# Difference between revisions of "Statistics"

From QuaBiNet

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== Statistical Concepts for Biologists == | == Statistical Concepts for Biologists == | ||

: Mingshu Huang et al., Harvard University | : Mingshu Huang et al., Harvard University | ||

− | : | + | : An overview of selected statistical concepts, and examples of how they are used in biology |

− | :: [http://www.quabinet.org/boxplot.html Box Plots] | + | :: [http://www.quabinet.org/stats_modules/boxplot.html Box Plots] |

+ | :: [http://www.quabinet.org/stats_modules/ci.html Confidence Intervals] | ||

+ | :: [http://www.quabinet.org/stats_modules/sv.html Sampling Distribution] | ||

+ | |||

+ | == A visual introduction to Machine Learning == | ||

+ | : r2d3 | ||

+ | : A beautiful introduction to the ideas behind machine learning | ||

+ | :: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ | ||

+ | |||

+ | == Warning Signs in Experimental Design and Interpretation == | ||

+ | : Peter Norvig, google | ||

+ | : http://norvig.com/experiment-design.html |

## Latest revision as of 04:30, 11 September 2015

## Contents

- 1 MOOCS
- 2 Online Resources
- 2.1 Handbook of Biological Statistics
- 2.2 How to Share Data with a Statistician
- 2.3 What Statistical Test should I use?
- 2.4 Power and Sample Size Programs
- 2.5 Statistical Concepts for Biologists
- 2.6 A visual introduction to Machine Learning
- 2.7 Warning Signs in Experimental Design and Interpretation

# MOOCS

## Mathematical Biostatistics Boot Camp 1

- Coursera, Johns Hopkins
- https://www.coursera.org/course/biostats
- Thorough, but quite mathsy and formula-heavy. No programming required.

## Statistics 2.*x (1 to 3)

- EdX, Berkeley
- https://www.edx.org/course/berkeley/stat2-1x/introduction-statistics/1138
- Basic intro to statistics and probability. Extremely well-taught

## Data Science Specialization

- Coursera, Johns Hopkins
- https://www.coursera.org/specialization/jhudatascience/1?utm_medium=listingPage
- A series of ten courses including data processing, R programming, and statistics

# Online Resources

## Handbook of Biological Statistics

- John H. McDonald
- http://www.biostathandbook.com/
- A comprehensive and well-written online textbook on biological data analysis

- http://simplystatistics.org/2013/11/14/the-leek-group-guide-to-sharing-data-with-a-statistician-to-speed-collaboration/
- This excellent guide by Jeffrey Leek from Johns Hopkins Bloomberg School of Public Health explains how best to prepare, process and annotate raw data before sending it to a statistician for analysis. Also helpful if you do your own data analysis!

## What Statistical Test should I use?

- http://www.ats.ucla.edu/stat/mult_pkg/whatstat/default.htm
- Table of setatistical tests
- Institute for Digital Research and Education, UCLA

## Power and Sample Size Programs

- http://www.epibiostat.ucsf.edu/biostat/sampsize.html
- List of resources for power and sample size calculations
- UCSF Division of Biostatistics

## Statistical Concepts for Biologists

- Mingshu Huang et al., Harvard University
- An overview of selected statistical concepts, and examples of how they are used in biology

## A visual introduction to Machine Learning

- r2d3
- A beautiful introduction to the ideas behind machine learning

## Warning Signs in Experimental Design and Interpretation

- Peter Norvig, google
- http://norvig.com/experiment-design.html