Example syllabus only – exact content subject to change. Please see your instructor’s syllabus for the current term for your specific course’s guidelines.
Instructor: Jianjing Lin (Office: 306C Stockbridge Hall; Email: @email)
Lectures: TuTh 1:00PM – 2:15PM, in-person at Holdsworth Hall room 202
Office hour: Thursday 2:30PM – 4:00PM at 306C Stockbridge Hall or by appointment
Course website: Canvas
TA: Said Arslan (Email: @email)
TA office hour: TBD
Course description and objective
This course introduces a wide range of analytical techniques and decision-making tools that are commonly used in the business environment. We will cover standard approaches in data management and analysis, including linear regression, the difference-in-differences framework, hedonic evaluation, survey designs, and cost- benefit analysis. Moreover, we will also discuss classic economic models that can be applied to a variety of management and public policy domains. The goal of this course is to demonstrate the application of these techniques and equip students with tools that will be useful for their future career.
Prerequisites
- Intermediate microeconomics (Res-Econ 202 or Econ 203)
- Intermediate statistics (Res-Econ 213)
- Decision analysis (Res-Econ 313)
- Financial analysis for consumers and firms (Res-Econ 314 or Accounting 221 or Finance 301)
Student learning objectives
After completing the course, students will be able to
- Understand and effectively explain the methodologies covered in this course
- Integrate theoretical principles with quantitative techniques to promote decision-making
- Creatively apply the acquired knowledge from their respective fields to make optimal choices in their professional and personal lives
- Develop analytical ability of resolving business decision problems in a variety of settings and improve communication skills via problem solving and group work presentations
Textbook and references
- No required textbooks.
- Supplementary references:
- William J. Baumol, Alan S. Blinder, John L. Solow, Microeconomics: Principles & Policy, 14th Edition, Cengage Learning
- James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris, Managerial Economics: Application, Strategy, and Tactics (used), Cengage Learning.
- Robert Pindyck, Daniel Rubinfeld, Microeconomics (used), Pearson.
Required technology
- Microsoft Excel and its Analysis Toolpak
- The Microsoft Office 365 Education suite, which includes Excel, is available for free to all students. See Microsoft in the following link for more information: https://www.umass.edu/it/services/software.
- Alternatively, you may use any campus lab computer for access. Instructions for loading Excel’s Analysis Toolpak are available at: https://support.microsoft.com/en-us/office/load-the-analysis-toolpak-in- excel-6a63e598-cd6d-42e3-9317-6b40ba1a66b4.
- Canvas
- All course materials—lecture slides, homework assignments, etc.—are on the course’s Canvas page.
- Echo360
All classes will be recorded and uploaded to Echo360: echo360.org.
Assessment
I will use the following grading scale:
| Grade | A | A- | B+ | B | B- | C+ | C | C- | D+ | D | D- | F |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Score | 93–100 | 90–93 | 87–90 | 83–87 | 80–83 | 77–80 | 73–77 | 70–73 | 67–70 | 63–67 | 60–63 | 0–60 |
I will calculate grades according to the following percentages:
| Assessment | Percentage |
|---|---|
| Attendance | 10% |
| Practice problems | 15% |
| Quizzes | 15% |
| Group project presentation | 15% |
| Midterm exam | 20% |
| Final exam | 25% |
- Attendance: I randomly take attendance for 12 times throughout the entire semester. The first two absence will be waived. After that for each time of absence, the student will lose 10% of the attendance credit. If you expect you cannot attend a lecture, please DO email me BEFORE the lecture to avoid loss of attendance credits.
- Practice problems: There are 5 – 8 problem sets. Each is usually assigned after a section or an important topic. All problem sets are due at the beginning of class. Late submission will not be accepted.
- Quizzes: Quizzes (open-book) will be assigned in class from time to time. Roughly 10 – 20 minutes will be devoted to the quiz, depending on the length and level of difficulty. Make-up quizzes are not allowed unless it is due to legitimate reasons AND the instructor is notified for absence in advance.
- Midterm exam: Tentatively scheduled during the regular lecture on March 10 (Tuesday).
- Final exam: Students are advised NOT to schedule long-distance travels until the final exam schedule is finally determined. Note that all exams are closed- book. Make-up exams will NOT be administered except for extraordinary circumstances (accidents, family emergencies, UMass approved official activities, etc.) and will require proper documentation.
- Group project presentation: Each student is required to participate in a group presentation, with each group comprised of no more than four students. Students in the same group should work together and communicate well with each other on selecting the topic and preparing the presentation. Group presentations are tentatively scheduled in classes after the midterm exam. More details will be sent out later.
- Bonus?Yes!: You can get bonus points from active participation in class. How the bonus points are calculated into the final grade will be determined towards the end of the semester.
Important dates for this class
- February 19 (Thursday), no class (on Monday schedule)
- March 10 (Tuesday), Midterm exam
- March 16-20, Spring break – no classes
Course topics and schedule
(subject to change as the semester progresses)
- Weeks 1-3: Common tools for policy evaluation with illustrations in Excel
- Data basics in Excel
- Linear regression and difference-in-differences
- Regression in Excel
- Weeks 4-6: Market failures and regulakons
- Efficient markets
- Imperfect compekkons
- Externalikes
- Regulakons
- Weeks 7-9: Valuation and decision making
- Non-market valuation
- Hedonic and forecasting
- Survey design
- Cost-benefit analysis
- Weeks 10-11: Applications in the healthcare sector
- Select policy issues
- Common data sources and analytical approaches
Diversity and inclusion
The University of Massachusetts Amherst is committed to policies that promote inclusiveness, social justice, and respect for all, regardless of race, color, religion, creed, gender, sexual orientation, age, national or ethnic origin, physical or mental disability, political belief or affiliation, marital status, veteran status, immigration status, gender identity and expression, genetic information, or any other characteristic or status protected by state or federal laws.
Academic Integrity Statement
UMass Amherst is strongly committed to academic integrity, which is defined as completing all academic work without cheating, lying, stealing, or receiving unauthorized assistance from any other person, or using any source of information not appropriately authorized or attributed. As a community, we hold each other accountable and support each other’s knowledge and understanding of academic integrity. Academic dishonesty is prohibited in all programs of the University and includes but is not limited to: Cheating, fabrication, plagiarism, lying, and facilitating dishonesty, via analogue and digital means. Sanctions may be imposed on any student who has committed or participated in an academic integrity infraction. Any person who has reason to believe that a student has committed an academic integrity infraction should bring such information to the attention of the appropriate course instructor as soon as possible. All students at the University of Massachusetts Amherst have read and acknowledged the Commitment to Academic Integrity and are knowingly responsible for completing all work with integrity and in accordance with the policy (https://www.umass.edu/senate/book/academic-integrity-policy).
Accommodation Statement
The University of Massachusetts Amherst is committed to making reasonable, effective, and appropriate accommodations to meet the needs of students with disabilities and help create a barrier-free campus. If you have a disability and require accommodations, please register with Disability Services, meet with an Access Coordinator in Disability Services, and send your accommodation letter to your faculty. Information on services and registration is available on the Disability Services website (https://www.umass.edu/disability-services/).
Artificial Intelligence (AI)
Use of AI tools (UMass enterprise licensed version of Copilot is the recommended option) is permitted in this course for students who wish to use them. To adhere to our scholarly values, students must cite any AI-generated material that informed their work (this includes in-text citations and/or use of quotations, and in your reference list). Using an AI tool to generate content without proper attribution qualifies as academic dishonesty.
Title IX Statement
In accordance with Title IX of the Education Amendments of 1972 that prohibits gender-based discrimination in educational settings that receive federal funds, the University of Massachusetts Amherst is committed to providing a safe learning environment for all students, free from all forms of discrimination, including sexual assault, sexual harassment, domestic violence, dating violence, stalking, and retaliation. This includes interactions in person or online through digital platforms and social media. Title IX also protects against discrimination on the basis of pregnancy, childbirth, false pregnancy, miscarriage, abortion, or related conditions, including recovery. There are resources here on campus to support you. A summary of the available Title IX resources (confidential and non-confidential) can be found at the following link: https://www.umass.edu/titleix/resources. You do not need to make a formal report to access them. If you need immediate support, you are not alone. Free and confidential support is available 24 hours a day / 7 days a week / 365 days a year at the SASA Hotline 413-545-0800.