STATS 1000 - Statistical Practice I
General Course Information
Course Details | Detailed Course Information | Course Staff | Course Timetable | Related Links
Course Details
| Course Code | STATS 1000 |
| Course | Statistical Practice I |
| Coordinating Unit | School of Mathematical Sciences, Faculty of Engineering, Computer & Mathematical Sciences |
| Term | Semester 1/2 2013 |
| Mode | Internal |
| Level | Undergraduate |
| Location/s | North Terrace |
| Units | 3 |
| Contact | Up to 5 hours per week |
| Prerequisites | Not applicable |
| Corequisites | Not applicable |
| Incompatible | STATS 1004, STATS 1005, ECON 1008, STATS 2004, APP MTH 2009, APP MTH 2010 or STATS 1504 |
| Assumed Knowledge | SACE stage 2 Mathematical Methods or equivalent |
| Restrictions | May not be presented towards the Bachelor of Mathematical and Computer Science degree. |
| Quota | Not applicable |
| Course Description | Statistical ideas and methods are essential tools in virtually all areas that rely on data to make decisions and reach conclusions. This includes diverse fields such as medicine, science, technology, government, commerce and manufacturing. In broad terms, statistics is about getting information from data. This includes both the important question of how to obtain suitable data for a given purpose and also how best to extract the information, often in the presence of random variability. This course provides an introduction to the contemporary application of statistics to a wide range of real world situations. It has a strong practical focus using the statistical package SPSS to analyse real data.Topics covered are: organisation, description and presentation of data; design of experiments and surveys; random variables, probability distributions, the binomial distribution and the normal distribution; statistical inference, tests of significance, confidence intervals; inference for means and proportions, one-sample tests, two independent samples, paired data, t-tests, contingency tables; analysis of variance; linear regression, least squares estimation, residuals and transformations, inference for regression coefficients, prediction |
Detailed Course Information
Includes Learning Objectives, Learning Resources, Teaching & Learning
coming soonCourse Enrolment Dates and Fees
The enrolment dates, fees and full timetable of all activities for this course can be accessed from the Course Planner.
Course Staff
Associate Professor Gary Glonek
School of Mathematical Sciences
Faculty of Engineering, Computer & Mathematical Sciences
Room 639
Ingkarni Wardli
North Terrace
Telephone: +61 8 8313 3218
Email
Administrative Enquiries: School of Mathematical Sciences Office, Level 6 Ingkarni Wardli
