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 . 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. Overview of Supervised Learning. Outline. Regression vs. Classification. Two . Basic Methods: Linear Least Square vs. Nearest Neighbors. C. lassification via Regression. C. urse of Dimensionality and . 3. Jefferson Davis. Research Analytics. Day . 2 . stuff. From . yesterday and the day before. R values have types/classes such as numeric, character, . logical, . dataframes. , and matrices.. Much of R functionality is in libraries. How to predict and how it can be used in the social and behavioral sciences. How to judge the accuracy of predictions. INTERCEPT and SLOPE functions. Multiple regression. This week. 2. Based on the correlation, you can predict the value of one variable from the value of another.. Day . 1 Part 1: Introduction. Sam Buttrey. December 2015. Who Am I?. A.B., Princeton, Statistics;. . M.A., Ph.D., . U. California-Berkeley, Statistics. Naval Postgraduate School, Department of Operations Research, 1996-Present. 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. ;. 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?. 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. Linear Regression Formula: . Used for prediction purposes for values beyond the region of the given data.. Equation: . and . are the means of x and y. is the standard deviation of x. is the covariance. Nisheeth. Linear regression is like fitting a line or (hyper)plane to a set of points. The line/plane must also predict outputs the unseen (test) inputs well. . Linear Regression: Pictorially. 2. (Feature 1). 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.. Outline. Regression vs. Classification. Two . Basic Methods: Linear Least Square vs. Nearest Neighbors. C. lassification via Regression. C. urse of Dimensionality and . M. odel Selection. G. eneralized Linear Models and Basis Expansion. 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.