Business Decision Making
University of Newcastle
Area of Study
Business Management, Statistics
Taught In English
This course has similarities to STAT1020 or STAT1070. If you have successfully completed any of these courses you cannot enrol in this course.
Course Level Recommendations
ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.
Host University Units10
Recommended U.S. Semester Credits3 - 4
Recommended U.S. Quarter Units4 - 6
Hours & Credits
STAT1060 is an introductory course in qualitative and quantitative methods which underpin effective business decision making. It is taught within a business context having been designed for undergraduate students in the Faculty of Business & Law. The course develops a student's ability to incorporate statistical thinking and to take account of variation in the real world during processes of establishing project initiatives, defining objectives, data collection, data presentation, data analysis, reporting and decision making.
On successful completion of the course students will be able to:
1. Describe and implement a structured approach to problem solving and decision making in business
2. Explain why data and the understanding of variation are important in making business decisions
3. Explain various qualitative techniques fundamental to the decision making process
4. Explain and apply basic statistical concepts and techniques
5. Identify and use appropriate data collection and analysis techniques
6. Identify flaws in data collection techniques and discuss approaches to reducing error
7. Interpret and present data in a form that makes information accessible for decision-makers
The process of problem solving and decision making in business including:
- Qualitative techniques for data collection and investigation;
- Primary and secondary data sources;
- The concept of variation;
- Quantitative and exploratory data techniques;
- Presenting data;
- Descriptive and Inferential Statistics;
- Data types in quantitative analysis;
- Developing hypotheses;
- Errors in data collection and sampling;
- Assessing risk;
- Quantifying and incorporating variation in decision making;
- Confidence intervals;
- Hypothesis testing;
- Presentation of findings;
- Correlation and regression.
Face to Face On Campus 2 hour(s) per Week for Full Term starting in week 1
Students are expected to complete 4 hours of guided learning via online preparation, lectures, interactive workshops, tutorials, discussion groups or self-directed learning and an additional 6 hours of independent study per week.
Courses and course hours of instruction are subject to change.
Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.