PPT-Lecture notes 5: sampling distributions and the central lim

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Highlights The law of large numbers The central limit theorem Sampling distributions Formalizing the central limit theorem Calculating probabilities associated with

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Lecture notes 5: sampling distributions and the central lim: Transcript

Highlights The law of large numbers The central limit theorem Sampling distributions Formalizing the central limit theorem Calculating probabilities associated with sample means Two important results in inferential statistics. Slide . 1. Sample bold display. P. B. #text. #text. nextSibling. prevSibling. nextSibling. prevSibling. firstChild. lastChild. parentNode. parentNode. parentNode. Slide . 1. <. p>Sample . bold display. P. B. #text. #text. nextSibling. prevSibling. nextSibling. prevSibling. firstChild. lastChild. parentNode. parentNode. parentNode. Understanding the meaning of the terminology we use.. Quick calculations that indicate understanding of the basis of methods.. Many of the possible questions are already sprinkled in the lecture slides.. Slide . 1. <. p>Sample . bold display. P. B. #text. #text. nextSibling. prevSibling. nextSibling. prevSibling. firstChild. lastChild. parentNode. parentNode. parentNode. Slide . 1. Intelligent Systems (AI-2). Computer Science . cpsc422. , Lecture . 11. Oct, 2, . 2015. 422 . big . picture: Where are we?. Query. Planning. Deterministic. Stochastic. Value Iteration. Approx. Inference. Slide . 1. Access Matrix. File A. File B. File C. Printer 1. Alice. RW. RW. RW. OK. Bob. R. R. RW. OK. Carol. RW. David. RW. OK. Faculty. RW. RW. OK. CS 140 Lecture Notes: Protection. Slide . 2. Access Matrix. Parameter & Statistic. Parameter. Summary measure about population. Sample Statistic. Summary measure about sample. P. . in. . P. opulation. . &. . P. arameter. S. . in. . S. ample. . Slide . 1. CSS Rule. body {. font-family: Tahoma, Arial, sans-serif;. color: black;. background: white;. margin: 8px;. }. Selector. Declaration. Block. Attribute Name. Value. CS 142 Lecture Notes: CSS. Slide . 1. Sample bold display. P. B. #text. #text. nextSibling. prevSibling. nextSibling. prevSibling. firstChild. lastChild. parentNode. parentNode. parentNode. Slide . 1. CSS Rule. body {. font-family: Tahoma, Arial, sans-serif;. color: black;. background: white;. margin: 8px;. }. Selector. Declaration. Block. Attribute Name. Value. CS 142 Lecture Notes: CSS. Slide . 1. Google Datacenter. CS 142 Lecture Notes: Datacenters. Slide . 2. Datacenter Organization. Rack:. 50 machines. DRAM: 200-800GB @ 300 . µs. Disk: 100TB @ 10ms. Single server:. 4-8 cores. DRAM: 4-16GB @ 100ns. 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.. Objectives. In this chapter, you learn:. The concept of the sampling distribution. To compute probabilities related to the sample mean and the sample proportion. The importance of the Central Limit Theorem. Lecture PowerPoint Slides. Basic Practice of Statistics. 7. th. Edition. In chapter 15, we cover …. Parameters and statistics. Statistical estimation and the Law of Large Numbers. Sampling distributions.

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