STAT207 - Business Data Analysis

Year

Credit points

Campus offering

Prerequisites

Nil

Incompatible

STAT102 Business Data Analysis

Teaching organisation

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. It covers gathering and describing data, the role of probability in measuring uncertainty, statistical inference using various common parametric and non-parametric techniques and analysis of relationships between variables.

A knowledge of Statistics is critical for many professions including economics, financial analysis, marketing, management and accounting. Numbers and figures are used every day in business to make predictions. If you invest in financial markets, statistics can be used to predict the price of a stock 12 months from now based on company performance measures and other economic factors both locally and globally. This is just one example that illustrates how statistics are used in our modern society.

Students will be able to apply data analysis tools with a focus on the application of those tools to understand the issues of vulnerable populations. The unit provides students with the necessary knowledge and skills needed to apply data analysis techniques for business decision making.

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 - Understand a variety of statistical techniques to datasets and explain the benefits and limitations of these techniques (GA4, GA5)

LO2 - Use computer-based statistical programs to analyse and interpret financial and non-financial data to inform business decisions (GA5, GA10)

LO3 - Apply commonly used quantitative methods and techniques to collect and analyse financial and non- financial data (GA5, GA8)

LO4 - Critically evaluate real-world problems and issues using statistical tools with a focus on vulnerable populations (GA3, GA5)

Graduate attributes

GA3 - apply ethical perspectives in informed decision making

GA4 - think critically and reflectively 

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession

GA8 - locate, organise, analyse, synthesise and evaluate information 

GA10 - utilise information and communication and other relevant technologies effectively.

Content

Topics will include: 

• Role of statistics in decision making, including the focus on vulnerable populations
• 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

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 the 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

Representative texts and references

• Lind, D., Marchal, W., & Wathen, S., 2021, Basic Statistics in Business and Economics 10th Edition, McGraw-Hill, ISBN: 9781260716313
• Cooksey, Ray W. "Descriptive Statistics for Summarising Data." In Illustrating Statistical Procedures: Finding Meaning in Quantitative Data, pp. 61-139. Springer, Singapore, 2020.
• 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.
• Smith, P.A. and Lorenc, B., 2020. Robust Official Business Statistics Methodology During COVID-19-related And Other Economic Downturns. Statistical Journal of the International Association for Official Statistics.
• Almazon, E. P. (2017) An introduction to descriptive & inferential statistics (Available online from ACU Library).