## Recently Published

##### How does the math gap grow

Learners start with the same level of math knowledge (none) but over time a gap grows between the strongest students and the weakest students.
At some schools the gap grows wide. At others the gap remains narrow. Let's dig into the trends.

##### Pete and Susan

The answer to Oliver Roeder's Riddle on fivethirtyeight [1].
[1]:http://fivethirtyeight.com/features/can-you-solve-the-impossible-puzzle/

##### Publish Document

Code folding example.

##### Pretty knitring

Making output nicer in R markdown.

##### hello magrittr

The forward piping operator, %>%, allows us to avoid intermediate variables and indent code nicely, making life easier for the reader.

##### Need to survive

I think Sean’s analysis should use survival statistics. Let me try to prove that point.

##### Homebrewed metaregression

We can roll our own meta regression using the mle function and our own definition of likelihood.

##### Homebrewed weighted regression

Let's see if we can roll our own weighted linear regression using the mle function and our own definition of likelihood.

##### Is the median significant?

Test whether a median is significantly different than zero without using bootstrap.

##### The significance of Pete and repeat

Why repeated sampling leads to false positives.

##### Dose response for atomic bomb survivors

[Little 2008] proposes that the best fit to atomic bomb survivor data is a linear-quadratic-exponential fit. What does this look like?
[Little 2008]: http://www.jstor.org/stable/30119601

##### Brenner's G

The radiation reduction factor from Brenner 1996. Let's see how it works.

##### Forecast

Rob Hyndman's forecast package is neat.

##### interact.gbm

Exploring the interact.gbm function to find interactions in a gbm model.

##### GBM can!

(but perhaps its buggy)

##### Publish Document

Notes from working through Data Analysis: A Bayesian Tutorial [1].
[1]: http://www.cogsci.northwestern.edu/Bayes/Sivia_1996.pdf

##### Category theory for programmers (challenges)

I’m working through Bartosz Milewski’s excellent series on category theory for programmers [1]. Category theory is a set of concepts that helps to drive Haskell’s design. These are my responses to the challenge questions.
[1]: http://bartoszmilewski.com/2014/10/28/category-theory-for-programmers-the-preface/

##### Logit percent rank is normal

Data behaves nice and normal after we find the percentiles and then apply the logit function to it.

##### Specifying variance in linear regression

I have an overall estimate of an effect based on many data strata. But I want to figure out the estimate based on each individual strata. The problem is that some strata have only a few points. On their own they build a point estimate for my effect. But that is over confident. I want to see the estimate of the effect based on their data, but without this over confidence.

##### Pointwise Variance in Polynomial Regression

How does error increase when polynomial regression is applied?

##### The final frontier

Using R to draw space.

##### Rollaboard or Spinner

Which suitcase is better?

##### Preview-35ae6e8b7fbc

test

##### Effect of uncertainty on dose response

Deviation from a linear quadratic model is just an artifact of noise in the dose.

##### bootstrap_n

An experiment to prove that bootstrapped results became more significant as n increases.

##### pretty_table

How to make boss looking tables in Rmarkdown using xtable.

##### transformation

Learning the rules of image transformation

##### convolution

Automatically discovering the ways images can be distorted

##### tricks

Tips and tricks for rpubs.
By Benjamin Haley

##### DDREF

Work in progress on re-estimating DDREF.