PPT-Random Sampling using Ranint

Random Sampling using Ranint for an interval 1200 Ranint is in Red Random Sampling using Ranint for an interval 1200 Random Sampling using Ranint for an interval 1200 We want our interval to be. Basic Terms. Research units – subjects, participants. Population of . interest (all humans?). Accessible . population – those you can actually try to sample. Intended . sample – those you select for participation. Population = group of people you need to know information about. Census = information obtained from every person in population. Sample = small group from within the population. Sample survey = investigation done using a sample. 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. Michael Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, . Richard . Peng. , Aaron Sidford . M.I.T.. Outline. Reducing Row Count. Row . S. ampling and Leverage Scores. Adaptive Uniform Sampling. MTH . 494. LECTURE-13. Ossam Chohan. Assistant Professor. CIIT Abbottabad. 2. STRATIFIED SAMPLING. 3. STRATIFIED SAMPLING. 1.. . Stratification. : The elements in the population are divided into layers/groups/ strata based on their values on one/several auxiliary variables. The strata must be non-overlapping and together constitute the whole population.. How do Sociologists choose the participants for their research?. Starter. Think. - Work independently for 2 minutes to write in as many key concepts into the worksheet as you can. . Pair. - Now, work in a pair with the person sitting next to you for 2 minutes and help each other with any key concepts you couldn't do on your own.. SAI India. September 2011. Sampling is used by SAI-India extensively in. Financial Audit. Compliance Audit. Performance Audit. Sampling. Planning. – selection of units for audit. Audit Execution – selection of transactions for detailed scrutiny. 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.. How . can it be that mathematics, being after all a product of human thought independent of experience, is so admirably adapted to the objects . of reality. Albert Einstein. Some parts of these slides were prepared based on . Random Sampling using Ran#. The Ran#: Generates . a pseudo . random number to 3 decimal places that . is less than 1.. i.e. . it generates a random number in the range . [0, 1. ]. . Ran#. . is in Yellow. 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. Uses of sampling in Quality. 1. Why sample?. Samples give us information about a Population.. For us, the population could be manufactured items, internet orders from Amazon. , . Skype calls. , customers in . 7. Introduction. In . a typical statistical inference problem, you want to discover one or more characteristics of a given population. .. However, it is generally difficult or even impossible to contact each member of the population.. 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.