PPT-Sample-Separation-Margin Based Minimum Classification Error

Discriminant Functions Yongqiang Wang 12 Qiang Huo 1 1 Microsoft Research Asia Beijing China 2 The University of Hong Kong Hong Kong China qianghuomicrosoftcom ICASSP2010 Dallas Texas USA March 1419 2010. Recitation 13. (11/27/2012). TA: Zhen . (Alan) . Zhang. zhangz19@stt.msu.edu. Office hour: (C500 WH) 1:45 – 2:45PM . Tuesday. (office . tel.: . 432-3342). Help-room: (A102 WH) 11:20AM-12:30PM, . Monday, Friday. Estimates and Sample Sizes. Chapter . 7, Part 2. Example Problems. Finding Margin of Error. Question:. Assume that a random sample is used to estimate a population proportion p. Find the margin of error E that corresponds to the given statistics and confidence level.. For each of the following please state the population, sample, parameter, and statistic.. The . local school board posts average SAT Math scores for each high school in the district. For one particular high school, the average math score is given as 516. A statistics teacher at that high school thinks that this is too high. He takes a random sample of 100 students and finds that their average SAT Math score is 486. . . Zvonimir . Pavlinovic. Tim King Thomas . Wies. . . New York University. An example. Rank error sources by some criterion. Report top ranked sources to the programmer. x: . int. Basics. m. Since about of the samples are . if we create intervals based on a sample mean, x, and. go up and down by , then of the . time, we. ’ll create an interval that .. Recitation 12. (4/2/2013). TA: Zhen Zhang. zhangz19@stt.msu.edu. Office hour: (C500 WH) 3-4 PM Tuesday. (. office . tel.: . 432-3342). Help-room: (A102 WH) . 9:00AM-1:00PM. , . Monday. Class meet on Tuesday: . Department of Statistics. Penn State . University. USA. Making computing skills part of learning introductory stats. Royal Statistical Society. 10/13/16. ASA 2016 Recommendations for Intro Stat. GAISE: Guidelines . If the individuals in the population differ in some qualitative way, we often wish to estimate the proportion / fraction / percentage of the population with some given property.. For example: We track the sex of purchasers of our product, and find that, across 400 recent purchasers, 240 were female. What do we estimate to be the proportion of all purchasers who are female, and how much do we trust our estimate?. Dru. Rose . I wonder what percentage of all 600 . Kare. . Kare. College students travel to school by car ?. . Population . 600. students. S. ample. n. = 25. Dru. Rose . Estimating a Population Proportion. Practice . Pg. 340-341. #6-8 (Finding Critical Values). #9-11 (Expressing/Interpreting CI). #17-20 (Finding Margin of Error). Answers: Pg. . 340-341. #. 5. -8 . (Finding Critical Values. Dr.. . Tingting. Mu. Email: . tingting.mu@manchester.ac.uk. Chapter . 4. : Support Vector Machines. Outline. Understand . concepts such as . hyperplane. , distance.. Understand the basic idea of support vector machine (SVM).. Population . Proportion. Objective. : . To estimate population proportions under specific conditions with various confidence levels. Warm-Up: Estimating Population Means. A coffee machine dispenses coffee into paper cups. You’re supposed to get 10 ounces of coffee, but the amount varies slightly from cup to cup. Here are the amounts measured in a random sample of 20 cups. Find a 95% confidence interval based off of these sample data to capture the true mean. Is the true mean actually captured in your interval?. Recitation 13. (11/27/2012). TA: Zhen . (Alan) . Zhang. zhangz19@stt.msu.edu. Office hour: (C500 WH) 1:45 – 2:45PM . Tuesday. (office . tel.: . 432-3342). Help-room: (A102 WH) 11:20AM-12:30PM, . Monday, Friday. Choosing a sample size. Sampling methods. Stratified sampling, cluster sampling. Sampling problems. Non-response bias, measurement . bias. Optimization (Exce. l. ’s “Solver”). Adverse . selection.