Lecture Notes in Quantitative Biology

Lecture 1a	Revised: 28 May 2004

Introductions .

Introduce prof: at OSC, research interest in "scaling theory"

-what happens if you double size?

-for organisms, what happens if you double body size ?

same shape ? same activity level ?

-for ecosystems, what happens if you halve the area ?

same number of species ? same predation rates ? same dispersal rates ?

Also interested in application of stats to biology,

how best to teach stats to science majors

Introduction around the room, name and major or area of each person (1988-1995)

Identify groups, with follow-up questions (80 people 1996)

How many undergrads in 4th year ? How many in 3rd year ?

How many field oriented honours students ? (ask for topics)

How many lab oriented honours students (ask for several topics)

How many field oriented grad students ?

How many lab oriented grad students ? (ask for topics)

Organizational Matters

First some organizational matters, before moving on to the course summary sheet.

Going to pass around the current Roster.

There are 3: afternoon lab undergrad, evening lab undergrad, and grad students

We need this to set up correct spreadsheets

Who is really here ?

Student id correct ?

If you are not on roster, add your name and id to correct roster.

You will likely need to have an add form signed.

Email. You have our email addresses (on syllabus) and we need yours.

So please put your email address on the roster next to your name.

Going to use email in this course by putting together an email list as soon as possible.

We will use the list to provide data, and information on labs and problem sets.

You can use email to query us.

You will get an individual reply.

Or, you will get reply broadcast to entire class, if of general interest.

If broadcast reply, source will be anonymous.

Your question, but not your name.

I assume every one has a MUN account with email ?

If not you need to obtain one before next week.

Lab Afternoon, evening, or both ?

Several labs in SN3119,the rest will be in computer labs.

Computer labs in C2003 for both afternoon and evening lab.

You can use C2004 (same machines) anytime you want.

To use these machines you need a password CS username and password.

Some of you might already have CS account that gives you access to computers in

these labs.

If not, then you should go to C2003/C2004, find the student on

duty, and obtain username and password, before next meeting.

Access to packages

-You can use any package you like in this course.

-There will be 3 packages available in the computer labs, C2003/C2004

Minitab, SPSS, SAS

-You can use other packages, but they must have a general linear model command.

Come see me if you plan to use package you already use.

As you fill out the information sheet, here are few comments on statistical packages in this course.

Statistical packages are an important part of a course in statistics, as they free us from drudgery, and reduce computational errors of computing by hand.

Most statistical packages these days permit a model-based approach to statistics, and any package with a model based approach can be used in the course.

Examples: Minitab, SAS, Systat, DataDesk, S-Plus. Even SPSS has caught up.

So almost any statistical package can be used, you don't have to use Minitab.

In this course I tend to emphasize Minitab, even tho I usually use other packages.

The reason for the emphasis is that Minitab is the easiest to learn, of the packages with a model-based approach. If you don't already know one of the packages, then learning Minitab will be the easiest course. If you are a grad student and forsee extensive use of statistics, then you should think about using one of the other packages, however.

For this course, you can use Minitab in a MUN PC lab, Minitab on your own PC, or one of the other packages on the computer of your choice. Whatever you use, you will need to have access to email and a package before the first lab meeting (ie 11 September in 2003).

Course Summary Back to the Handout.

Demonstrators. Names and email addresses on board.

Demonstrators mark work in the course, are available in addition to me if you have questions. They will be in the lab this year, based on last fall experience in switching to graphics interface version of Minitab.

Goals of course:

1. Principles of good quantitative analysis

2. Increase your skill in applying quant. methods

to the solution of problems in biology.

3. Self instruction in quantitative methods. Do-it-yourself

4. To confer with statistician

5. To develop critical capacity

6. To increase your ability to evaluate quantitative presentation of

research results.

List of activities in course, under Evaluation.

Course is structured around a regular cycle of labs, quizzes, and problem sets.

Here is the rationale for this structure.

1 Quantitative methods, including statistics, are an important part of science.

Importance of understanding the scientific enterprise, which shapes our daily lives, regardless of whether we end up practicing science.

2 Quantitative analysis is an important part of biology

It has been traditionally important in

population biology, ecology, env. biology.

Quantitative analysis becoming increasingly important in areas of biology

that traditionally were not quantitative:	classification

phylogenetic trees

molecular biology

This will certainly increase, as new methods and devices generate large

amounts of data, and as researchers seek more compact and

general summaries of knowledge.

3 Facility comes with practice. The only way to learn it is to practice.

Like music and sports.	Not a spectator sport.

Organization of material in this course reflects this philosophy

Frequent practice, through weekly cycle of labs, quizzes, exercises

labs on Tuesday, due following Monday

quiz on Wednesday

problem set due Friday

Curve of time investment in this course:

rises rapidly, peaks in middle, slacks off late

(no term paper, fewer assignments)

Evaluation spread across all of these.

4 (next page)

4 Familiarity and facility in use in quantitative methods is important, but the underlying theme is that of a formal model rather than series of recipes.

This can be learned best by cyclic approach.

Evaluation is based on this rationale.

Evaluation spread out across many activities, in keeping with concept that facility comes with practice and frequent contact.

Problem sets and quizzes are weekly. We try to mark these quickly and get them back to you so you can see problems, if any.

Exams are open book. Emphasize ability to use tools, rather than memorize.

Quizzes.

Practice is essential to acquiring skill, this is another occasion for practice.

These are also open-book.

Tried at beginning and at end of lecture.

Seem to work best 10 minutes before end.

Most people finish in 10 minutes, some stay a few minutes longer.

Grad students are required to do more work than undergrads, to get graduate credit.

This will be a written report of an in-depth analysis of data, topic decided during conference early in the semester.

In 1996-98 the grad students met in tutorial sessions of 3-4 people working on similar kinds of problems. The advantage for the students is that this increases contact with problems in quant. biology. This worked very well, so I plan to continue this.

These tutorials are open to honours students as well.

Tutorials will be grouped by topic, which will be announced in class.

You are welcome to attend these, and will likely get the most out of the first few meetings.

Text Sokal and Rohlf now in its 3rd edition.

It is an excellent recipe book. It has all of the most commonly used procedures in biology. Often with important modifications or qualifications. If you are thinking of going on in biology you will find this book a useful set of recipes.

Two traditions in teaching statistics to science majors.

One is to emphasize the underlying math, so as to have the capacity to solve any problem that might come along. Unfortunately, this latter approach often fails because there is a gap between principles and execution. Ok, I have learned about the expectation operator, and I can compute probabilities, now that I can do this, what do I do with my data ?

The other philosophy, which addresses this problem, is recipe based. The emphasis is on learning the recipes well. This is the philosophy of commonly used statistics texts for biologists. This approach is not mathematically rigorous, but people do apply statistics after learning the recipes.

Unfortunately, the recipes don't always cover new situations.

In this course we will do something that is unusual, but works well. We will use a model-based approach. The lectures will demonstrate the use of model based methods in statistics, and how to apply them to problem solving. This approach will be applied in labs and to the problem sets from the text. This increases your skill in applying principles of model based statistics. When you are done with the course, you will have be able to analyze almost any data that you are likely to encounter in biology. You will have enough skill and confidence that you will be able to help others with analyzing their data.

The readings from Sokal and Rohlf, listed on the outline, are for the same material as the lecture. You will soon discover, however, that the approach in the text and that in the lectures are very different. You will have two different presentations of a particular statistical analysis, such as say regression. The model based approach presented in this course lets you analyze your data without having to worry about 'searching for the right test.'

Handouts in Quantitative Biology. On web. On reserve. In bookstore.

These will be referred to during the lectures. Saves you from having to copy lots of material from the board. You can concentrate on understanding, rather than copying.

Laboratories in Quantitative Biology. On web. On reserve. In bookstore.

These have all the material you need for the labs.

This is all you will need to bring to the labs. Note that the web version differs slightly from the printed version (2004). The differences are minor, except for labs 9 and 10. You will need to use the web version of labs 9 and 10.

Lecture Notes. These were put on reserve in library in 1997. These were written for my use in lectures, but students told they found the lecture notes useful. Beginning in 2000 these have been revised toward greater readability. They were posted on the web in 2001. They were revised substantially in 2002 and in 2004.

Schedule/Evolution of course (Pages 2 and 3 of course outline)

Course began in 1988 as a series of lectures taken straight from Sokal and Rohlf.

In 1990 this was supplemented by lectures organized around theme of a formal model, including hypothesis testing.

In 1991 supplementary material added.

spatial statistics, classifications, multivariate methods

estimating population size

In 1992, added more material on quantities (2 weeks)

In 1993 the material on quantities and equations expanded and integrated with the

general linear model approach to statistics. The course focussed to an

increasing degree on problem solving with the model based approach to

statistics.

In 1994 the course was organized to an increasing degree around topics that arise

repeatedly in consulting setting (grad students, honours students, colleagues) or

that were most useful to graduate students taking the course for credit.

Choice of topics was based on 'what came through the door' rather than the

topics typically found in texts emphasis, or topics that might be consider of

theoretical importance.

Material became organized into three parts evident in the schedule:

1. Quantitative reasoning (first 7 lectures)

Essential background for the model based approach to statistics.

Models: quantities, units & dimensions, equations,

Statistics:	frequency distributions, hypothesis testing

2. Data analysis with the General Linear Model (approx 17 lectures)

(t-tests, anova, regression, etc)

3. Extension to count data (Generalized Linear model, non-normal errors)

In 2002 the material on analysis of counts (binomial and poisson) was developed from the model based approach (generalized linear model), rather than from the traditional contingency test approach.

Willing to replace some of the miscellaneous material at end if anybody would like to see something covered that is not in the outline. Think about it, based on your background and goals. Write topics in on the questionnaire at end.

Laboratories (page 4 of course outline)

Three of the labs are group efforts that require 2-3 hours in the lab:

inferential cards - how do we discover (or infer) rules (lab 1)

practice in using equations - course is model based (lab 2)

criticizing graphs and figures to learn better practice (lab 5)

Rest of labs will use the computer to execute the analyses presented in the lecture and text.

Attendance during computer labs is not required. HOWEVER. We are not going to answer questions about the lab if you don't come. After the first hour, you may want to go off and do the rest of the lab on your own time. The lab is your best chance to ask questions and get help with the computer. It is also a good chance to ask questions about any material from the lectures or problem sets, but please save these until everything has quieted down.

Labs are Due as shown on course outline. Late labs will not be marked, this is too hard on the demonstrators because it makes them go back, switch back and forth from one marking scheme to another.

Group participation is strongly encouraged. You will learn more be working in groups than on your own. You can work out the labs in groups, but each person should turn in their own report.

Don't duplicate your lab reports. It is too easy to spot. You are cheating yourself, by missing an opportunity to actively review the material as you write it up for your report.

Problem sets. (Page 5 of outline)

These are another occasion for active engagement with the material. Not extensive. They do force you to think about problem set-up, which is often the hardest part of quantitative biology. We will return these to you within a week, so you can see how you are doing.

Purpose of 1st problem set is to apply the definition of a quantity. You will likely discover that the practice of quantitative biology, in the published literature, is surprisingly poor.

Purpose of 2nd problem set is to put data into the computer for remaining problem sets. You will have to do this at some point, so the idea is to do this now, so as to gain mastery of computer input and output. This gets everybody up to speed in using computers.

Several problem sets have multiple problems, but only one needs to be turned in. You should try to set up the rest, however, to make sure you know how to set up these analyses.

Structured format for the problem that you turn in, sets 6-8. This is partly for your benefit, to state what is expected. It is also to reduce the load on the demonstrators marking the question, making it easier to find and mark your answer.

The work you turn in under your name should be your work, not someone else's.

Misrepresenting is easy to spot, it annoys the markers, who bring it to me.

I can't tell who did the work, so both get a zero.

Besides, you are cheating yourself, you are short-changing yourself on practice,

which will catch up with you on the exams and quizzes.

Questionnaire

(last page of outline)

Purpose is to adjust course to backgrounds and interests of students.

How much instruction in use of computers ?

What level of facility in mathematical operations to expect ?

Please start filling this out now. You can "Time-share" while I provide an introduction to model based statistics.