# benjaminhaley

## 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?
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.