Tech Notes

My notes on Statistics, Big Data, Cloud Computing, Cyber Security

Data Collection – Experiments

We are concerned with the analysis of data generated from an experiment. It is wise to take time and effort to organise the experiment properly to ensure that the right type of data, and enough of it, is available to answer the questions of interest as clearly and efficiently as possible. The specific questions that the experiment is intended to answer must be clearly identified before carrying out the experiment. We should also attempt to identify known or expected sources of variability in the experimental units since one of the main aims of a designed experiment is to reduce the effect of these sources of variability on the answers to questions of interest. That is, we design the experiment in order to improve the precision of our answers

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Explanatory variables are usually categorical and are called factors and values of factors are called levels

A combination of values are called treatment. A treatment is something that researchers administer to experimantal units . For example, a corn field is divided into four, each part is ‘treated’ with a different fertiliser to see which produces the most corn; a teacher practices different teaching methods on different groups in her class to see which yields the best results;

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Experiment Design

  • Control Extraneous variable
  • Randomize selection of experimental unit
  • Apply treatment to multiple experimental unit

Disclaimer : These are my study notes – online – instead of on paper so that others can benefit. In the process I’ve have used some pictures / content from other original authors. All sources / original content publishers are listed below and they deserve credit for their work. No copyright violation intended.

References for these notes :

The study material for the MOOC “Making sense of data” at Coursera.org

http://www.stats.gla.ac.uk/steps/glossary/anova.html

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