STAT 205-02H: Elementary Statistics for the Biological and Life Sciences, Fall 2017
Instructor: Tim Hanson. E-mail: hansont@stat.sc.edu.
Office Hours: Tuesday/Thursday 10am-11am and by appointment.
Office: 219C LeConte College, (803) 777-3859.
Class Meeting: Tuesday/Thursday 1:15pm-2:30pm in Leconte College 201A.
Textbook: Samuels, M.L., Witmer, J.A., and Schaffner, A.A. (2012). Statistics for the Life Sciences, 4th Ed.
Prerequisites: MATH 111 or higher or consent of department Carolina Core: ARP .
Course description
This course gives students
in biology, ecology, public health, pharmacy, nursing and other life sciences a non-
calculus based introduction to the application of modern statistical methods including
descriptive and inferential statistics. Statistics is a foundational research tool within
the biological and life sciences. Topics include descriptive statistics, probability, and
inference for statistical models including: one and two sample problems for continuous
and discrete data, 2 × 2 tables (independence; comparing odds ratios, relative risks,
and differences in proportions; diagnostic testing), one-way ANOVA, linear and logistic
regression, and survival analysis.
Learning outcomes
Learning Outcomes: by the end of the course students should be able to:
► understand and interpret common graphical displays and summary statistics
from data,
► apply the rules of probability to solve basic problems,
► understand
aspects of one and two sample problems, including confidence intervals, hypothesis testing,
sample size calculation, power, and checking assumptions,
► understand basic
ideas underlying one-way analysis of variance,
► understand aspects of the simple linear
regression model: least squares estimation, the normal-errors model, confidence interval
and hypothesis tests for slope β1,
► understand the logistic regression model and
its use for analyzing Bernoulli outcomes with a continuous predictor,
► understand
aspects of 2 × 2 contingency tables: relative risk, odds ratio, difference in proportions,
case-control studies, independence, sensitivity, specificity, and prevalence, predictive
values positive and negative, Simpson's paradox and the Cochran-Mantel-Haenszel
test,
► have a basic understanding of related ideas including receiver operator characteristic
(ROC) curves, disease rates, incidence versus prevalence, and survival curves,
and
► be able to carry out common statistical methods in the computing package R.
Expectations
Read the sections of the text to be covered prior to the class
session. Attend class and arrive on time; note that regular attendance is required. Do
assigned homework after every lecture. Ask questions to clarify concepts.
Textbook and reading
(1) Statistics for the Life Sciences, 4th Ed., by M.L. Samuels,
J.A. Witmer, and A. Schaffner. Addison Wesley, 2012.
(2) Course notes. The notes present the most important ideas and tools in the textbook with much of the mathematics removed; they also provide many examples of analyzing real data in R.
Computing
Statistical analyses will be carried out via R, free software for statistical
computing and graphics. If you have your own Windows-based machine or a Macintosh,
you can install R now from the Comprehensive R Archive Network.
Accommodations for disabilities
If you require special accommodations for a disability, these must be arranged in advance through the Office of Student Disability Services in room 112A LeConte (777-6142, TDD 777-6744, sasds@mailbox.sc.edu).
Homework and exams
Homework assignments will be posted on the course website each week, except exam weeks. I cannot overstress how important these assignments are to learning the material. There will also be one midterm (in October) and a Final Exam Tuesday, Dec. 12 at 12:30pm. The exams are non-cumulative.
Grading
Each exam is worth 20%, your homework is worth 50%,
attendance is 10%. Grades: 90%-100% A, 85%-89% B+, 80%-84% B, 75%-79% C+,
70%-74% C, 65%-69% D+, 60%-64% D, under 60% F.