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The table below shows an outline of the material we plan to cover throughout the semester. Students do not need to read ahead of lecture, however after each class should review the covered topics from the Scientific Methods for Health Sciences EBook. Although we meet for half a day each month, we will try to cover most sections listed around each face-to-face class time. The weekly and daily breakdown of topics is provides to aid students that may prefer to have a more structured self-guided study plan.
HS 550 Fall'14 Week Dates Monday Wednesday Friday
1 9/1/2014 Syllabus EDA/Charts Intrinsic/Extrinsic Process variability
EDA/Charts Gradebook, Expectations, HWs, Website    
2 9/8/2014 Fundamentals Fundamentals Fundamentals/Rules
3 9/15/2014 Rules Rules Counting
Bayesian, Total Prob      
4 9/22/2014 Odds Ratio Relative Risk Applications 
5 9/29/2014 Measures of Centrality Measures of Variability Measures of Shape 
6 10/6/2014 Random Variables/Expectation/Variance Discrete Probability Distributions Continuous Probability Distributions
7 10/13/2014 Distributome Resource Normal Probability Model  Applications
Distributions Distribution Modeler    
8 10/20/2014 Limiting/Asymptotic results Simulation and Experiments Normal/Poisson Approximation to Binomial
Distribution Relationships CLT, LLN CLT Activity  
    Estimation of π  
9 10/27/2014 Design of Experiments Intro to Epidemiology Intro to Epidemiology
Design of Experiments Experiments vs. Observational studies    
Experiments vs. Observational studies     Project2
10 11/3/2014 Estimation Bias/Precision of Estimators Approaches for point-based estimation
Estimation Margin of Error    
11 11/10/2014 Midterm Exam Small/Large Samples Small/Large Samples
Parametric Inference Fundamentals of Inference Means, Proportions, Variances Means, Proportions, Variances
Hypothesis testing Review of End-of-term Research Paper/Project    
Power, sample-size, effect-size, sensitivity, specificity      
12 11/17/2014 1, 2, or more samples Type I and Type II errors Type I and Type II errors
Power, sample-size, effect-size, sensitivity, specificity Paired data Power Power
Clinical vs. Stat significance   Sample-sizes Sample-sizes
13 11/24/2014 Correlation   Regression
Correlation Properties   Project3
14 12/1/2014 Resampling and Simulation Bootstrap Inference Bootstrap Inference
15 12/8/2014 Rate of Change Association vs. Causality Applications 
Rate of Change Review of End-of-term Research Paper/Project    
Association vs. Causality      
Final 12/15/2014 Research Paper Due    
SOCR Resource Visitor number Dinov Email