QMET204 Statistics for Business Semester 2, 2022
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QMET204 Statistics for Business
Semester 2, 2022
Course Aims and Learning Outcomes
Aims
The main aims of this course are:
1. To help students understand the role, importance, and benefits of data-driven decision-making.
2. To develop students’ abilities to construct and communicate compelling narratives from raw data using statistical reasoning and methods.
Learning outcomes
After successfully completing this course, students will be able to:
LO1. Understand and explain the role of analytics in the formulation of business strategy.
LO2. Describe data and communicate actionable insights using appropriate visuals.
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LO3. |
Apply statistical methods to analyse data. |
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LO4. |
Visualize data using the R programming language |
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LO5. |
Write SQL queries to retrieve, manipulate, and analyse data. |
Course Content
The following table gives an indication of the timing of the content for this course. It may be necessary to make adjustments to the timetable.
Learning and Teaching Arrangements
Learning and Teaching Approach
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The course provides a range of delivery methods and learning opportunities for students including lectures, self-study materials, group work, and office hours. Students are strongly advised to make full use of all available learning opportunities. |
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The learning and teaching strategies may include, but are not limited to lectures and tutorials, demonstrations, peer learning, group discussions, self-directed learning, tuākana tēina, pōwhiri. Students are strongly advised to make full use of all available learning opportunities. |
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The learning and teaching approach is based on a combination of face-to-face lectures, interactive lecture/tutorials and on-line resources which will be available on LEARN. |
Face-to-face Learning Activities
Lectures
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Activity |
Day |
Time |
Room |
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Lecture |
Tuesday |
11:00 am |
D4 |
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Lecture |
Thursday |
9:00 am |
D1 |
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Lecture |
Friday |
11:00 am |
D4 |
Labs
Dates, times, and venues will be announced during the lectures. This information will also be communicated via the LEARN page.
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Online Learning Activities Formally registered students in this course will be able to access the course 73ARN site via http://learn.lincoln.ac.nz. Self-study material, review material, other relevant course material, and assessment activities will be made available on the course webpage. The course webpage will also be used as a means of communication with the class and students are advised to check the site and their “@lincolnuni.ac.nz” email regularly. |
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As this course is delivered by distance, online lectures and tutorials will be used. Students are expected to be able to use Skype and Lincoln’s LEARN site, and are also expected to be fully conversant with using the Internet to locate information. |
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Resources The following textbooks will be used: Statistical Techniques in Business and Economics by Douglas Lind, 18e Predictive Analytics for Business Strategy by Jeffrey Prince; ISBN 978-1-260-08464-1 A list of supplementary readings will be uploaded to the learn page. Students will be asked to use software such as MS Excel and R for conducting statistical analyses. Other software such as Minitab may be used. |
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Lecture Notes |
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Lecture notes will be posted on LEARN. It is important to note that the images shown in lectures will not all be available in the PDFs of the notes, as copyright regulations prevent this. Some readings will be placed on the relevant LEARN site. |
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Teaching on Field Trip Days Face-to-face activities and office hours will not be held on field trips days. |
Assessment
Formal assessment items
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The schedule of assessment activities and their contribution to the overall mark for the course is as follows: |
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Assessment |
Weighting |
Due date |
Learning outcomes covered |
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Test 1 |
25% |
Aug 25 |
L01, L02 |
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Test 2 |
25% |
Oct 20 |
L03, L04 |
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Presentation |
10% |
Oct 21 |
LO1, LO2, LO3, L04 |
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Final Exam |
40% |
TBA |
All |
Assessment Summaries
Tests
The tests will be held online during lecture time, on the dates mentioned above. Students will be apprised of the relevant topics for the test in advance. The tests will be open-book.
Presentations
Students will be asked to present various topics (which we will cover) during the semester. The dates of the presentations will be announced during the lectures. The presenter is expected to address questions and comments from the lecturer as well as from the students. The goal of the presentations is to help students develop the ability to critically assess economic phenomena, and communicate economic theory and data to a diverse audience.
Final Examination
The final examination will be comprehensive. Material covered during the lectures, self-study and online material, review material, assigned readings and supplementary material are examinable unless otherwise stated by the Examiner.
Penalties
Students who do not submit a reasonable attempt of the following items of internal
assessment may be awarded a grade of NC (Not Complete).
In order to be awarded a pass grade in the course students must attain 50 percent or more in
the course overall.
Late Submission of Assessment
Unless alternative arrangements have been made with the Examiner, items of assessment that are submitted after the due date and time will be awarded a mark of zero. University regulations apply for the final examination.
Academic Dishonesty
The examiner will apply the discipline regulations to any incidents of academic dishonesty, e.g. cheating or plagiarism. Your attention is drawn to theUniversal Course Regulations.
Office Hours and Feedback Opportunities
Office hours
Dr. Puneet Vatsa
Students are welcome to drop-by the Examiner’s office at other times (although they may not always be available), and to contact the Examiner to make an appointment at a mutually agreeable time. Towards the end of semester students will be consulted about what additional support they require before the final examination. Students will be advised of the details via the News Forum on the course webpage.
Feedback Opportunities
Feedback is welcomed and appreciated throughout the semester. Contact information for staff is provided at the top of this course outline. Students may give feedback in any format you feel comfortable with (e.g. in person, with a support person, through a student rep, via a note, or email). Constructive feedback is welcomed and appreciated throughout the semester to allow the Examiner to improve the course and their lecturing style. There will be an opportunity to formally evaluate the course at the end of the semester.
Guest lecturers
Professionals from a range of industry sectors may be invited to presented lectures on relevant and topical issues. The provision of guest lecturers is dependent upon availability of external individuals and may be affected by external circumstances.
Health and Safety off-campus
Field Trips: full details will be provided separately. Refer to theCode of Conduct for Trips, Tours and other External Activities.
Student Workload
The total student workload of 150 hours for 15-credit courses represents the minimum amount of time that an average or B grade student might be expected to spend in tuition and applied learning to receive a passing grade. The total student workload for a course is not spread evenly from week to week and students are expected to proactively manage their workload through the semester. Achievement in a course is based on how well a student performs, not on the time committed to studying the course. No matter how many hours a student puts into this course, they are not guaranteed a pass.
The following time-use guidelines are provided as an example of how the 150 hours may be allocated in this course.
2022-07-19