PPT-Cluster Sampling STAT262

A new sampling method Motivating example Want to study the average amount of water used per person per month How would you design a survey 2 A new sampling method Consider the two strategies Sample person by person. Module 8 Survey sample builder. Learning objectives. Slide. . 2. By the end of this module, you should understand:. how to use the survey sample builder to:. determine the survey design. select random clusters and children or households. 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. brian. . schnick. BASIC CONCEPTS IN SAMPLING. Advantages of Sampling. Sampling Error. Sampling Procedure. Advantages of Sampling. Sampling is a necessity in many geographic research problems. Population may be simply too big for 100% contact. Introduction Cluster One Cluster Two Cluster Sampling is perhaps the most important step in assuring that good quality aggregates are being used on INDOT contracts. Since a sample is just a small portion of the total material, the importance th Approximations in Probabilistic Programming. The computing . stack (approximation). Algorithms. Compiler and runtime. Architecture. The APPROX view: with . probabilities. and approximations!. The computing stack. Researchers studied the effects that improving vision with eyeglasses had on educational outcomes. They identified 2,069 students who could improve their vision with eyeglasses. 750 were not offered eyeglasses and 1,319 were. Of the 1,319 offered eyeglasses, 928 accepted the eyeglasses. Students who received the eyeglasses scored significantly higher in both math and science. What was the treatment in this study?. Main role of randomization:. Assign treatments to the experimental units.. Sampling. Main role of randomization:. Random selection of the sample of cases from the population.. Why randomize?. Experiments. Alex Beutel. Joint work with Kenton Murray, . Christos . Faloutsos. , Alex . Smola. April 9, 2014 – Seoul, South Korea. Online Recommendation. 2. 5. Users. Movies. 5. 3. 5. 5. 2. Online Rating Models. 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. Motivating example: enrollment in California Schools. California schools: E=4421, H=775, M=1018. We are interested in the number of enrolled students. 25%. What is stratified sampling. ?. Stratify. : make . merayxu@gmail.com. Person . Re-identification by Matching Compositional Template with . Cluster Sampling. Problem. Identifying The Same Person Under Different Cameras. Person Re-identification. Basic Assumption. Background. We have learned ways to display, describe, and summarize data, but have been limited to examining the particular batch of data we have.. To make decisions, we need to go beyond the data at hand and to the world at large.. Mark Bun (Boston University), . Jörg. Drechsler (IAB), . Marco . Gaboardi. (Boston University), Audra McMillan (Apple), . Jayshree Sarathy (Harvard University). Outline. Background. Cluster sampling.