Year
2021Credit points
10Campus offering
No unit offerings are currently available for this unitPrerequisites
NilTeaching organisation
3 hours per week for twelve weeks or equivalent.Unit rationale, description and aim
This unit provides students with an ethical and practical approach to the analysis of business data uncertainty with emphasis on generating useful information for business and personal decision-making. Students need to understand the concepts of the data collection methods and presentation, descriptive statistics, inferential statistics, hypothesis testing, analysis of variance, regression analysis, chi-squared testis, and time series forecasting. In addition, students need to apply Excel skills in data analysis. Students need to embrace how data analysis tools can be applied to understand vulnerable populations. The unit is designed to help students to develop the statistical literacy, competency, thinking and reasoning that would be advantageous for potential job settings in a spectrum of disciplines, thus accomplishing the contemporary practical, ethical and global expectations. The unit provides students with the necessary knowledge and data analysis skills needed for a work-ready graduate.
Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
On successful completion of this unit, students should be able to:
LO1 - Interpret data with various descriptive techniques using computer-based applications (GA5, GA8)
LO2 - Analyse statistical inferences with appropriate statistical techniques (GA4, GA5)
LO3 - Examine statistical methods in real life settings for various purposes including society's common good (GA2, GA5)
LO4 - Evaluate the nature and limitations of statistical inferences and opinions for the purpose of problem-solving in business (GA5, GA6)
Graduate attributes
GA2 - recognise their responsibility to the common good, the environment and society
GA4 - think critically and reflectively
GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession
GA6 - solve problems in a variety of settings taking local and international perspectives into account
GA8 - locate, organise, analyse, synthesise and evaluate information
Content
Topics will include:
- Role of statistics in decision making
- Data collection and presentation
- Measuring uncertainty, including probability distributions, normal and other continuous distributions, and sampling distributions
- Inference statistics, including confidence interval estimations, hypothesis testing, analysis of variance
- Forecasting including regression analysis and time series forecasting
- Hypothesis testing including chi-square and non-parametric test, model building, and decision making
- Application of Excel in statistics
- Statistical methods in real life settings, considering the common good
Learning and teaching strategy and rationale
ACU’s teaching policy focuses on learning outcomes for students. Our teaching aims to engage students as active participants in the learning process while acknowledging that all learning must involve a complex interplay of active and receptive processes, constructing meaning for oneself, and learning from others. ACU promotes and facilitates learning that is autonomous and self-motivated, is characterised by the individual taking satisfaction in the mastering of content and skills, and is critical, looking beneath the surface level of information for the meaning and significance of what is being studied.
The workshop's schedule is designed so that students can achieve intended learning outcomes sequentially. Teaching and learning activities will apply the experiential learning model, which encourages students to apply higher-order thinking. The unit ensures that learning activities involve real-world scenarios that in turn, assist with ‘real-world’ preparedness. The unit also uses a scaffolding technique that builds a student’s skills and prepares them for the next phase of the learning process.
This unit is structured with required upfront preparation before workshops; most students report that they spend an average of one hour preparing before the workshop and one or more hours after the workshop practicing and revising what was covered. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise. It is up to individual students to ensure that the out-of-class study is adequate for optimal learning outcomes and successes.
Mode of delivery: This unit is offered in different modes. These are: “Attendance” mode, “Blended” mode and “Online” mode. This unit is offered in three modes to cater to a range of participants' learning needs and preferences and maximise effective participation for isolated and/or marginalised groups.
Attendance Mode
In a weekly attendance mode, students will require face-to-face attendance in a specific physical location/s. In addition, students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops; most students report that they spend an average of one hour preparing before the workshop and one or more hours after the workshop practicing and revising what was covered. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise.
Blended Mode
In a blended mode, students will require intermittent face-to-face attendance determined by the School. In addition, students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise.
Online Mode
In an online mode, students are given the opportunity to attend facilitated synchronous online seminar classes with other students and participate in the construction and synthesis of knowledge, while developing their knowledge. Students are required to participate in a series of online interactive workshops, which include activities, knowledge checks, discussion and interactive sessions. This approach allows flexibility for students and facilitates learning and participation for students with a preference for virtual learning.
Assessment strategy and rationale
Assessments are used primarily to foster learning. ACU adopts a constructivist approach to learning which seeks alignment between the fundamental purpose of each unit, the learning outcomes, teaching and learning strategy, assessment and the learning environment. In order to pass this unit, students are required to achieve an overall score of at least 50% and attempt all assessment items. Using constructive alignment, the assessment tasks are designed for students to demonstrate their achievement of each learning outcome.
Assessment one and two are the same regardless of whether the teaching mode is attendance, blended, or online. Assessment three is the same for attendance and blended modes, but changes for online mode. This is indicated in overview of assessment table below. Both methods assess the same learning outcomes.
Overview of assessments
Brief Description of Kind and Purpose of Assessment Tasks | Weighting | Learning Outcomes | Graduate Attributes |
---|---|---|---|
Assessment Task 1: Report As an introduction to statistics and its applications, the 1000-word assessment comprises a report, presenting and interpreting the data collected with appropriate descriptive statistics, elaborating the “stories” behind the real-life data, and formulating an appropriate opinion on the subject matter of interest. Submission Type: Individual Assessment Method: Report Artefact: Written report | 25% | LO1, LO2, LO4 | GA4, GA5, GA6, GA8 |
Assessment Task 2: The Microsoft Office Specialist: Excel Associate Certification This task requires students to attempt the Microsoft Office Specialist: Excel Associate Certification. This Certification covers the fundamentals of creating and managing worksheets and workbooks, creating cells and ranges, creating tables, applying formulas and functions and creating charts and objects. An individual earning this Certification has approximately 150 hours of instruction and hands-on experience with the product, has proven competency at an industry associate level, and is ready to enter the job market. In addition, they can demonstrate the correct application of the principal features of Excel and can complete tasks independently. Submission Type: Individual Assessment Method: Microsoft Certification Artefact: Microsoft Certification | 25% | LO1, LO3 | GA2, GA5, GA8 |
Assessment Task 3: Attendance and blended modes: Open-book Final Exam This task requires students to draw on and analyse relevant information to demonstrate their knowledge of business data analysis gained throughout the entire semester, including considering ethical perspectives in decision making. Submission Type: Individual Assessment Method: Open-book Exam Artefact: Exam paper | 50% | LO2, LO3, LO4 | GA2, GA4, GA5, GA6 |
Assessment Task 3: Online mode: Case Study This assessment task consists of a case study where students are provided a real-world scenario and dataset that requires them to apply their statistical skills to support their written arguments, including considering ethical perspectives in decision making. Submission Type: Individual Assessment Method: Case study Artefact: Written paper | 50% | LO2, LO3, LO4 | GA2, GA4, GA5, GA6 |
Representative texts and references
- Lind, D., Marchal, W., & Wathen, S., 2021, Basic Statistics in Business and Economics 10th Edition, McGraw-Hill, ISBN: 9781260716313
- Amrhein, V. et al. (2019) Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication. The American statistician. [Online] 73 (sup1), 262–270.
- Almazon, E. P. (2017) An introduction to descriptive & inferential statistics (Available online from ACU Library).