Course Overview

This course is an introduction to statistics, covering

  • Data collection
  • Summary statistics and data visualization
  • Elementary probability theory
  • Parameter estimation by confidence intervals
  • Hypothesis tests for means and proportions
  • Linear and logistic regression

In addition to statistics, this course serves as an introduction to the R programming language and the associated RStudio development environment.

Learning objectives

The goal of this course is to give you the tools to describe, interpret, and analyze a data set, including an analysis of the methods used to collect the data and the limitations and biases that may be present. We take the perspective that uncertainty is an intrinsic aspect of working with empirical data, and that After completing this course, you will be able to use data to draw informed and statistically justified conclusions–and, equally importantly, to critically evaluate data-based arguments and analyses for the strength of their conclusions and the scope of their applicability.

Who this course is for

This course is for anyone interested in using statistical methods to understand, consume, and present data. Unlike most introductory-level statistics courses, we use the powerful and flexible statistical software package RStudio. This will give you the ability to go as far as you need or want to in your study of statistics, without being limited by your tools.

Prerequisites and required technology

This course requires a basic competency in algebra and familiarity with elementary functions, such as square roots and logarithms. This can be demonstrated by course credit in MATH 112 (College Algebra) or a sufficient score on the mathematics placement exam.

Programming experience is not required for this course, but if this is your first experience with a programming language, be aware that you will probably have to take some extra time toward the beginning of the course to gain the skill of thinking programmatically. It is important that you don’t fall behind on this aspect of the course, as the complexity of tasks increases as the course progresses.

It is strongly recommended that you have a computer capable of running R and RStudio, so that you are able to complete assignments outside of the lab period. R and RStudio run on Windows, Mac, and several distributions of Linux, but notably not on Chrome OS. If you use a Chromebook and you wish to run R, you must either install Linux on it, or use a cloud-computing service to run RStudio. Feel free to contact me for assistance with getting this set up.

Assignments

Submitted assignments in this course consist of 14 lab assignments, which are submitted in the form of R scripts. Each week you will be in the lab for a 2 hour session, where there will be a short presentation by your section leader, after which you will have time to work on the lab in a pair with another student.

Attendance at lab sessions is required and accounts for a substantial portion of your grade. In order to be considered to have attended the lab, you must sign in at the beginning of lab and then work on the lab until you and your partner have completed the lab or the period is over. If you would like to leave early, you must show your section leader your completed lab and get it approved by them.

Here are a few examples of behavior that will not earn you attendance credit:

  • Showing up to the lab section, signing in, and immediately leaving
  • Showing up to the lab section, signing in, and then doing something besides working on the lab for the entire period
  • Showing up to the lab section with the lab already completed and immediately leaving, while your partner has not yet completed the lab

Generally, lab assignments will be due one week after the corresponding lab section.

Grading policies

The grade breakdown is as follows:

  • Lab assignments: 14 * 2% = 28%
  • Lab attendance: 14 * 1% = 14%
  • In-class quizzes and polls: 18%
  • Exams: 2 * 20% = 40%

Academic Honesty

You are expected to abide by all University policies regarding academic honesty.

Specific to this course: it is encouraged that you work together with your fellow students on labs. However, we ask that you:

  • cite your fellow students as “collaborators” in the header of your lab file
  • do not simply copy code wholesale from other students

Failing to cite your collaborators or copying code straight from other students may result in loss of credit for the assignment or formal Academic Integrity proceedings.

Special needs and accommodations

Students who need special accommodation or services must register with the Disability Resources Center (DRC). See https://drc.arizona.edu/ for details.

In addition, you should request that the DRC send notification to your instructor of your need for accommodations as soon as possible. If your accommodations will impact your ability to participate in the normal course activities, please arrange a meeting with the instructor to discuss them.