PPT-Regression Model Building

Predicting Number of Crew Members of Cruise Ships Data Description n158 Cruise Ships Dependent Variable Crew Size 100s Potential Predictor Variables Age 2013 Year Built Tonnage 1000s of Tons. 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 Austin Troy. NR 245. Based primarily on material accessed from Garson, G. David 2010. . Multiple Regression. . Statnotes. : Topics in Multivariate Analysis.. http://faculty.chass.ncsu.edu/garson/PA765/statnote.htm. 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 . 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 . 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. 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. 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 . 1. 2. 3. Outline. Jinmiao. Fu—Introduction and History . Ning. Ma—Establish and Fitting of the model. Ruoyu. Zhou—Multiple Regression Model in Matrix Notation. Dawei. . Xu. and Yuan Shang—Statistical Inference for Multiple Regression. In linear regression, the assumed function is linear in the coefficients, for example, . .. Regression is nonlinear, when the function is a nonlinear in the coefficients (not x), e.g., . T. he most common use of nonlinear regression is for finding physical constants given measurements.. 6-4.1 . Polynomial Models. 6-4 Other Aspects of Regression. 6-4.1 . Polynomial Models. 6-4 Other Aspects of Regression. 6-4.1 . Polynomial Models. Suppose that we wanted to test the contribution of the second-order terms to this model. In other words, what is the value of expanding the model to include the additional terms?. : A British biometrician, Sir Francis Galton, defined regression as ‘stepping back towards the average’. He found that the offspring of abnormally tall or short parents tends to regress or step back to average.. 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.