Department of Accounting and Business Analytics MGTSC501
Data Analysis and Decision Making Syllabus - Fall 2021
Instructor: Ray Hagtvedt Phone: 780-248-1262
Office: Bus 2-43 Email: [email protected]
Lecture A1: BUS 5-40 A/B Tuesdays 0900-1150 Lab: D1 in BUS B-24/28 T 1300-1350 Lecture X03: BUS 5-13 Tuesdays 1830-2130 Lab: X04 in BUS B-18 T 1300-1350 Lecture X01: BUS B-09 Thursdays 1830-2130 Lab: X02 in BUS B-28 T 1300-1350 Lab Manager: Ozan Ozdemir Email: [email protected] TAs: See eClass
COURSE DESCRIPTION AND OBJECTIVES
Business decision-making uses increasingly advanced models, new tools, and innovative concepts, and even new kinds of data. As analytics evolves, artificial intelligence and machine learning enters the mainstream, and entirely new kinds of data become tractable, the fundamentals of data analysis and modeling have become more, rather than less, important. This course introduces you to tools and reasoning that will help you in analyze data yourself or interpret and evaluate 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.
Learning Outcomes:
Learn about how data analysis fits into the MBA program, knowledge, society, new technology, and business; gain an introduction to terms such as ‘big data’ & analytics.
Review graphical and numerical methods of descriptive statistics
Understand basic probability theory, with selected distributions such as the Binomial, Poisson, Exponential, and Normal
Gain an introduction to 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, & make 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
COURSE MATERIALS
Required Textbook:
Statistics for Business and Economics, 14
thedition, by Anderson, Sweeney, Williams, Camm & Cochran. Previous editions are fine, but problems will be assigned from the newest edition. Referred to as ASW. We will also use
WebAssign, the publisher’s homework tool. ISBN- 9780357031391
eClass: You will be able to obtain materials via eClass at https://eclass.srv.ualberta.ca/
Other than information provided in class, eClass will be where announcements and other information regarding the course will be made. Please plan to check this site regularly and/or make sure your settings in eClass will send you emails when there are updates.
COURSE ACTIVITIES AND EVALUATION
Your grade in this course will be based on the deliverables below. The grades will be weighted as follows to determine your percentage mark in the course:
Homework and Labs 25%
Midterm Exam 25%
Final Exam 50%
Total 100%
Letter grades will be assigned to the percentage marks in accordance with University
Regulations [Section 23.4(4) of the University Calendar]. Grades in this course will be based on a combination of absolute achievement and relative performance.
These grades represent the only marks available to students. No additional work or extra credit is available.
In-class activities
Class participation may earn up to ten percentage points in extra credit, in order to incentivize good citizenship and helping the class move forward. Attendance per se is not required, but students are responsible for everything delivered in class.
Online activities
You are responsible for keeping up with eClass and turning in homework and lab assignments online. Some office hours may be online.
Exams
Engineering: https://www.ualberta.ca/engineering/student-services/student-policies/calculator- use-specifications.html
Exams Remarking Policy
Solutions to textbook problems will not be provided, beyond videos and WebAssign. Solutions to the homework will be posted on eClass. Midterm exams will be returned & solutions will be posted.
Students should review the grades carefully and bring to my attention any questions about their grades as soon as possible. No regrading requests will be accepted more than one week after the results have been made available.
All appeals for regrading must be in writing and the original copy (unless available on-line) must be attached. Exams submitted for appeal will be subject to an entire review. This may result in your grade being lowered.
Final exam solutions will not be posted, but the exam will be made available for viewing in your instructor’s office.
For information regarding applying for a reappraisal of your final exam please see Section 23.5.4 (2) of the Calendar.
Link to official U of A grading policy documents
https://policiesonline.ualberta.ca/PoliciesProcedures/Pages/DispPol.aspx?PID=101 Absences from Exams
Occasionally life events occur that require a student to miss term work, term examinations, or final examinations. However, excused absences are not granted automatically and will be considered only for acceptable reasons such as incapacitating illness, severe domestic affliction, or religious convictions.
Unacceptable reasons include, but are not limited to, personal events such as vacations, weddings, or travel arrangements. When a student is absent without an acceptable excuse, a final grade will be computed using a raw score of zero for the work missed. Any student who applies for or obtains an excused absence by making false statements will be liable under the Code of Student Behaviour.
If a student is absent from a midterm exam for a legitimate and adequately documented reason, the weight of the missed exam will be reallocated to a comprehensive final exam. There will be no deferred mid-term exams in this course.
If a student is absent from the final exam, he or she must obtain permission from the Faculty of Graduate Studies and Research to write a deferred final exam. The deferred exam will be written at a time determined by the MBA office in consultation with the instructor.
Section 23.3(2) and 23.5.6 (1) of the Calendar provides the University Regulations regarding procedures in the case of a missed final examination. If a deferred final exam is required for this course, it will tentatively be held on the Friday before Reading Week at 0800.
CLASSROOM ETIQUETTE
Showing courtesy and professionalism to the instructor and other students is an important part of every class. It is expected that students observe business etiquette online & off, and in particular:
a) Will arrive in class before it begins and will stay until it is over;
b) Will not engage in sidebar conversations or otherwise disrupt;
c) Will keep video on if possible when using Zoom or similar;
d) Will keep audio off if not speaking (again, when using Zoom or similar) Class Attendance
Attendance is not mandatory, but it is your responsibility to attend lectures, labs, office hours, and problem sessions, as you deem necessary.
ACADEMIC SUPPORTS
The Academic Success Centre provides professional academic support to help students maximize their academic success and achieve their academic goals. The Centre offers
appointments, advising, group workshops, online courses, and specialized programming year- round to students in all university programs, and at all levels of achievement and study.
Location:
1-80 Students' Union Building University of Alberta, North Campus
Website:
https://www.ualberta.ca/current- students/academic-success- centre
Phone: 780-492-2682 Email: [email protected]
ACADEMIC INTEGRITY
Because we regard this class as we would any job responsibility, it seems prudent to clarify, in advance, the policy on academic integrity. Given the professional nature of the MBA program, it is unlikely that a student in this class would turn in work which is not their own. However, if we determine that the work is not entirely that of the student(s) whose name(s) appear on the work, the student(s) involved may not pass this course and be further subject to program-level
discipline. Specifically, in order to protect the integrity of the MBA degree, the University may expel, suspend, reprimand, or reduce a course mark of any student who breaches the Code of Student Behavior.
Absolute and complete academic honesty is expected of you in this course. It is important for you as a student to behave in an ethical manner. 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 http://www.governance.ualberta.ca/StudentAppeals.aspx) and avoid any behaviour that 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.
Policy about course outlines can be found in Section 23.4(2) of the University Calendar.
TENTATIVE COURSE SCHEDULE
Class# Dates Topics Chapters
1 September 2 & 7 Introduction & review;
Descriptive Statistics 1, 2, 3
2 September 9 & 14 Probability 4
3 September 16 & 21 Discrete Probability Distributions 5 4 September 23 & 28 Continuous Probability Distributions 6 5 October 5 & 7 Sampling and Sampling Distributions 7 6 October 12 & 14 Inferential Statistics: Confidence
Interval Estimation 8
7 October 19 & 21 Inferential Statistics: Hypothesis Testing 9 8 October 26 & 28 Midterm
9 November 2 & 4 Simple Linear Regression; Ordinary
Least Squares 14
November 9-11 Reading Week
10 November 16 & 18 Multiple Regression 15.1-8 11 November 23 & 25 Models of multiple OLS regression
Regression and Time Series 16.1-
16.3 12 November 30 &
December 2 Chi-squared methods;
Decision Analysis 12.1-2
13 December 7 Review 19
Final Exam Dec 10-22, 2021
