What This Document Is
This is a comprehensive course syllabus for STAT 371, an introductory applied statistics course offered at the University of Wisconsin-Madison. It outlines the expectations, policies, and logistical details for students enrolled in the course, designed for those in the life sciences and related fields. It serves as the foundational guide for navigating the course requirements and understanding how success will be measured.
Why This Document Matters
This syllabus is essential for any student considering enrolling in or currently registered for STAT 371. It clarifies crucial information regarding course objectives, grading criteria, required materials, and instructor contact details. Reviewing this document *before* the course begins will help you assess your preparedness and understand the commitment required. Throughout the semester, it serves as a central reference point for all course-related questions and policies, ensuring clarity and minimizing potential misunderstandings. Students will find it particularly useful when planning their semester, understanding assessment weights, and knowing available resources.
Common Limitations or Challenges
This syllabus provides a high-level overview of the course structure and expectations. It does *not* contain the actual statistical content, methods, or problem sets that will be covered in the course. It also doesn’t include specific lecture schedules or detailed explanations of statistical concepts. While it outlines the grading breakdown, it doesn’t provide examples of exam questions or homework assignments. Access to the full syllabus is required to understand the specifics of each component.
What This Document Provides
* A clear outline of the course’s primary learning goals and objectives.
* Information regarding required course materials, including the textbook.
* Details about instructor contact information and availability (office hours).
* A breakdown of the grading components and the associated weightings.
* Policies regarding exams, homework submissions, and make-up work.
* An overview of the statistical computing software utilized in the course.
* Prerequisites and any assumed background knowledge.
* The grading scale used to determine final course grades.
