PDF-Frank Wood, fwood@stat.columbia.eduLinear Regression Models Lecture 4,

Frank Wood fwoodstatcolumbiaeduLinear Regression Models Lecture 4 Slide 2Today Normal Error Regression Model. isavectorofparameterstobeestimatedand x isavectorofpredictors forthe thof observationstheerrors areassumedtobenormallyandindependentlydistributedwith mean 0 and constant variance The function relating the average value of the response to the pred washingtonedu Reading KF Ch 45 10111012 1042 For MinMax Spanning Tree algorithm KF A31 or for more detail CRLS Ch 22 1 The decomposable models class The 64257gure below illustrates the relationship between Bayes nets Markov nets and decomposable mode Dr. Kari Lock Morgan. Simple Linear Regression. SECTION 2.6. . Least squares line. Interpreting coefficients. Prediction. . Cautions. Want More Stats???. . If you have enjoyed learning how to analyze data, and want to learn more: . Introduction. Course Information. Your instructor: . Hyunseung. (pronounced Hun-Sung). Or HK (not Hong Kong . ). E-mail. : khyuns@wharton.upenn.edu . Lecture:. Time: Mon/Tues/Wed/. Thur. . at 10:45AM-12:15PM. Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models. Part . 8 . – . Multicollinearity,. Diagnostics. Multiple Regression Models. Operations Research . and Control Systems . in Health Care. Spring/Summer 2016. Forecasting - Introduction. Forecasting in Health Care. Forecasting Models. Structural Models. Time Series Models. Expert Judgment. Statistical models in . R. --- Recap ---. Stefan Bentink . bentink@jimmy.harvard.edu. Linear Regression Models. residual error. regression coefficient. dependent variable. intercept. independent variable. 1. Drawing the reg. line.. 2. Making predictions.. 3. Interpreting b and r.. 4. RMS residual.. 5. r. 2. .. 6. Residual plots.. Final exam is Thur, 6/7, in class. . Hw7 is due Tue, 6/5, and is from the handout, which is from “An Introduction to the Practice of Statistics” 3. 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. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models. Part . 9 . – . Model Building. Multiple Regression Models. Using Binary Variables . Logs and Elasticities. 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.. . intensities. in . the. . claim. . frequency. and. Claim. . frequency. . regression. Overview. . pricing. (1.2.2 in EB). Individual. Insurance . company. Premium. Claim. Due to . the. . law.