PPT-Checking Regression Model Assumptions

NBA 201314 Player Heights and Weights Data Description Model Heights X and Weights Y for 505 NBA Players in 201314 Season Other Variables included in the Dataset Age Position Simple Linear Regression Model Y . In regression analysis with . Stata. In multi-level analysis with . Stata. (not much extra). In logistic regression analysis with . Stata. NOTE: THIS WILL BE EASIER IN STATA THAN IT WAS IN SPSS. Assumption checking . Monotonic but Non-Linear. The relationship between X and Y may be monotonic but not linear.. The linear model can be tweaked to take this into account by applying a monotonic transformation to Y, X, or both X and Y.. Instructional Materials. http://. core.ecu.edu/psyc/wuenschk/PP/PP-MultReg.htm. aka. , . http://tinyurl.com/multreg4u. Introducing the General. Linear Models. As noted by the General, the GLM can be used to relate one set of things (. Cattram Nguyen, Katherine Lee, John . Carlin. Biometrics by the Harbour, 30 Nov, 2015. Motivating example: Longitudinal Study of Australian Children (LSAC). 5107 infants (0-1 year) recruited in 2004. Intro to PS Research Methods. Announcements. Final on . May 13. , 2 pm. Homework in on . Friday. (or before). Final homework out . Wednesday 21 . (probably). Overview. we often have theories involving . Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Regression and Forecasting Models. Part 0 - Introduction. . Professor William Greene; . Economics . and IOMS Departments. . Logistic Regression III. Diagnostics and Model Selection. 2. Outline. • . Checking model assumptions. - outlying and influential points. - linearity. • . Checking model adequacy . . - Hosmer- Lemeshow test. David J Corliss, PhD. Wayne State University. Physics and Astronomy / Public Outreach. Model Selection Flowchart. NON-LINEAR. LINEAR MIXED. NON-PARAMETRIC. Decision: Continuous or Discrete Outcome. PROC LOGISTIC. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models. Part . 9 . – . Model Building. Multiple Regression Models. Using Binary Variables . Logs and Elasticities. Definition. Dependent variable,. LHS variable,. explained. variable,. response. variable,…. Independent variable,. RHS variable,. explanatory variable,. Control variable,…. Error term,. disturbance,. Instructor: Prof. Wei Zhu. 11/21/2013. AMS 572 Group Project. Motivation & Introduction – Lizhou Nie. A Probabilistic Model for Simple Linear Regression – Long Wang. Fitting the Simple Linear Regression Model – . IFPRI. Westminster International University in Tashkent. 2018. 2. Regression. Regression analysis. is concerned with the study of the . dependence. of one variable, the . dependent variable. , on one or more other variables, the . 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?. Hierarchical Systems. Truong Khanh Nguyen. 1. , Jun Sun. 2. , Yang Liu. 1. , and Jin Song Dong. 1. 1 . National University of Singapore. 2 . Singapore University of Technology and Design. Binary Decision Diagram (BDD) based model checking is capable of verifying systems with a large number of states. .