This subject is designed to foster the development of foundational statistical skills that are necessary for day-to-day business analysis. The objectives of this subject are to (a) provide a basic knowledge of the application of mathematics and statistics to business disciplines; (b) develop the ability to analyse and interpret data to provide meaningful information to assist in making management decisions; and (c) develop an ability to apply modern quantitative tools (Microsoft Excel) to data analysis in a business context. Particular emphasis will be placed on determining when it is appropriate to use the various techniques and how to use Excel to conduct analyses.
|Academic unit:||Bond Business School|
|Subject title:||Business Statistics|
Delivery & attendance
|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:|| |
|[email protected] & Email:||[email protected] is the online learning environment at Bond University and is used to provide access to subject materials, lecture 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
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.
- Use a statistical package frequently used by practitioners to analyse the data using techniques from SLOs 1-5.
|Written Report||Multiple Homework Assignments - short answer analytical questions||20%||Ongoing||1, 2, 3, 4, 5, 6.|
|*Online Quiz||Online Quiz||15%||Ongoing||1, 2, 3, 4, 5, 6.|
|Computer-Aided Examination (Open)||Comprehensive Final examination in computer labs. Exam format is a combination of statistical and spreadsheet software applications (e.g., Excel) and written answers.||40%||Final Examination Period||1, 2, 3, 4, 5, 6.|
|Computer-Aided Examination (Open)||Mid-semester Examination - in computer labs. Exam format is a combination of statistical and spreadsheet software applications (e.g., Excel and written answers.||25%||Week 7 (Mid-Semester Examination Period)||1, 2, 3, 4, 5, 6.|
- * 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.
|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.|
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.
Students must check the [email protected] subject site for detailed assessment information and submission procedures.
Policy on late submission and extensions
Due to the voluntary nature of assignments (best two out of total of three homeworks and best three out of total of four quizzes), the late submission will result in zero marks.
Policy on plagiarism
University’s Academic Integrity Policy defines plagiarism as the act of misrepresenting as one’s own original work: another’s ideas, interpretations, words, or creative works; and/or one’s own previous ideas, interpretations, words, or creative work without acknowledging that it was used previously (i.e., self-plagiarism). The University considers the act of plagiarising to be a breach of the Student Conduct Code and, therefore, subject to the Discipline Regulations which provide for a range of penalties including the reduction of marks or grades, fines and suspension from the University.
Feedback on assessment
Feedback on assessment will be provided to students within two weeks of the assessment submission due date, as per the Assessment Policy.
Accessibility and Inclusion Support
If you have a disability, illness, injury or health condition that impacts your capacity to complete studies, exams or assessment tasks, it is important you let us know your special requirements, early in the semester. Students will need to make an application for support and submit it with recent, comprehensive documentation at an appointment with a Disability Officer. Students with a disability are encouraged to contact the Disability Office at the earliest possible time, to meet staff and learn about the services available to meet your specific needs. Please note that late notification or failure to disclose your disability can be to your disadvantage as the University cannot guarantee support under such circumstances.
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.
This topic begins with an introduction to basic statistical concepts and definitions. A variety of graphs are then discussed including pie charts, bar charts, boxplots, histograms, line charts and scatter plots.
Basic numerical descriptive statistics are also covered including measures of central location, variability, shape, relative standing and linear association.
This topic introduces the concept of probability and basic probability rules. It also covers expectation and variance of discrete distributions.
Expectation, variance and a variety of probability calculations within business contexts are covered.
After discussing the difference between non-probability and probability sampling, the different types of probability sampling are discussed – these include simple random sampling, systematic sampling, stratified sampling and cluster sampling. Following that, sampling distributions of the Mean and Proportion are presented. This includes an explanation and application of the important statistical theorem known as the Central Limit Theorem.
The differences between point and intervals estimates are first discussed. Confidence intervals for both the Mean and Proportion are then explained. This knowledge is then used to estimate the sample size needed in specific circumstances to inform the data collection process.
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 Mean (one and two populations) and Proportions are then explained.
Two-sample tests for paired and independent samples.
The relationship between two variables is established through both correlation and simple linear regression model. The use of the least squares approach to estimate the regression parameters is also introduced.
An extension from simple regression is introduced through additional classical regression assumption of multicollinearity. The interpretation of regression coefficients, hypothesis testing and confidence intervals are examined in depth.