This morning’s lecture gave a general overview of statistics and their role in analysing quantities of data. Most of the technical constructions — mean, median, mode, standard deviation — are probably familiar to many, but the setting for their application and the computational context may not be.
Mathematical statistics turns out to be a tool of exceptional power for distilling information from data, with computer assistance having particular impact where there are large quantities of data, or very thinly-spread information.
Specific items covered in this lecture were:
- Individual statistics: mode, median, mean, variance, standard deviation. When you really should use a median, and the seductive danger of the mean.
- Sampling: estimating statistics of a large population from a small sample; mean, variance and standard deviation.
- Correlation: what it is and is not; how to measure and estimate correlation coefficient.
Finally, a problem: how to draw this example of self-descriptive statistics? Which frame do you begin with? Could you do it without the aid of a computer? Is there more than one possible way the drawing might come out?
Link: Slides for Lecture 17
Complete the coursework assignment. Write out your solutions to all three questions, staple them together, and post in the box outside the ITO in Forrest Hill before 4pm on Thursday 23 March.
- Tom Stafford. The way you’re revising may let you down in exams — and here’s why. The Guardian, 7 May 2016.
A book for learning about statistics, what they can do, and how to use them effectively:
- P. Hinton. Statistics Explained: A Guide for Social Science Students. Routledge, third edition, March 2014.
Link: Library copy of second edition
Two more for reading about statistics in the real world. Both easy to digest and recommended for interest. Get them out of the library, buy a copy, or read online.
M. Blastland and A. Dilnot. The Tiger That Isn’t: Seeing Through a World of Numbers. Profile, 2008.
Link: Electronic copy available through the University Library