362 search results for "ANOVA"

Multiple Regression (sans interactions) : A case study.

September 16, 2016
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Multiple Regression (sans interactions) : A case study.

Dataset: state.x77 – Standard built-in dataset with 50 rows and 8 columns giving the following statistics in the respective columns. Population: population estimate as of July 1, 1975 Income: per capita income (1974) Illiteracy: illiteracy (1970, percent of population) Life Exp: life expectancy in years (1969–71) Murder: murder and non-negligent manslaughter rate per 100,000 population (1976) HS Grad: percent high-school graduates (1970) Frost: mean…

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Variables can synergize, even in a linear model

September 1, 2016
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Introduction Suppose we have the task of predicting an outcome y given a number of variables v1,..,vk. We often want to “prune variables” or build models with fewer than all the variables. This can be to speed up modeling, decrease the cost of producing future data, improve robustness, improve explain-ability, even reduce over-fit, and improve … Continue...

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Gotta catch them all

August 21, 2016
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Gotta catch them all

Introduction When data becomes high-dimensional, the inherent relational structure between the variables can sometimes become unclear or indistinct. One, might want to find clusters for numerous amounts of reasons - me, I want to use it to better unde...

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Are you lazy? No worries, tadaatoolbox got your back.

August 18, 2016
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Are you lazy? No worries, tadaatoolbox got your back.

A while ago, we started developing the tadaatoolbox R package. The goal is simple: There are certain things we tend to always do one after another, like performing effect size calculations after a t-Test. The convenience tadaatoolbox aims to provide is exactly this: Do the usual stuff and leave me alone. As an example, take

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New Course: Learn R Statistics Online With Our Introduction to Statistics

July 11, 2016
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New Course: Learn R Statistics Online With Our Introduction to Statistics

The best way to learn is at your own pace. Combining the interactive R learning environment of DataCamp and the expertise of Prof. Conway of Princeton, we offer you an extensive online course on introductory statistics with R.  Start learning now...Wh...

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Introduction to the RMS Package

July 4, 2016
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Introduction to the RMS Package

The rms package offers a variety of tools to build and evaluate regression models in R. Originally named ‘Design’, the package accompanies the book “Regression Modeling Strategies” by Frank Harrell, which is essential reading for anyone who works in the ‘data science’ space. Over the past year or so, I have transitioned my personal modeling

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R for Publication by Page Piccinini: Lesson 6, Part 2 – Linear Mixed Effects Models

July 4, 2016
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R for Publication by Page Piccinini: Lesson 6, Part 2 – Linear Mixed Effects Models

In today’s lesson we’ll continue to learn about linear mixed effects models (LMEM), which give us the power to account for multiple types of effects in a single model. This is Part 2 of a two part lesson. I’ll be taking for granted that you’ve completed Lesson 6, Part 1, so if you haven’t done that Lesson 6, Part...

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Simulation and power analysis of generalized linear mixed models

June 28, 2016
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Simulation and power analysis of generalized linear mixed models

Simulation and power analysis of generalized linear mixed models Brandon LeBeau University of Iowa Overview (G)LMMs Power simglm package Demo Shiny App! Linear Mixed Model (LMM) Power Power is the ability to statistica...

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WifRA: a quick walkthrough

June 26, 2016
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WifRA: a quick walkthrough

After the quick overview, here is a quick walkthrough to some categorical analysis. Open the app: Here 1. Import the data: Here are some homemade data, done with the following R code: set.seed(3467) x=1:400+rnorm(400,0,1) y1=x*2.5+40+rnorm(400,0,50) y2=x*4.5+80+rnorm(400,0,50) group=rep(c('G1','G2'),each=400) x=c(x,x) y=c(y1,y2) DF=data.frame(x=x,y=y,group=group) write.csv(DF,'DF.csv') Click on import data, select your data and set rownames to first column. You

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R for Publication by Page Piccinini: Lesson 6, Part 1 – Linear Mixed Effects Models

June 26, 2016
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R for Publication by Page Piccinini: Lesson 6, Part 1 – Linear Mixed Effects Models

In today’s lesson we’ll learn about linear mixed effects models (LMEM), which give us the power to account for multiple types of effects in a single model. This is Part 1 of a two part lesson. I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done that Lesson 6, Part...

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