'Variance explained' presentation slideshows

Regression Analysis with SPSS

Regression Analysis with SPSS . Robert A. Yaffee, Ph.D. Statistics, Mapping and Social Science Group Academic Computing Services Information Technology Services New York University Office: 75 Third Ave Level C3 Tel: 212.998.3402 E-mail: yaffee@nyu.edu February 04. Outline .

1.29k views • 96 slides

Microarray Normalization

Microarray Normalization. Issues in High-Throughput Data Analysis BIOS 691-803 Spring 2010 Dr Mark Reimers. Normalization of Expression Arrays. Historical approaches 1995-2005 Current standard approaches Q.75 for Agilent Quantile for Affymetrix and Nimblegen VSN for Illumina

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David Weisburd Hebrew University George Mason University

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EVAL 6970: Meta-Analysis Subgroup Analysis

EVAL 6970: Meta-Analysis Subgroup Analysis. Dr. Chris L. S. Coryn Spring 2011. Agenda. Subgroup analyses In-class activity. Subgroup Analyses. Three methods Method 1: -test Method 2: -test based on ANOVA Method 3: -test for heterogeneity

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EVAL 6970: Meta-Analysis Subgroup Analysis

EVAL 6970: Meta-Analysis Subgroup Analysis. Dr. Chris L. S. Coryn Kristin A. Hobson Fall 2013. Agenda. Subgroup analyses In-class activity. Subgroup Analyses. Three methods Method 1: -test Method 2: -test based on ANOVA Method 3: -test for heterogeneity

333 views • 33 slides

Central limit theorem - go to web applet

Central limit theorem - go to web applet. Correlation maps vs. regression maps. PNA is a time series of fluctuations in 500 mb heights PNA = 0.25 * [ Z(20N,160W) - Z(45N,165W) + Z(55N,115W) - Z(30N,85W) ]. index vs. time. PNA - Correlation map (r values of each point with index).

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Multiple regression

Multiple regression. Regression. Problem: to draw a straight line through the points that best explains the variance. Regression. Problem: to draw a straight line through the points that best explains the variance. Regression.

369 views • 31 slides

Multiple regression

Multiple regression. Regression. Problem: to draw a straight line through the points that best explains the variance. Regression. Problem: to draw a straight line through the points that best explains the variance. Regression.

326 views • 31 slides

In the name of GOD

In the name of GOD. Zeinab Mokhtari. 1-Mar-2010. In data analysis, many situations arise where plotting and visualization are helpful or an absolute requirement for understanding. Plotting = visualization = graphing. Geographical maps satellite images Cartesian plotting Contour plots

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Bildung und Bildungssysteme im internationalen Vergleich

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Some 2-D Data (X)

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Disturbance

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Dep Var: TOTALSDLGS N: 33 Multiple R: 0.728 Squared multiple R: 0.529

Dep Var: TOTALSDLGS N: 33 Multiple R: 0.728 Squared multiple R: 0.529 Adjusted squared multiple R: 0.372 Standard error of estimate: 2.075 Effect Coefficient Std Error Std Coef Tolerance t P(2 Tail) CONSTANT -0.245 1.237 0.000 . -0.198 0.844

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PSY 1950 ANCOVA November 17, 2008

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Understanding Observed Stratocumulus Variability and Implications for Aerosol Indirect Effects

Understanding Observed Stratocumulus Variability and Implications for Aerosol Indirect Effects. Nathan Johnson. Outline. Effects of aerosols on Climate Susceptibility Observations of Microphysical Quantities MArine Stratocumulus Experiment Principle Components Analysis. Introduction.

247 views • 15 slides

Bringing Evaluation In: Moving from Evaluation to Evidence-based Decision Support

Bringing Evaluation In: Moving from Evaluation to Evidence-based Decision Support. Ben Pollard Director, VPS Portfolio Initiatives, UBC Former Director, Program Management and Evaluation, Immigrant Integration Branch. Immigrant Integration Overview.

226 views • 13 slides

Multiple Regression

Multiple Regression. From last time. There were questions about the bowed shape of the confidence limits around the regression line, both for limits around the mean and the individuals should be curved because they are estimates.

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Principal Components Analysis

Introduction Prevalence and increasing frequency of diabetes underscore need for screening Subject studies (see adjacent poster) demonstrate that NIR & fluorescence can noninvasively classify diabetes patients with performance comparable to FPG

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Sea level hindcast

Sea level hindcast. Red: Data Blue: Prediction Difference everywhere < 7 mm, 98% of variance explained. Vermeer & Rahmstorf, PNAS 2009.

101 views • 1 slides