Handbook : Unit informationEdith Cowan University

School: Business and Law

This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.

• Unit Title

  Quantitative Skills for Business

• Unit Code

  ECF6102

• Year

  2018

• Enrolment Period

  2

• Version

  2

• Credit Points

  20

• Full Year Unit

  N

• Mode of Delivery

  On Campus
  Online

• Unit Coordinator

  Dr Anna GOLAB

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Description

In this unit students will identify and implement appropriate statistical procedures to assist managers in making sound decisions in the face of uncertainty. The unit initially concentrates on developing an understanding of averages, variability and probability along with the various models of data behaviour in business enabling managers to choose between different investment strategies. From here the students learn the different methods of sampling, and an understanding of inferential statistics and an appreciation of estimates of the population parameters is established through confidence intervals. This leads to the main themes of the unit including hypothesis testing of means, proportions, variances and categorical responses and finally regression and multiple regression analysis. These techniques are used to authenticate the model, and to estimate and predict with confidence future outcomes for business. These applications are all applied to real data sets.

Learning Outcomes

On completion of this unit students should be able to:

1. Appraise the limitations and outline alternative approaches to resolving analytical problems.
2. Choose between the alternative approaches to analyse a problem.
3. Classify the variable to be examined & calculate the appropriate statistic to employ.
4. Demonstrate an advanced understanding of the science of statistical thinking, concepts, techniques and the application to business decision making.
5. Demonstrate an understanding of the value and limitations of the statistical and quantitative approach to business decision making.
6. Differentiate between the alternative probability distributions to apply in a business context & choose the appropriate model.
7. Generate computer solutions using different software.
8. Identify the variable of interest.
9. Interpret the results from an analysis and explain in everyday language the implications for management.
10. Interpret the statistical output from various software packages.
11. Justify the appropriate sampling distribution to be employed.
12. Predict future trends fromtheir analysis.

Unit Content

1. Introduction to statistical concepts.
2. Determining cause and reliable forecasting with correlation and regression analysis. Testing & inference of parameters.
3. Discrete & continuous probability models in business behaviour.
4. General probability concepts and probability distributions.
5. Point and interval estimation of population parameters.
6. Analysis of all topics using real data and appropriate computer software.
7. Review of measures of central tendency and variation.
8. Analysis of skewness and relative dispersion.
9. Hypothesis testing for means, proportions & variances.
10. F tests for testing variances of two populations.
11. Non parametric analysis for categorical variables.
12. Two sample tests - confidence intervals and hypothesis tests for - comparing means for two independent populations, two related populations, and comparing proportions.
13. Multiple regression analysis.
14. Sampling distributions for both small and large samples.

Learning Experience

ON-CAMPUS

Students will attend on campus classes as well as engage in learning activities through ECU Blackboard.

For more information see the Semester Timetable

ONLINE

Students will engage in learning experiences through ECU Blackboard as well as additional ECU learning technologies.

Additional Learning Experience Information

This Unit will be delivered as a blended model integrating face-to-face and digital learning experiences. Students will be required to access and use a variety of digital learning materials to prepare for and engage in class discussion and activities. Students are required to do self-study before coming to weekly sessions. Its well-established that pre-reading about upcoming topics improves performance in class and on exams. Students attend a weekly one hour seminar and a one hour tutorial. Seminars consolidate the key concepts of the unit and guide students through the theoretical and practical issues and data analysis. Tutorials provide students with the opportunity to discuss critically the techniques employed and generate ideas about how to best analyse a problem. A group assignment task using real data, which is initiated in week 1 and submitted in week 12, develops their ability to work in teams and share ideas across different cultures. Engaged teaching and learning is applied by a series of real data sets. By the end of the semester students have a working example of their skills for their portfolio. Draft assignment submissions throughout the semester enable their progress to be monitored and provide valuable feedback to enhance their learning. This approach to `active learning' is supported in education research and enhances their learning experience. Students studying the unit using an online mode work through a study program with resources provided online via Blackboard. Students are required to review learning materials each week and to complete assigned questions on each topic. Regular online access is required. The assignment enables students to work in teams and aims to improve students' problem solving and critical thinking skills when they apply statistical concepts and knowledge to analyse problems presented in the Research Papers and the real data set in the major assignment/case study.

Assessment

GS1 GRADING SCHEMA 1 Used for standard coursework units

Students please note: The marks and grades received by students on assessments may be subject to further moderation. All marks and grades are to be considered provisional until endorsed by the relevant Board of Examiners.

Core Reading(s)

• Berenson, M. L., Levine, D. M., & Szabat, K. A. (2015). Basic business statistics: Concepts and applications. (13th ed.). Essex, England: Pearson Education.

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