'Missing data' presentation slideshows

Todd D. Little University of Kansas Director, Quantitative Training Program

1.37k views • 117 slides

RStats Statistics and Research Camp 2014

RStats Statistics and Research Camp 2014. Welcome!. Helen Reid, PhD. Dean of the College of Health and Human Services Missouri State University. Welcome. Goal : keeping up with advances in quantitative methods Best practices : use familiar tools in new situations Avoid common mistakes.

1.37k views • 86 slides

Data Preprocessing

Data Preprocessing. Why preprocess the data? Descriptive data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Why Data Preprocessing?. Data in the real world is dirty

1.36k views • 74 slides

Data Management Approaches: handling, storage, & missingness

599 views • 52 slides

Data Preprocessing

Data Preprocessing. CSC 576: Data Science. Today. Data Preprocessing Handling Missing Values Handling Outliers Covariance and Correlation Normalization Binning, Discretization Sampling Aggregation. Data Preprocessing. Data Exploration phase results in finding data quality issues

838 views • 44 slides

Todd D. Little University of Kansas Director, Quantitative Training Program

674 views • 37 slides

Single cell RNAseq Kathie Mihindukulasuriya , PhD Senior Scientist, Cruchaga Lab

Single cell RNAseq Kathie Mihindukulasuriya , PhD Senior Scientist, Cruchaga Lab Department of Psychiatry Washington University in St. Louis. Plan: Single cell RNA- seq vs bulk RNA- seq C urrent single cell protocols and platforms Processing single cell RNA- seq data

703 views • 36 slides

Data Quality Assurance Beverly Musick

Data Quality Assurance Beverly Musick. Introduction. Electronic data play a critical role in health care delivery By providing decision support to health-care providers By forming the basis for periodic reporting to administrators and funders

649 views • 35 slides

Keeping Data Confidential in an Era of No Privacy

Keeping Data Confidential in an Era of No Privacy. Prof. Jerry Reiter Department of Statistical Science Duke University. Disclosure limitation setting. Agency seeks to release data on individuals Risk of re-identifications from matching to external databases

430 views • 28 slides

Precipitation & Interception

Precipitation & Interception. Ali Fares Chapter 2. Finish the lecture Lab: how to spatially distribute rainfall and calculate average rainfall rate using two methods: Thiessen Polygon Method Isohyeth method Lab assignment. Rainfall Spatial Distribution.

980 views • 26 slides

Modeling Missing Data in Distant Supervision for Information Extraction

Modeling Missing Data in Distant Supervision for Information Extraction. Alan Ritter Luke Zettlemoyer Mausam Oren Etzioni. Distant Supervision For Information Extraction. [ Bunescu and Mooney , 2007] [Snyder and Barzilay , 2007] [ Wu and Weld , 2007] [ Mintz et al., 2009]

482 views • 22 slides

Reports of gangs in Antiguan schools

Reports of gangs in Antiguan schools. Julie Meeks Gardner Caribbean Child Development Centre, Consortium for Social Development and Research, University of the West Indies, Open Campus Charles Katz

314 views • 20 slides

APPLICATION OF THE DEVELOPED SAS MACRO FOR EDITING AND IMPUTATION AT STATISTICS LITHUANIA

216 views • 19 slides

C4.5 and the K-Means Clustering Algorithms

C4.5 and the K-Means Clustering Algorithms. By: Clint Tomer MATH 4200 Final Project. Outline. Introduction to the C4.5 Algorithm Introduction to the K-Means Clustering Algorithm Dataset Overview Description of the Experiment. Outline cont. Graph of Experiment

485 views • 17 slides

Managing and Curating Data

Managing and Curating Data. Chapter 8. Introduction. Data organization Data management Data curation Raw data is required to repeat a scientific study Any data supported by public funds is legally required to be available for other scientists and the public. Step 1: Managing Raw Data.

179 views • 16 slides

MAT 155 Chapter 1

MAT 155 Chapter 1. The following is a brief review of Chapter 1. This does NOT cover all the material in that chapter. Click on Slide Show and View Slide Show. Read and note your answer to the question. Advance the slide to see the answer.

327 views • 15 slides

Spatially Complete Global Surface Albedos Derived from MODIS Data

Spatially Complete Global Surface Albedos Derived from MODIS Data. Operational MODIS surface albedo data product (MOD43B3/MCD43B3) 0.47, 0.56, 0.67, 0.86, 1.24, 1.64, 2.1, 0.3-0.7, 0.7 - 5.0, 0.3-5.0 1 arc min (~2 km) latitude-longitude spatial resolution ( C004 )

456 views • 12 slides

E-Marketing Solution

Providing solutions to email marketing and offering data appending services. Data appending is a quick and effective way to build an email database, if wrongly handled, it can turn into a disaster.

555 views • 6 slides

Opinionated

176 views • 6 slides

XClean in Action

XClean in Action. Melanie Weis , HPI Potsdam, Germany Ioana Manolescu, INRIA Futurs, France CIDR 2007. What is XClean?. XClean is an XML data cleaning system. Types of errors that require data cleaning: Typos Different data formats (e.g., date, abbreviations, language) Missing data

155 views • 6 slides