MA508 - Business Statistics

Credit Points: 15 credit points

Workload: 36 hours

Prerequisite: N/A

Co-requisite: N/A

Aims & Objectives

This is a Year 1, Core Unit in the Master of Professional Accounting. For Course Learning Outcomes and further information relating to Master of Professional Accounting program please visit our website:

Managers often have to solve problems and make decisions for which they need useful information. Sometimes such information is contained within numerical data sets, which may be formed from a range of sources such as sales records, procurement records, industry data and other internal and external sources to the firm. Although the data has been acquired, it often has to be converted into information with statistical techniques for it to be useful for decision-making. Understanding the managerial problem and what data is available the choice of statistical technique and data used to solve the problem is determined. There is a range of statistical techniques available to the manager that can be employed to screen data and extract information to help make decisions. However, in many cases a limited number (usually one) of statistical techniques is appropriate based on the type of data available and the questions to be answered. Being able to select the appropriate statistical method for dealing with a problem requires knowledge of the various techniques available their limitations and the assumptions associated with them. In addition, it is important that the decision maker can interpret the results of applying statistical techniques to the problem.

This subject aims to provide students with the knowledge of statistical techniques used by managers in decision-making. The subject is practical and provides students with the opportunity to apply the knowledge acquired to the solving of problems and case studies that are presented throughout the course.

This unit will cover the following topics:

  • Descriptive statistics/ types of data, graphs and charts to illustrate Statistical data 
  • Central location and measures of variability 
  • Probability
  • Discrete and continuous probability distribution
  • Sampling Distributions 
  • Confidence Interval Estimation
  • Chi-squares test
  • Hypothesis Testing
  • Correlation and Linear Regression

Learning Outcomes

Course Learning Outcomes

The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology’s website:  

Unit Learning Outcome

At the completion of this unit students should be able to:
a. Demonstrate advanced and integrated understanding of situations in which statistical analysis may be useful.
b. Critically analyse, reflect on and solve statistical problems using analytical methods.
c. Generate, interpret and transmit knowledge, skills and ideas from a range of output from statistical analysis software and interpret the results.
d. Apply knowledge and skills using a range of statistical measures and techniques to real life situations and business decisions to demonstrate autonomy, expert judgement and adaptability as a practitioner or learner. 


Assessment Task Due Date A B Unit Learning Outcomes
1. Contribution and Participation Weeks 1-12 - 6% a-d
2. Formative Assessment Week 3 - 4% a
3. Assignment [Individual] Week 7 20% - a,b
4. Assignment [Group] Week 11 20% - a-d
5. Case Study Analysis [Individual] (3 hours) TBA - 50% a,b,d
TOTALS   40% 60%  

Task Type: Type A: unsupervised, Type B: supervised.

Contribution and Participation (6%)

This unit has class participation as an assessment. The assessment task and marking rubric will follow the Guidelines on Assessing Class Participation ( Further details will be provided in the assessment specification on the type of assessment tasks and the marking rubrics.

Teaching Methods

NOTE: All School of Business units 3-hour workshops Flipped Classroom Mode.

Textbook and Reference Materials

Note: Students are required to purchase the prescribed textbook and have it available each week in class.

Copies of the textbook are available in the MIT Library.

Prescribed Text Book

  • Selvanathan, E.A., Selvanathan, S., Keller, G. (2017) Business Statistics, ABRIDGED, Ed. 7; Cengage learning. 
  • Other recommended references:
  • Anderson, D., Sweeney, D., & Williams, T. (2016). Essentials of Modern Business Statistics with Microsoft Office Excel. (6th ed.). USA: Cengage Learning.
  • Berenson, M., Levine, D., Szabat, K., O'Brien, M., Watson, J., & Jayne, N., (2015). Basic Business Statistics. (4th ed.). Australia: Pearson.
  • Larson, R., & Farber, E. (2015). Elementary Statistics: Picturing the World, Global Edition. (6th ed.). England: Pearson.

Journal articles

  • Tonidandel, S.,  LeBreton, JM. (2011) Relative importance analysis: A useful supplement to regression analysis, Journal of business and psychology, 26, 1-9. 
  • Anderson, M. J., (2008) A new method for non‐parametric multivariate analysis of variance, Austral Ecology,
  • Anderson, M. J., Ellingsen, K, E., and McArdle B. H., (2006) Multivariate dispersion as a measure of beta diversity, Ecology Letters, 9, 683-693.

Check the unit Moodle page for additional recommended readings throughout the trimester.

The Referencing style for this unit is APA: See the MIT Library Referencing webpage: and the Unit Moodle page for additional referencing support material and weblinks.

Graduate Attributes

MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:

Ability to communicate Independent and Lifelong Learning Ethics Analytical and Problem Solving Cultural and Global Awareness Team work Specialist knowledge of a field of study


Levels of attainment Extent covered
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations.
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations.
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations.
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials.
The attribute is not considered, there is no theory or practice or activities associated with this attribute.