Views: 14730 SAF Business Analytics
1. Introduction to Statistics
 
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*NOTE: This video was recorded in Fall 2017. The rest of the lectures were recorded in Fall 2016, but video of Lecture 1 was not available. MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: https://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet talked about the importance of the mathematical theory behind statistical methods and built a mathematical model to understand the accuracy of the statistical procedure. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 336790 MIT OpenCourseWare
Statistics: Correlation and Regression Analysis in SPSS
 
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This video shows how to use SPSS to conduct a Correlation and Regression Analysis. A simple null hypothesis is tested as well. The regression equation is explained despite the result of the hypothesis conclusion. ====================================================== Ways to support my channel: 1. Like, Share and Subscribe. 2. Buy Andy Field's textbook here: http://amzn.to/2yxomuQ 3. Buy SPSS (Student's version) here: http://amzn.to/2g19Ofc 4. Buy this book written by Dr. Everett Piper, President of Oklahoma Wesleyan University. Analyzes the current higher education system: http://amzn.to/2y6tpRk 5. Donate at PayPal.Me/AGRONKACI ============================ MORE VIDEOS: Watch Using Excel to find the Correlation Coefficient r here: https://youtu.be/y3bgaLwdm50 Watch ANOVA in SPSS here: https://youtu.be/Bx9ry1vBbTM Watch Sampling Distribution of Sample Means here: https://youtu.be/anGsd2l5YpM Watch Using Excel Charts to calculate Regression Equation here: https://youtu.be/qZjTtnyaV70 Watch Using Excel to calculate Regression Equation here: https://youtu.be/LDC0p9iZY8g Watch ANOVA in Microsoft Excel (One-Way) here: https://youtu.be/WhBkgWL3_3k Useful stuff: 6. Robot Vacuum Cleaner: http://amzn.to/2xpNGCH 7. Roku Express: http://amzn.to/2yvvAPQ 8. Mini Coffee Maker: http://amzn.to/2y7S1tq 9. Xbox One S 1TB Console - Forza Horizon 3 Bundle: http://amzn.to/2xoycPA 10. Xbox One 1TB Console - Tom Clancy's The Division Bundle: http://amzn.to/2yxYi2J ============================
Views: 239393 Agron Kaci
Warsaw POL242Y1 Crosstabulation Bivariate Presentation
 
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*Initial Setup* set width=80/length=none/header=no/messages=none. get file="/homes/josephf/webstats/Arabbarometer_07.sav". *Select Algeria* select if COUNTRY = 3 *Declaring Missing Values. These values are useless* missing values Q2321 Q2322 Q2323 Q2451 (97, 98, 99) *Recodes* recode Q2321 Q2322 Q2323 (1=3) (3=1) (4=0) recode Q2451(1=0) (2=1) (3=2) (4=3) *Putting the questions into different variables easy reading* compute DEMECO = Q2321 compute DEMIND = Q2322 compute DEMORD = Q2323 *Reliability Check on the indicators* RELIABILITY VARIABLES= DEMECO DEMIND DEMORD /scale(all)=all /summary=all. *Making the Index* compute INDEX = DEMECO+DEMIND+DEMORD. recode INDEX (0 Thru 1 = 0) (2 thru 4 = 1) (5 thru 7 = 2)(8 thru 9 = 3) value labels INDEX 0'V. Supportive' 1'Supportive' 2'Non Supportive' 3'V. Non' fre var= INDEX /stats=mode median mean std skewness kurtosis. *X1 variable recoding* missing values Q609 (97, 98, 99) recode Q609 (1=3)(2=2)(3=1)(4=0) into USALOV. value labels USALOV 0'Str. Disagree' 1'Disagree' 2'Agree' 3'Str. Agree' *ANOVA X1* oneway INDEX by USALOV (0,3) /ranges=scheffe /statistics=all *X2 Variable Recoding* missing values Q4013 (97, 98, 99) recode Q4013 (4=0)(3=1)(2=2)(1=3) value label Q4013 0'Str. Disagree' 1'Disagree' 2'Agree' 3'Str. Agree' *ANOVA X2* oneway INDEX by Q4013 (0,3) /ranges=scheffe /statistics=all *X3 Variable Recoding* missing values Q5072 (98, 99) recode Q5072 (1=3)(2=2)(3=1)(4=0) value label Q5072 0'Str. Disagree' 1'Disagree' 2'Agree' 3'Str. Agree' *ANOVA X3* oneway INDEX by Q5072 (0,3) /ranges=scheffe /statistics=all *Crosstabs and significance* *X1* crosstab tables Index by USALOV /cells=column count /statistics=btau CHISQ. *X2* crosstab tables Index by Q4013 /cells=column count /statistics=btau CHISQ. *X3* crosstab tables Index by Q5072 /cells=column count /statistics=btau CHISQ.
Views: 69 Warsaw2012POL242Y
Joint Modeling of Incomplete Data with Diverse Variable Types using Latent-Variable Models
 
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CHIPTS Methods Seminar - UCLA-Semel Institute Center for Community Health Joint Modeling of Incomplete Data with Diverse Variable Types using Latent-Variable Models Presented by: Thomas R. Belin, Ph.D. Professor, UCLA Department of Biostatistics Tuesday, October 8, 2013 2pm -- 3pm Center for Community Health, UCLA Wilshire Center Abstract: In incomplete data sets with many variables and diverse variable types (e.g., continuous, ordinal categorical, nominal categorical), it is challenging to develop general-purpose strategies for handling missing data. After reviewing sequential regression imputation methods (e.g., IVEWare, ICE, MICE, MIDAS) that might be viewed as competitors, this presentation will discuss joint modeling strategies based on latent-variable models that allow for the inclusion of diverse data types. In particular, we will focus on the use of models that can be fit with the help of a parameter-extended Metropolis-Hastings strategy for drawing correlation matrices in an MCMC inference framework. Illustrative examples will be presented and future directions for research in this area will be considered.
Views: 314 uclachipts

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