Syllabus for Statistics 185

Professor: Mark McClure

Course purpose

Statistics is an applied field with a wide range of practial applications that is of growing importance in today's data driven world. In this course, you will:

• Learn the qualitative language of data: What are the basic structures, variables, summaries, and charts that we use to describe and understand data? What data can we find and how do we collect more?
• Learn how to analyze data quantitatively: What specific distribution should we use to model a specific data set? What do quantitative measures associated with a distribution tell us about the data set?
• Learn how to make inferences from the data: What conclusions can we draw from a given data set or collection of observations? And how confident can we be in those conclusions?
• Deal with large, real world data in all it's messiness: This is meant to be a practical class where you can develop some skills that help throughout your college career and beyond. As such, we'll learn how to use the computer to work with actual data.

Materials

• The Text: We will use the Open Intro statistics text. This is an open text made freely available through a Creative Commons license.
• You are not required to purchase a hardcopy of the text but, if you want one, you can find links to several versions at the Open Intro website. There are also links to other ancillaries, like video summaries, computer tutorials, and data.
• Technology: This class will have a significant online component. As such, a decent computer and reliable internet connection are essential. All required software is either open source, available via a UNCA site license, or will be accessed via the web - including:
• Zoom: For attending class remotely.
• MyOpenMath: We will have regular auto-graded homework using MyOpenMath. You can login there, using the information presented in class.
• Stat Talk: I've set up an online forum called Stat Talk where we can discuss all aspects of statistics. Not only is this a great place ask HW questions but participation is mandatory. You will earn points for certain assigned questions.
• Python/Anaconda: An open source language and environment for scientific computing and graphics. This is the tool that will allow us to work with large, real world data sets. You can download a copy from here, though you will probably be able to get away with the environment I'll build into our web-based labs. Anaconda is also installed in the math lab and on the computers in our classroom.

Course structure

This course is offered as a "hybrid" course, meaning that it will run partly in person and partly online. According to the "official" schedule, we're supposed to meet MW in the classroom and F online. I'd prefer to offer as much flexibility as possible, however. With that in mind, all classes will be available via zoom. I might come in on Fridays to deliver lecture as well, if the room is available.

Having said that, you are expected to attend class and participate; it will affect your grade, as described below.

All assessment will be done remotely; there will be no in class quizzes or exams.

• Homework: There will be two types of graded homework:
• MyOpenMath, which is online and automatically graded.
• Forum posts.
• Quizzes: We'll have fairly frequent quizzes, which will essentially be short but timed MyOpenMath sessions.
• Computer Labs: A major part of the class will involving using the computer to work with large data sets. Thus, we'll have several computer labs to help with this.
• Participation: You will earn up to 60 points simply for attending regularly (in-person or online), asking questions, and participating in any in class labs or problem sessions.
• Final grades: I will determine final grades using a scale not more stringent than the standard 90-80-70-60 scale. You will be apprised of your standing as the term progresses.
• Late work: In general, I don't accept late work.