QNM225 - Quantitative Decision Management for Business

Outline information
Semester
Schools offering this subject
Last revision date 2023-10-02 00:20:56.874
Last review date 2023-12-04 00:15:04.738

Subject Title
Quantitative Decision Management for Business

Subject Description
This course develops decision management skills for business using quantitative data analysis. We start with a traditional introduction to statistics to collect, analyze, present, and interpret quantitative data. This is not just a statistics course. We also introduce new technologies and methods such as data science, business intelligence, and data analytics to improve critical decision-making skills to solve common business problems. We cover critical thinking, estimation, forecasting, prediction, modeling, sensitivity analysis, multiple regression, model optimization, risk analysis, and goal seeking. Students write reports communicating recommendations to senior management from their quantitative analysis. A computer is mandatory.

Credit Status
This course is equivalent to an introductory college statistics course for a college diploma and provides transfer credit for statistics toward a business degree. This course is not appropriate if you intend to take the Accounting CGA exam which tests calculator rather than computer abilities.)   

Learning Outcomes
Upon successful completion of this subject the student will be able to:

  1. Explain basic statistical concepts such as data gathering sampling, and seven levels of measurement such as qualitative, quantitative, geo-positioning, images, email, and microblogging.
  2. Apply a systematic method to produce a high quality reports for managers.
  3. Explain critical thinking skills used to detect deception with numbers.
  4. Summarize data using a spreadsheet to detect visual data patterns using graphs such as histograms, scatter diagrams, tables, and charts.   
  5. Calculate measures of central tendency and variation by applying spreadsheet and statistical methods.
  6. Evaluate and explain uncertainty using classical probability theories such as Bayes Theorem.
  7. Solve, predict, and build models using probability distributions.
  8. Compute minimum sample size and discuss big data analytics.
  9. Interpret data and provide estimations using confidence intervals
  10. Evaluate and construct hypothesis tests.
  11. Build quantitative models, forecast, and interpret using multiple regression.
  12. Decision making using modeling, sensitivity analysis, optimization, and goal-seeking.
 

Essential Employability Skills
    •  Execute mathematical operations accurately.

    •  Apply a systematic approach to solve problems.

    •  Use a variety of thinking skills to anticipate and solve problems.

    •  Analyze, evaluate, and apply relevant information from a variety of sources.

Academic Integrity
Seneca upholds a learning community that values academic integrity, honesty, fairness, trust, respect, responsibility and courage. These values enhance Seneca's commitment to deliver high-quality education and teaching excellence, while supporting a positive learning environment. Ensure that you are aware of Seneca's Academic Integrity Policy which can be found at: http://www.senecapolytechnic.ca/about/policies/academic-integrity-policy.html Review section 2 of the policy for details regarding approaches to supporting integrity. Section 2.3 and Appendix B of the policy describe various sanctions that can be applied, if there is suspected academic misconduct (e.g., contract cheating, cheating, falsification, impersonation or plagiarism).

Please visit the Academic Integrity website http://open2.senecac.on.ca/sites/academic-integrity/for-students to understand and learn more about how to prepare and submit work so that it supports academic integrity, and to avoid academic misconduct.

Discrimination/Harassment
All students and employees have the right to study and work in an environment that is free from discrimination and/or harassment. Language or activities that defeat this objective violate the College Policy on Discrimination/Harassment and shall not be tolerated. Information and assistance are available from the Student Conduct Office at student.conduct@senecapolytechnic.ca.

Accommodation for Students with Disabilities
The College will provide reasonable accommodation to students with disabilities in order to promote academic success. If you require accommodation, contact the Counselling and Accessibility Services Office at ext. 22900 to initiate the process for documenting, assessing and implementing your individual accommodation needs.

Camera Use and Recordings - Synchronous (Live) Classes
Synchronous (live) classes may be delivered in person, in a Flexible Learning space, or online through a Seneca web conferencing platform such as MS Teams or Zoom. Flexible Learning spaces are equipped with cameras, microphones, monitors and speakers that capture and stream instructor and student interactions, providing an in-person experience for students choosing to study online.

Students joining a live class online may be required to have a working camera in order to participate, or for certain activities (e.g. group work, assessments), and high-speed broadband access (e.g. Cable, DSL) is highly recommended. In the event students encounter circumstances that impact their ability to join the platform with their camera on, they should reach out to the professor to discuss. Live classes may be recorded and made available to students to support access to course content and promote student learning and success.

By attending live classes, students are consenting to the collection and use of their personal information for the purposes of administering the class and associated coursework. To learn more about Seneca's privacy practices, visit Privacy Notice.