'Log odds' diaporamas de présentation

Hidden Markov Models

1.02k views • 90 slides

Hidden Markov Models

1.1k views • 88 slides

Logistic Regression

825 views • 58 slides

Educational data mining overview & Introduction to Exploratory Data Analysis with DataShop

Educational data mining overview & Introduction to Exploratory Data Analysis with DataShop. Ken Koedinger CMU Director of PSLC Professor of Human-Computer Interaction & Psychology Carnegie Mellon University. Overview. DataShop Overview Logging model DataShop Features

634 views • 45 slides

Introduction to Probabilistic Sequence Models: Theory and Applications

David H. Ardell,Forskarassistent. Introduction to Probabilistic Sequence Models: Theory and Applications. Lecture Outline: Intro. to Probabilistic Sequence Models. Motif Representations: Consensus Sequences, Motifs and Blocks, Regular Expressions

560 views • 44 slides

Logistic Regression III: Advanced topics

Logistic Regression III: Advanced topics. Conditional Logistic Regression for Matched Data. Recall: Matching. Matching can control for extraneous sources of variability and increase the power of a statistical test.

530 views • 40 slides

Lecture 2

The analysis of cross-tabulations. Lecture 2. Cross-tabulations. Tables of countable entities or frequencies Made to analyze the association, relationship, or connection between two variables This association is difficult to describe statistically

409 views • 31 slides

April 11

April 11. Logistic Regression Modeling interactions Analysis of case-control studies Data presentation. Subgroup Analyses Journal Tables. Treatment A Treatment B OR Overall 100 150 0.67 Men 40 90 x.xx Women 60 60 x.xx Age <50 25 30 x.xx Age 51-60 35 50 x.xx

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More about Markov model.

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PH6415 Review Questions

PH6415 Review Questions. Question 1. A journal article reports a 95%CI for the relative risk (RR) of an event (treatment versus control as (0.55, 0.97). What can be said of the p-value associated with testing Ho: RR=1 vs. Ha: RR not equal 1? The p-value is < 0.01. The p-value is < 0.05.

319 views • 25 slides

Dr. Joni L. Swanson Assistant Superintendent for Curriculum and Instruction Geneseo CUSD #228

The Impact of Dual Enrollment Course Participation upon Post-Secondary Achievement and Degree Attainment. Dr. Joni L. Swanson Assistant Superintendent for Curriculum and Instruction Geneseo CUSD #228 Geneseo, IL jswanson@dist228.org. Research on Dual Enrollment. Prior Studies.

417 views • 23 slides

Propensity Score Models

Propensity Score Models . Michael Massoglia Department of Sociology University of Wisconsin Madison . General Overview. The logic of propensity models Application based discussion of some of the key features Emphasis on working understanding use of models

563 views • 22 slides

Hidden Markov model

Hidden Markov model. BioE 480 Sept 16, 2004. In general, we have Bayes theorem: P(X|Y) = P(Y|X)P(X)/P(Y) Event X: the die is loaded, Event Y: 3 sixes.

360 views • 22 slides

Lecture 9 Hidden Markov Models

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233 views • 17 slides

‘Doing’ Gender In Context: Household Bargaining & Risk of Divorce in Germany and the US

‘Doing’ Gender In Context: Household Bargaining & Risk of Divorce in Germany and the US. Dr. Lynn Prince Cooke University of Kent - Canterbury Special PSID 40 th Anniversary Session ASA Annual Meeting, Boston, 3 August 2008. PROS

270 views • 16 slides

Home Management of Malaria study

Home Management of Malaria study. By Victor Chalwe , MD, MSC . ICIUM , Turkey. Introduction.

337 views • 13 slides

Figure 1. Forest plot presenting the weighted mean difference on the IIEF erectile function domain final score of trials comparing sildenafil versus placebo.

319 views • 12 slides

Exponential Random Graph Models

Exponential Random Graph Models. A brief introduction. www.statnet.org. Fair use. Feel free to use these slides for purposes such as teaching (including multiple copies for classroom use), scholarship, research, criticism, comment, or reporting.

226 views • 11 slides

Logistic Regression

Logistic Regression. The logistic function: The logistic function is useful because it can take as an input any value from negative infinity to positive infinity, whereas the output is confined to values between 0 and 1.

200 views • 5 slides