国际学生入学条件
Completion of the equivalent of an American upper-secondary school education (approximately 12 years of formal education beginning at age six) as well as the appropriate diplomas or satisfactory results on leaving examinations.
Academic Preparation. Strong academic preparation and a U.S.-equivalent cumulative grade point average of 2.5 on a 4.0 scale (for freshman applicants) or 2.0 college/university grade point average on a 4.0 scale.
ILETS - 6.0
TOEFL iBT- 68
TOEFL PBT - 520
展开![](/img/arrow_bottom_blue.png)
IDP—雅思考试联合主办方
![](/img/yasi_logo_b.png)
雅思考试总分
6.0
了解更多
雅思考试指南
- 雅思总分:6
- 托福网考总分:68
- 托福笔试总分:520
- 其他语言考试:Duolingo English Test - 95
CRICOS代码:
申请截止日期: 请与IDP顾问联系以获取详细信息。
课程简介
Statistics is the science of collecting and analyzing data. Statisticians interact with researchers in all the various disciplines of science, engineering, medicine, social science and business to develop scientifically sound methods in those areas. Most course work in the department is devoted to understanding current methods and the reasoning behind them. A degree in statistics prepares students for careers in industry, government, academia, and research institutes, as well as being excellent preparation for professional programs in medicine, law, business administration and public policy and administration.<br><br>Industrial Statistics is concerned with maintaining and improving the quality of goods and services. It involves a broad range of statistical tools but maintaining and improving quality involves an overall approach to the management of industrial processes that transcends the use of these specific tools. Variability is inherent in all processes, whether they be manufacturing processes or service processes. This variability must be controlled to create high quality goods and services and must be reduced to improve quality. Industrial Statistics focuses on the use of statistical thinking, i.e., the appreciation of the inherent variability of all processes. It also focuses on developing skills for modeling data and designing experiments that can lead to improvements in performance and reductions in variablity.
展开![](/img/arrow_bottom_blue.png)