Search results “Examining distribution data management”
Examining Distributions
Unit 1, Part 1 Quantitative Data & Categorical Data Descritptive Statistical Methods
Views: 3363 Robert Emrich
Math Antics - Mean, Median and Mode
Learn More at mathantics.com Visit http://www.mathantics.com for more Free math videos and additional subscription based content!
Views: 1026724 mathantics
01 - Looking at Data
Materials Looking at Data - Distributions Slides: Looking at Data Lecture Normal Distributions Lecture Looking at Data - Relationships Slides: Looking at Data - Relationships Lecture Producing Data Slides: Producing Data Lecture Objectives Examine distributions. Summarize and describe the distribution of a categorical variable in context. Generate and interpret several different graphical displays of the distribution of a quantitative variable (histogram, stemplot, boxplot). Summarize and describe the distribution of a quantitative variable in context: a) describe the overall pattern, b) describe striking deviations from the pattern. Relate measures of center and spread to the shape of the distribution, and choose the appropriate measures in different contexts. Apply the standard deviation rule to the special case of distributions having the "normal" shape. Explore relationships between variables using graphical and numerical measures. Classify a data analysis situation (involving two variables) according to the "role-type classification," and state the appropriate display and/or numerical measures that should be used in order to summarize the data. Compare and contrast distributions (of quantitative data) from two or more groups, and produce a brief summary, interpreting your findings in context. Graphically display the relationship between two quantitative variables and describe: a) the overall pattern, and b) striking deviations from the pattern. Interpret the value of the correlation coefficient, and be aware of its limitations as a numerical measure of the association between two quantitative variables. In the special case of linear relationship, use the least squares regression line as a summary of the overall pattern, and use it to make predictions. Recognize the distinction between association and causation, and identify potential lurking variables for explaining an observed relationship. Recognize and explain the phenomenon of Simpson's Paradox as it relates to interpreting the relationship between two variables. Sampling. Examine methods of drawing samples from populations Identify the sampling method used in a study and discuss its implications and potential limitations. Designing Studies. Distinguish between multiple studies, and learn details about each study design. Identify the design of a study (controlled experiment vs. observational study) and other features of the study design (randomized, blind etc.). Explain how the study design impacts the types of conclusions that can be drawn.
Views: 713 Lollynonymous
Power law distributions in entrepreneurship: Implications for theory and research
A long-held assumption in entrepreneurship research is that normal (i.e., Gaussian) distributions characterize variables of interest for both theory and practice. We challenge this assumption by examining more than 12,000 nascent, young, and hyper-growth firms. Results reveal that variables which play central roles in resource-, cognition-, action-, and environment-based entrepreneurship theories exhibit highly skewed power law distributions, where a few outliers account for a disproportionate amount of the distribution's total output. Our results call for the development of new theory to explain and predict the mechanisms that generate these distributions and the outliers therein. We offer a research agenda, including a description of non-traditional methodological approaches, to answer this call.
Views: 235 Elsevier Journals
How to Analyze Satisfaction Survey Data in Excel with Countif
Purchase the spreadsheet (formulas included!) that's used in this tutorial for $5: https://gum.co/satisfactionsurvey ----- Soar beyond the dusty shelf report with my free 7-day course: https://depictdatastudio.teachable.com/p/soar-beyond-the-dusty-shelf-report-in-7-days/ Most "professional" reports are too long, dense, and jargony. Transform your reports with my course. You'll never look at reports the same way again.
Views: 376423 Ann K. Emery
Binomial Distribution   15 Expected Frequencies and Fitting of binomial distribution
#Statistics #Binomial Distribution Fit a Binomial Distribution to the following data: x 0 1 2 3 4 F 28 62 46 10 4 MBA, MCA, CA, CPT, CS, CWA, CMA, FOUNDATION, CPA, CFA, BBA, BCOM, MCOM, Grade-11, Grade-12, Class-11, Class-12, CAIIB, FIII, UPSC, RRB, Competitive Exams, Entrance Exams #Binomial Distribution - 15 - Basics Problem: Fit a Binomial Distribution to the following data: x 0 1 2 3 4 F 28 62 46 10 4 - www.prashantpuaar.com
Views: 35860 Prashant Puaar
What is GEOSPATIAL ANALYSIS? What does GEOSPATIAL ANALYSIS mean? GEOSPATIAL ANALYSIS meaning - GEOSPATIAL ANALYSIS definition - GEOSPATIAL ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Geospatial analysis, or just spatial analysis, is an approach to applying statistical analysis and other analytic techniques to data which has a geographical or spatial aspect. Such analysis would typically employ software capable of rendering maps processing spatial data, and applying analytical methods to terrestrial or geographic datasets, including the use of geographic information systems and geomatics. Geographic information systems (GIS), which is a large domain that provides a variety of capabilities designed to capture, store, manipulate, analyze, manage, and present all types of geographical data, and utilizes geospatial analysis in a variety of contexts, operations and applications. Geospatial analysis, using GIS, was developed for problems in the environmental and life sciences, in particular ecology, geology and epidemiology. It has extended to almost all industries including defense, intelligence, utilities, Natural Resources (i.e. Oil and Gas, Forestry ... etc.), social sciences, medicine and Public Safety (i.e. emergency management and criminology), disaster risk reduction and management (DRRM), and climate change adaptation (CCA). Spatial statistics typically result primarily from observation rather than experimentation. Vector-based GIS is typically related to operations such as map overlay (combining two or more maps or map layers according to predefined rules), simple buffering (identifying regions of a map within a specified distance of one or more features, such as towns, roads or rivers) and similar basic operations. This reflects (and is reflected in) the use of the term spatial analysis within the Open Geospatial Consortium (OGC) “simple feature specifications”. For raster-based GIS, widely used in the environmental sciences and remote sensing, this typically means a range of actions applied to the grid cells of one or more maps (or images) often involving filtering and/or algebraic operations (map algebra). These techniques involve processing one or more raster layers according to simple rules resulting in a new map layer, for example replacing each cell value with some combination of its neighbours’ values, or computing the sum or difference of specific attribute values for each grid cell in two matching raster datasets. Descriptive statistics, such as cell counts, means, variances, maxima, minima, cumulative values, frequencies and a number of other measures and distance computations are also often included in this generic term spatial analysis. Spatial analysis includes a large variety of statistical techniques (descriptive, exploratory, and explanatory statistics) that apply to data that vary spatially and which can vary over time. Some more advanced statistical techniques include Getis-ord Gi* or Anselin Local Moran's I which are used to determine clustering patterns of spatially referenced data. Geospatial analysis goes beyond 2D and 3D mapping operations and spatial statistics. It includes: Surface analysis —in particular analysing the properties of physical surfaces, such as gradient, aspect and visibility, and analysing surface-like data “fields”; Network analysis — examining the properties of natural and man-made networks in order to understand the behaviour of flows within and around such networks; and locational analysis. GIS-based network analysis may be used to address a wide range of practical problems such as route selection and facility location (core topics in the field of operations research, and problems involving flows such as those found in hydrology and transportation research. In many instances location problems relate to networks and as such are addressed with tools designed for this purpose, but in others existing networks may have little or no relevance or may be impractical to incorporate within the modeling process....
Views: 2259 The Audiopedia
Median Polish - Exploratory Data Analysis
[NOTE: Good CC/Subtitles Added] Median Polish is an Exploratory Data Analysis technique for analyzing two-way tables. This video shows a step-by-step example of working the Median Polish on a simple 3x3 two-way table: -15 4 1 6 16 30 -5 4 -12 Here is a simple R program that will create 3x3 two-way tables for you to practice with, and the median polish results generated by R: tbl = matrix(data=as.integer(runif(9) * 10), nrow=3, ncol=3) tbl medpolish(tbl)
Views: 4834 Timothy Chen Allen
Data Sense 01: Introduction / Section 03: Measurement
Lecture video for "Data Sense: An Introduction to Statistics for the Behavioral Sciences" by Barton Poulson. This video covers Chapter 01: Introduction, Section 01: Storytelling and defines levels of measurement (i.e., nominal, ordinal, interval, and ratio level variables). The book and complete set of videos may be purchased at http://kendallhunt.com/poulson/.
Views: 1346 Barton Poulson
Using Excel to illustrate a uniform probability distribution
This is for Data Management courses where we study uniform PDs as one kind of many probability distributions. We are using an Excel simulation to show that dice rolls give a uniform probability distributions by examining their relative frequencies ... ( or *are* they uniform...?)
Views: 18223 Paul King
Using "big data" for transportation analysis: A case study of the LA Metro Expo Line
The video begins at 2:25. Friday, October 3, 2014 Mohja L. Rhoads, Senior Research Associate, South Bay Cities Council of Governments Friday, October 3, 2014 Access to a comprehensive historical archive of real-time, multi-modal multi-agency transportation system data has provided a unique opportunity to demonstrate how “big data” can be used for policy analysis, and to offer new insights for planning scholarship and practice. We illustrate with a case study of a new rail transit line. We use transit, freeway, and arterial data of high spatial and temporal resolution to examine transportation system performance impacts of the Exposition (Expo) light rail line (Phase 1) in Los Angeles. Using a quasi-experimental research design, we explore whether the Expo Line has had a significant impact on transit ridership, freeway traffic, and arterial traffic within the corridor it serves. Our results suggest a net increase in transit ridership, but few effects on traffic system performance. Given the latent travel demand in this heavily congested corridor, results are consistent with expectations. The benefits of rail transit investments are in increasing transit accessibility and person throughput within high-demand corridors; effects on roadway traffic are small and localized.
Views: 2849 TREC at PSU
Distributed Local Outlier Detection in Big Data
Distributed Local Outlier Detection in Big Data Yizhou Yan (Worcester Polytechnic Institute) Lei Cao (Massachusetts Institute of Technology) Caitlin Kuhlman (Worcester Polytechnic Institute) Elke Rundensteiner (Worcester Polytechnic Institute) In this work, we present the first distributed solution for the Local Outlier Factor (LOF) method—a popular outlier detection technique shown to be very effective for datasets with skewed distributions. As datasets increase radically in size, highly scalable LOF algorithms leveraging modern distributed infrastructures are required. This poses significant challenges due to the complexity of the LOF definition, and a lack of access to the entire dataset at any individual compute machine. Our solution features a distributed LOF pipeline framework, called DLOF. Each stage of the LOF computation is conducted in a fully distributed fashion by leveraging our invariant observation for intermediate value management. Furthermore, we propose a data assignment strategy which ensures that each machine is self-sufficient in all stages of the LOF pipeline, while minimizing the number of data replicas. Based on the convergence property derived from analyzing this strategy in the context of real world datasets, we introduce a number of data-driven optimization strategies. These strategies not only minimize the computation costs within each stage, but also eliminate unnecessary communication costs by aggressively pushing the LOF computation into the early stages of the DLOF pipeline. Our comprehensive experimental study using both real and synthetic datasets confirms the efficiency and scalability of our approach to terabyte level data. More on http://www.kdd.org/kdd2017/
Views: 1957 KDD2017 video
EXPERT INSIGHT at TMF: Digital Integration - Standardizing Event Data Collection and Management
Neural Technologies' VP of Marketing Claus Nielsen delivers a seminar to Operators at TM Forum's Digital Transformation Asia 2018. - Understanding the importance of standardizing event data collection and management to facilitate faster and easier event data distribution/consumptions by downstream applications & partners - Examining the challenges involved in: Event Data Collection & Distribution Event Data Life Cycle Management Event Data Driven Decision Making - The Introduction of the Event Data Lake (ELD) Platform - How are various stakeholders working together to drive standardization?
Radiolarian Micropalaeontology: Analysing Radiolarian Microfossil Data.
Professor Simon Haslett discusses analysing radiolarian microfossil data. Radiolaria are marine single-celled organisms that possess a silica shell and are preserved in the fossil record. Radiolarian data can be analysed by individual species plots/graphs, or through numerical and statistical analysis of assemblage datasets. Such data can be used in stratigraphy to date layers of rock and sediment through geological time, and also to reconstruct past environments and establish palaeoclimate history. Simon Haslett is Professor of Physical Geography and Director of the Centre for Excellence in Learning and Teaching at the University of Wales, Newport. New videos are regularly added so please subscribe to the channel. Camera operator and editor: Jonathan Wallen.
Views: 550 ProfSimonHaslett
How Enhanced IT/OT Integration can Help Enable the Future Distribution Network
Driving effective IT/OT integration will be critical for utilities, enabling data and operations to seamless work together to achieve business outcomes and greater future performance. Learn more: http://bit.ly/2jcfmmn
Views: 1476 Accenture
Pressure Management: Industry Practices and Monitoring Procedures
04/10/2014 Water Research Foundation Webcast. ​Most systems tend to operate at much higher pressure than needed, resulting in increased energy use, increased non-revenue water loss, and excessive main breaks. Project #4321, Pressure Management: Industry Practices and Monitoring Procedures developed guidance on best practices and cost/benefits of implementing an optimized pressure management program. The project included an analysis of a year-long pressure monitoring program from 22 utilities. This Webcast will focus on these results and a survey of pressure management practices, examining the case study examples and providing recommendations to improve pressure management in drinking water distribution systems. The final deliverables for this project are available on the website.
Data Transformation for Positively and Negatively Skewed Distributions in SPSS
This video demonstrates how to transform data that are positively or negatively skewed using SPSS. Concepts such as log10 transformation, determining skewness, reflection, adjusting for zeros, and adjusting for negative numbers are described.
Views: 75855 Dr. Todd Grande
Statistics I
Download the Show Notes: http://www.mindset.co.za/learn/sites/files/LXL2013/LXL_Gr11Mathematics_30_Statistics_30Sept.pdf In this live Grade 11 Maths show we take a close look at Statistics I. In this lesson we revise how to represent data using histrograms and frequency polygons. We analyse data by examining box and whisker plots & finally we revise measures of central tendency. Visit the Learn Xtra Website: http://www.learnxtra.co.za View the Learn Xtra Live Schedule: http://www.learnxtra.co.za/live Join us on Facebook: http://www.facebook.com/learnxtra Follow us on Twitter: http://twitter.com/learnxtra ( E00200242 )
Views: 26500 Mindset Learn
Exploring GIS: Why spatial is special?
An overview of the spatial thinking process in geographic information systems and science. The presentation includes the spatial thinking questions that a GIS can answer.
Views: 2746 GIS VideosTV
Analyzing the Cloudera Hortonworks Merger
Breaking Big Data News last was about the Cloudera Hortonworks merger. What does that mean for the Hadoop Ecosystem? In this episode of the Big Data Beard YouTube show Brett Roberts and Thomas Henson will analyze the merger of the two premier Hadoop Ecosystem distributors. Find out our predictions for the future of Cloudera-Hortonworks and the Hadoop Community as a whole. Be sure to leave comments on your prediction from the Cloudera Hortonworks merger. ► GROW YOUR BIG DATA BEARD - Site devoted to "Exploring all aspects of Big Data & Analytics" ◄ https://bigdatabeard.com/ ► BIG DATA BEARD PODCAST - Subscribe to learn what's going on in the Big Data Community ◄ https://bigdatabeard.com/subscribe-to-podcast/ ► CONNECT ON TWITTER ◄ https://twitter.com/bigdatabeard
Views: 517 Big Data Beard
Examining ZFS On-Disk Format Using mdb and zdb: Max Bruning
In this video, Max Bruning presents on the ZFS On-Disk Format Using mdb and zdb. Recorded at the Open Solaris Developer Conference in Prague on June 28, 2008. Sun Microsystems
Views: 2223 overheardinpdx
Large Data Management, Data Standards, Data Sharing - Owen White
July 24-26, 2013 - Human Microbiome Science: Vision for the Future More: http://www.genome.gov/27554404
Variability of Stock Return Standard Deviation | Corporate Finance | CPA Exam BEC|CMA Exam |Chp12 p3
The variance essentially measures the average squared difference between the actual returns and the average return. The bigger this number is, the more the actual returns tend to differ from the average return. Also, the larger the variance or standard deviation is, the more spread out the returns will be. The way we will calculate the variance and standard deviation will depend on the specific situation. In this chapter, we are looking at historical returns; so the procedure we describe here is the correct one for calculating the historical variance and standard deviation. If we were examining projected future returns, then the procedure would be different. NORMAL DISTRIBUTION For many different random events in nature, a particular frequency distribution, the normal distribution (or bell curve), is useful for describing the probability of ending up in a given range. For example, the idea behind “grading on a curve” comes from the fact that exam score distributions often resemble a bell curve.
Spend Analysis Series Episode 4 - Standardizing and Categorizing Data in Spend Analysis Final
In this fourth installment of the Spend Analysis Series, Spend Consultant Jennifer Ulrich explains how to standardize and categorize spend data when examining your company's spend profile.
Percentiles and Quartiles
statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!
Views: 416023 statslectures
Steve Lohr: "Data-ism" | Authors at Google
Steve Lohr, a technology reporter for the New York Times, chronicles the rise of Big Data, addressing cutting-edge business strategies and examining the dark side of a data-driven world. Coal, iron ore, and oil were the key productive assets that fueled the Industrial Revolution. Today, Data is the vital raw material of the information economy. The explosive abundance of this digital asset, more than doubling every two years, is creating a new world of opportunity and challenge. Data-ism is about this next phase, in which vast, Internet-scale data sets are used for discovery and prediction in virtually every field. It is a journey across this emerging world with people, illuminating narrative examples, and insights. It shows that, if exploited, this new revolution will change the way decisions are made—relying more on data and analysis, and less on intuition and experience—and transform the nature of leadership and management. Lohr explains how individuals and institutions will need to exploit, protect, and manage their data to stay competitive in the coming years. Filled with rich examples and anecdotes of the various ways in which the rise of Big Data is affecting everyday life it raises provocative questions about policy and practice that have wide implications for all of our lives.
Views: 4787 Talks at Google
SPSS: Analyzing Subsets and Groups
Instructional video on how to analyze subsets and groups of data using SPSS, statistical analysis and data management software. For more information, visit SSDS at https://ssds.stanford.edu.
Strategies for Ensuring Good Documentation Practices (GDP) Trailer
Good Documentation Practice (GDP) in clinical research is a baseline expectation; however, there are no set guidelines around what comprises GDP in a Good Clinical Practice (GCP) environment. In this web seminar, we will look closely at the key features of GDP by first examining the question: What is a document? At its core, a document is information (meaningful data) and its supporting medium, which could be in the form of paper, CD, computer files, or microfilm. Documentation is a process which comprises multiple steps: Recording of data, review of documents, approval of documents, issuance and disposal of documents, retrieval of documents, and presentation of documents. In addition, this web seminar will examine the issues identified when documentation has been subject to agency review, and the steps that can be taken to ensure that your approach to clinical trial documentation demonstrates the quality processes that have been applied to your documentation efforts.
Views: 2325 Kathy Barnett
Generalized Extreme Value Distributions: Application in Financial Risk Management
http://demonstrations.wolfram.com/GeneralizedExtremeValueDistributionsApplicationInFinancialRi The Wolfram Demonstrations Project contains thousands of free interactive visualizations, with new entries added daily. This Demonstration illustrates the Fisher?Tippett?Gnedenko theorem in the context of financial risk management. A sample of n=1000 observations is drawn from a parent distribution ?^parent that describes the probability of historical losses of a portfol... Contributed by: Pichet Thiansathaporn Audio created with WolframTones: http://tones.wolfram.com
Views: 2651 wolframmathematica
What's New In Oracle Manufacturing Analytics? [Examining Oracle BI Applications 11g: The Series]
http://www.kpipartners.com/watch-whats-new-in-oracle-manufacturing-analytics ... KPI endorses the Oracle Manufacturing Analytics solution as one that provides end-to-end visibility into manufacturing operations by integrating data from across the enterprise value chain. The Oracle offering enables organizations to reduce production costs, improve product quality, minimize inventory levels and respond faster to customer demands. Manufacturing Analytics, as part of the latest release of the BI Applications (, can provide support for Discrete Manufacturing analysis and produce pegging reports to show the relationship between demand and supply. Watch this 'Examining Oracle BI Applications 11g: The Series' session that takes a deep dive into the latest version of this Oracle BI Applications solution and how this can extend and organization's business intelligence footprint to support the Manufacturing modules in Oracle E-Business Suite. Manufacturing Analytics can also provide tremendous analytical value to organizations who wish to: gain visibility into manufacturing schedules gain visibility into cost gain visibility into quality and service levels correlate work order information with production plans reduce work order cycle time and aging of open work orders perform non-conformance and disposition analysis improve insight into raw materials and finished goods. Areas of examination for this session include: Common Business Questions for Manufacturing Departments Overview of Oracle Manufacturing Analytics The Manufacturing Executive Dashboard The Production Performance Dashboard The Inventory Dashboard The Production Cost Dashboard The Plan-To-Produce Dashboard Performance Summary By Plant Reporting Supply and Demand Analysis Reports Resource Utilization Reporting Work Order Details Reporting Inventory Snapshot Reporting Inventory Aging Reports Production Costs By Top 10 General Ledger Accounts Cost Distribution Trend Reporting Plan-To-Produce Linearity Report Plan Comparison Report
Views: 793 kpipartners
Cloudera surges on merger with software rival Hortonworks
CNBC's Seema Mody reports on Cloudera stock jumping after it announced an all-stock merger of equals with competitor Hortonworks.
Views: 991 CNBC Television
Busting Myths About China’s Overseas Development Program With New Data with Dr. Brad Parks
Over the last decade, China has emerged as one of the largest suppliers of international development finance, with a large and growing overseas development budget. Consequently, no other non-Western country has drawn as much scrutiny for its development activities. Yet China does not release detailed information about the “where, what, how, and to whom” of its development aid. This presents an obstacle for policy makers, practitioners, and analysts who seek to understand the distribution and impact of Chinese development finance. Since 2013, AidData has led an ambitious effort to correct this problem by developing an open source data collection methodology called Tracking Underreported Financial Flows (TUFF) and maintaining a publicly available database of Chinese development projects around the world. AidData has also teamed up with a group of economists and political scientists from leading universities around the world to conduct cutting-edge research with this database, examining differences and similarities in the levels, priorities, and consequences of Chinese and American development finance. On March 13, Dr. Brad Parks, executive director of AidData and a faculty member at the College of William and Mary, will discuss the organization’s work with the National Committee in New York City. Drawing on advanced techniques that include using nighttime light and deforestation data from high-resolution, satellite imagery, Dr. Parks will present new findings on the intended economic development impacts and the unintended environmental impacts of Chinese development projects. Bio: Brad Parks is AidData’s executive director and a research faculty member at the College of William and Mary’s Institute for the Theory and Practice of International Relations. His research focuses on the cross-national and sub-national distribution and impact of international development finance, and the design and implementation of policy and institutional reforms in low-income and middle-income countries. His publications include Greening Aid?, Understanding the Environmental Impact of Development Assistance (Oxford University Press, 2008) and A Climate of Injustice: Global Inequality, North-South Politics, and Climate Policy (MIT Press, 2006). He is currently involved in several empirical studies of the upstream motivations for, and downstream effects of, Chinese development finance. His research in this area has been published in the Journal of Conflict Resolution, the Journal of Development Studies, China Economic Quarterly, and the National Interest. From 2005 to 2010, Dr. Parks was part of the initial team that set up the U.S. Government's Millennium Challenge Corporation (MCC). As acting director of Threshold Programs at the MCC, he oversaw the implementation of a $35 million anti-corruption and judicial reform project in Indonesia and a $21 million customs and tax reform project in the Philippines. Dr. Parks holds a Ph.D. in international relations and an M.Sc. in development management from the London School of Economics and Political Science.
Modelling Data with the Generalized Extreme Value Distribution This Modelling Data with the Generalized Extreme Value Distribution shows how to fit the generalized extreme value distribution using maximum likelihood estimation. The extreme value distribution is used to model the largest or smallest value from a group or block of data.
Does normalizing your data affect outlier detection?
It is common practice to normalize data before using an outlier detection method. But which method should we use to normalize the data? Does it matter? The short answer is yes, it does. The choice of normalization method may increase or decrease the effectiveness of an outlier detection method on a given dataset. In this talk we investigate this triangular relationship between datasets, normalization methods and outlier detection methods.
Views: 529 R Consortium
Aimia's Data Philanthropy Event (2014)
Aimia employees volunteer their time to help non-profit organizations analyze their data, providing insights and recommendations doing good for these charities.
Views: 1001 Aimia Inc
Using data to improve the sustainability of livestock production
The choice to eat animal products is a complex one. While our ancestors depended on animal source foods for vital nutrients, modern diets can provide essential nutrition through plant-based ingredients alone. And yet, globally, average meat consumption per person is higher than ever before. This upsurge has raised serious concerns over the impact of animal- based foods on global sustainability. Now, a special issue of the journal animal has brought together seven articles examining various aspects of livestock production to provide an evidence-driven starting point for sustainable practice. The special issue stems from the 2016 conference on ‘Steps to Sustainable Livestock’, organized by the Global Farm Platform Initiative. The GFP comprises 15 model farms in 11 countries. Although each facility is distinct, they hold a shared mission: to understand the environmental impact of different agricultural practices across varied climates and ecosystems, while also assessing the ability to meet global food requirements. One area of research focuses on the link between livestock and human health. A large proportion of human disease originates from or is otherwise linked to livestock disease. One article proposes a classification system for this relationship that can help prioritize, identify, and deliver appropriate health interventions. Another examines how consumption of meat and milk can help humans maintain a healthy diet at different life-stages. The issue also explores the importance of ruminant animals in turning otherwise indigestible plant material into high-quality food. Three articles explore feeding strategies for ruminants — including the use of insects — to decrease the use of cereal grains as a feed source. The final subject examined is the trade-offs that arise when looking at different ways to minimize the environmental impact of livestock production. One article uses mathematical modeling to explore ways of enhancing phosphorous recycling, while another focuses on the potential of a farm “platform” — an actual farm equipped with high-tech instruments to measure water flow and nutrient distribution — to identify metrics that can serve as surrogates for environmental health. Although seven papers can’t address all the ways to improve the societal and environmental sustainability of livestock production, the collection provides a solid foundation to help guide the continued evolution of best practices that help address societal concerns about livestock production. View the special topic here: https://www.cambridge.org/core/journals/animal/issue/AEB72BDDF9BD83C90714D94AF0A297C2 Editorial: Gill et al. “Livestock production evolving to contribute to sustainable societies.” animal (2018). https://doi.org/10.1017/S1751731118000861 Review: Dairy foods, red meat and processed meat in the diet: implications for health at key life stages I. Given https://doi.org/10.1017/S1751731118000642 Closing the phosphorus cycle in a food system: insights from a modelling exercise R. J. van Kernebeek, S. J. Oosting, M. K. van Ittersum, R. Ripoll-Bosch, I. J. M. de Boer https://doi.org/10.1017/S1751731118001039 Review: Use of human-edible animal feeds by ruminant livestock M. Wilkinson, M. R. F. Lee https://doi.org/10.1017/S175173111700218X Review: Optimizing ruminant conversion of feed protein to human food protein A. Broderick https://doi.org/10.1017/S1751731117002592 Review: Feed demand landscape and implications of food-not feed strategy for food security and climate change P. S. Makkar https://doi.org/10.1017/S175173111700324X Review: Animal health and sustainable global livestock systems D. Perry, T. P. Robinson, D. C. Grace https://doi.org/10.1017/S1751731118000630 Roles of instrumented farm-scale trials in trade-off assessments of pasture-based ruminant production systems Takahashi, P. Harris, M. S. A. Blackwell, L. M. Cardenas, A. L. Collins, J. A. J. Dungait, J. M. B. Hawkins, T. H. Misselbrook, G. A. McAuliffe, J. N. McFadzean, P. J. Murray, R. J. Orr, M. J. Rivero, L. Wu, M. R. F. Lee https://doi.org/10.1017/S1751731118000502 Video produced by https://www.researchsquare.com
Views: 277 Cambridge Core
Identifying Optimal Cut-Points for Continuous Predictors to Discriminate Disease Outcomes
Variables are often dichotomized for decision making in clinical practice and appropriate management of patients requires optimizing a cut-point to discriminate disease risk. If true cut-points for one or more variables exist, the challenge is identifying them. We examine dichotomization methods to identify which methods recover a true cut-point and present evidence that maximizing odds ratio, Youden's statistic, Gini Index, chi-square statistic, relative risk and kappa statistic theoretically recover a cut-point. Simulations evaluating these statistics for recovery of a cut-point indicate that the chi-square statistic and Gini Index have the smallest bias and variability. There are limited methods for simultaneously optimizing cut-points for more than 1 variable. We propose a method for jointly dichotomizing two or more variables and conduct simulations to compare joint and marginal dichotomization for the ability to recover the cut-points. Our results show that cut-points selected jointly exhibit smaller error and similar bias relative to marginal selection. Dr. Wolf is an Assistant Professor of Biostatistics in the Department of Public Health Sciences at the Medical University of South Carolina (MUSC). She has a PhD in biostatistics from MUSC, a Master’s degree in environmental chemistry from UNC Wilmington, and a Bachelor’s degree in chemistry and anthropology from Rice University. Her statistics research interests focus on developing statistical methods for biomarker discovery and disease prediction modeling. Her translational interest focus on development of prediction models and diagnostic tools for rheumatic diseases and on examining the impact of environmental contaminants in the food chain of human populations.
Views: 423 DE-CTR ACCEL
Statistics of Extremes: Animation 2
An animation from the 2015 review by A.C. Davison and R. Huser, "Statistics of Extremes," from the Annual Review of Statistics and Its Application: http://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-010814-020133?utm_source=youtube&utm_medium=st.davison&utm_campaign=suppvideo Illustration of the point process of exceedances and the convergence to the GPD. For increasing values of n, the plots display the point process of rescaled times and rescaled variables, namely (j/(n + 1), (Yj − bn)/an), for data simulated from the uniform (top left), standard Gaussian (top right), unit exponential (bottom left), and 0.2-Pareto distributions. The side plots are histograms of the exceedances over the threshold u (horizontal blue line), i.e., an−1(Yj − bn)|an−1(Yj − bn) > u. The solid red curves are the corresponding asymptotic GPD densities.
http://bestsolutions.cu.cc/acc-340-final-exam-2/ 1 Three logical database structures include _________ 2 A notation showing the relationship among entities is called 3 The process of examining and arranging file data in a way that helps avoid problems when these files are used or modified later is called _______________. a) insertions anomaly b) data manipulation language c) normalization 4 Data definition commands, data query commands and report generators are features of a) data modeling b) DBMS packages. c) Systems development life cycle. 5 Organizational structure for the general ledger is provided by a) special journals b) subsidiary ledgers c) chart of accounts. 6 AISs depend heavily on the use of codes to record, classify and retrieve financial data. a) True b) False 7 The practice of examining business practices and redesigning them from scratch is called a) lean manufacturing b) business process reengineering c) resource management. d) just in time system. 8 Software solutions that include financial functions interfaced with manufacturing, sales and distribution, and human resources are called a) value added reseller b) application service provider c) enterprise resource planning systems 9 Label the four stages in the systems development life cycle. 1. Research and Planning 2. Design 3. Implementation and Testing 4. Maintenance 10 When installing a new system, the 3 changeover possibilities are _________ 11 Name two tools used to plan, schedule and monitor the activities during a systems implementation project. 12 The objective in designing any internal control system is to provide foolproof protection against all internal control risks. a.) True b.) False 13 A good ______ enables an accounting manager as well as auditors to follow the path of the data recorded in transactions form the initial source. 14 A control activity of an internal control system that focuses on structuring work assignments among employees so that one employee's work activities serve as a check on those of another employee is called _________ 15 What kind of analysis should be performed when considering if an internal control procedure should be implemented? a.) expected loss b.) risk assessment c.) cost benefit 16 Which of the following are examples of fault tolerant systems. a.) disk mirroring b.) rollback processing c.) data backup d.) all of the above 17 Control totals are an example of a(n) a.) output control b.) input control c.) processing control 18 A criticism of the traditional architecture is a lack of integration across functional areas of the organization. a.) True b.) False 19 __________________ ________________means the computer input device is connected to the CPU so that master files are updated as transactions data are entered. a.) Report time processing b.) Batch processing c.) Online processing 20 The _______________ has been one of the most exciting developments in telecommunications and networking. a.) file server b.) wireless network card c.) Internet
Views: 211 green anderson
How Big Data Analysis Guides Hurricane Sandy Response
The complete process used by Direct Relief to prepare for and respond to Hurricane Sandy using Palantir's analytics suite. With this technology, Direct Relief connects clinics with essential medical resources by using the best insights available to assess needs, scale problems and track the rapid pace of events. Smart preparation is the best defense against Hurricanes. That's why, at the start of each #hurricane season, Direct Relief pre-positions hurricane prep packs and modules in secure locations in disaster-prone areas, providing health facilities with the medications and medical supplies they’ll need in a storms’ wake. To learn more about Direct Relief's efforts in response to hurricanes, visit: https://www.directrelief.org/emergency/hurricanes/.
Views: 2270 Direct Relief
Using Distributed Data Networks to Understand Heterogeneity in Real-World Data
Kimberly Westrich, director of health services research at the National Pharmaceutical Council, explains how large observational datasets like electronic health records and claims data can provide a glimpse into real-world patient settings. In particular, she talks about the Observational Medical Outcomes Project (OMOP), a distributed data network, and research NPC and Auburn University have undertaken utilizing OMOP data. Ms. Westrich says one of the biggest challenges in examining this type of data is accounting for heterogeneity, which is why NPC and Auburn have developed a process to help researchers better investigate and understand this issue. For more information, visit http://www.npcnow.org/issue/real-world-evidence.
Views: 329 npcnow
Change Management Simulation Data Collection
In this video we will conduct an overview of the data collection process that we recommend when using simulation as a change management tool.
Views: 200 Phil Whitehead
Stata Video 4 - Recoding Existing Variables and Frequency Tables
Besides generating new variables, we often need to change current values/coding schemes of existing variables. In this video, we show you how to do so via "recode" and "replace" commands in Stata. Also, you will see how to list frequency tables in Stata.
Views: 1591 Lei Zhang
Beyond the Plasticene: How #blockchain is working towards a world without plastic pollution
Examining how transformative innovation can create a world without waste, Danielle Russo moderates an expert panel of Joseph Lubin, Saskia Bruysten, Michael Goltzman, Satya Tripathi at #Ethereal Davos 2019. Michael Goltzman explains how Coca Cola is helping drive change by creating a portfolio that is going to be 100% recyclable. Joe Lubin explains how mechanism design will transform and incentive ethical environmental behavior. Joe continues to explain how market dynamics will be required to determine the best solution and incentivize environmental policies. Saskia Bruysten, CEO of Yunus Social Business, explains her firm’s goal of eliminating poverty in developing countries by creating waste management systems and thereby providing jobs to people in need. The group considers various approaches to removing plastics from the environment. Throughout the entire panel, the group intensely examines the importance of plastic mitigation and sustainable financing to prevent further distribution of plastics. Satya Tripathi shares his expertise about environmental policies that are being introduced around the world. He continues to expand on how private firms are financing innovations including reusable materials, new rainforests, and sustainable farming practices. Finally, the panel answers questions from the audience. Speakers: ​Joseph Lubin, Co-Founder of Ethereum and Founder, ConsenSys Andrew Morlet, CEO, Ellen MacArthur Foundation Diego Donoso, President of Packing and Specialty Plastics, DowDuPont Satya S. Tripathi, Assistant Secretary General, The United Nations Moderated by Daniella Russo, Co-Founder and CEO, Think Beyond Plastic ConsenSys website: https://consensys.net/ Join us at #Ethereal Events & Blockchain Conferences: https://etherealsummit.com/
Views: 128 ConsenSysMedia
Evo Pricing: What we do and how we do it
http://www.evopricing.com Last week we asked the data scientists in our Turin office to explain, in their own words, what Evo Pricing does and the "secret sauce" we use to get great results for our clients. TRANSCRIPT: [Intro Music] Fabrizio Fantini (Founder of Evo Pricing): Evo Pricing is based on my PhD research work that I was doing while I was in Boston, at Harvard University. The company essentially takes data from customers, takes data from the market and estimates the probability of sales, and figures out what are the right promotions, product prices and the optimal product assortment. Elena (Data Scientist): Based on sales of the last week I can recommend which stores need specific items, which ones can exchange their items. I give a specific suggestion, for example: Store A has to send these 3 items to Store B, rather than put them in stock. Fabrizio Fantini: Our solutions cover a wide spectrum of decisions: planning, strategy, bid structure, the placing of articles in the stores, discounts, price optimization, sale management, targeted promotions - for example in the insurance sector in order to retain customers. Viola (Data Scientist): We help customers to improve their market prices by examining, for example, which competitors are moving. We study their story in a certain way, what they have in stock, the performance of past sales. Fabrizio Fantini: We often liken ourselves to satellite navigators. Why? First of all, because of our working method. A sat-nav has some complicated logic inside but then the interface with the user is very simple: it tells you if you have to go straight, left or right; it's a bit like what we are doing. We use a lot of data and algorithms that are quite complex to then give fairly simple indications to the management. Giuseppe (Senior Data Scientist): Companies have a lot of data but sometimes they do not find the right way to look at them. We try to help them to interpret what's going on. Our recommendation comes from a deep understanding of the phenomenon. Fabrizio Fantini: The amount of data available to people and companies is exploding and it’s exploding specifically because the cost is decreasing exponentially. But all of these data are like a noise and so in reality the difficulty of our job is increasing, not diminishing. Giuseppe: Since reality is complex and data is complex, fragmented, we need to use tools to capture data fragmentation and their complexity, tools that go beyond classical enterprise productivity analysis done by using Excel. Blanca (Data Scientist): We're studying what the results are, what's going on... you can see if things are getting better or getting worse. When they're going well you try to make them go even better while, when they're going worse, you say, "OK, maybe I would change some things here, maybe this item is too expensive and I would do it cheaper, or maybe it's too cheap". Elena: And then there's also the direct intervention of the managers in the stores, so every week the shopkeeper can give us their opinion on what they think will be selling or not selling in the next few weeks. Amedeo (Data Scientist): The strategy must always adapt to the needs of the individual case, of the client. Viola: We have to understand well what our customer expectations are. Amedeo: We start with an idea that can be a good approximation of the reality but, moving forward, we can find what might be the problems, things to improve or to change. Fabrizio Fantini: Just like a car driver that changes the route and then the navigator updates the entire route, so we learn from decisions that management takes and we try to adapt all this automatically, improving the quality of our recommendations. Elena: We work on all these things together, to merge the machine prediction with the human factor. Fabrizio Fantini: Our first fashion client in Italy, Miroglio Group, has publicly talked about one of our most successful and scientifically interesting experiences. We did a research project with them on distribution of items in retail fashion stores. We demonstrated that the involvement of people working in the store helps artificial intelligence to improve the quality of solutions. Algorithms improve predictions but do not win alone. We believe in what we call a new alliance between man and machine. Elena: It's a collaboration between the two things. Fabrizio Fantini: The quality of human intuition doubles the effectiveness of solutions, so it is a very significant improvement. Elena: We've seen that this alliance between man and machine brings good results. [Outro music]
Views: 436 Evo Pricing
Accounting  -  Encode Analytic Distributions in Odoo
Accounting, Encode Analytic Distributions in Odoo
Supply Chain Design | MITx on edX | Course About Video
Learn how to design and optimize the physical, financial, and information flows of a supply chain to enhance business performance ​– part of the MITx Supply Chain Management MicroMaster's Credential. Take this course free on edX: https://www.edx.org/course/supply-chain-design-mitx-ctl-sc2x#! ABOUT THIS COURSE Supply chains, especially global supply chains, are complex structures that involve several businesses crossing multiple time zones and continents. The design of a supply chain is critical to its overall success, and ultimately, the firm’s success. This business and management course will cover all aspects of supply chain design. We will start with learning how to design for the flow of physical products or goods. Variously referred to as Network Design, Facility Location, or Flow Optimization, the physical design is a core requirement for any supply chain planner. Following this, we will dive into the design of the financial flows involved with supply chains. Specifically, you will learn how to translate supply chain terms and metrics into financial terms used in the C-suite. We will then proceed into the design and management of the information flow of a supply chain by examining three critical phases: procurement, production, and demand planning. Our primary objective here is to understand how a business works with different partners in these three basic supply chain activities. The procurement lessons will cover basic approaches as well as more advanced techniques such as combinatorial auctions and supply contracts. The production lessons will concentrate on material resource planning (MRP) and fixed horizon planning. The demand management lessons will examine collaboration as well as sales and operations planning (S&OP). After covering the design of the three flows (physical, financial, and information), we will turn to the design of the supply chain organization itself as well as the metrics. Real cases and examples from practice will be used throughout the entire course. This course is part of the MITx MicroMaster’s Credential in Supply Chain Management that is specifically designed to teach the critical skills needed to be successful in this exciting field. In order to qualify for the MicroMaster’s Credential you will need to earn a Verified Certificate in each of the five courses as well as pass a Capstone Exam. When you sign up for a CTL.SC2x Verified Certificate you will also be granted access to supplemental content such as additional practice problems and complementary videos. MITx requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. MITx will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the MITx course; revocation of any certificates received for the MITx course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. WHAT YOU'LL LEARN - How to design supply chain networks and flow - How to translate supply chain actions into financial terms - How to source and procure products and services - How to plan, demand and run operations planning - How to design a supply chain organization - How to assess supply chain performance metrics
Views: 2950 edX
Examining the area of interest in Google Earth
Google Earth is a great tool for doing some quick preliminary examination of the area of study or area of interest.
Views: 741 CrossTrainingVideos
Market Research Analysts CareerSearch.com
Career Search Market and survey researchers gather information about what people think. Market, or marketing, research analysts help companies understand what types of products people want and at what price. They also help companies market their products to the people most likely to buy them. Gathering statistical data on competitors and examining prices, sales, and methods of marketing and distribution, they analyze data on past sales to predict future sales. Market research analysts devise methods and procedures for obtaining the data they need. Market and survey researchers generally have structured work schedules. They often work alone, writing reports, preparing statistical charts, and using computers, but they also may be an integral part of a research team. Market researchers who conduct personal interviews have frequent contact with the public. Most work under pressure of deadlines and tight schedules, which may require overtime. Travel may be necessary. Median annual earnings of market research analysts in May 2006 were $58,820. CareerSearch.com
Views: 127 careersearchcom