'Statistical confidence' diaporamas de présentation

Alex Lewin (Imperial College) Sylvia Richardson ( IC Epidemiology) Tim Aitman (IC Microarray Centre) In collaboration

Bayesian Modelling for Differential Gene Expression. Alex Lewin (Imperial College) Sylvia Richardson ( IC Epidemiology) Tim Aitman (IC Microarray Centre) In collaboration with Anne-Mette Hein, Natalia Bochkina ( IC Epidemiology) Helen Causton (IC Microarray Centre)

719 views • 56 slides

Advanced Design Application & Data Analysis for Field-Portable XRF

564 views • 42 slides

Continuous Improvement

Continuous Improvement. Bill Pedersen, PE. Distort the System Downsizing example Reduced inventory example. Distort the figures End of Quarter Example. There are 3 ways to get better numbers:. Improve the System!!!!!!.

676 views • 39 slides

451 views • 35 slides

A systematic approach for the identification of classes of gamma-ray sources

A systematic approach for the identification of classes of gamma-ray sources. Diego F. Torres & Olaf Reimer. More details in Torres & Reimer: ApJ Letters 629, 141 (2005). Institute for Space Sciences Barcelona, Spain. Plausible diversity of high-energy gamma-ray sources.

428 views • 30 slides

Confidence Interval of a Proportion

Confidence Interval of a Proportion. FPP 20 - 21. Using the sample to learn about the box. Box models and CLT assume we know the contents of the box (the population). In real-world problems, we do not.

438 views • 27 slides

Confidence Interval of a Proportion

Confidence Interval of a Proportion. FPP 20 - 21. Using the sample to learn about the box. Box models and CLT assume we know the contents of the box (the population). In real-world problems, we do not.

271 views • 27 slides

Sampling Decisions in Your TC Program Evaluation

Sampling Decisions in Your TC Program Evaluation. TCEC. Overview. Why sampling? Terms Sampling methods Sample size Sample size and analysis Answers to program questions Resources Announcements. Why sampling?. Evidence from data drive the program

451 views • 26 slides

IRIS & @RISK

IRIS & @RISK . Presentation Overview. The Challenges (Colin Wheeler, Istria Ltd) Background The Challenge: Aligning Quantitative and Qualitative Techniques Our Solution: IRIS Software Client Benefits The Implementation. About Istria. Established in 1985

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Introduction to Inference

Introduction to Inference. Confidence Intervals. Statistical Inference. Provides a method for drawing conclusions about a population from sample data. We use probability to express the strength of our conclusions. Remember!.

282 views • 17 slides

Confidence Intervals for proportions

Confidence Intervals for proportions. When we select a sample, we know the responses of the individuals in the sample. Often we are not content with information about the sample. We want to infer from the sample data some conclusion about a wider population that the sample represents.

415 views • 16 slides

PARAMETRIC STATISTICAL INFERENCE

PARAMETRIC STATISTICAL INFERENCE. INFERENCE: Methodologies that allow us to draw conclusions about population parameters from sample statistics TYPES OF INFERENCE: Estimation Hypothesis testing Methods based on statistical relationships between samples and populations

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Introduction The GPM project

206 views • 7 slides

Optimized Nearest Neighbor Methods

Cam Weighted Distance vs. Statistical Confidence. Optimized Nearest Neighbor Methods. Robert R. Puckett. Cam-Weighted Distance. Deforms the distribution by transformation Simulates strengthening and weakening effects between prototypes.

190 views • 6 slides