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

Materials

• The Text: We will use the Open Intro statistics text. This is an open text made freely available through the Creative Commons license.
  • You may download our PDF for free.
  • You are not required to purchase a hardcopy of the text but, if you want one, you can find links to several version at the Open Intro website. There are also links to other ancillaries, like video summaries, computer tutorials, and data.
• Technology: There are several technological tools that we'll use including:
  • R: An open source language and environment for statistical 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 The R-Project site. We'll have several labs this term that will require the use of R.
  • WebWork: We will have regular auto-graded homework using WebWork. You can login here, using the information presented in class.
  • Discourse: I've set up an online discussion forum where we can all talk about the statistical material we learn in this class together. Note that participation in Discourse is mandatory; you will earn actual points. In addition, our computer labs will be turned in via Discourse.

Topics covered

We'll cover most of the first 7 chapters of the Open Intro textbook. That includes

• Data:
  • Numerical and categorical data
  • Understanding data tables and visualizations
  • Gathering data
• Probability theory:
  • Definition and basic computations
  • Random variables
  • Distributions with an emphasis on the normal distribution
• Inference:
  • Random samples, standard error, and margin of error
  • Hypothesis testing
  • Confidence intervals
• Regression:
  • Computation of the regression line
  • Inference from regression

In addition, we'll learn how to use the statistical software package R fairly well.

Grades

• Exams: There will be two exams during the term. Likely dates for the exams are:
  • Friday, June 30
  • Tuesday, July 18
  Note that both exams will be comprehensive to that point of the term. The first exam will be worth around 100 points and the second will be worth around 150 points.
• Quizzes: There will be four quizzes during the term. Likely dates for those quizzes are:
  • Friday, June 9
  • Friday, June 16
  • Friday, June 23
  • Friday, July 14
  Each quiz could be worth anywhere from 20 to 60 points.
• Homework: There will be two types of homework:
  • WebWork, which is online and automatically graded.
  • Textbook assignments, which will not be collected but offer important practice.
  These homeworks will be posted as the term progresses.
• Computer labs: We'll have 4 or 5 computer labs worth 20-30 points apiece.
• Discourse: Participation in our online discussion forum, called Discourse, is mandatory. Discourse has built in trust levels; your trust level increases as you participate. Everyone will start at trust level 1 and can increase up to trust level 3. You will earn 15 points for each increase in your trust level. In addition, there will be several assigned problems each of which will earn you points. Also, your computer labs will be turned in via Discourse.
• In class problems: We will work problem sheets together most days. Quiz and exam problems will be closely related to these sheets. In addition, you will receive a 40 point class participation grade simply for participating regularly.
• 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.
• Cheating: I don't deal with cheating. If I suspect cheating strongly enough, I simply refer you to the provost and fail you for the class.

Advice

• Summertime statistics: You don't have to be a mathematical genius to absorbs the fundamentals of statistics and learn how to apply statistics to real world problems. You do need to work fairly hard and consistently, however. This is particuarly tru in a condensed Summer class.
• The typical day: Class is 1 hour and 45 minutes long and will usually be divided between lecture and problem sessions. On Fridays, we will typically have an assessment, either a quiz or an exam.
• Help: You are not undertaking this challenging task alone. Here are a few sources of assistance:
  • Me: I like to talk to people about math and stat! That's why I chose this profession. I will typically stick around in my office after class for about an hour. Please feel free to approach me any time you have questions.
  • Your classmates: Most people learn best by talking things through with others. You will find that you can both learn from and help your fellow classmates. In particular, if your classmate is explaining a fine point to you, then you are helping them!
  • Discourse: As already mentioned, I've set up an online discussion forum for this class. I'll check the site regularly and, I hope, many of your fellow students will as well.
  • The Math Lab: We all know the Math Lab rocks! It's open long hours and is located right across the hall from my office. You will be welcome there and will definitely find people to talk to about statistics, as well as mathematics.