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Claudia Vallinon 10 January 2013
Transcript of Statistics
Claudia Vallin MIT Quantifying Biology Workshop 2013 Overview Statistics are a tool to analyze and interpret numerical data
Two main branches : descriptive and inferential.
Descriptive: used to say something about a set of information that has been collected.
Inferential: used to make predictions or comparisons about a larger group (a population) using information gathered about a small part of that population. Examples Descriptive Statistics Inferential Statistics Mean: average
Median: middle value Standard deviation: how the mean varies
Standard error: how accurate the sample represents the population Which tells us HOW is our sample Involves generalizing beyond the data, something that descriptive statistics does not do.
How likely is something to happen or comparing differences between groups. Applications With such predictions, you can test the probability, which is the likelihood that a specific event will occur.
Measured comparisons of groups can be mathmatically stated if groups are different or have a pattern rather than just chance.
This analysis of prediction or comparisons becomes a tool to test the proposed explanation of a phenomenon (h a vs. h n). Some tests include:
Analysis of Correlation
..and some more Why MatLab? Functionality for basic numerical computations.
Contains umpteen variety of toolboxes that allows users to perform a wide range of applications. Users may also create their own toolboxes.
Stores information in matrices, making data analysis easier.
Easy to use and common in many fields. Calculator
Elementary and “advanced” operations.
Graphs and and visualization of dataPlotting (x,y) and (x,y,z) data, bar graphs, histograms
Standard deviation, average or mean value of array, regression techniques (including linear, non linear), max valyes, median, most frequent value Examples Wild et al. 2011 "R" Statistic
Software Examples Kolmogorov-Smirnov Test
T-test Pfannkuche et al., 2011 Applications Bartlett Test of Homogeneity of Variances (“stats” package)
different age groups (24hr,48hr & 96hr)
Robust fitting of linear model (“mass” package)
Other tests such as: Mann-Whitney U-Tests, Wilcoxon matched-pairs signed rank tests T-tests & ANOVAs. Python Programming language
1. Faster and more effective
3. Multi-paradigm system Examples The Molecular Modeling Toolkit (MMTK) Open source Python library for molecular modeling and simulation with a focus on biomolecular systems
Written in a mixture of Python and C Mavuluri, 2011
http://www.r-bloggers.com/author/pradeep-mavuluri/ Thank you for attention Males: t(13) = 2.743, p = 0.017
Females: n = 16, df = 14, t = 0.581, p = 0.570 Treatment effect: (F2,28 = 4.804; p = 0.037)
Gender effect: (F2,28 = 14.039; p = 0.001) LOG transformed data p value >0.15