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. Types of Biological Data. Summary Descriptive Statistics. Measures of Central Tendency. Measures of Dispersion. Assignments. Scales of Measurement: General Comments . Any observation or experiment in biology involves the collection of information (observe plants). clouds. when . it broke, and with such violence I fell to the ground . that I found myself stunned, and in a hole nine fathoms under the grass,. when I recovered, hardly knowing how to get out again. Looking down, I observed that I had on a pair of . 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. 1. SAMPLING DISTRIBUTIONS —. SAMPLING DISTRIBUTIONS. Population and samples, parameters and statistics. A . POPULATION. is the set of . all possible subjects . of a given . experiment. or . study. Dr. Halil . İbrahim CEBECİ. Chapter . 06. Continuous. . Probability. . Distributions. a . continuous random variable. . is one that can assume an . uncountable. number of values..  . We cannot list the possible values because there is an infinite number of them.. 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.. 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). and Estimators. EXAMPLE . Because of rude sales personnel, a poor business plan, ineffective advertising, and a poor name, Polly Esther’s Fashions was in business only three days. On the first day 1 dress was sold, 2 were sold on the second day, and only 5 were sold on the third day. Because 1, 2, and 5 are the entire population, the mean is . 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. . Sample . statistics. will be slightly off from the true values of its population’s . parameters. Sampling error:. The difference between a sample statistic and a population parameter. Probability theory.