statistics.qmd

# General Statistical Resources

This is a list of sites etc. that have some resources statistics in general or
for multiple statistical programs or programming languages. For specific
programs, see the program-specific wiki page.

## General

[Cross-validated](http://stats.stackexchange.com/): question and answer site for
people interested in statistics, machine learning, data analysis, data mining,
and data visualization.

## Statistical Programming

[UCLA ARC](https://stats.oarc.ucla.edu)

[Princeton dss](http://dss.princeton.edu/online_help/)


## Mixed Models

[Hierarchical Model Visualization](http://mfviz.com/hierarchical-models/)

[Mixed Model FAQ](https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html)



## Machine learning

[Statistical Learning Glossary](http://alumni.media.mit.edu/~tpminka/statlearn/glossary/glossary.html)

['Awesome list' of freebies](https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md)



## Power Calculations

[Effect Size Conventions](http://core.ecu.edu/psyc/wuenschk/docs30/EffectSizeConventions.pdf) Some notes on choices of small vs large effect sizes for use in power calculations.

[Beyond Significance Testing](http://www.apa.org/pubs/books/4316151.aspx) Rex Kline's text on issues with significance testing, how to do power calculations, and calculate effect size in a variety of settings.


## Missing Data

[When is complete case analysis unbiased?](http://thestatsgeek.com/2013/07/06/when-is-complete-case-analysis-unbiased/)

[Flexible Imputation of Missing Data](https://stefvanbuuren.name/fimd/) (Book) - terrific text covering all aspects of multiple imputation.

## Structural Equation Modeling

See the [SEM section](sem.html).


## Suveys

[How Big of a Problem is Analytic Error in Secondary Analyses of Survey Data?](http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0158120) West et al. (2016)


## Miscellaneous/Frequently Asked Questions

[Oh no I got the wrong sign](http://www.stat.columbia.edu/~gelman/stuff_for_blog/oh_no_I_got_the_wrong_sign.pdf)

[Factor Analysis vs. Principal Component Analysis](http://stats.stackexchange.com/questions/1576/what-are-the-differences-between-factor-analysis-and-principal-component-analysi)

[Change scores vs. ANCOVA](http://m-clark.github.io/docs/lord/)

[Problems Caused by Categorizing Continuous Variables](http://biostat.mc.vanderbilt.edu/wiki/Main/CatContinuous)

[How to Spot Visualization Lies](https://flowingdata.com/2017/02/09/how-to-spot-visualization-lies/)

[What are pseudo R-squareds?](https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-what-are-pseudo-r-squareds/) Describes how pseudo R-squareds come about, compares and contrasts the various versions, and describes their limitations.

[How much lower should AIC be before I prefer that model?](https://stats.stackexchange.com/questions/232465/how-to-compare-models-on-the-basis-of-aic)