PPT-Assessing Student Learning about Statistical Inference

Beth Chance Cal Poly San Luis Obispo USA John Holcomb Cleveland State University USA Allan Rossman Cal Poly San Luis Obispo USA George Cobb Mt Holyoke College USA Background. . A School Leader’s Guide for Improvement. 1. Georgia Department of Education . Dr. John D. Barge, State School Superintendent . All Rights Reserved. The Purpose of this Module is to…. p. rovide school leaders an opportunity to strengthen their understanding of low inference feedback.. Meeting 5: Chunk 2. “I can infer…because…and…I know”. Today’s Cluster:. Objective: . By the end of the meeting, teachers will be prepared to introduce “I can infer…because…and I know…” using the critical attributes which. The truth, the whole truth, and nothing but the truth.. What is inference?. What you know + what you read = inference. Uses facts, logic, or reasoning to come to an assumption or conclusion. Asks: “What conclusions can you draw based on what is happening . S. M. Ali Eslami. September 2014. Outline. Just-in-time learning . for message-passing. with Daniel Tarlow, Pushmeet Kohli, John Winn. Deep RL . for ATARI games. with Arthur Guez, Thore Graepel. Contextual initialisation . Rahul Sharma and Alex Aiken (Stanford University). 1. Randomized Search. x. = . i. ;. y = j;. while . y!=0 . do. . x = x-1;. . y = y-1;. if( . i. ==j ). assert x==0. No!. Yes!.  . 2. Invariants. Kari Lock Morgan. Department of Statistical Science, Duke University. kari@stat.duke.edu. . with Robin Lock, Patti Frazer Lock, Eric Lock, Dennis Lock. ECOTS. 5/16/12. Hypothesis Testing:. Use a formula to calculate a test statistic. There is a hierarchy of truths:. Mathematical truth. is independent of our perceptions. . Examples are facts like (. x. + . y. ) . z. = . xz. + . yz. and (for right triangles) . a. 2 . + . Chris . Mathys. Wellcome Trust Centre for Neuroimaging. UCL. SPM Course. London, May 11, 2015. Thanks to Jean . Daunizeau. and . Jérémie. . Mattout. for previous versions of this talk. A spectacular piece of information. Suhas Lohit, . Kuldeep. Kulkarni, . Pavan. . Turaga. ,. . Jian Wang, . Aswin. . Sankaranarayanan. Arizona . State . University. . Carnegie Mellon University. Sergio Pissanetzky. Sergio@SciControls.com. Emergent Inference. Any system. VISION. ROBOT. SOFTWARE. your mom. grab. an. object. computer. program. eyes. cameras,. sensors. translation. 100,000,000. Susan Athey, Stanford GSB. Based on joint work with Guido Imbens, Stefan Wager. References outside CS literature. Imbens and Rubin Causal Inference book (2015): synthesis of literature prior to big data/ML. An.  inference is an idea or conclusion that's drawn from evidence and reasoning. . An . inference.  is an educated . guess.. When reading a passage: 1) Note the facts presented to the reader and 2) use these facts to draw conclusions about . Stat-GB.3302.30, UB.0015.01. Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Statistical Inference and Regression Analysis. Part 0 - Introduction. . Professor William Greene; Economics and IOMS Departments. 1. Based on. “Inference . to the Best Explanation: The General . Account”. “Inference . to the Best Explanation: . Examples”. Chapters 8 and 9 in. John D. Norton, . The Material Theory of Induction..