jerid.francom + statistics   117

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Tutorials on Advanced Stats and Machine Learning With R
A good introduction to ggplot plotting and regression models for data science.
datascience  r  statistics  tutorial  textbook
july 2018 by jerid.francom
Hierachical Models
An excellent visual presentation on understanding hierarchical modeling (mixed-effects models).
r  statistics  hierarchical-models  lmer  visualization
november 2017 by jerid.francom
In a post-truth world, statistics could provide an essential public service | John Pullinger
Statisticians can now amass more data more quickly than ever. This could help us to make decisions based on real numbers, not prejudice
textbook  statistics  datascience
february 2017 by jerid.francom
A MODERN DIVE into Data with R
Getting away from the traditional introductory statistics curriculum, more focused on reproducible research and modern data analysis techniques and tools
380  textbooks  r  bookdown  statistics  datascience
september 2016 by jerid.francom
Chi-Squared Test
Before we build stats/machine learning models, it is a good practice to understand which predictors are significant and have an impact on the response variable.
380  r  statistics  chi-squared
august 2016 by jerid.francom
The Mathematics of Machine Learning | R-bloggers
This post was first published on my Linkedin page and posted here as a contributed post. In the last few months, I have had several people contact me
machinelearning  statistics  380
july 2016 by jerid.francom
John Oliver Explains How The Media Distorts Study Results Like ‘A Game Of Telephone’
What shows up as headlines on news sites and TV isn't always an accurate depiction of what scientific studies really find.
380  data  statistics  science  public
may 2016 by jerid.francom
Power, Difference and Sample Sizes
In my earlier posts on hypothesis testing and confidence intervals, I covered how there are two hypotheses - the default or null hypothesis, and the alternative hypothesis (which is like a logical opposite of the null hypothesis). Hypothesis testing is fundamentally a decision making activity, where you reject or fail to reject the default hypothesis.…
r  statistics  power
september 2015 by jerid.francom
Introduction to Data Analysis
Very good, step-by-step tutorial on basic data analytics
programming  data  r  statistics  tutorials  analytics
december 2014 by jerid.francom
Centering several variables « HLP/Jaeger lab blog
myCenter= function(x) {
if (is.numeric(x)) { return(x - mean(x, na.rm=T)) }
if (is.factor(x)) {
x= as.numeric(x)
return(x - mean(x, na.rm=T))
}
if (is.data.frame(x) || is.matrix(x)) {
m= matrix(nrow=nrow(x), ncol=ncol(x))
colnames(m)= paste("c", colnames(x), sep="")
for (i in 1:ncol(x)) {
m[,i]= myCenter(x[,i])
}
return(as.data.frame(m))
}
}
r  centering  statistics  data  analysis
january 2012 by jerid.francom
histogram
curve(dnorm(x, mean=m, sd=std),
histogram  r  statistics  normal.curve
january 2012 by jerid.francom
plyr
plyr is a set of tools for a common set of problems: you need to split up a big data structure into homogeneous pieces, apply a function to each piece and then combine all the results back together. For example, you might want to:

fit the same model to subsets of a data frame
quickly calculate summary statistics for each group
perform group-wise transformations like scaling or standardising
r  plyr  package  data  transformation  statistics
january 2012 by jerid.francom
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