PPT-Abstract In this report we developed and analyzed several linear regression models to

Keywords Data Analysis Linear regression Nosocomial Analysis of Hospital Stays in a Nosocomial Infection Control Data Jessica Hathaway Matthew Hill Lilshay Rogers Heaven Tate Mentor Dr Julian AD . Di64256erentiating 8706S 8706f Setting the partial derivatives to 0 produces estimating equations for the regression coe64259cients Because these equations are in general nonlinear they require solution by numerical optimization As in a linear model PSY505. Spring term, 2012. February . 27, . 2012. Today’s Class. Regression and . Regressors. Two Key Types of Prediction. This slide adapted from slide by Andrew W. Moore, Google. http://www.cs.cmu.edu/~awm/tutorials. Jennifer Kensler. Laboratory for Interdisciplinary Statistical Analysis. Collaboration. . From our website request a meeting for personalized statistical advice. Great advice right now:. Meet with LISA . Linear Function. Y = a + bX. Fixed and Random Variables. A FIXED variable is one for which you have every possible value of interest in your sample.. Example: Subject sex, female or male.. A RANDOM variable is one where the sample values are randomly obtained from the population of values.. 1. 3.6 Hidden Extrapolation in Multiple Regression. In prediction, exercise care about potentially extrapolating beyond the region containing the original observations.. Figure 3.10. An example of extrapolation in multiple regression.. NBA 2013/14 Player Heights and Weights. Data Description / Model. Heights (X) and Weights (Y) for 505 NBA Players in 2013/14 Season. . Other Variables included in the Dataset: Age, Position. Simple Linear Regression Model: Y = . Model . the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed . data.. Formally, the model for multiple linear regression, given . ;. some. do’s . and. . don’ts. Hans Burgerhof. Medical. . S. tatistics. and . Decision. Making. Department. of . Epidemiology. UMCG. . Help! Statistics! Lunchtime Lectures. When?. Where?. What?. 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 – . Total Credit Hours: 123 Core History 2 (3) Core Social Science (3) Required Human Sciences Core Courses (9 hours) CADS 2000 Global Consumer Culture (3) HDFS 2000 Marriage & Family in a Global Context Frank Wood fwoodstatcolumbiaeduLinear Regression Models Lecture 3 Slide 2Least Squares MaxminimizationFunction to minimize wrt Minimize this by maximizing QFind partials and set both equal to zero go Lecture Outline. 1. Simple Regression:. . Predictor variables Standard Errors. Evaluating Significance of Predictors . Hypothesis Testing. How well do we know . ?. How well do we know . ?. Multiple Linear Regression: . 1. 2. Office Hours. :. More office hours, schedule will be posted soon.. . On-line office hours are for everyone, please take advantage of them.. . Projects:. Project guidelines and project descriptions will be posted Thursday 9/25.. Regression Trees. Characteristics of classification models. model. linear. parametric. global. stable. decision tree. no. no. no. no. logistic regression. yes. yes. yes. yes. discriminant. analysis.