PPT-Sampling : Your data is only as good

as your field technicians Jeff Martin Astrea Taylor amp Nancy Zikmanis Next Phase of the Project Life Cycle PLANNING SAMPLING ASSESSMENT EVALUATION Plan for data collection using the DQO . Church Main St Watergrasshill O Mahonys Bar Sars64257elds Court Opp Hospital Sallybrook Opp Brook Inn Riverstown Opp Veterinary Hospital Hazelwood Opp Community School Glanmire Opp Stone Arch Lwr Glanmire Rd Water Street Jctn Cork Bus Station Parnel Approximating the . Depth. via Sampling and Emptiness. Approximating the . Depth. via Sampling and Emptiness. Approximating the . Depth. via Sampling and Emptiness. Example: Range tree. S = Set of points in the plane. Impact Evaluations. Marie-H. é. lène. . Cloutier. 1. Introduction. Ideally, want to compare what happens to the . same. schools with and without the program. But . impossible. → use . statistics. Big Question: How do you know when you have collected enough data and done it appropriately?. Today’s Agenda. Tips and Tricks. Article Review Discussion. Assignment for Feb 24. Sampling Review. Observation Approaches (Qualitative/ Quantitative) and Practice. . for. . Qualitative. . Research. Assoc. . Prof. Dr. Şehnaz . Şahinkarakaş. Sampling. S. ample. :. any . part of a population of individuals on whom information is obtained: students, teachers, young learners, etc. Yu Su*, Gagan Agrawal*, . Jonathan Woodring. #. Kary Myers. #. , Joanne Wendelberger. #. , James Ahrens. #. *The Ohio . State University. #. Los . Alamos National . Laboratory. Motivation. Science becomes increasingly data driven;. Zhizhuo. Zhang . Outline. Review of Mixture Model and EM algorithm. Importance Sampling. Re-sampling EM. Extending EM. Integrate Other Features. Result. Review Motif Finding: Mixture modeling. Given a dataset . Robert Christensen. , Feifei Li. University of Utah. Lu Wang, Ke Yi. Hong Kong University. Of Science and Technology. Motivation. Geo Spatial Data is being collected on a massive scale. Approximate aggregations is fast and often effective for this data. From Surveys to Big . D. ata. Edith Cohen. Google Research. Tel Aviv University. Disclaimer:. Random sampling is classic and well studied tool with enormous impact across disciplines. This presentation is biased and limited by its length, my research interests, experience, understanding, and being a Computer Scientist. I will attempt to present some big ideas and selected applications. I hope to increase your appreciation of this incredible tool.. Lemonade from Lemons. Bugs manifest themselves every where in deployed systems.. Each manifestation gives us the chance of inspection and hence the resolutions.. Deployment gives more test cases than the test suite.. A link between Continuous-time/Discrete-time Systems. x. (. t. ). y. (. t. ). h. (. t. ). x. [. n. ]. y. [. n. ]. h. [. n. ]. Sampling. x. [. n. ]=. x. (. nT. ), . T. : sampling period. x. [. n. ]. x. General Sampling Issues. Thinking of the steps in sampling (from theoretical population to respondents)—what are some biases that can come in at each point?. What is the proximity similarity model? What are issues with that model?. Krishna . Pacifici. Department of Applied Ecology. NCSU. January 10, 2014. Designing studies. Why, what, and how?. Why collect the data?. What type of data to collect?. How should the data be collected in the field and then analyzed?. Ke. Yi. Hong Kong University of Science and Technology. yike@ust.hk. Random Sampling on Big Data. 2. “Big Data” in one slide. The 3 V’s. : . Volume. External memory algorithms. Distributed data.