PPT-Linear Regression CS771: Introduction to Machine Learning

Nisheeth Linear regression is like fitting a line or hyperplane to a set of points The lineplane must also predict outputs the unseen test inputs well Linear Regression Pictorially 2 Feature 1. e Ax where is vector is a linear function of ie By where is then is a linear function of and By BA so matrix multiplication corresponds to composition of linear functions ie linear functions of linear functions of some variables Linear Equations Xi Chen. Machine Learning Department. Carnegie Mellon University. (joint work with . Han Liu. ). . Content. Experimental Results. Statistical Property . Multivariate Regression and Dyadic Regression Tree. Cardiovascular fitness among skiers. Cardiovascular fitness is measured by the time required to run to exhaustion on a treadmill. In the following study, cardiovascular fitness is compared to performance in a 20-km ski race.. 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. H104: Building . Hadoop. Applications. Abhik Roy. Database Technologies - Experian. roy.abhik@gmail.com. ; abhik.roy@experian.com. LinkedIn Profile: . https. ://. www.linkedin.com/in/abhik-roy-98620412. 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 = . Netezza. Abhik Roy. Experian. Session Code: E03. May 23, 2016 (03:45 PM – 04:45 PM) | Platform: . Cross Platform. Photo by . Steve from Austin, TX, USA. Succeeding with Predictive Analytics and unlocking the power of Data Science with R on . 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 . 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. 1. Correlation indicates the magnitude and direction of the linear relationship between two variables. . Linear Regression: variable Y . (criterion) . is predicted by variable X . (predictor) . using a linear equation.. 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. 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 – . 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: . 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.