Managerial Economics & Statistics DECS B725 061 Term: Summer 2019
Course Location: Miller Hall 205 Class Hours: TR, 6:00 - 9:15 p.m.
Instructor: Nate Straight Office Location: Marquette 320C Phone: 504-864-7910 (office), 504-421-2281 (cell) Email: [email protected], [email protected] Office Hours: Tuesday – Thursday, 9:00 a.m. – 4:30 p.m.
Terms of Use A student's continued enrollment in this course signifies acknowledgment of and agreement with the statements, disclaimers, policies, and procedures outlined within this syllabus and elsewhere in the Blackboard environment. This Syllabus is a dynamic document. Elements of the course structure (e.g., dates and topics covered, but not policies) may be changed at the discretion of the professor.
College of Business Mission In the Ignatian tradition, the mission of the College of Business is to provide a superior value-laden education that motivates and enables students to become effective and socially responsible business leaders. We strive to contribute quality research, serve local and intellectual communities, and graduate students who possess critical thinking skills and courage to act justly in a global business environment.
Course Description The course will develop qualitative and quantitative approaches for problem-solving and decision- making in the field of management. Students will learn how to use theoretical and analytical tools from the fields of economics and statistics to enhance their ability to understand real world problems, identify solutions, and make the right managerial decisions to achieve organizational performance goals.
Course Materials There are no required physical textbooks for this course. There will be many readings.
For the economics portion of the class, we will be referencing 2 online textbooks: • “Principles of Economics” by Libby Rittenberg and Timothy Tregarthen (can be accessed at http://open.lib.umn.edu/principleseconomics/) • “Principles of Economics” by Steven Greenlaw and Timothy Taylor (can be accessed at https://opentextbc.ca/principlesofeconomics/)
For the statistics portion of the class, we will be referencing the textbook: • “Introductory Statistics” by Douglas Shafer and Zhiyi Zhang (can be accessed at: saylordotorg.github.io/text_introductory-statistics/)
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Alternatively, if you want a traditional physical textbook that will cover similar content as the above reading materials, I recommend the following textbooks:
• Economics for Managers by Paul Farnham for the economics portion • Statistics: The Art and Science of Learning from Data by Alan Agresti, Christine Franklin, and Bernhard Klingenberg or for the statistics portion
Specific readings for each week will be announced and linked on Blackboard in advance of the relevant class session. The class will entail significant student-led discussion of the practical application of topics.
Completing the assigned readings prior to each class session is mandatory. You will be given specific instructions for each week and/or class session of topics or concepts which you should focus on in the readings. You should be prepared to give concrete examples of these concepts in class if called upon.
Expected Student Course Learning Objectives Upon completing this course in applied economics and quantitative analysis, students will be able to:
Economics • Apply fundamental economic concepts such as subjective value, incentives, module opportunity cost, and marginal analysis to managerial decision-making (MBA 2.2) • Contrast fixed, variable, average, marginal, or sunk costs; explain their relation to marginal productivity and their role in production decisions (MBA 2.2) • Understand the dynamics of “supply and demand” in determining market prices, and explain the pricing decisions made by firms in various conditions (MBA 3.2) • Explain the role of elasticity, cost structure, and competition on pricing (MBA 3.2) • Identify types of competitive and economic factors relevant to strategy (MBA 3.2)
Statistics • Understand how data is created and avoid potential biases in data collection (MBA 5) module • Prepare and interpret descriptive statistics and summary tables or charts (MBA 5) • Apply the tools of statistical inference to produce estimates of unknown values, accounting for potential sampling biases and statistical error appropriately (MBA 5) • Translate claims about the behavior of real world phenomena into statistically testable statements, and evaluate the claims using statistical testing (MBA 5) • Construct and interpret a reasonable linear model of a statistical relationship, and use it to explain and predict the behavior of a variable of interest (MBA 5)
Course Design & Approach The first half of the class will focus on foundational tools of economic analysis as applied to managerial decision-making. We will review various principles of microeconomics that relate to firm and consumer behavior, discuss production and pricing decisions, and analyze the impact of competition on the same.
The second half of the class will focus on the quantitative methods which are used most commonly for data and/or decision analysis in business. We will focus on an understanding of variation and on the essential processes of statistical inference.
“Certain professors of education must be wrong when they say that they can put knowledge into the soul which was not there before, like sight into blind eyes. [But] the power and capacity of learning exists in the soul already; and just as the eye is unable to turn from darkness to light without the whole body, so too the instrument of knowledge can only by the movement of the whole soul be turned from the world of becoming into that of being” – Plato, Republic, Book VII
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Grading Criteria & Grading Scale You have the following coursework to complete, which will be scored as follows:
9 “quant check" homework problem sets @ 50 points = 450 points 4 weekly mini case-study analysis reports @ 75 points = 300 points 1 comprehensive final exam (given online) @ 250 points = 250 points
Your final grade will be calculated as your earned points divided by: 1000 points
Your grade will be calculated and adjusted as described above and mapped to the scale below:
<59.9 60-66.9 67-69.9 70-72.9 73-76.9 77-79.9 80-82.9 83-86.9 87-89.9 90-94.9 95-100 F D D+ C- C C+ B- B B+ A- A
I reserve the right to round edge cases (e.g., 79.96%) either up or down at my sole discretion.
Academic Integrity & Expected Behavior This is a graduate class in a professional degree program. I expect you to behave accordingly; please do so.
Academic misconduct will not be tolerated, but dealt with per the bulletin. In particular, if you are found to have copied work from the Internet or copied on an exam, you will receive an immediate F in the course.
The most significant other aspects of academic integrity are engaged participation in class, authentic representation of original work, adherence to all rules regarding collaboration or group work, and avoidance of even an appearance of dishonesty. Refer to bulletin.loyno.edu/academic-honor-code and to your MBA Student Code of Conduct. If you have any doubt if an activity constitutes academic misconduct, ask me first
Attendance & Participation Policies My attendance policy is that you should consistently come to class. I will not be grading or penalizing for absences, but the nature of a summer course is that you are liable to fall irreparably behind if you skip class.
Exam Format & Project Information The exam is open-note, open-book and will involve calculation and interpretation of economic values and statistical quantities. The exam format is nevertheless primarily “short answer” with questions that are designed to test your conceptual understanding more than your rote ability to churn out numbers.
Group Work Policy for Projects You may work the mini case-study analyses (not homework) with 1 class partner. You are not required to do so, nor is it necessarily beneficial to your performance. Both group members must be involved in answering every part of the analysis. My expectation for group work is that your partner serve as a sounding board and that you learn from others, and categorically not that you merely divvy up work.
Late Work Policy for Assignments This class will involve a significant amount of take-home work which will require material time investment. In order not to fall insurmountably behind on homework, you must put in a serious attempt to complete assignments in a timely fashion.
I am very understanding of events that cause work to be late, but you must keep me posted on progress in completing any coursework that is late. Any assignment that is turned in after I have returned others’ graded work may be penalized 50%.
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Coursework & Anticipated Schedule The following is the set of topics we will cover in class and estimated exam / assignment due dates.
Week 0, pre-class: Cost, Value, and Decision-Making Rittenberg & Tregarthen, Chapter 1, Sections 1.1–1.2 Taylor & Greenlaw, Chapter 2, Section 2.1 Robert Murphy, “Problems with the Cost Theory of Value” Taylor & Greenlaw, Chapter 7, Section 7.1
Week 1, July 1: Cost, Value, and Decision-Making Rittenberg & Tregarthen, Chapter 8, Sections 8.1–8.2 Taylor & Greenlaw, Chapter 7, Section 7.2
Week 2, July 8: Producers, Consumers, and Markets Quant 1 due Taylor & Greenlaw, Chapter 8, Sections 8.1–8.2 Quant 2 due Rittenberg & Tregarthen, Chapter 3, Sections 3.1–3.3 Rittenberg & Tregarthen, Chapter 5, Sections 5.1–5.3 Rittenberg & Tregarthen, Chapter 6, Sections 6.2
Week 3, July 15: Pricing, Competition, and Strategy Case 1 due Rittenberg & Tregarthen, Chapter 10, Sections 10.1–10.3 Quant 3 due Rittenberg & Tregarthen, Chapter 11, Section 11.3 Quant 4 due Taylor & Greenlaw, Chapter 11, Section 11.1 Besanko, “Strategic Positioning for Competitive Advantage”
Week 4, July 22: Data Visualization and Summarization Case 2 due Shafer & Zhang, Chapter 1, Sections 1.1–1.3 Quant 5 due Shafer & Zhang, Chapter 2, Sections 2.1–2.4 Quant 6 due Jacob Joseph, “How to Compare Apples and Oranges”
Week 5, July 28: Sampling Theory and Statistical Inference Case 3 due Shafer & Zhang, Chapter 4, Section 4.1 Quant 7 due Shafer & Zhang, Chapter 5 Quant 8 due Shafer & Zhang, Chapter 6, Sections 6.1–6.2 Tomi Mester, “Statistical Bias Types Explained”
Week 6, August 5: Significance and Statistical Correlation Case 4 due Shafer & Zhang, Chapter 7, Section 7.1 Quant 9 due Shafer & Zhang, Chapter 8, Sections 8.1–8.2 Shafer & Zhang, Chapter 10, Sections 10.1–10.2 Alex Reinhart, “The Power of the P-Value”
Final Exam due midnight, Satuday, August 10
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