Approaches to Quantitiative Analysis in the Life Sciences (Graduate)

NJIT MATH 615. Rutgers 48:120:615. 3 Credits.

Now being taught (Spring 2011).

Files for download

Note: class guides will be posted after class. This is so that students will be more interactive during class itself, and so that I can make any necessary modifications based in our discussions.

General Syllabus — Class policies, grading, weekly topics, etc.

Subject Syllabus (PDF) — General listing of topics. This is a rough guide. Timing and order of topics are likely to change. Further down this page is a more detailed syllabus, with dates, that will be updated as the semester progresses.

Concepts and definitions (.doc) — You should download this and begin answering the questions as we cover them in class. Each week I will update it with new questions. You should get the updated version, and copy and paste the new questions to the end of your own version. (Make sure you don't acidentally overwrite the copy that has your answers! I suggest you rename your working copy.)

Template for class project (.doc) — A guide to the structure of your project. I will go through this with you in class.

Semester syllabus and weekly downloads

1/24/2011 — Probability, (lack of) intuition, data types, probability distributions, random numbers
Class 1 Guide (.pdf) — Class 1 Guide (.nb) — Mathematica version (only for those with Mathematica)

1/31/2011 — Probability distributions continued, quantiles, moments, goodness of fit
Class 2 Guide (.pdf) — Class 2 Guide (.nb) — Mathematica version (only for those with Mathematica)

2/7/2011 — Fitting models, maximum likelihood, least squares, bias
Class 3 Guide (.pdf) — Class 3 Guide (.nb) — Mathematica version (only for those with Mathematica)
Matrix multiplication refresher guide

Example data for linear regression: resting-R.dat

2/14/2011 — Three frameworks for inference: hypothesis testing, model choice, Bayesian
Class 4 Guide (.pdf) — Class 4 Guide (.nb) — Mathematica version (only for those with Mathematica)

2/21/2011 — ANOVA, linear models, general linear models, generalized linear models
Class 5 Guide (.pdf) — Class 5 Guide (.nb) — Mathematica version (only for those with Mathematica)

2/28/2011 — Prediction, cross-validation, overfitting, multiple tests (this contains some material we did not get to in class)
Class 6 Guide (.pdf) — Class 6 Guide (.nb) — Mathematica version (only for those with Mathematica)

Example data for ANOVA: eysenck.txt. Description: Why do older people often seem not to remember things as well as younger people? Do they not pay attention? Do they just not process the material as thoroughly? One theory regarding memory is that verbal material is remembered as a function of the degree to which is was processed when it was initially presented. Eysenck (1974) randomly assigned 50 younger subjects and 50 older (between 55 and 65 years old) to one of five learning groups. The Counting group was asked to read through a list of words and count the number of letters in each word. This involved the lowest level of processing. The Rhyming group was asked to read each word and think of a word that rhymed with it. The Adjective group was asked to give an adjective that could reasonably be used to modify each word in the list. The Imagery group was instructed to form vivid images of each word, and this was assumed to require the deepest level of processing. None of these four groups was told they would later be asked to recall the items. Finally, the Intentional group was asked to memorize the words for later recall. After the subjects had gone through the list of 27 items three times they were asked to write down all the words they could remember. Link for original data: http://www.statsci.org/data/general/eysenck.html
Example data for multiple regression: bodyfat.dat. Description: The data give the body fat, triceps skinfold thickness, thigh circumference and midarm circumference for twenty healthy females aged 20 to 34. The body fat measurement was obtained by an accurate but cumbersome and expensive procedure requiring the immersion of the person in water. It would therefore be very helpful if a regression model with some or all of these predictor variables could provide reliable predictions of the amount of body fat, since the measurements needed for the predictor variables are easy to obtain. Link for original data: http://www.sci.usq.edu.au/staff/dunn/Datasets/applications/health/bodyfat.html

3/7/2011 — Experimental design, types of errors, power
Class 7 Guide (.pdf) Class 7 Guide (.nb) — Mathematica version (only for those with Mathematica)

3/14/2011 — SPRING BREAK

3/21/2011 — Non-parametric statistics
Class 8 Guide (.pdf)Class 8 Guide (.nb) — Mathematica version (only for those with Mathematica)

3/28/2011 — Ordination, plus recap of Bayesian approach
Class 9 Guide
Bayesian refresher guide.

Class 10 —First R tutorial.

4/4/2011
Class 11 Guide — Autocorrelation and timeseries

4/11/2011 — Second R tutorial.
Example data for second R tutorial: right-click (or CTRL-click on a Macintosh) to download, and save the file somewhere sensible where you can find it again.

4/18/2011 — PRESENTATIONS

4/25/2011 — NO CLASS

5/2/2011 — PRESENTATIONS

5/9/2011 — FINAL EXAM PERIOD. PROJECTS DUE by 6pm!