代写AB1202 Statistics & Analysis 2024-2025代写C/C++编程

日期：2024-08-27 07:10

CURRENT COURSE OUTLINE

Academic Year        2024-2025

Semester                Sem 1

Course Code           AB1202

Course Title            Statistics & Analysis

Pre-requisites          N/A

No of AUs               3 AUs

Contact Hours         3hrs per week (1hr online E-lecture; 2hr in-person seminar)

A) Course Aims

This course introduces the concepts and methods of statistical inferences: the process of inferring unknowns based on collected data. Students of this course will also learn basic programming skills to conduct statistical analyses in the R environment.

This course consists of three main modules. Module 1 introduces elements of probability theory. Module 2 covers the method of statistical inferences. Module 3 introduces two applications of statistical inferences, linear regression and simulation analysis.

B) Intended Learning Outcomes (ILO)

By the end of this course, you (as a student) should be able to:

1. Relate the theory of statistical inferences to business applications.

2. Run simulation and regression analyses.

3. Use R to conduct statistical analysis and interpret the results.

C) Course Content

Module 1: Elements of probability

? Understand probability.

? Conditional probability and statistical independence.

? Random variables and probability distributions.

? Expectations.

Module 2: Statistical inferences

? Sampling and sampling distribution.

? Confidence interval (CI).

? Null hypothesis statistical testing.

Module 3: Simulation and Regression analysis

? Regression analysis and variable coding.

? Conduct simulation analysis in the R environment.2

D) Assessment (includes both continuous and summative assessment)

Component                                   ILO tested           NBS Learning goals (Apx. 1)              Weight          Team/Individual

Individual participation                    ILO1                       C                                                        10%                     Individual

Computer Quizzes                           ILO2, ILO3             AK, PSDM                                             50%                     Individual

E-Learning and online assessment    ILO2, ILO3             AK, PSDM                                              20%                      Individual

Group project                                 ILO1                            C                                                    20%                      Team

Total                                                                                                                                      100%

Explanations and course policies

1. Participation

Students are rated based on the frequency and quality of their interactions with their peers and instructor. “Quality” refers to opinions that are correct, thorough, and open up a productive path of inquiry (i.e., Wow!). To meaningfully participate in class discussions, come to your class prepared.

2. Quizzes

Students of this course will complete two (2) computer-based quizzes.

               Date                                                  Coverage               Weight

Quiz 1      Sep. 27, Friday (6:30pm to 9:30pm)      Weeks 1 to 5          20%

Quiz 2      Nov. 15, Friday (6:30pm to 9:30pm)      Weeks 6 to 12        30%

The quiz questions will be set based on lecture videos, online self-assessment exercises, and tutorial questions. Note that the use of Gen-AI tools and search engines will not be allowed during the quizzes. Further details will be released in due course.

3. E-learning component

For every teaching week, students should go to the main course website to (1) watch the lecture videos, and (2) complete/submit the e-learning exercises. These weekly learning activities should be completed by Sunday, 11pm, prior to the class.

Evaluation

To receive the full 20% of this component, students must (1) complete all weekly exercises before Sunday, 11pm, prior to the class, (2) obtain full marks for all weekly exercises.

? Late submissions

Students should complete the e-learning exercises of the week latest by 11:00pm on the Sunday of the seminar. That is, 11:00 pm on the Sunday before the week’s class.

The completion status is recognized by the timestamps at which you complete all exercises on the NTULearn page. Please be reminded that there are multiple sets of exercises each week (under the same tab), typically one set after each video. Ensure you finish all exercises from the top to the bottom of the page. Incomplete submissions will be considered late as well. You can check the submission status of current and past exercises under “My Grades.”

Penalty for late or incomplete submissions:

1 week of exercises = 2%

2 weeks of exercises = 5%

3 weeks of exercises = 9%

4 weeks of exercises =14%

5 weeks of exercises or more = 20% (i.e., zero marks for this component)

NOTE 1: The penalties for late or incomplete submissions kicks in once the deadline has passed. If a student’s first submission of an exercise occurs after the deadline, the above late penalties will apply. There is no dependency on the amount of lateness.

NOTE 2: The late assignment is counted by week. E.g., the penalty for not completing one or five questions for a particular week is the same.

NOTE 3: As students may add/drop during Weeks 1-2, the penalty for late submissions for Weeks 1- 2 will be automatically waived (no reason required). However, submissions are still required.

NOTE 4: International students or students travelling overseas (regardless of your travel arrangement) are subject to the same grading rule.

NOTE 5: Students can still view and submit answers after the due date (for self-study purposes). Grades will be calculated based on the last submission before the deadline.

Penalties will not be waived after W3 (W3 inclusive) for any reason, including but not limited to, computer/internet problems, sudden illness, family emergency, “I forgot,” etc. To avoid penalties, please plan ahead and refrain from last-minute submissions.

? Assignment grading

As noted, late submissions will receive a penalty and will not be marked. Submissions made before the deadline will be marked based on the last attempt.

Specifically, students are allowed unlimited attempts at the questions before the deadline. The marks will be decided only based on the last attempt. Note that detailed feedback and hints for the questions will be shown after you submit the 1st attempt. That is, students can obtain full marks by redoing the tests.

Finally, you can review your submission status, past e-learning questions and answers under My Grade on the NTULearn website.

4. Group project (20%)

Each project group should consist of a maximum of five students (exceptions must be granted by the instructor) from the same class.

The deliverables include (1) a group presentation video not longer than 8 minutes (15%) (2) a one-pager self-reflection + peer reviews (5%).

Please see Appendix 2 for further detail.

We will keep track of all assignments (current & past). Please follow the guideline of Academic Integrity when preparing this assignment to avoid disciplinary actions due to plagiarism.

Please note that we expect all students in a group to have the highest standard of work ethic and contribute earnestly and equally to the deliverables. As in any teamwork, students should also strive to resolve individual differences among group members and work collaboratively. As such, students from a project group in principle should receive the same marks (with necessary adjustments made according to individual presentation performance). The instructor will reserve the right to intervene and moderate the mark should major anomalies come to his/her attention.

E) Formative feedback

Students will receive formative feedback after completing the online exercises that follow the online lecture. Additional feedback will be provided by the instructor during the class.

F) Learning and Teaching approach

The course follows the blended learning design, whereby students pick up the main concepts through online learning materials before the week’s seminar. Students can assimilate the content at their own pace and are advised to repeat the online videos and exercises should they wish to. To maximize the learning outcome, students should properly review the e-learning materials and complete the accompanying exercises before each seminar. The seminar allows students to clear their queries interactively with their peers and the instructor.

Since students may use Gen-AI tools in their personal learning, a guiding document is provided to help students discover the uses and limitations of such tools in the context of learning Statistics. These exercises include evaluating Gen-AI capabilities for probability computations, data generation and code debugging. Instructors may facilitate discussions on these topics during the seminars. Note that the use of Gen-AI is not examinable in this course and students will not be allowed to use Gen-AI during the quizzes.