PPT-Lecture 3 Properties of Summary Statistics: Sampling Distribution

Main Theme How can we use math to justify that our numerical summaries from the sample are good summaries of the population Lecture Summary Today we focus on two summary statistics of the sample and study its theoretical properties. Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Statistics and Data Analysis. Part 10 – The Law of. Large Numbers . and the Central. Comparison of Group Means. Standard Deviations (. z . scores). 0. +1. +2. +3. -2. -1. -3. 34.13%. 34.13%. 13.59%. 13.59%. 2.14%. 2.14%. 0.13%. 0.13%. Percentile Equivalents. 1. 5. 10. 2. 0. 4. 0. 5. 0. Properties of Summary Statistics: Sampling Distribution. Main Theme . How can we use . math. to justify that our numerical . summaries from the sample are . good . summaries of the population?. Lecture Summary. . and Randomization Procedures. Dennis Lock. Statistics Education Meeting. October 30, 2012. 1. An introductory statistics book writing with my family. Robin H. Lock (St. Lawrence). Patti F. Lock (St. Lawrence). 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. Radford M. Neal. The Annals of Statistics (Vol. 31, No. 3, 2003). Introduction. Sampling from a non-standard distribution. Metropolis algorithm is sensitive to choice of proposal distribution. Proposing changes that are too small leads to inefficient random walk. Textbook: Sections . 8.7, 9.1 through 9.4. • Approximate binomial-distribution probabilities using a normal . distribution. • Explain statistical inference in terms of statistics and parameters.. 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. Jonathan W. Duggins; James Blum. NC State University; UNC Wilmington. Agenda. Introduction. SAS Studio Basics. Tasks. Snippets. Editing Code. Summary. Introduction. Motivation. Guidelines for Assessment and Instruction in Statistics Education (GAISE). Martina Litschmannová. m. artina.litschmannova. @vsb.cz. EA 538. Populations. vs. Sample. A . population. includes each element from the set of observations that can be . made.. A . sample. consists only of observations drawn from the population.. Room 310. Class . #14. Section 4.6 and 4.8. Section 4.6. We learn how to make inferences about the population on the basis of information contained in the sample. . Several . of these techniques are based on the assumption that the population is approximately normally distributed. It will be important to determine whether the sample of data come from a normal population before we can apply these techniques properly. . AP Statistics. Unit 5. The Central Limit Theorem for Sample Proportions. Rather than showing real repeated samples, . imagine. what would happen if we were to actually draw many samples.. Now imagine what would happen if we looked at the sample proportions for these samples. . 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.