Course Description and Learning Objectives

This course is an introductory course on two fundamental statistical packages, SAS and R.  Upon completing this course students will be able to

Course Logistics

Prerequisites

STAT 1221 or any course which mostly emphasize regression and includes an elementary statistical package such as MINITAB. No advanced programming experiences required.

Required Textbook

Computing:

We will use the statistical software packages SAS and R.

  • SAS is available on the PCs at all campus computing labs, such as Cathedral, Posvar, Forbes Quad and Benedum. If in addition you would like to have SAS on your PC, Pitt’s Software Download Service offers SAS for free. SAS can only be installed on Windows or Unix environments (No Mac OS). If you use MacBook, I recommend you to use Pitt’s virtual lab.
  • R is a free, open-source software package/programming language for statistical computing, and is available on the PCs at all campus computing labs, such as Cathedral, Posvar, Forbes Quad and Benedum. If in addition you would like to have R on your PC/Mac/Unix, it can be downloaded for free at http://www.r-project.org/.

Course Management System: Canvas

Assignments are submitted and graded electronically through Canvas. Many links and material will be made progressively available on Canvas:

  • Lecture slides
  • Reading material
  • Homework assignments and tests
  • Data sets

Grading components

  • Homework 50%
  • Exam I 15%
  • Exam II 15%
  • Exam III 20%
  • Attendance Bonus 2%
    Attendance is encouraged. Students who attend at least 20 out of 24 lectures will receive a 2% bonus. Attendance will be recorded from September 13, 2021.

Course Grades:

Grade Percentage
A+ [97%,100%]
A [93%,97%)
A- [90%,93%)
B+ [87%,90%)
B [83%,87%)
B- [80%,83%)
C+ [77%,80%)
C [73%,77%)
C- [70%,73%)
D+ [67%,70%)
D [63%,67%)
D- [60%,63%)
F [0,60%)

Online Resources

On Statistical Procedures:

On SAS:

On R

University Policies:

Academic Integrity

Students in this course will be expected to comply with the University of Pittsburgh’s Policy on Academic Integrity. Any student suspected of violating this obligation for any reason during the semester will be required to participate in the procedural process, initiated at the instructor level, as outlined in the University Guidelines on Academic Integrity. This may include, but is not limited to, the confiscation of the examination of any individual suspected of violating University Policy. Furthermore, no student may bring any unauthorized materials to an exam, including dictionaries and programmable calculators.
To learn more about Academic Integrity, visit the Academic Integrity Guide for an overview of the topic. For hands-on practice, complete the Understanding and Avoiding Plagiarism tutorial.

Statement on Classroom Recording

To ensure the free and open discussion of ideas, students may not record classroom lectures, discussion and/or activities without the advance written permission of the instructor, and any such recording properly approved in advance can be used solely for the student’s own private use.

Diversity and Inclusion

The University of Pittsburgh does not tolerate any form of discrimination, harassment, or retaliation based on disability, race, color, religion, national origin, ancestry, genetic information, marital status, familial status, sex, age, sexual orientation, veteran status or gender identity or other factors as stated in the University’s Title IX policy. The University is committed to taking prompt action to end a hostile environment that interferes with the University’s mission. For more information about policies, procedures, and practices, see: http://diversity.pitt.edu/affirmative-action/policies-procedures-and-practices.

I ask that everyone in the class strive to help ensure that other members of this class can learn in a supportive and respectful environment. If there are instances of the aforementioned issues, please contact the Title IX Coordinator, by calling 412-648-7860, or e-mailing . Reports can also be filed online: https://www.diversity.pitt.edu/make-report/report-form. You may also choose to report this to a faculty/staff member; they are required to communicate this to the University’s Office of Diversity and Inclusion. If you wish to maintain complete confidentiality, you may also contact the University Counseling Center (412-648-7930).

Disability Services

If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services (DRS), 140 William Pitt Union, (412) 648- 7890, , (412) 228-5347 for P3 ASL users, as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.

Accessibility

The Canvas LMS platform was built using the most modern HTML and CSS technologies, and is committed to W3C’s Web Accessibility Initiative and Section 508 guidelines. Specific details regarding individual feature compliance are documented and updated regularly.

Health and Safety Statement

Please visit https://www.coronavirus.pitt.edu/ and check your Pitt email for updates before each class.

Tentative Class Schedule

Lecture Day Date Software Topics HW Due
1 M Aug 30 R Ch.1 Basics and Ch.2 The R Environment
2 W Sep 1 R Ch.3 Probability and Distribution
M Sep 6 Labor Day: No Class
3 W Sep 8 R Ch.4 Descriptive Statistics and Graphics (1)
4 M Sep 13 R Ch.4 Descriptive Statistics and Graphics (2) HW1
5 W Sep 15 R Ch.5 One- and Two-Sample Tests (1)
6 M Sep 20 R Ch.5 One- and Two-Sample Tests (2) HW2
7 W Sep 22 R Ch.7 Analysis of Variance (1)
8 M Sep 27 R Ch.7 Analysis of Variance (2) HW3
9 W Sep 29 Exam I
10 M Oct 4 R Ch.8 Tabular Data (1)
11 W Oct 6 R Ch.8 Tabular Data (2)
12 M Oct 11 R Ch.6 Correlation
13 W Oct 13 R Ch.6 Regression HW4
14 M Oct 18 R Linear Models (1)
15 W Oct 20 R Linear Models (2) HW5
16 M Oct 25 R Selected Topics in Coding
17 W Oct 27 Exam II
18 M Nov 1 SAS Ch.1 Introduction (1)
19 W Nov 3 SAS Ch.1 Introduction (2) HW6
20 M Nov 8 SAS Ch.2 Data Description
21 W Nov 10 SAS Ch.2 Simple Inference HW7
22 M Nov 15 SAS Ch.3 Inference for Categorical Data
23 W Nov 17 SAS Ch.4 ANOVA I HW8
Nov 22-26 Thanksgiving Break: No Class
24 M Nov 29 SAS Ch.5 ANOVA II
25 W Dec 1 SAS Ch.6 Simple Linear Regression HW9
26 M Dec 6 SAS Ch.7 Multiple Linear Regression
27 W Dec 8 SAS PROC SQL HW10
- F Dec 17 Exam III