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R for psychologists

R for psychologists

R for psychologists. Professor Thom Baguley, Psychology, Nottingham Trent University Thomas.Baguley@ntu.ac.uk. 0. Overview. 1. What is R ? 2. Why use R ? 3. R basics 4. R graphics 5. Linear models in R 6. ANOVA and ANCOVA 6. Writing your own R functions

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706 views • 57 slides
HRP 223 - 2008

HRP 223 - 2008

HRP 223 - 2008. Topic 9 - Regression. Height and Resting Pulse. The spreadsheet RESTING.xls has height and pulse measures on 50 people. On average, does pulse go up or down with height?. Look before you leap!. Root MSE = Estimated standard deviation of the error in the model (eta)

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637 views • 51 slides
Inferential statistics

Inferential statistics

Doing inferential statistics = doing pairwise comparisons of models. Inferential statistics. Every comparison answers a conceptual question, e.g. - Is there a relationship between X and Y?. - Is the difference between the experimental conditions statistically significant?.

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641 views • 44 slides
Introduction to Data Analysis in R

Introduction to Data Analysis in R

Introduction to Data Analysis in R. Department of Statistical Sciences and Operations Research Computation Seminar Series Speaker: Edward Boone Email: elboone@vcu.edu. What is R?.

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551 views • 43 slides
Valuation 4: Econometrics

Valuation 4: Econometrics

Valuation 4: Econometrics. Why econometrics? What are the tasks? Specification and estimation Hypotheses testing Example study. Last week we looked at. What is so special about environmental goods? Theory of consumer demand for market goods

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426 views • 24 slides
Regression with ARMA Errors

Regression with ARMA Errors

Regression with ARMA Errors. Example: Seat-belt legislation. Story: In February 1983 seat-belt legislation was introduced in UK in the hope of reducing the number of deaths and serious injuries on the road. Goal: Check whether or not this law is effective

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327 views • 16 slides
Microeconometric Modeling

Microeconometric Modeling

William Greene Stern School of Business New York University. Microeconometric Modeling. Bootstrapping and Quantile Regresion. Estimating the Asymptotic Variance of an Estimator. Known form of asymptotic variance: Compute from known results

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202 views • 13 slides
Zinc Data

Zinc Data

Zinc Data. SPH 247 Statistical Analysis of Laboratory Data. zinc <- read.delim ("zinc.txt ") > names(zinc) [1] "Concentration" " Peak.Area " > zinc.lm <- lm( Peak.Area ~ Concentration,data =zinc) > summary( zinc.lm ) Call:

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225 views • 5 slides

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