'Predicted values' presentation slideshows

Objectives of this class

Objectives of this class. By the end of the class you should be able to: Explain why OLS should not be used when the dependent variable is discrete Understand and use logit and probit models Use multinomial, ordered and count-data models in the correct situations

1.81k views • 158 slides

Chapter 6: Model Assessment

Chapter 6: Model Assessment. Chapter 6: Model Assessment. Summary Statistics Summary. Prediction Type. Statistic. Decisions. Accuracy/Misclassification Profit/Loss Inverse prior threshold. ROC Index (concordance) Gini coefficient. Rankings. Average squared error SBC/Likelihood.

773 views • 59 slides

Basic linear regression and multiple regression

846 views • 58 slides

Model Based Techniques for DATA RELIABILITY in Wireless Sensor Networks.

Model Based Techniques for DATA RELIABILITY in Wireless Sensor Networks. - Hambi. Problem Definition. Assuring the reliability of the data is going to be one of the major design challenges of future sensor networks

624 views • 48 slides

GLOBAL WARMING

GLOBAL WARMING. Keynote Address: WCNA 2008 Orlando Florida July 03, 2008. Dr. Chris P. Tsokos Distinguished University Professor Vice President of IFNA July 03, 2008. GLOBAL WARMING. Research Seminar Team Chris P. Tsokos Gan Ladde Rebecca Wooten Shou Hsing Shih Bongjin Choi Yong Xu

555 views • 45 slides

Unpacking LogM: Toward a More General Theory of Party System Density

Unpacking LogM: Toward a More General Theory of Party System Density. David Lowery, Simon Otjes, Sergiu Gherghina University of Leiden Arjen van Witteloostuijn University of Antwerp Gabor Peli University of Utrecht Holly Brasher University of Alabama at Birmingham.

478 views • 35 slides

Simultaneous Modeling of Developer Behavior and Land Prices in UrbanSim

Simultaneous Modeling of Developer Behavior and Land Prices in UrbanSim. Daniel Felsenstein Eyal Ashbel. UrbanSim European Users Group meeting, ETH Zurich, 17-18 th March 2008. The Motivation. In UrbanSim, interdependence between developer behavior and land prices is noted.

296 views • 29 slides

Regression

Regression. Understanding relationships and predicting outcomes. Key concepts in understanding regression. The General Linear Model Prediction and errors in prediction Coefficients/weight Variance explained, variance not accounted for Effect of outliers Assumptions.

430 views • 28 slides

An Introduction to Regression with Binary Dependent Variables

An Introduction to Regression with Binary Dependent Variables. Brian Goff Department of Economics Western Kentucky University. Introduction and Description. Examples of binary regression Features of linear probability models Why use logistic regression? Interpreting coefficients

532 views • 27 slides

i) the greater the covariance between X and Y, the larger the (absolute value of) the slope

So. are how the computer determines the size of the intercept and the slope respectively in an OLS regression. The OLS equations give a nice, clear intuitive meaning about the influence of the variable X on the size of the slope, since it shows that:.

322 views • 23 slides

Simple Linear Regression

Simple Linear Regression. Statistics 700 December 4-7, 2001.

255 views • 18 slides

Simple Linear Regression

Simple Linear Regression. Statistics 515 Lecture.

305 views • 17 slides

Prediction

Prediction. Prediction. Prediction is a scientific guess of unknown by using the known data. Statistical prediction is based on correlation. If the correlation among variables is high, the success in prediction will be high.

355 views • 15 slides

Respiratory Function Tests

Respiratory Function Tests. Fiona Gilmour SHO 03/06/04. Respiratory Function Tests. Mechanical Function Spirometry Lung Volumes Diffusion Capacity Gas Exchange Function Arterial Blood Gases. Use?. Do not predict individual risk of pulmonary complications Smoking Exercise tolerance

366 views • 13 slides

Intro to Regression

Intro to Regression. POL 242. Summary. Regression is the process by which we fit a line to depict the relationship between two variables (usually both interval or close to it). This line allows us to predict a value of the dependent variable at each level of the independent variable.

188 views • 12 slides

Excel Modeling Non Linear Regression

Excel Modeling Non Linear Regression. Anchored By: Renu Rao Kaveh Saba. Features of Solver. Solver can optimize the value in the target cell by varying the adjustable cells. Cells are related by a given model equation, which is then used to find predicted values. NonLinear Regression.

297 views • 12 slides

Interpreting Effectiveness

Interpreting Effectiveness. Making Use of EVAAS Data. What is EVAAS. Education Value Added Assessment System Dr. William Sanders. Where do they get these numbers?. 8 th Grade Reading test score gives English I EOC predicted score AC=CS-(.92 * ATPA) AC= Academic Change CS=Current Score

244 views • 9 slides

What if Anything is Wrong

What if Anything is Wrong. Current in a Series Circuit.

127 views • 5 slides

Team A: VME+1/[4-9] (ME+1 far side)

Team A: VME+1/[4-9] (ME+1 far side). Goal: Determine synchronization parameters good enough to take data See https://cmsdaq.cern.ch/elog/CSC/3014. Team A: Nick Kypreos (Florida), Shih-Chuan Kao (UCR). Communication phase parameters measured for VME+1/[4-9].

151 views • 4 slides

AGE BASED PREDICTION BIAS IN SPIROMETRY REFERENCE EQUATIONS

162 views • 1 slides