代写22803 Quantitative Skills in Accounting and Finance Spring 2024帮做R语言

SUBJECT OUTLINE

22803 Quantitative Skills in Accounting and Finance

Subject description

This subject enables students to develop their ability to collect, analyse and interpret accounting and business data and to use the results of the analysis for decision making. The subject emphasises the development of a practical understanding of various analytical techniques. Students learn how to apply these techniques using statistical tools and to implement the results of their analysis in practical business decisions. These skills and competencies are essential for a future career in accounting and finance in either industry and practice.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. explain the role of statistical analysis for decision making in accounting and business

2. identify the sources of accounting and business data and collect data from these sources for analysis

3. identify and effectively apply commonly used statistical methods and techniques to financial and non-financial data

4. generate, analyse, and review statistical data including hypothesis testing

5. effectively interpret, communicate and present the results of quantitative analyses and make relevant predictions to facilitate decision making including the use of charts and tables

Contribution to the development of graduate attributes

The subject provides students with essential knowledge and skills required in the career of a professional accountant. The subject introduces students to quantitative methods and statistical analysis who have not had any academic research background. It enables students to understand the role that quantitative methods, data collection and analysis could play in business decision making and the importance of ethics and professional integrity in the practice of professional accountants.

The subject contributes to the development of the following Program Learning Objective:

. Apply ethical principles and professional integrity in the context of professional accounting practice (4.2)

Teaching and learning strategies

This subject is taught using a mix of face-to-face teaching, online resources and self-directed study.

Tutorials: In the first part of the class, students are expected to work in groups to solve in-class exercises. Tutorials provide students with the opportunity to review and discuss concepts and receive feedback. Students are expected to contribute and to participate in group discussions and to ask questions. Pre-class preparation is required to solve the in-class problems.

Lectures: The second part of class is dedicated to a lecture. Lectures are designed to introduce the analytical tools  which are the focus of the subject and to demonstrate practical examples. Lectures do not cover the course content  in-depth but provide an overview of each topic and explain the more difficult concepts using practical examples and   demonstration questions. Lecture slides are available prior to class on the course site. Students are expected to add their own additional notes and explanations. It is recommended that students read the relevant textbook chapter/s prior to attending the scheduled lecture on that topic. This background reading is essential to setting the scene for the lecture. This activity will be completed in collaboration with other students and provides students with the opportunity to obtain in-class peer feedback on their learning.

Pre-class preparation: Students are expected to review prior week’s lectures notes, undertake the relevant readings, watch videos and complete the self-study exercises before attending class in order to successfully engage in in-class  exercises. The assigned weekly self-study questions and solutions are made available on the learning management system.

Discussion board: Student learning in this subject is also facilitated through online discussion forums which are available throughout session to all students on the learning management system. These discussion forums can be used to ask questions relating to subject content and subject administration. The forums therefore provide an alternative method of obtaining feedback on your learning from subject staff or other students.

Content (topics)

Sources and collection of accounting data

Basic descriptive statistics

Probability and discrete probability distributions

Normal and continuous distributions

Confidence interval estimation

Hypothesis testing

Regression analysis and forecasting

Decision analysis and big data analytics

Assessment

X grades are awarded in this subject

Assessment task 1: In-class Quizzes (Individual)*

Objective(s): This addresses subject learning objective(s): 1, 3 and 5

Weight: 20%

Task: Students are to complete four online quizzes in-class administrated through Canvas. These quizzes

will be spread throughout the session and examine content covered in the weeks prior. Questions

consist of multiple choice and short answer. Students must be present in class to complete the quiz. Students may refer to lecture notes and subject materials but are not permitted to use search

engines, Artificial Intelligence (AI) or any other web-based software of any kind to assist in answering questions (doing so will result in Academic Misconduct).

Length: Each quiz will run for 40 minutes.

Criteria: This assessment will be graded on the following criteria:

. accuracy of answers;

. level of detail and the clarity of your answers

*Note: Late submission of the assessment task will not be marked and awarded a mark of zero.

Students who do not complete half of the learning quizzes will have the weighting of that assessment added to the final examination conditional on the students submitting, receiving approval and

complying with the requirements of special consideration in accordance with the UTS rules. If the

composite mark for the final exam then totals more than 50 per cent and the student is in the final

subject of their degree, the UTS rules on borderline result (range of 45-49) shall apply whereby

students will be allowed to undertake a supplementary final examination. Where a student completes and passes a supplementary examination, the maximum mark awarded for the subject will be 50 Pass.

Assessment task 2: Assignment (Group)

Objective(s): This addresses subject learning objective(s): 2, 3, 4 and 5

Weight: 30%

Task: Students work in groups of up to 5 members to prepare a report addressing the business

case/problems presented in the assignment brief. The assignment brief on Canvas provides details of the problems to be addressed as well as the submission guidelines and marking rubric.

Length: The report is to be a maximum of 15 pages with 1.5 line spacing and 12-point font size, including any

tables/figures/charts but excluding the title page, executive summary, references and appendices (excel attachments etc.). Please refer to the assignment brief on Canvas for submission details.

Due: 11.59pm Sunday 27 October 2024

Criteria: This assessment will be graded on the following criteria:

· appropriate application of statistical methods and techniques to financial and non-financial data . depth and scope of the analysis/evaluation

. validity of conclusions and recommendations

· presentation and readability of the report

Assessment task 3: Final Exam (Individual)

Intent: This assessment task is a compulsory component of the subject. A student must attain no less than

40% of the marks in this assessment to pass the subject irrespective of the subject’s total marks.

Objective(s): This addresses subject learning objective(s): 1, 3, 4 and 5

Weight: 50%

Task: Final exam. Questions require students to apply concepts and skills learned during the subject to

business cases and scenarios.

Length: 2 hours

Criteria: This assessment will be graded on the following criteria:

. accuracy of answers,

. level of detail and the clarity of answers

Minimum requirements

A student must achieve 50% or more of the subject’s total marks AND achieve 40% or more in the final exam to pass the subject.

A Fail (X) Grade is awarded to a student who attains 50% or more of the overall subject assessment marks but attains less than 40% of the final exam marks. To pass the subject, the student must then attain 50% or more of the marks in the supplementary task, in which case the student is awarded an overall mark of 50P.

Recommended texts

Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D. and Cochran, J.J., 2020. Modern business statistics with Microsoft Excel. Cengage Learning. 7th Edition. ISBN: 9780357131381.

Assessment: faculty procedures and advice

Extensions and late assessment protocol

Any assessment task (excluding take-home final exams, frequent assessment tasks that must be submitted on a

regular basis prior to or during a synchronous class such as weekly homework preparation, pre-class or in-class

quizzes or single assessments that are performed and assessed during class time such as presentations) submitted after the due date and time, will be either:

. penalised by way of loss of marks where 10 per cent (10%) of the marks for the assessment task will be deducted per day for assessment tasks submitted after the due date. A day is defined as a 24-hour period or part thereof

following the published due date and time of the assignment, and

. will be rejected and not marked if the assessment is submitted more than five (5) calendar days for subjects offered in the Main Calendar (Autumn, Spring and Summer) and seven (7) calendar days for subjects offered in the UTS Online (Sessions 1 to 6), after the stated submission date, unless a formal short-term extension has been granted    by the Subject Coordinator, or

. rejected without marking (where the subject outline states that this will be the consequence of an assessment task being submitted after the due time on the due date without an approved extension)

The maximum penalty that can be applied is 50% of the assessment marks. Students cannot receive a negative score for the assessment task after a penalty is levied.

A penalty for late work will not apply in cases of approved extensions by the Subject Coordinator. Approved

extensions cannot be made without a request for extension sent directly to the coordinator (or nominated approving

tutor as designated by the subject coordinator) for short-term extensions (within the timeframe set out above) or via an application for special consideration (within the UTS time frames for submission). Any direct requests must be

received by the Subject Coordinator at least 24 hours prior to the due date and time.

Students enrolled in UTS Online courses must complete the  Request for Short Extension without Academic Penalty form. to apply for a short-term extension.

A penalty may not apply after due consideration of any submission (request for extension or application for special  consideration) by the Academic Liaison Officer (ALO), on behalf of students registered with Accessibility Services .