Biostatistics (Biol/Math 218 HP-01)
Monday and Wednesday
8:30a – 9:51a
Carson Hall 411
Professor: Dr. Matthew Lundquist
Office: Carson 603
Email: [email protected]
Office hours: Monday 10-11a and Thursday 1-2p or by appointment
Whitlock & Schluter (2019). The analysis of biological data (3rd edition). W. H. Freeman. ISBN: 9781319226299
- eBook available and encouraged
- 2nd edition is OK.
- OpenStax Introductory Statistics is available, but is much more limited than Whitlock book
Much of what we know about the biological world is based on rigorous collection and analysis of data. By the end of this course, students will be able to:
- Organize, analyze, and present biological data.
- Work with real-world data from ecology, medicine, and other fields in the biological sciences.
- Choose and perform proper statistical tests for a variety of data and interpret their results.
- Use R and Python, leading statistical analysis tools, to perform basic statistical analyses.
Students success in this class depends on your input and effort. Biostatistics is a large and complex subject, and all the material cannot be adequately covered just during the weekly class meetings. There will be no traditional lectures in this course. Instead, students will be assigned readings in the course textbook and provided with many short videos related to the week’s topics on YouTube and an assignment to complete and hand in the following Wednesday.
It is the responsibility of each student to come to the course sessions well prepared, having watched the videos and done the readings, ready to participate in the week’s activities.
- Statistics portfolio (400 points): In conjunction with the textbook readings, lectures, and live activities, students will be required to compile an electronic portfolio of their statistical work. This includes the data, methodology with justification, statistical and graphical outputs, computer code, and reflection for each of the statistical tests that they learn in class. The point of the portfolio is for students to create a reference manual for themselves that they can use in the future.
Professor Lundquist will provide a template for each portfolio section on GitHub. Portions of the portfolio will be due weekly as .html reports generated from the .Rmd files on Brightspace by 8 am before class on Wednesdays. See tentative calendar for particular due dates. Students may work collaboratively on portfolios, but all work must be submitted in their own words.
Portfolios will be graded on completeness, accuracy, and lack of errors. All errors in code (bugs) must be fixed before final submission to receive full credit. Debugging is a great use of office hours.
- Pop quizzes (200 points): A number of short “pop” quizzes based on the most recent content will be assigned at the end of a class period. Dr. Lundquist will provide a problem in an .Rmd file to solve. A complete and accurate .html report with the answer will need to be submitted via Brightspace before the beginning of the next class period. These could be assigned during any class period. Students are expected to solve these problems independently.
- Independent statistical project (300 points): Towards the end of the semester, students will be given a choice of data to analyze independently. Students will prepare a short (5-6 page) summary of their findings, including the statistical analyses, graphical outputs, and interpretation. Students will also include any computer code as an additional appendix to their report (i.e. not included in the number of pages).
- Participation (100 points): This course will rely heavily on in-class discussions and activities done individually or in groups during the class sessions. Participation is key to success in this class. Also, statistics is not learned by everyone at the same pace and students are highly encouraged to work together and help each other out during discussions. Collaboration will also go towards participation grades. Participation will always be considered when tabulating final grades.
Participation will be determined according to attendance and in-class activity submissions.
|A ≥ 930||B+:860-894||C+: 755-789||D: 600-684|
|A-: 895-929||B: 825-859||C: 720-754||F < 600|
Recording of Classes
Please be aware that audio recording or photographing online or in-person classes is strictly prohibited unless a student has received explicit permission from the instructor. An exception is made for students who have registered with the Office of Disability Services and have been granted prior approval to receive audio recordings, which can be provided by the course instructor. Students with approval to receive recordings must sign a contract agreeing to keep all recordings confidential, not share or disseminate them in any form, and to destroy all recordings after completing the course. Instructors are also required to inform students if they will be recording a class session.
This course is 100% in-person and will meet at its regular time and all students are expected to attend during those meeting times. To be successful in this class, you must attend all class meetings and all homework and in-class assignments must be handed in by their assigned times.
Students must notify Dr. Lundquist via email for any missed classes to make sure that they do not fall behind and that they have access to pop quizzes. Consistently missing class can negatively impact participation grades.
This class will make limited use of Brightspace , instead using a dedicated course website. Brightspace will be used for assignment submissions and a basic grade book will be kept on Brightspace.
Important: the grading capabilities on Brightspace are limited and Dr. Lundquist holds the official grade book. Dr. Lundquist will be happy to address any questions about grades or status in the class via email or during office hours. Do not rely on Brightspace for continuously updated grades.
Dr. Lundquist will be available by email if you have concerns or questions about the class. However, please understand that since he teaches multiple classes, he may take up to 24 hrs to respond. If you email during the weekends, they might not be responded to until the following Monday.
Students with disabilities (learning, physical or psychological) who require reasonable accommodations or academic adjustments for a course must be registered with the Office of Disability Services or enrolled in the Academic Access Program. With students’ permission, faculty members are notified each semester by CONFIDENTIAL email that a student with documented disabilities is enrolled in their class and is eligible for accommodations. If a student has questions regarding the Office of Disability Services or accommodations, please email [email protected]. This office is located in Nugent 353. Please be aware that audio recording class lectures and discussions is an accommodation some students may use when it is approved through the Office of Disability Services. If approved, the student signs a contract agreeing to keep all recordings confidential, not share them with others, and to destroy all recordings after completing the course.
MMC fosters an academic community where students and faculty work together to create a learning experience that imparts knowledge and forms character. To achieve this, the College requires all members of the community to adhere to the policy of Academic Honesty that can be found in the Student Handbook, the College Catalogue and on the College website (bit.ly/mmc-ah)
ChatGPT and other generative AI
Recently, artificial intelligence companies like OpenAI have introduced generative AI programs that take simple prompts and generate new content including essays, images, audio, and programming code. While this technology is extremely exciting and useful, students in particular should be cautious when utilizing these tools.
For example, ChatGPT is a large language model trained by OpenAI that can generate human-like text. It can be used to check biostatistics code and concepts by providing explanations and generating examples. However, it is important to note that ChatGPT is a machine learning model and may not always provide accurate or appropriate information. It is recommended to verify any information provided by ChatGPT with additional sources and to consult with your professor with complex or critical issues. Additionally, it is important to remember that ChatGPT and any other AI technology is only as good as the data it was trained on, so it may not be able to answer questions or provide information on newer developments or specialized topics.
If students have any concerns related to using ChatGPT or any other AI technology for their assignments, they should bring them to the attention of their instructor.
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Please be aware that all MMC staff and faculty members are “responsible employees”, which means that if you share a situation involving an incident of bias, discrimination, harassment, or sexual misconduct, they must share that information with the Chief Equity, Diversity and Inclusion Officer and Title IX Coordinator. Although faculty and staff are obligated to share this information, you are in control of how to proceed with a reported incident, including whether or not you wish to pursue a formal complaint. Our goal is to make sure you are aware of the range of options available to you and have access to the resources you need.
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Counseling and Wellness Center
Dow Zanghi Health Center
231 E. 55th St. (in the 55th St. Residence Hall)
|01||1/30-2/01||Chapter 01: Intro to statistics and data collection||Intro assignment due 2/08|
|02||2/06-2/08||Chapter 02: Displaying data||Portfolio 01 due 2/15|
|03||2/13-2/15||Chapter 03: Describing data||Portfolio 02 due 2/22|
|04||2/20-2/22||No class 2/20, Chapter 04: Estimating with uncertainty||Portfolio 03 due 3/01|
|05||2/27-3/01||Chapter 05: Probability||Portfolio 04 due 3/08|
|06||3/06-3/08||Chapter 06 and 14: Hypothesis testing and experimental design||Portfolio 05 due 3/15|
|07||3/13-3/15||Chapter 07: Analyzing proportions and the binomial distribution, No class 2/15||Portfolio 06 due 3/29|
|08||3/20-3/22||No class, spring break|
|09||3/27-3/29||Chapter 08: Chi-square goodness of fit||Portfolio 07 due 4/05|
|10||4/03-4/05||Chapter 10 and 11: Numerical data and the normal distribution||Portfolio 08 due 4/12|
|11||4/10-4/12||Chapter 12: Comparing means (parametric tests)||Portfolio 09 due 4/19|
|12||4/17-4/19||Chapter 15: ANOVA + data for independent project assigned||Portfolio 10 due 4/26|
|13||4/24-4/26||Chapter 13/15: Comparing means (non-parametric tests)||Portfolio 11 due 5/03|
|14||5/01-5/03||Chapter 16/17: Correlation and regression||Portfolio 12 due 5/10|
|15||5/08-5/10||Support session for projects||Work on projects|
|16||5/15–5/17||Project presentations||Projects due 11:59pm 5/17|
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