General Information
This subject introduces students to fundamental quantitative theory and tools to support the data analysis and decision making needs of modern organisations. This subject covers descriptive statistics, probability distributions, sampling, hypothesis testing and regression. This subject focuses on developing practical computational skills and systematic problem-solving capabilities to analyse and interpret data for various business problems and decisions. The tools and techniques introduced in this subject, including the use of spreadsheets for data management and analysis, can be applied to exploratory big data analysis.
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Details
Academic unit: Bond Business School Subject code: STAT11-111 Subject title: Business Statistics Subject level: Undergraduate Semester/Year: January 2024 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Forum: x12 (Total hours: 24) - Forum
- Computer Lab: x12 (Total hours: 24) - Computer Lab
- Personal Study Hours: x12 (Total hours: 72) - Recommended study time & reviewing materials
Attendance and learning activities: Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. -
Resources
Prescribed resources: Books
- Selvanathan et.al. (2017). Business Statistics - Abridged. 7th, Cengage Learning
iLearn@Bond & Email: iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications.
Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.
To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au
| Academic unit: | Bond Business School |
|---|---|
| Subject code: | STAT11-111 |
| Subject title: | Business Statistics |
| Subject level: | Undergraduate |
| Semester/Year: | January 2024 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
|
| Attendance and learning activities: | Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. |
| Prescribed resources: | Books
|
|---|---|
| iLearn@Bond & Email: | iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student. To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au |
Enrolment requirements
| Requisites: |
Nil |
|---|---|
| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.
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| Restrictions: |
Nil |
Assurance of learning
Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.
At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.
Program Learning Outcomes (PLOs)
Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Produce appropriate graphical and numerical descriptive statistics for different types of data.
- Apply probability rules and concepts relating to discrete and continuous random variables to answer questions within a business context.
- Demonstrate knowledge of the importance of the Central Limit Theorem (CLT) and its applications.
- Conduct and interpret a variety of hypothesis tests to aid decision making in a business context.
- Use simple/multiple regression models to analyse the underlying relationships between the variables through hypothesis testing.
Generative Artificial Intelligence in Assessment
The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.
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Assessment details
Type Task % Timing* Outcomes assessed Computer-Aided Examination (Open) Comprehensive Final examination in supervised computer lab. 40.00% Final Examination Period 1,2,3,4,5 Computer-Aided Examination (Open) Mid-semester Examination in supervised computer lab 30.00% Week 7 (Mid-Semester Examination Period) 1,2,3,4,5 Written Report Multiple Homework Assignments - short answer analytical questions. Weeks 2,4,9 and 11. 30.00% Ongoing 1,2,3,4,5 - * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
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Assessment criteria
Assessment criteria
High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment. Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
| Type | Task | % | Timing* | Outcomes assessed |
|---|---|---|---|---|
| Computer-Aided Examination (Open) | Comprehensive Final examination in supervised computer lab. | 40.00% | Final Examination Period | 1,2,3,4,5 |
| Computer-Aided Examination (Open) | Mid-semester Examination in supervised computer lab | 30.00% | Week 7 (Mid-Semester Examination Period) | 1,2,3,4,5 |
| Written Report | Multiple Homework Assignments - short answer analytical questions. Weeks 2,4,9 and 11. | 30.00% | Ongoing | 1,2,3,4,5 |
- * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
Assessment criteria
| High Distinction | 85-100 | Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. |
|---|---|---|
| Distinction | 75-84 | Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. |
| Credit | 65-74 | Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. |
| Pass | 50-64 | Usually awarded to students whose performance meets the requirements set for work provided for assessment. |
| Fail | 0-49 | Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. |
Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Study Information
Submission procedures
Students must check the iLearn@Bond subject site for detailed assessment information and submission procedures.
Policy on late submission and extensions
A late penalty will be applied to all overdue assessment tasks unless the Lead Educator grants an extension. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.
Academic Integrity
Bond University‘s Student Code of Conduct Policy , Student Charter, Academic Integrity Policy and our Graduate Attributes guide expectations regarding student behaviour, their rights and responsibilities. Information on these topics can be found on our Academic Integrity webpage recognising that academic integrity involves demonstrating the principles of integrity (honesty, fairness, trust, professionalism, courage, responsibility, and respect) in words and actions across all aspects of academic endeavour.
Staff are required to report suspected misconduct. This includes all types of plagiarism, cheating, collusion, fabrication or falsification of data/content or other misconduct relating to assessment such as the falsification of medical certificates for assessment extensions. The longer term personal, social and financial consequences of misconduct can be severe, so please ask for help if you are unsure.
If your work is subject to an inquiry, you will be given an opportunity to respond and appropriate support will be provided. Academic work under inquiry will not be marked until the process has concluded. Penalties for misconduct include a warning, reduced grade, a requirement to repeat the assessment, suspension or expulsion from the University.
Feedback on assessment
Feedback on assessment will be provided to students according to the requirements of the Assessment Procedure Schedule A - Assessment Communication Procedure.
Whilst in most cases feedback should be provided within two weeks of the assessment submission due date, the Procedure should be checked if the assessment is linked to others or if the subject is a non-standard (e.g., intensive) subject.
Accessibility and Inclusion Support
Support is available to students where a physical, mental or neurological condition exists that would impact the student’s capacity to complete studies, exams or assessment tasks. For effective support, special requirement needs should be arranged with the University in advance of or at the start of each semester, or, for acute conditions, as soon as practicable after the condition arises. Reasonable adjustments are not guaranteed where applications are submitted late in the semester (for example, when lodged just prior to critical assessment and examination dates).
As outlined in the Accessibility and Inclusion Policy, to qualify for support, students must meet certain criteria. Students are also required to meet with the Accessibility and Inclusion Advisor who will ensure that reasonable adjustments are afforded to qualifying students.
For more information and to apply online, visit BondAbility.
Additional subject information
As part of the requirements for Business School quality accreditation, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Subject curriculum
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Introduction to Statistics
This topic introduces basic statistical concepts and definitions, including quantitative and qualitative data. A variety of graphical displays are explored, including pie charts, bar charts, boxplots, histograms, line charts and scatter plots.
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Descriptive Statistics
Fundamental concepts of statistics are covered in this topic, including measures of central location, variability, shape, relative standing and linear association. Students will learn how to calculate these measures and how to interpret them.
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Probability Distributions
This topic introduces the concept of probability and basic probability rules. Students will learn how to calculate and interpret probabilities from discrete probability distributions and how to apply the rules of expectation and variance to discrete random variables.
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Continuous Probability Distributions
This topic introduces the normal distribution and its properties. Students will learn how to calculate probabilities and quantiles using the normal distribution and to interpret the results to make informed business decisions.
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Sampling Distributions
Types of samples are examined, including both probability and non-probability samples, and the properties of probability distributions of sample statistics explored. Students will learn how to apply the central limit theorem to sampling distributions of the sample mean and sample proportion and interpret the results.
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Interval Estimates
Point and intervals estimates are compared. Confidence intervals for both the population mean and population proportion are calculated and interpreted. Students will then estimate the sample size needed in specific circumstances to inform the data collection process.
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Hypothesis Testing – One Population
The fundamentals of hypothesis testing are presented. This includes the concepts of the null and alternative hypotheses, one-tailed and two-tailed tests, possible test outcomes, possible error types and statistical significance levels. Specific tests for population mean and population proportion are then explored and used to make informed business decisions.
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Hypothesis Testing - Two Populations
Hypothesis testing is further developed with two-sample tests for paired and independent samples, and population proportions.
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Regression Analysis
The relationship between two variables is established through both correlation and the simple linear regression model. The use of the least squares approach to estimate the regression parameters is examined and the assumptions underpinning the model explored.
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Multiple Regression Analysis
The relationship between two or more independent variables and one dependent variable is examined. Students will learn how to use dummy variables, interpret the regression coefficients, examine the reliability of the model and conduct relevant hypothesis tests.