University of Alberta School of Business
Fall 2019 MGTSC501
Dr Ray Hagtvedt Data Analysis and Decision Making
COURSE SYLLABUS
Course Description_______________________________________________________
Business decision-making uses increasingly advanced models, new tools, and even new kinds of data. As terms such as big data, analytics, artificial intelligence, and machine learning enter the mainstream, and entirely new kinds of data become tractable, the fundamentals of data analysis and modeling have become increasingly important. This course introduces you to tools and reasoning that will help you in analyze data yourself and make proper use of statistical reports. As a mandatory course for all AACSB approved MBA programs, the core material is standard.
Although the priority is understanding concepts, a lab component introduces students to software for data analysis.
Class details_____________________________________________________________
A1 LEC: T 9:00am - 11:50am, located in BUS 3-5 X01 LEC: R 6:30pm - 9:30pm, located in BUS 3-5 X03 LEC: T 6:30pm - 9:30pm, located in BUS 3-5
Learning Goals_______________________________________________________
Learn about how data analysis fits into the MBA program, knowledge, society, new technology, and business
Gain familiarity with the language of data analytics and statistics
Review graphical and numerical methods of descriptive statistics
Understand basic probability theory, with selected distributions such as the Binomial, Poisson, Exponential, and Normal
Learn the basics of decision analysis and the pricing of information
Understand what a sampling distribution is, and how such sampling distributions for sample statistics tie probability theory to inferential statistics
Use and correctly interpret confidence intervals and hypothesis testing
Understand, apply, and compute simple linear regression models (Ordinary Least Squares [OLS]), starting from raw data, and finishing with confidence intervals and hypothesis tests, plus specific predictions
Understand and apply multiple regression models (OLS), develop models, including non- linearity and dummy variables, make predictions and specify the expected errors
Understand the limitations, as well as the power, of the statistical tools
Materials_______________________________________________________________
Textbook: Statistics for Business and Economics, 14
thedition, by Anderson, Sweeney, Williams, Camm, Cochran, Fry, & Ohlmann. Previous editions are fine, but problems will be assigned from the newest edition.
eClass will have all relevant downloads
This syllabus is subject to revision. For instance, it will be updated to contain key
information such as final exam time and place. Any information contained in the syllabus
is of necessity considered “received”.
Course Grade___________________________________________________________
Your grade will be determined as follows:
Class Participation 10 %
Homework & Labs 20 %
Midterm(s) 15 %
Project 15 %
Final Exam 40 %
Attendance Policy _________________________________________________________
Attendance is not required, but Class Participation is difficult without. NB! Each student is always responsible for the material covered in class (missed or not), as well as all messages delivered in class.
Contact Information: ______________________________________________
Office: Business Building 2-43 Email: [email protected] Phone: 780-248-1262
Office hours; Tuesdays & Thursdays 1200-1300
Lab Manager: Email
Daniel Kurian dkurian@ualberta
Teaching Assistants: Email:
Courtney Boschmann [email protected] Chan Woo Youn [email protected] Brenden Bullok [email protected] Cole Stroeder [email protected] Braeden Veeneman [email protected]
Assignments___________________________________________________________________
There will be four assignments, each with a combination of course, lab, project, and (some) administrative questions. These are to be completed individually, although you are welcome to work on them with others.
We will normally do textbook problems during the lecture, and these are listed at the end of the PowerPoint slides (available for download). Your solutions to these problems will not be collected, and no solutions to these problems can be posted due to restrictions from the publisher. Discussing these problems in class is a great way to measure the level of class understanding.
Labs: These are becoming more tightly integrated, but you will still receive separate grades for course and lab portions of the assignments.
Exams, Grading, & Appeals_____________________________________________________
We will have one midterm and one final exam. The rules for these exams will be posted, but in general:
you may not bring anything to an in-class exam that will give an unfair advantage over classmates. In particular, you may not bring advanced calculators (defined as those that can communicate with other devices), computers, or printed material. You may bring up to four crib-sheets to each in-class exam, front and back, in your own hand-writing, with anything you care to write on these four sheets of paper. The basic idea is to provide a level playing field for all students. The crib sheets are the single best pedagogical tool I have ever come across.
Show your work. If there is no work you may earn 0% for an otherwise “close to” correct answer.
Any missed work, including the mid-term exam, will have their weight added to the final exam. No two exams are exactly the same, so no makeup exams will be given under any circumstances.
If the final exam itself is missed, formal University of Alberta guidelines and procedures will be followed.
This means in particular that applications to take the make-up exam must be submitted to the MBA office.
The planned time for the make-up exam will be at 0800 on the Friday prior to Reading Week the following semester, but this may change. Since this is too long a delay for most people, and grades tend to be much lower after any significant time, it is important to try hard to take the final exam at the assigned time.
If there are multiple sections and dates for the final, it may be possible to take the exam at another time if the reason is good (e.g. medical). If you wish to do so, please contact your instructor prior to your regularly scheduled exam in order to get permission to take the exam at another specific date.
For all deliverables, raw scores may be translated to z-scores (after all, this is a class in statistics). The z- scores will be weighted using the numbers above, in the following way.
Course grade = (Weight 1)*(Score 1)
+ (Weight 2)*(Score 2)
+ (Weight 3)*(Score 3) + (Weight 4)*(Score 4) + (Weight 5)*(Score 5)
Example: Assume 90 homework & lab, 85 midterm,90 for the project, 80 final, 90 labs, and 90 for class participation, which are the only scores that count in this example
:
Example grade = 90*0.10 + 90*0.20 + 85*0.15 + 90*0.15 + 80*0.40 = 85.25 (B course grade) Numerical guidelines (approximate): an A is 90 or more, B is 80 or more, C+ is 70 or more.
The University of Alberta Guidelines will be followed explicitly and implicitly:
https://policiesonline.ualberta.ca/PoliciesProcedures/Pages/DispPol.aspx?PID=101
Adjustments may be made by over-weighting the final exam. For example, doing well on the final exam is a method that may salvage a poor grade prior to the final exam, while doing well only on homework will not. There will be no make-up work, and no work after the final exam.
Appeals of any grade are to be delivered in writing to the instructor or the grader within one week or the grade being made available on eClass. Email is fine, but sufficient detail to understand the error the grader made has to be included. The weight of a mistake the student made is not grounds for appeal. The procedure is as follows (until steps within the course are exhausted):
1. Appeal in writing.
2. The grader will provide a first response.
3. The next step, if the first response is not sufficient, is to re-submit an additional explanation as to why the grader’s response is incorrect, this time only to the instructor. The deadline for this step is one week after the response from the grader is available.
4. Instructor responds.
Selected University of Alberta Guidelines__________________________________________
Policy about course outlines can be found in Section 23.4(2) of the University Calendar. (GFC 29 SEP 2003)
“The University of Alberta is committed to the highest standards of academic integrity and honesty.
Students are expected to be familiar with these standards regarding academic honesty and to uphold the policies of the University in this respect. Students are particularly urged to familiarize themselves with the provisions of the Code of Student Behaviour (online at
www.uofaweb.ualberta.ca/governance/studentappealsregulations.cfm) and avoid any behaviour which could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or
participation in an offence. Academic dishonesty is a serious offence and can result in suspension or expulsion from the University.” (GFC 29 SEP 2003)
Further information, including a handbook of Academic Integrity, is available here:
http://www.osja.ualberta.ca/
Procedures for Registering Complaints About Marking, Grading and Related Issues
a. Where the above guidelines have not been followed or where students have concerns about the instructor’s teaching, the student should make the concern known to the appropriate individual in the following sequence:
1) Instructor
2) Chair of the Department in which the course is taught
3) Dean of the Faculty in which the course is taught (some Faculties have delegated this authority to Departments)
b. A student needing advice on these matters should see the student advisors in the Office of the Dean of Students.
c. These procedures do not constitute a mechanism for appeals and grievances regarding the academic standing or individual grades of a student. Appeals and grievances of that nature are dealt with in §23.8 of the University Calendar.
Plagiarism, Cheating, Misrepresentation of Facts, and Participation in an Offence
The University of Alberta considers plagiarism, cheating, misrepresentation of facts and participation in an offence to be serious academic offences. Plagiarism, cheating, misrepresentation of facts and
participation in an offence can be avoided if students are told what these offences are and if possible sanctions are made clear at the outset. Instructors should understand that the principles embodied in the Code of Student Behaviour are essential to our academic purpose. For this reason, instructors will be fully supported by Departments, Faculties and the University in their endeavours to rightfully discover and pursue cases of academic dishonesty in accordance with the Code.At the beginning of each term,
instructors should review with their students the definitions of plagiarism and cheating. We are now also asking you to review with your students the definition of Misrepresentation of Facts and participation in an Offence. Your cooperation and assistance in this matter are much appreciated. The Don’t Cheatsheet summarizes the appropriate sections of the Code that deals with the four offences noted and is available on the University Governance website at www.ualberta.ca/governance/StudentAppealsCheatsheet.cfm.
Instructors are also requested to inform students that when academic offences occur, a number of
sanctions can be imposed, such as mark or grade reduction, failure in the course, a remark on a transcript of 8 (or 9 in failing graduate student grades), indicating Inappropriate Academic Behaviour, and students may be suspended or expelled (outlined in §30.4.2 of the GFC Policy Manual).”
On re-examinations [Source: Calendar 23.5.5. (2)e]:
“Re-examinations are not permitted in the Faculty of Graduate Studies and Research.”
Miscellaneous Policy____________________________________________________________
This course is taught in day sections on Tuesdays and evening sections on Tuesdays and Thursdays.
Feel free to make up classes during the alternate time(s), provided we have sufficient seats.
If you find yourself in trouble, do not ignore the problem, but come see me a.s.a.p. I have additional problem sets, as well as additional work, available. However, require both time and effort, and will not be useful if started too late.
Keep up with the course website & the syllabus. Information provided either in class or via the website has to be considered received after 24 hours.
In general, treat everyone in this class with common courtesy, and expect consideration in return.
If you have a work assignment, or travel, that conflicts with the course, I do not have a problem with any student who prioritizes work or family emergencies. Note again that attendance is not required; you are responsible for your own learning and making the best use of your time.
Due to the ethics applications guidelines at the University of Alberta, you are not allowed to collect any data for this class.
Deviations from policy will be unusual, especially because there is a great deal of flexibility designed into this course. If such a deviation has been granted, e.g. extending a project deadline for
attending a case competition, only written documentation counts. In particular, if you get a verbal assent to a special request, it must be followed up in writing (preferably email), immediately.
eClass will contain links to “Past or representative evaluative course material, consistent with the Access to Evaluative Material Procedure of the Assessment Policy, found at the University of Alberta Policies and Procedures Online (UAPPOL) website at https://policiesonline.ualberta.ca.”
Recording Policy (mandatory inclusion)_______________________________________
Audio or video recording, digital or otherwise, of lectures, labs, seminars or any other teaching
environment by students is allowed only with the prior written consent of the instructor or as a part of an approved accommodation plan. Student or instructor content, digital or otherwise, created and/or used within the context of the course is to be used solely for personal study, and is not to be used or distributed for any other purpose without prior written consent from the content author(s).
Anticipated Class Schedule________________________________________________
The following schedule may change as the semester progresses.
Week in Year
Topic(s)
Textbook Chapter Reference36 Introduction & review; Descriptive Statistics Chapter ref: 1, 2, 3
37 Probability Chapter ref: 4
38 Discrete Probability Distributions Chapter ref: 5
39 Continuous Probability Distributions Chapter ref: 6
40 Sampling and Sampling Distributions Chapter ref: 7
41 Inferential Statistics: Confidence Interval Estimation Chapter ref: 8 42 Inferential Statistics: Hypothesis Testing Chapter ref: 9 43 Midterm (October 22 & 24)
44 Simple Linear Regression; Ordinary Least Squares Chapter ref: 14
45 Multiple Regression Chapter ref: 15.1-8
46 Reading Week
47 Models of multiple OLS regression; time-series regression;
Chapter ref: 16.1-16.3
48 Chi-squared methods; Decision Analysis Chapter ref: 12.1-2 & 21 49 Project presentations & review
Tentative from the exam planner:
A1: Thursday, December 19, at 0900 X01: Thursday, December 12, 2019 at 17:30
X03: Tuesday, December 10, 2019 at 17:30
http://www.registrarsoffice.ualberta.ca/en/Examinations.aspx