'Randomized experiment' presentation slideshows

Propensity Score Analysis A tool for causal inference in non-randomized studies

Propensity Score Analysis A tool for causal inference in non-randomized studies. Summer Statistics Workshop 2010 Felix Thoemmes Texas A&M University. Agenda. Motivating examples Definition of causal effects with potential outcomes Definition of propensity scores

2.16k views • 78 slides

1.7k views • 57 slides

Weighting

804 views • 55 slides

Making Change Happen

Making Change Happen. Kari Lock Morgan STAT 790: Teaching Statistics 10/24/12. Introducing Inference with Simulation Methods. Sleep versus Caffeine.

688 views • 55 slides

WHY RANDOMIZE

WHY RANDOMIZE. Presented by: Raul Sanchez de la Sierra. March 2014. Why randomize. ?. Because correlation is not causation. Example: Impact of a medicine. Does not take a drug. Does the medicine increase weight??. Takes the drug. Overview. Causal impact and counterfactual

599 views • 50 slides

Comparing Results from RCTs and Quasi-Experiments that share the same Intervention Group

Comparing Results from RCTs and Quasi-Experiments that share the same Intervention Group. Thomas D. Cook Northwestern University. Why RCTs are to be preferred. Statistical theory re expectations

603 views • 49 slides

Hypothesis Testing: p-value

STAT 101 Dr. Kari Lock Morgan. Hypothesis Testing: p-value. SECTION 4.2 Randomization distribution p-value. Paul the Octopus. http://www.youtube.com/watch?v=3ESGpRUMj9E. Hypotheses. In 2008, Paul the Octopus predicted 8 World Cup games, and predicted them all correctly

1.1k views • 49 slides

STAT 101: Day 3 Data Collection: Experiments and Observational Studies 1/23/12

STAT 101: Day 3 Data Collection: Experiments and Observational Studies 1/23/12. Association versus Causation Confounding Variables Observational Studies vs Experiments Randomized Experiments. Section 1.3. Professor Kari Lock Morgan Duke University.

617 views • 43 slides

Multiple Regression

STAT 101 Dr. Kari Lock Morgan. Multiple Regression. SECTION 10.3 Variable selection Confounding variables revisited Brief look at logistic regression. Model Output. R 2 versus Adjusted R 2.

548 views • 42 slides

Lecturing (leading a class, giving a talk, etc.)

459 views • 38 slides

Hypothesis Testing: p-value 2/13/12

Hypothesis Testing: p-value 2/13/12. Randomization distribution p-value Statistical significance. Section 4.2. Professor Kari Lock Morgan Duke University. To Do. P roject 1 Proposal (due Wednesday) Homework 4 (due Monday) NO LATE HOMEWORK ACCEPTED!. Exercise and Pulse.

463 views • 38 slides

Probability

STAT 101 Dr. Kari Lock Morgan. Probability. SECTION 11.1 Events and, or, if Disjoint Independent Law of total probability. Confounding Variables. Once GDP is accounted for, electricity use is no longer a significant predictor of life expectancy.

476 views • 37 slides

Simple Linear Regression

STAT 101 Dr. Kari Lock Morgan 11/6/12. Simple Linear Regression. SECTION 2.6 Interpreting coefficients Prediction Cautions Least Squares regression. Crickets and Temperature. Can you estimate the temperature on a summer evening, just by listening to crickets chirp?

444 views • 34 slides

Christel Vermeersch

(RD). Christel Vermeersch. The World Bank. REGRESSION DISCONTINUITY. Technical Track Session V. Main Objective of an Evaluation (Reminder). Estimate the effect of an intervention D on a results indicator Y. For example:.

472 views • 28 slides

Using Simulation Methods to Introduce Inference

Using Simulation Methods to Introduce Inference. Kari Lock Morgan Department of Statistical Science Duke University In collaboration with Robin Lock, Patti Frazer Lock, Eric Lock, Dennis Lock CAUSE Webinar 12/13/11. Simulation Methods. Inference: confidence intervals and hypothesis tests

393 views • 28 slides

8.3: Paired t-Test

8.3: Paired t-Test. Objective: To test claims about inferences for two sample means from matched-pair tests or blocked samples, under specific conditions. Paired Data.

2.11k views • 20 slides

Early Inference: Using Randomization to Introduce Hypothesis Tests

Early Inference: Using Randomization to Introduce Hypothesis Tests. Kari Lock, Harvard University Eric Lock, UNC Chapel Hill Dennis Lock, Iowa State Joint Mathematics Meetings New Orleans, 1/9/11. Traditional Hypothesis Testing.

273 views • 19 slides

Anthony E Brown, MD, MPH J Travis Gossey , MD, MS, MPH Susan G Nash, PhD

Using the Electronic Health Record to Improve Transfer of Medical Information after a Primary Care Office Visit. Anthony E Brown, MD, MPH J Travis Gossey , MD, MS, MPH Susan G Nash, PhD Adriana Linares, MD, DrPH Valory Pavlik , PhD.

385 views • 17 slides

Building Assets and Opportunity for Low-Income Canadians

251 views • 15 slides

Common Core State Standards for Mathematics

Common Core State Standards for Mathematics. Making Inferences and Justifying Conclusions S-IC Math.S-IC.5. Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters* are significant.

272 views • 14 slides