Search results “Regression analysis of count data cambridge”
Binomial distribution | Probability and Statistics | Khan Academy
PWatch the next lesson: https://www.khanacademy.org/math/probability/random-variables-topic/binomial_distribution/v/visualizing-a-binomial-distribution?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/random-variables-topic/expected-value/v/law-of-large-numbers?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 885263 Khan Academy
NLP - Text Preprocessing and Text Classification (using Python)
Hi! My name is Andre and this week, we will focus on text classification problem. Although, the methods that we will overview can be applied to text regression as well, but that will be easier to keep in mind text classification problem. And for the example of such problem, we can take sentiment analysis. That is the problem when you have a text of review as an input, and as an output, you have to produce the class of sentiment. For example, it could be two classes like positive and negative. It could be more fine grained like positive, somewhat positive, neutral, somewhat negative, and negative, and so forth. And the example of positive review is the following. "The hotel is really beautiful. Very nice and helpful service at the front desk." So we read that and we understand that is a positive review. As for the negative review, "We had problems to get the Wi-Fi working. The pool area was occupied with young party animals, so the area wasn't fun for us." So, it's easy for us to read this text and to understand whether it has positive or negative sentiment but for computer that is much more difficult. And we'll first start with text preprocessing. And the first thing we have to ask ourselves, is what is text? You can think of text as a sequence, and it can be a sequence of different things. It can be a sequence of characters, that is a very low level representation of text. You can think of it as a sequence of words or maybe more high level features like, phrases like, "I don't really like", that could be a phrase, or a named entity like, the history of museum or the museum of history. And, it could be like bigger chunks like sentences or paragraphs and so forth. Let's start with words and let's denote what word is. It seems natural to think of a text as a sequence of words and you can think of a word as a meaningful sequence of characters. So, it has some meaning and it is usually like,if we take English language for example,it is usually easy to find the boundaries of words because in English we can split upa sentence by spaces or punctuation and all that is left are words.Let's look at the example,Friends, Romans, Countrymen, lend me your ears;so it has commas,it has a semicolon and it has spaces.And if we split them those,then we will get words that are ready for further analysis like Friends,Romans, Countrymen, and so forth.It could be more difficult in German,because in German, there are compound words which are written without spaces at all.And, the longest word that is still in use is the following,you can see it on the slide and it actually stands forinsurance companies which provide legal protection.So for the analysis of this text,it could be beneficial to split that compound word intoseparate words because every one of them actually makes sense.They're just written in such form that they don't have spaces.The Japanese language is a different story.
Views: 905 Machine Learning TV
Multiple regression (Minitab)
Currell: Scientific Data Analysis. Analysis for Fig 9.10(a) http://ukcatalogue.oup.com/product/9780198712541.do © Oxford University Press
Large-scale regression with sparse data
Rajen Shah's presentation on large-scale regression with sparse data at the RSS 2013 International Conference
Views: 980 RoyalStatSoc
How to analyse concordances systematically
A screencast explaining a systematic process of analysing concordances with the help of a brief example analysis (Observation, Classification, Generalisation, Interpretation). Feel free to use in your own teaching of corpus linguistics.
Views: 711 CorpusLingAnalysis
Chi-square tests for count data: Finding the p-value
I work through an example of finding the p-value for a chi-square test, using both the table and R.
Views: 179266 jbstatistics
Introduction to the Linguistic Inquiry and Word Count
This 7:20 video introduces the viewer to the software tool known as LIWC ('Luke'), aka the Linguistic Inquiry and Word Count program. This video is supported by the Centre for Human Evolution, Cognition and Culture, and its Cultural Evolution of Religion project, at University of British Columbia. The HECC website has accompanying instructional blog posts about the use of LIWC here http://www.hecc.ubc.ca/cerc/.
Views: 8013 ryantatenichols
What Is Statistics: Crash Course Statistics #1
Welcome to Crash Course Statistics! In this series we're going to take a look at the important role statistics play in our everyday lives, because statistics are everywhere! Statistics help us better understand the world and make decisions from what you'll wear tomorrow to government policy. But in the wrong hands, statistics can be used to misinform. So we're going to try to do two things in this series. Help show you the usefulness of statistics, but also help you become a more informed consumer of statistics. From probabilities, paradoxes, and p-values there's a lot to cover in this series, and there will be some math, but we promise only when it's most important. But first, we should talk about what statistics actually are, and what we can do with them. Statistics are tools, but they can't give us all the answers. Episode Notes: On Tea Tasting: "The Lady Tasting Tea" by David Salsburg On Chain Saw Injuries: https://www.cdc.gov/disasters/chainsaws.html https://www.ncbi.nlm.nih.gov/pubmed/15027558 https://www.hindawi.com/journals/aem/2015/459697/ Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark Brouwer, Nickie Miskell Jr., Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Divonne Holmes à Court, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, Robert Kunz, SR Foxley, Sam Ferguson, Yasenia Cruz, Daniel Baulig, Eric Koslow, Caleb Weeks, Tim Curwick, Evren Türkmenoğlu, Alexander Tamas, Justin Zingsheim, D.A. Noe, Shawn Arnold, mark austin, Ruth Perez, Malcolm Callis, Ken Penttinen, Advait Shinde, Cody Carpenter, Annamaria Herrera, William McGraw, Bader AlGhamdi, Vaso, Melissa Briski, Joey Quek, Andrei Krishkevich, Rachel Bright, Alex S, Mayumi Maeda, Kathy & Tim Philip, Montather, Jirat, Eric Kitchen, Moritz Schmidt, Ian Dundore, Chris Peters, Sandra Aft, Steve Marshall Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashC... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 374697 CrashCourse
Machine Learning with Text in scikit-learn (PyData DC 2016)
Although numeric data is easy to work with in Python, most knowledge created by humans is actually raw, unstructured text. By learning how to transform text into data that is usable by machine learning models, you drastically increase the amount of data that your models can learn from. In this tutorial, we'll build and evaluate predictive models from real-world text using scikit-learn. (Presented at PyData DC on October 7, 2016.) GitHub repository: https://github.com/justmarkham/pydata-dc-2016-tutorial Enroll in my online course: http://www.dataschool.io/learn/ Subscribe to the Data School newsletter: http://www.dataschool.io/subscribe/ == OTHER RESOURCES == My scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A My pandas video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y == JOIN THE DATA SCHOOL COMMUNITY == Blog: https://www.dataschool.io Twitter: https://twitter.com/justmarkham Facebook: https://www.facebook.com/DataScienceSchool/ YouTube: https://www.youtube.com/user/dataschool?sub_confirmation=1 Join "Data School Insiders" to receive exclusive rewards! https://www.patreon.com/dataschool
Views: 12296 Data School
Machine Learning - Text Classification with Python, nltk, Scikit & Pandas
In this video I will show you how to do text classification with machine learning using python, nltk, scikit and pandas. The concepts shown in this video will enable you to build your own models for your own use cases. So let's go! _About the channel_____________________ TL;DR Awesome Data science with very little math! -- Hello I'm Jo the “Coding Maniac”! On my channel I will show you how to make awesome things with Data Science. Further I will present you some short Videos covering the basic fundamentals about Machine Learning and Data Science like Feature Tuning, Over/Undersampling, Overfitting, ... with Python. All videos will be simple to follow and I'll try to reduce the complicated mathematical stuff to a minimum because I believe that you don't need to know how a CPU works to be able to operate a PC... GitHub: https://github.com/coding-maniac _Equipment _____________________ Camera: http://amzn.to/2hkVs5X Camera lens: http://amzn.to/2fCEU9z Audio-Recorder: http://amzn.to/2jNu2KJ Microphone: http://amzn.to/2hloKBG Light: http://amzn.to/2w8J92N _More videos _____________________ More videos in german: https://youtu.be/rtyJyzqeByU, https://youtu.be/1A3JVSQZ4N0 Subscribe "Coding Maniac": https://www.youtube.com/channel/UCG0TtnkdbMvN5OYQcgNFY1w More videos on "Coding Maniac": https://www.youtube.com/channel/UCG0TtnkdbMvN5OYQcgNFY1w _Social Media_____________________ ►Facebook: https://www.facebook.com/codingmaniac/ _____________________
Views: 17273 Coding-Maniac
Statistics: Analysis of discrete data I
IGCSE 0580 syllabus, E9, Statistics: Calculate the mean, median, mode and range for discrete data. For more math content and preparation for IGCSE exams 0580 and 0606, subscribe to my YouTube channel!
Views: 129 Thomas Davis
Stat 130 - Correlation and Regression @ Duke University
Stat 130 - Correlation and Regression @ Duke University
Views: 212 DukeClasses
What are Statistical Methods
Sets the stage for learning about statistics as a simplification (through numbers) of a complex reality.
Views: 1805 Gerald Swanson
How to read F Distribution Table used in Analysis of Variance (ANOVA)
Short visual tutorial on how to read F Distribution tables used in Analysis of Variance (ANOVA). Visual explanation on how to read ANOVA table used in ANOVA test. PlayList on Analysis of Variance https://www.youtube.com/playlist?list=PL3A0F3CC5D48431B3 F Distribution Calculator http://www.danielsoper.com/statcalc3/calc.aspx?id=4 Like MyBookSucks on Facebook! http://www.facebook.com/PartyMoreStudyLess PlayList on Hypothesis Testing http://www.youtube.com/playlist?list=PL36B9F916FA0FD039 Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 156538 statisticsfun
Intro. to Statistics for the Social Sciences - On Analysis of Variance (AnoVa - Part One)
Kyle T. of Veritas Tutors in Cambridge, MA, expands his presentation of foundational statistics in the social sciences, to include analyses of three or more social groups. These analyses collectively are called analyses of variance (AnoVa), though as Kyle explains an AnoVa is really not very different from the t-tests that preceded it in this seminar.
Views: 1460 VeritasTutors
Welcome to the SupderDataScience series on PySpark! Looking to learn more about Big Data and Machine Learning? Want to dive into projects using Python and Spark to harness the power of cloud computing? Get started with this incredible series that will progress to more complicated Pyspark tutorials to assist in providing you the knowledge and tools you need to create ground-breaking projects and big data algorithms. PySpark is a Python API built on Apache Spark which is an open-source cluster-computing framework. Big data operations are crucial from operations in Artificial Intelligence, Data Science to Cyber Security and much more. Get started learning today!
Views: 5472 SuperDataScience
Bioconductor Workshop 1: R/Bioconductor Workshop for Genomic Data Analysis
The Computational Biology Core (CBC) at Brown University (supported by the COBRE Center for Computational Biology of Human Disease) and R/Bioconductor Staff team up to provide training on analysis, annotation, and visualization of Next Generation Sequencing (NGS) data. For more info: https://www.brown.edu/academics/computational-molecular-biology/bioconductor-workshop-1-rbioconductor-workshop-genomic-data-analysis Wednesday, February 7th 2018 Brown University
Views: 466 Brown University
Statistical inference for networks: Professor Gesine Reinert, University of Oxford
Professor Gesine Reinert, Oxford University Research interests Applied Probability, Computational Biology, and Statistics. In particular: Stein’s method, networks, word count statistics Have you heard about the phenomenon that everyone is six handshakes away from the President? The six degrees of separation hypothesis relates to a model of social interactions that is phrased in terms of a network - individuals are nodes, and two individuals are linked if they know each other. Networks pop up in a variety of contexts, and recently much attention has been given to the randomness in such networks. My main research interest at the moment are network statistics to investigate such networks in a statistically rigorous fashion. Often this will require some approximation, and approximations in statistics are another of my research interests. It turns out that there is an excellent method to derive distances between the distributions of random quantities, namely Stein's method, a method I have required some expertise in over the years. The general area of my research falls under the category Applied Probability and many of the problems and examples I study are from the area of Computational Biology (or bioinformatics, if you prefer that name).
Geometric Deep Learning | Michael Bronstein || Radcliffe Institute
As part of the 2017–2018 Fellows’ Presentation Series at the Radcliffe Institute for Advanced Study, Michael Bronstein RI ’18 discusses the past, present, and potential future of technologies implementing computer vision—a scientific field in which machines are given the remarkable capability to extract and analyze information from digital images with a high degree of understanding.
Views: 9966 Harvard University
Sentiment Analysis in 4 Minutes
Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 88317 Siraj Raval
Statistical significance of experiment | Probability and Statistics | Khan Academy
Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/statistical-studies/hypothesis-test/e/hypothesis-testing-in-experiments?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/statistical-studies/hypothesis-test/v/statistical-significance-on-bus-speeds?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/statistical-studies/hypothesis-test/v/simple-hypothesis-testing?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 132306 Khan Academy
Introduction to Text Analytics with R: Overview
This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data is far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: - Tokenization, stemming, and n-grams - The bag-of-words and vector space models - Feature engineering for textual data (e.g. cosine similarity between documents) - Feature extraction using singular value decomposition (SVD) - Training classification models using textual data - Evaluating accuracy of the trained classification models Part 1 of this video series provides an introduction to the video series and includes specific coverage: - Overview of the spam dataset used throughout the series - Loading the data and initial data cleaning - Some initial data analysis, feature engineering, and data visualization Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam... The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/tu... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JLp0 See what our past attendees are saying here: https://hubs.ly/H0f5JZl0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 56540 Data Science Dojo
Text Analytics with R | Analyzing Sentiments with BoxPlot Chart | Data Science Tutorial
In this data science text analytics with R tutorial, I have talked about how you can analyze the sentiments from text using box plot chart in R. It helps us comparing sentiments of multiple texts or speeches or books to better analyze the sentiments from it. Text mining in R is done with help of sentimentr package and tm package. Text analytics with R,analyzing sentiments with boxplot chart,data science tutorial,boxplot chart,plotting sentiments,sentiment analysis in R,sentiment analysis with R,how to analyzing text in R,text processing in R,natural languge processing,NLP,nlp in R,r nlp,nlp anlaysis in R,what is text mining,how to do text mining in R,how to do NLP in R,NLP processing in R,process nlp in R,R tutorial for beginners,beginners tutorial for R,learn NLP using R
Integrative Science Symposium: Intergenerational Transmission of Psychopathology
Recorded 14 March, 2015, at the inaugural International Convention of Psychological Science, Amsterdam 0:37 - Discontinuity in the Intergenerational Transmission Process: A Role for Differential Susceptibility? - Jay Belsky, University of California 20:53 - How and How Much do Our Parents Determine Our Behavior: Was Philip Larkin Right? - Deborah M. Capaldi, Oregon Social Learning Center 39:03 - Beyond Interactionism - Michael E. Lamb, University of Cambridge 56:06 - Parental Regulation of the Epigenome - Michael J. Meaney, Douglas Mental Health University Institute and McGill University 1:19:20 - Discussion
Views: 1000 PsychologicalScience
Peter Bailis: MacroBase, Prioritizing Attention in Fast Data Streams | Talks at Google
Professor Peter Bailis of Stanford provides an overview of his current research project, Macrobase, an analytics engine that provides efficient, accurate, and modular analyses that highlight and aggregate important and unusual behavior, acting as a search engine for fast data. This is part of Google Cloud Advanced Technology Talks, a series dedicated to bringing cutting edge research and prestigious researchers to speak at Google Cloud. All speakers are leading experts and innovators within their given fields of research. Peter Bailis is an assistant professor from Stanford University.
Views: 1974 Talks at Google
IELTS - How to get a high score on Task 1 of the IELTS
Watch this lesson to get a better score on Task 1 of the academic IELTS. Panicked about the writing section of the IELTS? Or do you work in a field that requires you to present graphs? This English lesson will teach you key vocabulary to use when describing different types of graphs, a requirement in task 1 of the IELTS writing section. I will explain what you must do in Task 1, how you will be marked, and key expressions to use. I'll also give you some tips to help you achieve a high score on the IELTS. After the lesson, test yourself with the quiz at http://www.engvid.com/ielts-task-1-vocabulary/ For a complete, free guide to the IELTS, go to http://www.GoodLuckIELTS.com/
Lecture 15: Coreference Resolution
Lecture 15 covers what is coreference via a working example. Also includes research highlight "Summarizing Source Code", an introduction to coreference resolution and neural coreference resolution. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
Install and run Spark Notebooks for course projects
How to get the Spark Notebook system running on your personal computer. We'll use this to analyze archaeological data sets, as detailed in other videos in this playlist.
Views: 1518 Neel Smith
Inferring causality from Big Data by Samantha Kleinberg (P-1)
Massive amounts of medical data such as from electronic health records and body-worn sensors are being collected and mined by researchers, but translating findings into actionable knowledge remains difficult. The first challenge is finding causes, rather than correlations when the data are highly noisy and often missing, and relationships are more complicated than pairwise links between variables. The second challenge is then using these relationships to explain specific cases, such as why an individual’s blood glucose is raised. In this talk, I discuss new methods for both causal inference and explanation from complex and uncertain data, and how they can be used to make better decisions and provide individualized feedback. Speaker Bio: Samantha Kleinberg is an Assistant Professor of Computer Science at Stevens Institute of Technology. She received her Ph.D. in Computer Science from New York University in 2010 and was a Computing Innovation Fellow at Columbia University in the Department of Biomedical Informatics from 2010-2012. She is the recipient of NSF CAREER and JSMF Complex Systems Scholar Awards. She is the author of "Causality, Probability, and Time" (Cambridge University Press, 2012) and "Why: A Guide to Finding and Using Causes" (O’Reilly Media, 2015), a nontechnical introduction to causality. Join our Data Science + FinTech JC-NY here: https://www.meetup.com/Data-Science-Fintech-JC-NY/ Disclaimer: The views and opinions expressed in this video are purely those of the speaker and are not reflective of qplum. Investment strategies, results and any other information presented are for education and research purposes only. Investments involve risk and there are no guarantees of any kind. Carefully determine your risk tolerance and invest accordingly. Be sure to first consult with a qualified financial adviser and/or tax professional before implementing any strategy discussed herein. Past performance is not indicative of future performance.
Views: 199 qplum
Generating Rich Event Data on Civil Strife: A Progressive Supervised-Learning Approach Part 1
Paper/Presentation Description: This paper examines the challenges and opportunities that "big data" poses to scholars advancing research frontiers in the social sciences. It examines the strengths and weaknesses of machine-based and human-centric approaches to information extraction and argues for use of a hybrid approach, one that employs tools developed by data scientists to leverage the relative strengths of both machines and humans. The notion of a progressive, supervised-learning approach is developed and illustrated using the Social, Political and Economic Event Database (SPEED) project's Societal Stability Protocol (SSP). The SSP generates rich event data on civil strife and illustrates the advantages of employing a supervised-learning approach in contrast to conventional approaches for generating civil strife data. We show that conventional event-count approaches miss a great deal of within-category variance (e.g., number of demonstrators, types of weapons used, number of people killed or injured). We also show that conventional efforts to categorize longer periods of civil war or societal instability have been systematically mis-specified. To demonstrate the capacity of rich event data to open new research frontiers, SSP data on event intensities and origins are used to trace the changing role of political, class-based and socio-cultural factors in generating civil strife over the post WWII era. Speaker Bio: After completing his doctoral work in political science at Northwestern University, Professor Althaus joined the University of Illinois faculty in 1996 with a joint appointment in the Political Science and Communication departments. He is currently a professor in both departments, and also associate director of UIUC's Cline Center for Democracy, where he has been a faculty affiliate since 2004. Professor Althaus's research and teaching interests center on the communication processes by which ordinary citizens become (in theory, at least) empowered to exercise popular sovereignty in democratic societies, as well as on the communication processes by which the opinions of these citizens are conveyed to government officials, who (in theory, at least) must transform the will of the people into political action. His work therefore focuses on three areas of inquiry: (1) the processes and constraints that shape the journalistic construction of news about public affairs, (2) the processes and constraints that influence how news audiences receive and utilize public affairs information, and (3) the channels of communication that allow individual members of a polity to speak in a collective voice as a public. He has particular interests in the quantitative analysis of political discourse, opinion surveys as channels for mass communication and political representation, the impact of strategic communication activities on news coverage and public opinion, the psychology of information processing, and communication concepts in democratic theory. Professor Althaus serves on the editorial boards of Critical Review, Human Communication Research, Journal of Communication, Political Communication, and Public Opinion Quarterly. His research has appeared in the American Political Science Review, the American Journal of Political Science, Communication Research, Journalism and Mass Communication Quarterly, Journal of Broadcasting & Electronic Media, Journal of Conflict Resolution, Journal of Politics, Public Opinion Quarterly, and Political Communication. His book on the political uses of opinion surveys in democratic societies, Collective Preferences in Democratic Politics: Opinion Surveys and the Will of the People (Cambridge University Press, 2003) , was awarded a 2004 Goldsmith Book Prize by the Joan Shorenstein Center on the Press, Politics and Public Policy at Harvard University, and a 2004 David Easton Book Prize by the Foundations of Political Theory section of the American Political Science Association. He was named a Merriam Professorial Scholar by the UIUC Department of Political Science and the Cline Center for Democracy (2012-4, 2010-2), a 2004-5 Beckman Associate by the UIUC Center for Advanced Studies, and a 2003-4 Helen Corley Petit Scholar by the UIUC College of Liberal Arts and Sciences.
Views: 114 NanoBio Node
Advanced Statistics for Researchers (Part II)
Part II: Avoiding bias in literature review PROMISE AGEP, the Graduate School at UMBC and the Office of Post-Doctoral Affairs is proud to introduce a new series of seminars presented by Dr. Christopher Rakes, Assistant Professor, Department of Education. Systematic Review and Meta-Analysisis a set of methods for combining results from multiple studies to examine an overall effect. These techniques allow researchers to “step back” from individual studies and see a clearer picture of the field. This series will include methods for conducting systematic literature reviews and computing effect sizes. Structural Equation Modelingis a robust analytic framework that envelopes and improves upon many other familiar analytic methods (e.g., ANOVA, regression). Structural equation modeling, allows researchers to model both measured variables (such as items on a questionnaire) and the unobserved (latent) factors associated with those variables. This series will include methods for using structural equation modeling to conduct confirmatory factor analysis, testing causal structures, and comparing group differences in latent means. Session 2, 10/8/14 Meta-analysis and Systematic Review: Avoiding bias in literature review and calculating effect sizes. This session will focus on methods for obtaining a representative literature sample, developing a coding schema, measuring inter-rater reliability, and computing, analyzing, and interpreting effect sizes.
4-11 Other Useful Charts – Pie, Pareto, Scatter
In statistics, you will probably use bar charts and histograms the most, but here are some examples of graphing that are not used very often, but are still useful to know about. I am going to show you one chart that is very useful for count data, another that we will use later with correlation, plus a third that has been called, “easily the worst way to convey information ever developed in the history of data visualization.” Table of Contents: 00:51 - Pie Charts 02:20 - Pie Charts in SPSS 03:15 - Problems with Pie Charts 05:26 - Pareto Charts 05:59 - Pareto Charts in SPSS 07:14 - Scatterplot
Views: 1208 Research By Design
Inductive Statistics (STA2) - Chi-square (Hanze University)
This is the Inductive Statistics Chi-square lesson provide by International Business School of Hanzehogeschool University of Applied Sciences, the Netherlands. The lesson is given by Ning Ding.
Views: 1975 Ji Xiao
Working with repeated data to identify trends
Working with a table to identify trends. How to find empty cells in a table and then analyze the data.
Views: 480 Mike Theiss
Algorithms for Big Data (COMPSCI 229r), Lecture 1
Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' algorithm.
Views: 84325 Harvard University
Towards Machines that Perceive and Communicate
Kevin Murphy (Google Research) Abstract: In this talk, I summarize some recent work in my group related to visual scene understanding and "grounded" language understanding. In particular, I discuss the following topics: Our DeepLab system for semantic segmentation (PAMI'17, https://arxiv.org/abs/1606.00915). Our object detection system, that won first place in the COCO'16 competition (CVPR'17, https://arxiv.org/abs/1611.10012). Our instance segmentation system, that won second place in the COCO'16 competition (unpublished). Our person detection/ pose estimation system, that won second place in the COCO'16 competition (CVPR'17, https://arxiv.org/abs/1701.01779). Our work on visually grounded referring expressions (CVPR'16, https://arxiv.org/abs/1511.02283). Our work on discriminative image captioning (CVPR'17, https://arxiv.org/abs/1701.02870). Our work on optimizing semantic metrics for image captioning using RL (submitted to ICCV'17, https://arxiv.org/abs/1612.00370). Our work on generative models of visual imagination (submitted to NIPS'17). I will explain how each of these pieces can be combined to develop systems that can better understand images and words. Bio: Kevin Murphy is a research scientist at Google in Mountain View, California, where he works on AI, machine learning, computer vision, and natural language understanding. Before joining Google in 2011, he was an associate professor (with tenure) of computer science and statistics at the University of British Columbia in Vancouver, Canada. Before starting at UBC in 2004, he was a postdoc at MIT. Kevin got his BA from U. Cambridge, his MEng from U. Pennsylvania, and his PhD from UC Berkeley. He has published over 80 papers in refereed conferences and journals, as well as an 1100-page textbook called "Machine Learning: a Probabilistic Perspective" (MIT Press, 2012), which was awarded the 2013 DeGroot Prize for best book in the field of Statistical Science. Kevin is also the (co) Editor-in-Chief of JMLR (the Journal of Machine Learning Research).
Demeter Sztanko: Analysis and transformation of geospatial data using Python
PyData London 2015 A tutorial covering some general concepts of geospatial data, main formats in which it is distributed and some common places where this data can be acquired. We will also learn how to read, process and visualise this data using Python and QGIS. This talk will cover some typical problems one can experience when working with geospatial data. Full details — http://london.pydata.org/schedule/presentation/6/
Views: 466 PyData
Using the t Table to Find the P-value in One-Sample t Tests
I work through examples of finding the p-value for a one-sample t test using the t table. (It's impossible to find the exact p-value using the t table. Here I illustrate how to find the appropriate interval of values in which the p-value must lie.)
Views: 509258 jbstatistics
Recovering Optimal Solution by Dual Random Projection
Random projection has been widely used in data classification. It maps high-dimensional data into a low dimensional space in order to reduce the computational cost in solving the related optimization problem. While previous studies are focused on analyzing the classification performance of using random projection, in this work, we consider the recovery problem, i.e., how to accurately recover the solution to the optimization problem in the original high dimensional space based on the solution learned from the subspace spanned by random projections. We present a simple algorithm, termed Dual Random Projection, that uses the dual solution to the low-dimensional optimization problem to recover the solution to the original optimization problem. Our theoretical analysis shows that with a high probability, the proposed algorithm is able to accurately recover the optimal solution in the high dimensional space, provided that the data matrix is of low rank. We also examine the relationship between compressive sensing and the problem of recovering optimal solution using random projection.
Views: 214 Microsoft Research
#05 Flat Sorting, Mode, Pie Charts in Excel with XLSTAT
Here is how to describe a series of qualitative data using flat sorting, mode and pie charts. Go further: https://help.xlstat.com 30-day free trial: https://www.xlstat.com/en/download -- Stat Café - Question of the Day is a playlist aiming at explaining simple or complex statistical features with applications in Excel and XLSTAT based on real life examples. Do not hesitate to share your questions in the comments. We will be happy to answer you. -- Produced by: Addinsoft Directed by: Nicolas Lorenzi Script by: Jean Paul Maalouf
Views: 1133 XLSTAT
NLTK Stopwords Solution - Intro to Machine Learning
This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 2066 Udacity
Probabilistic Inference in Excel
Demonstration of Microsoft Research Cambridge project, Probabilistic Inference in Excel (Tabular).
Views: 79 Microsoft Research
What Is The Definition Of Parsimonious?
You know those people who count up every penny when it's time to split a restaurant bill? can define parsimonious (adjective) and get synonyms. Sparing, restrained parsimonious definition, characterized by or showing parsimonysee more meaning, what is not willing to spend money give something adjective mean, stingy, penny pinching (informal), miserly, near saving, grasping, miserable, stinting, frugal, niggardly, penurious, tightfisted, close fisted, tight arse (taboo slang), mingy (brit mnemonicdictionary meaning of and a memory aid (called mnemonic) retain that for long time in our person unwilling lot. Meaning, pronunciation, picture, example sentences, grammar, usage notes, definition of parsimonious written for english language learners from the merriam webster learner's dictionary with audio pronunciations, examples, parsimony principle is basic to all science and tells us choose in terms tree building, that means that, other things being equal, best apr 14, 2015. Dictionary and word of the day parsimonioususe parsimonious in a sentence. Oxford parsimonious synonyms, antonyms definedcollins english dictionarymba skool study model definition, ways to compare models. What is parsimonious (adjective)? Parsimonious (adjective) meaning, pronunciation and more by macmillan (comparative parsimonious, superlative most parsimonious)sparing in expenditure of moneyextreme unwillingness to spend money or use resources 'a great tradition public design has been shattered government parsimony' very unwilling pronunciation, example sentences, from oxford dictionaries synonyms for at thesaurus with free online thesaurus, antonyms, definitions. Aic, bic and different ways to choose definition of parsimonious adjective in oxford advanced learner's dictionary. Parsimonious (adjective) definition and synonyms parsimony of in english parsimonious. Parsimonious definition in the cambridge english dictionary. Definition of parsimonious by merriam websterdefine at dictionary. Link cite add to word listthe definition of parsimonious is people who are someone very unwilling spend money Meaning, pronunciation, translations and examples means the simplest model theory with least assumptions variables but greatest explanatory power. Definition of parsimonious by merriam webster. Psychology definition for occam's razor (law of parsimony) in normal everyday language, edited by psychologists, professors and leading studentsDefinition parsimonious merriam websterdefine at dictionary. Parsimonious meaning definition of parsimonious by mnemonic dictionary vocabulary. Definition of parsimonious. One of the principles reasoning jul 10, 2015 what is a parsimonious model? All about low and high parsimony tradeoff with goodness fit. Exhibiting or marked by parsimony; Especially frugal to the point of stinginess. Parsimonious definition of parsimonious by the free dictionary. Parsimonious adjective definition, pictures, pronunciation and parsimonious definition for english language learners from reconstructing trees parsimony understanding evolutionoccam's razor (law of parsimony).
Views: 79 Last Question
High Dimensional Functional Learning - Jason Klusowski
Jason Klusowski is a 4th year PhD student in the department of statistics at Yale University. He is interested in describing the balance between computational feasibility and optimality of statistical procedures in high dimensional settings. Abstract: General d-dimensional functions can be approximated by feed forward networks with one layer of nonlinearities. In a statistical setting, one observes sites in the domain of the function along with the evaluation of the function (with additive error) at these sites. The goal is to estimate the original function from this data. Greedy algorithms have significantly reduced the size of the required search dimension and consequently prompted the study of their theoretical properties. However, other computational challenges remain. In the first part of the talk, Jason will present new bounds for the average squared error of the target function from its greedily obtained estimator that are useful even when the dimension is significantly greater than the available sample size. He will also address the computational issue of finding the parameters of a new sigmoid that are fit to the residuals of the previous combinations of sigmoids, as required by the greedy scheme. One way is through adaptive annealing, in which small, internal modifications of the sigmoid are made that produce algorithmic stability and whose aggregated effect enable the realization of a desirable parameter. This work is joint with Dr. Andrew Barron of Yale University. Slides at https://github.com/gwulfs/bostonml#high-dimensional-function-learning
Views: 256 John O'Neill
Introductory Econometrics for Finance Lecture 11
This is the eleventh lecture in the series to accompany the book “Introductory Econometrics for Finance”. The videos build into a complete first course in econometrics with financial applications assuming no prior knowledge of the subject. You can buy the text and access other free resources at: http://www.cambridge.org/brooks3e The eleventh lecture is the first of two covering the problem of residual autocorrelation, focusing on definitions and testing, and links particularly with pages 188-199 of the third edition of the book.
Views: 4205 Chris Brooks
Psychometrics Meaning
Video is created with the help of wikipedia, if you are looking for accurate, professional translation services and efficient localization you can use Universal Translation Services https://www.universal-translation-services.com?ap_id=ViragGNG Video shows what psychometrics means. The design of psychological tests to measure intelligence, aptitude and personality; and the analysis and interpretation of their results.. Psychometrics Meaning. How to pronounce, definition audio dictionary. How to say psychometrics. Powered by MaryTTS, Wiktionary
Views: 382 SDictionary

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