PPT-ADVANCED MODELING: Interactions, Transformations, and Collinearity

I nteraction example Model EY β 0 β 1 Q 1 β 2 Q 2 β 3 Q 3 β 4 X 4 β 5 Q 1 X 4 β 6 Q 2 X 4 β 7 Q 3 X 4 This model implies. a 12 22 a a mn is an arbitrary matrix Rescaling The simplest types of linear transformations are rescaling maps Consider the map on corresponding to the matrix 2 0 0 3 That is 7 2 0 0 3 00 brPage 2br Shears The next simplest type of linear transfo AiDe. : A Tool for Cyber-Physical Oriented Analysis and Design of Body Area Networks and Devices. Authors: . Ayan. Banerjee, . Sailesh. . Kandula. , . Tridib. Mukherjee and . Sandeep. K. S. Gupta. Symptoms of . collinearity. Collinearity. between independent variables . High r. 2. High . vif. of variables in model. Variables significant in simple regression, but not in multiple regression. Variables not significant in multiple regression, but multiple regression model (as whole) significant. Ch. 2 Lesson 3. Pg. 123. What will you will learn?. Enlarge Photographs. Make something from a pattern. Identify Similarity. Two figures are . similar. if the second can be obtained from the first by a sequence of transformations and dilations. Kyra Stillman. Importance. Determine the actual occupancy. Monitor population fluctuations. Deduce what affects occupancy rates. Variables. Attempt to find . p. . and . ψ. ,. detection and occupancy probabilities. The Secrets to Effective Modeling for Systems Engineers. Dr.. Bruce Powel Douglass, Ph.D.. Chief Evangelist, Global Technology Ambassador. IBM Rational. Bruce.Douglass@us.ibm.com. Twitter: @. BruceDouglass. SketchUp. Dr. Carl Lee – University of Kentucky. Dr. Craig Schroeder – Fayette County Public Schools. Overview of Project. Integrated STEM Course – . 7. th. Grade. 8 weeks, 2-3 days a week. Students had completed instruction on transformational geometry. Learning Targets: 8.G.2,8.G.3, 8.G.4. Follow the slides to learn more about transformations. Students should have paper and a pencil for notes at their desk while going through this presentation.. Transformation: a transformation is a change in position, shape or size.. (b). 6 . questions. In the following questions, . complete the second sentence so that it has a similar meaning to the first sentence, using the word given. . Do not change the word given. . You must use between . Single Independent Variable. Questions. What is the linearity assumption? How can you tell if it seems met?. What is homoscedasticity (heteroscedasticity)? How can you tell if it’s a problem?. What is an outlier?. collinearity. Collinearity. between independent variables . High r. 2. High . vif. of variables in model. Variables significant in simple regression, but not in multiple regression. Variables not significant in multiple regression, but multiple regression model (as whole) significant. February 3, . 2011. Overview. Multivariate data simulation. Added . variable plots (review). Partial correlation. The problem of . collinearity. Regression diagnostics:. Review of assumption checking. Akira . Koseki. *, Italo . Buleje. *, . Prithwish. Chakraborty*, . Elif. . Eyigoz. *, Mohamed . Ghalwash. *, Takashi Itoh*, Toshiya . Iwamori. *, . Michiharu. Kudo*, Pablo Meyer*, Kenney Ng*, Parthasarathy . A.Ferrari. Catania, November 2012. CERN, . Geneva. …help/suggestions from several people. 14/11/2012. Alfredo Ferrari, Catania. 2. Mark Chadwick and Toshihiko Kawano (Gnash, MCNP, LANL). Sylvie . Leray.