Course Syllabus
Contact Information
Communication
To communicate with me, students may use my email (tabrizy@ou.edu) using their OU email address. I will get back to them in less than 36 hours.
Course Overview
This is a graduate-level course on applied statistics. In this course, we survey three topics:
- Description (using observed distributions, their central tendency, and dispersion)
- Probability and Sampling Distributions (with emphasis on Gaussian Distribution and its application in Sampling Distribution of Means)
- Multivariate Regression Analyses (using Ordinary Least Squares Method)
Emphasizing on applied aspects of the aforementioned topics, students are introduced to the existing tools in MS Excel and R. Students performance are assessed using an array of in-class activities, assignments (including students’ peer review), and exams, most of which are exclusively focused on applied aspects.
Course Prerequisites
Graduate standing and departmental permission.
Learning Outcomes and Assessment: Statistics
By the end of this course, students are expected to know how to:
- Describe observed variations in categorical variables using summary and contingency tables
- Describe observed variations using summary statistics
- Visualize observed variations using histograms
- Be able to distinguish between observed vs. probability distribution
- Compute and interpret central tendency and dispersion of a given probability distribution
- Make use of Gaussian Distribution to compute varying probabilities
- Be able to distinguish between probability vs. sampling distribution
- Make use of Gaussian Distribution to study the Sampling Distribution of Means
- Be able to distinguish between sample statistics vs. population parameters
- Conduct confidence interval estimation
- Be able to employ population regression function to estimate conditional correlations
- Conduct parameter estimation using Ordinary Least Squares (OLS) Method in bivariate and multivariate regression models
- Conduct inference using confidence interval estimation derived from OLS estimations
- Be able to understand the basics of time series analysis including Moving Average, Auto Regressions, and ARMA models
To assess students’ performance regarding the above learning outcomes, this course relies on an array of in-class activities, assignments, and exams.
Learning Outcomes and Assessment: Intellectual Virtues
Beyond the learning outcomes that directly relate to the contents of this course, students are expected to internalize and articulate two vital intellectual virtues:
- Love of learning
- Curiosity
This course offers an advanced survey of the basics of description and inference. Yet, it encourages students (and provides them with programming tools in R) to continue with their learnings beyond the scope of this course. This requires internalizing a love for learning about advanced quantitative methods and a curiosity about the existing tools that could be useful in employing those methods in real life. To assess students’ performance regarding the above learning outcomes, this course relies on in-class interactions (as part of in-class activities) and students peer review and interactions (as part of the assignments).
Grades Breakdown
| Activity | Description | Percentage |
|---|---|---|
| Exams | Your midterm and final exams include applied data analysis. Your exams are take-home and open-resource. They will be posted on Canvas, and they will also be submitted on Canvas. | 60% |
| Assignments |
You will be asked to submit 4 assignments.
|
25% |
| In-class Activities | There are a number of in-class activities that contribute to 15% of your final grade. Like exams and assignments, these activities will also be posted and submitted on Canvas. They will, mostly, be done in class in an interactive environment, cultivating the love of earning and curiosity. | 15% |
| Total | 100% |
Grade Scale
The sum of your exams, assignments, and in-class activities grades will determine your final letter grade on a scale where 90 or above is an A and less than 60 is an F. Tests won’t be curved, needs based appeals won’t be entertained, and grades will be determined in strict accordance with the above policy. Even if you end up with, say, 89.99999, you earn a grade of B.
Course Materials
Textbooks
Primary textbook: Introductory Econometrics for Finance (4th Ed.) by Chris Brooks, PhD
Published by Cambridge University Press, May 2019 (ISBN: 9781108436823)
Secondary textbook: Introductory Business Statistics by Alexander Holmes, PhD, Barbara Illowsky, PhD, and Susan Dean, PhD. Free Openstax Book: https://openstax.org/details/books/introductory-business-statistics
Software
MS Excel and MS Word (Office 365)
Note: The Data Analysis ToolPak must be added to Excel. Here is the instruction: https://support.microsoft.com/en-us/office/load-the-analysis-toolpak-in-excel-6a63e598-cd6d-42e3-9317-6b40ba1a66b4
R Studio Desktop Open Source Edition
Note: This software is freely available, and it can be downloaded from: https://www.rstudio.com/products/rstudio/
Course Components
Required Readings: The primary and secondary textbooks introduce some of the important introductory concepts, followed by key topics in description and inference. They also include an introduction to regression analysis (including OLS method) and time-series. Certain readings will be assigned from the primary textbook. They should all be done before the next class meeting.
Assignments: In the assignments, students put what they learn in their readings and lectures into practice. The assignments include data work, which must be done using MS Excel and reported using MS Word.
In-class Activities: In-class quizzes are designed as in-class learning tools. They are based on required readings and the concepts that are discussed in class. They usually include data work, computation, or estimation. And they are usually discussed in class.
Exams: The midterm and final exams include multiple questions that require data work. In terms of their structure, they are similar to assignments and in-class quizzes.
Late Policy: No assignments, in-class activities, or exams could be submitted later than their due-dates unless there are well-documented justifications (e.g., health reasons). Under exceptional circumstances, late or revised work will be accepted and receive 67% of credits.
Tentative Schedule (Subject to Change)
|
Week |
Topics |
Due |
|
Week 1 |
|
In-class activities and syllabus quiz are due before our 2nd meeting. The first assignment is due before our 3rd meeting. |
|
Week 2 |
|
In-class activities must be submitted before our 3rd meeting. The first assignment is also due before our 3rd meeting. |
|
Week 3 |
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In-class activities must be submitted before our 4th meeting. The second assignment is due before our 5th meeting. |
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Week 4 |
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In-class activities must be submitted before our 5th meeting. The second assignment is also due before our 5th meeting. |
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Week 5 |
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In-class activities must be submitted before our 6th meeting. The third assignment is due before our 7th meeting. |
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Week 6 |
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In-class activities must be submitted before our 7th meeting. The third assignment is also due before our 7th meeting. |
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Week 7 |
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In-class activities must be submitted before our 8th meeting. The last assignment, which is shorter than previous assignments, is due before the last day of this course. |
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Week 8 |
|
In-class activities must be submitted before the last day of this course. The last assignment is also due before the last day of this course. |
University Academic Policies and Student Support
Course Catalog
Search the OU Course Catalogue.
Student Handbook
Please familiarize yourself with the OU Student Handbook.
Online Library
Access digital materials and other resources at OU Libraries. In particular, students may use OU Business and Economics Library.
Academic Misconduct
In addition to the course conduct policies outlined in the syllabus, students must review the Graduate Student Handbook. It is also the responsibility of each student to be familiar with the definitions, policies, and procedures concerning academic misconduct. The Student Code is available from the Office of the Vice President for Student Affairs, and is contained in the Student's Guide to Academic Integrity. This site also defines misconduct, provides examples of prohibited conduct, and explains the sanctions available for those found guilty of misconduct.
Plagiarism
Plagiarism is the most common form of academic misconduct. There is basically no college-level assignment that can be satisfactorily completed by copying. OU's basic assumption about writing is that all written assignments show the student's own understanding in the student's own words. That means all writing assignments, in class or out, are assumed to be composed entirely of words generated (not simply found) by the student, except where words written by someone else are specifically marked as such with proper citation. Including other people's words in your paper is helpful when you do it honestly and correctly. When you don't, it's plagiarism. For more information about plagiarism, watch this video and then take this short course offered by University Libraries.
Reasonable Accommodation for Disabilities
The University of Oklahoma is committed to providing reasonable accommodation for all students with disabilities. Students with disabilities who require accommodations in this course should contact their professor as early in the semester as possible. Students with disabilities must be registered with the Accessibility and Disability Resource Center prior to receiving accommodations in this course.
Adjustments for Pregnancy/Childbirth Related Issues
Should you need modifications or adjustments to your course requirements because of documented pregnancy-related or childbirth-related issues, please contact me as soon as possible to discuss your options. Generally, modifications will be made where medically necessary and similar in scope to accommodations based on temporary disability. Learn more about the rights of pregnant and parenting students by consulting the FAQ sheets provided by the Institutional Equity Office.
Title IX Resources
For any concerns regarding gender-based discrimination, sexual harassment, sexual misconduct, stalking, or intimate partner violence, the University offers a variety of resources, including advocates on-call 24/7, counseling services, mutual no contact orders, scheduling adjustments, and disciplinary sanctions against the perpetrator. Please contact the Sexual Misconduct Office at 405-325-2215 (8-5, M-F) or OU Advocates at 405-615-0013 (24/7) to learn more or to report an incident.
Religious Holidays
It is the policy of the University to excuse absences of students that result from religious observances and to provide for the rescheduling of examinations and additional required classwork that may fall on religious holidays without penalty. It is the responsibility of the student to make alternate arrangements with the instructor at least one week prior to the actual date of the religious holiday.
Copyright Policy
It is illegal to download, upload, reproduce, or distribute any copyrighted material, in any form and in any fashion, without permission from the copyright holder or his/her authorized agent. The University of Oklahoma expects all members of its community to comply fully with federal copyright laws. If such laws appear to have been violated by any user, the university reserves the right (1) to terminate that user’s access to some or all of the university’s computer systems and information resources and (2) to take additional disciplinary actions as deemed necessary or appropriate. Repeat offenders will be sanctioned and their privileges terminated.
Registration and Withdrawal
If you choose to withdraw from this course, you must complete the appropriate University form and turn the form in before the deadline. If you stop attending the course and doing the coursework without doing the required paperwork, your grade will be calculated with missed homework and examination grades entered as zero. This could result in receiving a grade of F in the course. Deadlines are shown in the Academic Calendar, which is available from the Office of the Registrar.
Student Grievances
In addition to any policies outlined related to submitting an informal or formal grievance by your professor in the Course Syllabus in the online classroom, please review the Graduate Student Handbook for more information about the process of submitting a formal grievance.
Course Summary:
| Date | Details | Due |
|---|---|---|