PPT-Residuals

Modeling House Value Dependent Variable House value newval Independent Variables Size newsize East South Families Year Built Model Diagnostics Residuals Modeling House Value Logged Variables. Descriptionpredictcalculatespredictions,residuals,inuencestatistics,andthelikeafterestimation.Exactlywhatpredictcandoisdeterminedbythepreviousestimationcommand;command-specicoptionsaredocumentedwith 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 = . Example: Housing Prices in Boston. . CRIM. per capita crime rate by town. . ZN. proportion of residential land zoned for lots over 25,000 ft. 2. . INDUS. proportion of non-retail business acres per town. Professor William Greene. Stern School of Business. Department . of Economics. Econometrics I. Part . 3 – Least Squares Algebra. Vocabulary. Some terms. to be used in the discussion.. Population characteristics and entities vs. sample quantities and analogs. Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Inference and Regression. Part . 9 – Linear Model Topics. Agenda. Variable Selection – Stepwise Regression. Key concepts. Testing conditions of applications in complex study design. Residuals. Tests of normality. Residuals plots. Residuals vs. fitted. QQ plots. Cook’s distance. Conditions of applications. glmm. & the PIT-trap. Loïc Thibaut, David Warton. EagleHawk. Neck copepods. 1. 2. 3. 4. …. B1. 0. 10. 0. 0. B1. 0. 9. 0. 0. B1.t. 0. 51. 0. 2. B1.t. 2. 48. 0. 2. B2. 0. 6. 14. 0. B2. 1. 0. 7. Chapter 3 – Exploring Data. Day 3. Regression Line. A straight line that describes how a . _________ . variable, . __. ,. . changes as an . ___________ variable. , . ___. ,. . changes. used to . __________ . Disclaimer: I am not an expert!. When conducting any statistical analysis it is important to evaluate how well the model fits the data and that the data meet the assumptions of the model. . There are numerous ways to do this and a variety of statistical tests to evaluate deviations from model assumptions. . and Parallax from Spatial Scanning. Part 2. Adam Riess and Stefano . Casertano. Distortion at the . milli. -. pixel level. Spatial Scanning Provides measurement 1D. *. precision of < 1 . millipix. Dependent Variable: House value (. newval. ). Independent Variables: Size (. newsize. ); East; South; Families; Year Built. Model. Diagnostics. Residuals. Modeling House Value, Logged Variables. Dependent Variable: Logged House value (. Traumatic Brain Injury. Gerald W. . Shutt. Traumatic Brain Injury. Learning Objectives. Demonstrate an understanding of the basics of the TBI rating . Know the references. . . TBI. 38 CFR 3.304. - Direct service connection; wartime and peacetime. Px2inx2=ssxy ssxx=0:842754^=y^x=99:204givingthettedregressionliney=^+^x=99:200:84x:Sincethecoefcientofxisnegative,wecanimmediatelyconcludethatthemodelpredictsthattheassessedstresslevel(y) How close is the Line of Best Fit?. One additional . method to determine if a linear model is . appropriate for . a . data . set is . to analyze the . residuals. .. Do this by comparing the actual data point to the predicted outcome using the equation.