Bowdoin Math 1300 (Biostatistics), Fall ’15
Syllabus – 2nd DRAFT
September 4, 2015
Instructor: Prof. Mario MICHELI, micheli@bowdoin.edu, 105 Searles. Office phone: (207) 798-4286.
Class time and place: 11:30 a.m. – 1:00 p.m., MW, 217 Searles Science Building.
Lab time and place: 11:30 a.m. – 12:25 p.m., Fri., 216 Searles Science Building.
Office hours: Mon. 5–7 p.m., Tue. 2–4 p.m., & Thursday 2–3 p.m., and/or by appointment, in 105 Searles.
To make office hours more fruitful, I expect you to come to office hours after having made considerable effort
in both understanding the lecture material, and in solving the homework problems. This will be immensely
beneficial to both yourselves and to the fellow students who will come to office hours at the same time.
Textbooks: Diez, Barr, and Çetinkaya-Rundel, OpenIntro Statistics, 3rd edition; PDF available for free at
https://www.openintro.org/stat/. Samuel Goldberg, Probability: An Introduction, Dover Publications.
Peter Dalgaard, Introductory Statistics with R, 2nd edition, Springer.
Prerequisite: Placement in Math 1300. Biostatistics is an introduction to the statistical methods used in the
life sciences, and assumes a mathematics background equivalent to Mathematics 1600 (Differential Calculus).
However, you should not enroll in Math 1300 if you intend to enroll later in Math 2206 and Math 2606, the
mathematics major level probability and statistics courses.
Home page for the course: http://www.bowdoin.edu/~micheli/MATH1300F15/index.html
Blackboard: I will use Blackboard mostly to post grades for homework, labs, quizzes and exams. Log in at
http://blackboard.bowdoin.edu: your courses will be categorized by semester in the Semester Courses box.
The rest of the material (homework, lab assignments, solutions) will be posted on the course’s home page.
Course description: This course is mostly intended to provide a statistical toolbox for science majors who
are not planning to take upper division math courses in probability and statistics. Armed with such tools,
the students will learn to extract useful information from unstructured data sets. A substantial amount
of time will be spent learning and using R, a programming language and software environment that was
specifically created for statistical computing and graphics. Course topics include but are not limited to:
types of data, data collection, sampling data, histograms, boxplots, regression, basics of probability theory
(counting, permutations and combinations, sample spaces, axioms of probability, conditional probability,
discrete random variables, normal random variables, the Central Limit Theorem), point estimates confidence
intervals, hypothesis testing, ANOVA, statistical tests, goodness-of-fit, regression, residuals, correlation.
Class attendance/participation: While I will not be noting attendance, you are expected to be present
and engaged during class meetings, actively participating in group activities when appropriate. You are also
expected to read topics in the textbook alongside lectures and discussions in class. In the event that you
absolutely must miss a class, it is your responsibility to hand in any homework assignments, to understand
the material that was taught in class, and to be aware of any in-class announcements.
Also, while I will not be strictly implementing a “no electronics” policy in my class, I would urge you not
to use laptops or cell phones during our lectures and lab sessions. It is my opinion, and not only mine, that
having your laptop open during a lecture actually detracts from learning; studies show that taking notes by
hand in a math lecture helps your brain process and absorb the new information.
Labs: We will have weekly lab meetings, which we will use either to (1) learn and use R to perform statistical
operations on data sets, in which case I will ask you to produce short lab reports to be incorporated into the
homework assignments, or to (2) work in groups to solve some extra problems in preparation for tests.
Homework: There will be 11 homework assignments, typically due on Wednesdays at the beginning of
class. Each homework assignment will have a maximum score of 20 points. To receive full credit for your
work, you will have to (1) show your reasoning and (2) write clearly. No late homework will be
accepted, but the two lowest homework scores (out of 11) will be dropped in the computation of your final
grade. Some homework assignments will include programming parts.
Quizzes: We will have five in-class quizzes (on 9/21, 10/7, 10/28, 11/18, and 12/2, the first on a Monday,
the others on Wednesdays). They will take place during the first 15 minutes of class. Each will be given a
maximum score of 10 points. The lowest quiz score will be dropped for the computation of your final grade.
Midterm and final exams: we shall have two 85-minutes, in-class midterm exams on Wednesday,
September 30, 2015 and on Wednesday, November 11, 2015, respectively. The final exam will be on
Wednesday, December 16, 2015 in Searles 217. During the exams you will not be allowed to use books,
printed material, notes, calculators, cell phones, or other electronic devices. Besides pencil and paper, you
will be allowed to use the following “cheat sheets”:
• First midterm: one side of a letter-sized page (8.5” × 11”) of your own hand-written notes;
• Second midterm: two sides of a letter-sized page (8.5” × 11”) of your own hand-written notes;
• Final exam: three sides of a letter-sized page (8.5” × 11”) of your own hand-written notes.
For example, during the second midterm you can use the one-sided sheet that you prepared for the first
midterm, plus an additional side. As far as the material that you will be expected to know for the exams:
◦ the first midterm will be on what we cover until Wednesday, September 23 (included);
◦ the second midterm will be on what we cover until Wesnesday, November 4 (included);
◦ the final exam will be cumulative, but with more emphasis on the the last few weeks of class.
Final grade breakdown: Your grade will be will be computed as follows:
Homework + Labs
Quizzes
20%
10%
(will drop the two worst scores)
(will drop the worst score)
Midterm #1
Midterm #2
Final exam
20%
20%
30%
I will be grading the class on a curve. The course average (typically a B− or B) will actually depend on how
satisfied I will be about the general outcome of the class.
Students with Accommodations, and Student-Athletes: Students with disabilities or who require any
special accommodations should contact me as soon as possible, so that we can make appropriate arrangements.
If you are a student-athlete, also please contact me, so I can arrange make-up tests in case you miss some.
Academic honesty: All students are expected to adhere to Bowdoin’s Academic Honor Code. As mentioned
above, students are encouraged to work together on homework and lab assignments, but must write up their
own solutions individually. The work that a student writes down should be the result of his or her own
understanding. Of course, work should never be directly copied from another student.
A final word of advice: Use all of the support that Bowdoin is going to provide you.
• There will be a Math 1300 study group, run by Michael S. Butler, on Tuesday nights from 8 p.m. to
9 p.m., in Searles 217.
• Come to my office hours!!! I feel that my most successful students are are often those who make
full use of my office hours. If you feel that you need extra time, do not hesitate to email me and ask an
appointment for one-on-one office hours with me.