PPT-Portfolio theory DESCRIPTIVE STATISTICS

From prices to returns Mean gt AVERAGEX Variance gt VARPX Standard deviation gt STDEVPX Covariance and correlation Degree to which the returns on the . Larry Weldon. Department of Statistics and Actuarial Science. Simon Fraser University, Burnaby, CANADA. “Issue” Questions. Is Mathematical Statistics = Theory of Statistics?. Expert . vs. Practitioner . 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). Data. is information that is collected.. Qualitative (attribute) data. refers to . non – numerical data. . . Intro to Probability and Statistics. Quantitative (numerical) data. represents numerical observations. It falls into two classes: . StatKey. Kari Lock Morgan. Department of Statistical Science. Duke University. kari@stat.duke.edu. . Joint Mathematical Meetings, San Diego. 1/11/13. StatKey. A set of web-based, interactive, dynamic statistics tools designed for teaching simulation-based methods at . Review Terms from Day 1. Descriptive Statistics. Review I. Variable = any trait that can change values from case to case. Must be: . Exhaustive:. . variables should consist of all possible values/attributes. and. new diretions. Gautam Mitra. Co-authors: . Diana Roman. Csaba Fabian. Victor Zviarovich. LQG Investment Technology Day. Outline. The problem of portfolio construction. Models of Choice. Second order stochastic dominance. Data. is information that is collected.. Qualitative (attribute) data. refers to . non – numerical data. . . Intro to Probability and Statistics. Quantitative (numerical) data. represents numerical observations. It falls into two classes: . Winter . 2017. ‘Understanding Statistics in Psychology’. Instructor. : Geoffrey (Geoff) Boynton . TAs:. . . Adriana . Germano. (Sections AA and AB). . . Kelly . Chang (Sections . AC . and . AD). Many . studies generate large numbers of data points, and to make sense of all that data, researchers use statistics that . summarize. the data, providing a better understanding of overall tendencies within the distributions of scores. Inferential Statistics. What You Need to Know for The Exam….. You will not have to conduct these tests in an exam(cue… sigh of relief). However you will have to;. Know the purpose of using Inferential Statistics.. Descriptive Statistics Part I Each slide has its own narration in an audio file. For the explanation of any slide click on the audio icon to start it. Professor Friedman's Statistics Course by H & L Friedman is licensed under a  Economic Viability of Shared-use Kitchens Purdue University, Agricultural Economics Department Tomoko Hiramatsu Dr. Maria I. Marshall Economic Viability of Shared-Use Kitchens Members : Jodee Ellett Lecture 1. Harrison B. Prosper. Florida State University. European School of High-Energy Physics. Parádfürdő. , Hungary. . 5 . – . 18 . June, . 2013. 1. Outline. Lecture 1. Descriptive Statistics. in Statistics Courses. Larry Weldon. Simon Fraser University. Canada. Adapting Content to Modern Practice. Overview of Issue. How can we arrange to have statistics courses evolve with modern statistical practice? .