This course is an introductory course on two fundamental statistical packages, SAS and R. Upon completing this course students will be able to
STAT 1221 or any course which mostly emphasize regression and includes an elementary statistical package such as MINITAB. No advanced programming experiences required.
Peter Dalgaard, Introductory Statistics with R, Second Edition, Springer, New York, NY (ISBN: 978-0387790534).
The e-book of this book is available through Pitt Library:
https://pitt.primo.exlibrisgroup.com/permalink/01PITT_INST/e8h8hp/alma9998557195106236
Geoff Der and Brian S. Everitt, A Handbook of Statistical Analyses using SAS, Third Edition, Chapman and Hall/CRC, London, UK, (ISBN: 978-1584887843).
The e-book of this book is available through Pitt Library:
https://pitt.primo.exlibrisgroup.com/permalink/01PITT_INST/t5l303/alma9998678678206236
If you use the links from off campus, you will be routed through Pitt Passport to log in there before the e-book displays.
We will use the statistical software packages SAS and R.
Assignments are submitted and graded electronically through Canvas. Many links and material will be made progressively available on Canvas:
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%) |
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.
These materials may be protected by copyright. United States copyright law, 17 USC section 101, et seq., in addition to University policy and procedures, prohibit unauthorized duplication or retransmission of course materials. See Library of Congress Copyright Office and the University Copyright Policy.
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.
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 titleixcoordinator@pitt.edu. 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).
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, drsrecep@pitt.edu, (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.
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.
Please visit https://www.coronavirus.pitt.edu/ and check your Pitt email for updates before each class.
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 |