PPT-Simple Linear Regression

FTest for LackofFit Breaking Strength as Related to Water Pressure for Fiber Webs MS Ndaro Xy Jin T Chen Cw Yu 2007 Splitting of IslandsintheSea Fibers PA6COPET During Hydroentangling. The simple regression model formulas 4 Take aways 1 Introduction to linear regression Regression analysis is the art and science of fitting straight lines to patterns of data In a linear regression model the variable of interest the so called depend 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 . 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 = . ;. 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?. 9-. 1. 2. Objectives. Understand the basic types of data. Conduct basic statistical analyses in Excel. Generate descriptive statistics and other analyses using the Analysis . ToolPak. Use regression analysis to predict future values. 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.. Simple Linear Regression (SLR) Simple Linear Regression The purpose of regression ? Desc r ibe functional relationships bet w een v a r ia b les Control Prediction of outcomes Simple Linear Regression What. is . what. ? . Regression: One variable is considered dependent on the other(s). Correlation: No variables are considered dependent on the other(s). Multiple regression: More than one independent variable. 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 compiled by. Dr. . Parminder. . Kaur. Assistant Professor. Department of Commerce. For . B.Com. (. Prog. ) II . Sem. . Sec A. SIMPLE . LINEAR . REGRESSION. DEFINITION OF . REGRESSION . 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). 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.