FORS 201 – Biometrics
Autumn 2013
Instructor:
Dr. David Affleck
Email: david.affleck@umontana.edu
Office: C.H. Clapp Building 430
Phone: 243-4186
Office hours: Mondays 1 – 2 pm; Wednesdays 10 – 11 am
Assistant:
Trisha Singh
Email: trisha.singh@umontana.edu
Office hours: TBD in Stone Hall 106/107
Lectures:
MWF 9:10 – 10:00 am in Forestry 301
Labs:
Section 02 M 10:10 am – 12:00 pm in Stone Hall 106 &107
Section 03 W 12:10 pm – 2:00 pm in Stone Hall 106 &107
Course summary:
Introduction to data collection, data analysis, probability, and inferential statistics for forestry
and the environmental sciences. The course focuses on natural resource and
environmental applications of statistical methods, ranging from descriptive graphical and
quantitative analyses to the use of formal probability models, interval estimation,
significance tests, and linear regression modeling.
Course learning outcomes:
By the end of this course you will:
1. Recognize the ubiquity and importance of variation in natural systems and the need for
statistical reasoning.
2. Be able to effectively summarize data to characterize centrality, spread, bivariate
relationships and other important distributional features of natural resource data.
3. Appreciate the role of randomization in data collection and statistical inference.
4. Understand the concept of sampling distributions, and the utility of the Central Limit
Theorem.
5. Be familiar with probability distributions commonly used in statistical inference.
6. Be able to apply appropriate statistical methods to characterize uncertainty, to measure
evidence in support of hypotheses, and to make quantitative predictions.
Prerequisites:
Probability & linear math (M115) or (pre-) calculus (M121, M122, M151, M162, M171, or M172).
Textbook:
The Analysis of Biological Data, by Whitlock & Schluter (2009; Roberts & Co.)
Readings and exercises from this book will be assigned throughout the course. The first two
chapters are posted on the course website; a copy will be placed on reserve in the library.
Grading Policy:
There will be a midterm exam worth 20% of the course grade, a final exam worth 30%,
and a sequence of laboratory assignments collectively worth 50%. Traditional letter
grades will be assigned based on the combined percentage grade:
A
≥ 80%
B
79–70%
C
69–60%
D
59–50%
F
< 50%
Exams:
The midterm and final exam will draw on the material presented in class and labs, and
from that assigned as readings. A calculator and a formula sheet (double-sided but no
larger than 5”x 8”) will be permitted.
The final exam is scheduled for 8 – 10 am on Thursday, Dec. 12 (in Forestry 301).
Labs:
There are weekly assignments focusing on applications of the material covered in lectures.
These exercises consist of data analysis, problem solving, and computing. Exercises will
be assigned in lab period and due at the start of the subsequent lab (i.e. in one week’s
time), unless otherwise noted.
Group work is strongly encouraged in labs but every student must submit his or her own
work.
Notes:
Class materials and announcements will be posted on the FORS201 Moodle website,
which can be accessed from moodle.umt.edu.
Per university policy, all electronic communication associated with the course must be sent
to and from University of Montana email accounts. Ensure that your umontana.edu email
alias is properly configured and active!
All course activities are governed by the Student Conduct Code, which embodies the
ideals of academic honesty, integrity, human rights, and mutual respect. A copy of the
Student Conduct Code can be obtained from life.umt.edu/vpsa/student_conduct.php.
Any student with disabilities necessitating dispensation or assistance will please inform the
instructor immediately following the first class.
Tentative course schedule:
Week
1
Date
Lecture topics
Aug 26 Course introduction, overview.
Data, variables, & categorical data analysis.
Readings from Chapters 1 & 2 in Whitlock & Schluter
Lab 1: Introductions to the computer labs, course website, and Microsoft Excel.
2
Sept 2 Monday is Labor Day – no class, no lab meeting on Monday.
Graphical analysis of quantitative variables.
Measuring central tendency: mean(s), median, mode…and proportions too.
Readings from Chapters 2 & 3 in Whitlock & Schluter
Lab 2: Tabulating and visualizing data distributions.
3
Sept 9 Characterizing variation: ranges and standard deviations. Also ranks, percentiles,
standardizations, and z-scores.
Graphical analysis and description of bivariate relationships.
Readings from Chapters 2 & 3 in Whitlock & Schluter
Lab 3: Describing center, spread, position.
4
Sept 16 Quantifying strength of association: correlation.
Describing bivariate relationships using simple linear regression.
Readings from Chapters 16 & 17 in Whitlock & Schluter
Lab 4: Creating and interpreting scatterplots; correlation.
5
Sept 23 Data revisited: populations, samples, parameters, and estimates.
Data collection designs; observational studies and probability sampling.
Readings from Chapters 1 & 4 in Whitlock & Schluter
Lab 5: Simple linear regression.
6
Sept 30 Stratified, systematic, and cluster sampling designs.
Bias, precision, and sampling distributions.
th
Midterm Exam from 9:10 – 10 am in FOR 301 on Friday, Oct 4
Readings from Chapters 1 & 4 in Whitlock & Schluter
nd
No lab on Wednesday, Oct 2 .
7
Oct 7
Sampling distribution of a proportion: the binomial probability distribution.
Readings from Chapter 7 in Whitlock & Schluter
Lab 6: Sampling and sampling concepts.
8
Oct 14 The sampling distribution of a mean and the Central Limit Theorem.
Introduction to significance testing: mechanics, test statistics, critical values and p-values.
Readings from Chapters 6, 7, & 10 in Whitlock & Schluter
Lab 7: Sampling distributions and the binomial distribution.
Week
9
Date
Lecture topics
Oct 21 Testing population proportions and testing population means; Student’s t distribution.
Readings from Chapters 7, 10 & 11 in Whitlock & Schluter
Lab 8: Applications of the Central Limit Theorem; the normal distribution.
10
Oct 28 Introduction to interval estimation.
Confidence intervals for population proportions and means.
Readings from Chapters 4, 7, 10 & 11 in Whitlock & Schluter
Lab 9: Significance testing.
11
Nov 4 Regression redux: the sampling distribution of the regression line and its coefficients.
Interval estimates for coefficients and predictions.
Readings from Chapter 17 Whitlock & Schluter
Lab 10: Interval estimation.
12
Nov 11 No class or lab meeting on Monday: Veterans’ Day.
Contingency tables, tests of independence, and the chi-squared distribution.
Readings from Chapter 9 in Whitlock & Schluter
Lab 11: Inference for regression estimation.
13
Nov 18 Principles of experimental design.
Partitioning sums-of-squares: analysis of variance.
Readings from Chapters 14 & 15 in Whitlock & Schluter
Lab 12: Contingency tables and tests of independence.
14
Nov 25 More on analysis of variance and the F-distribution.
Wednesday and Friday are part of the Thanksgiving Break – no classes, no lab meeting.
Readings from Chapters 14 & 15 in Whitlock & Schluter
15
Dec 2 Course Review.
Lab 13: ANOVA and review problems.
16
Dec 9
th
Final exam from 8.00 – 10.00 am in FOR 301 on Thursday, December 12