sta1stm statistical methods
STATISTICAL METHODS
STA1STM
2019
Credit points: 15
Subject outline
In this subject you will be introduced to statistical methods which are frequently used in science, psychology, health science and the social sciences. Topics include descriptive treatment of sample data, elementary probability and distributions, estimation and hypothesis testing of means and proportions. Other topics may include sample survey techniques, introduction to regression and chi-squared distribution. The statistical package SPSS will also be introduced. The strengths and limitations of statistical models to enable informed thinking about sustainability are explored.
SchoolSchool Engineering&Mathematical Sciences
Credit points15
Subject Co-ordinatorChristopher Lenard
Available to Study Abroad StudentsYes
Subject year levelYear Level 1 - UG
Exchange StudentsYes
Subject particulars
Subject rules
PrerequisitesN/A
Co-requisitesN/A
Incompatible subjects STA1LS, STA2LS, STA1SS, STA2SS,STA1CTS, STA1PSY, ECO1ISB
Equivalent subjectsN/A
Special conditionsN/A
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | The basic practice of statistics & CDR | Prescribed | Moore, DS, Notz, WI and Fligner, MA 2013 | 6TH EDN, W.H. FREEMAN, NEW YORK. |
Graduate capabilities & intended learning outcomes
01. Summarise, numerically, graphically, and in words, the distribution of a data-set.
- Activities:
- In Test 1, students are required to summarise simple data sets.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Quantitative Literacy)
02. Describe and justify appropriate and reliable methods for gathering data via surveys and experiments.
- Activities:
- In Test 1 and the Exam, students are required to describe how a survey or experiment should be designed so as to gather usable data.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Quantitative Literacy)
03. Solve basic probability problems involving the normal distribution, and interpret the results within a specific context.
- Activities:
- In Test 1, students are required to solve a range of probability problems based on the normal distribution. Answers are required to be given in terms of the specific context of the problem. These skills are also implicitly assessed in Test 2 and the Exam since they are the basis of the particular hypothesis tests subsequently discussed.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Quantitative Literacy)
04. Analyse data using appropriate methods, including confidence intervals, hypothesis tests based on one and two sample means and matched pairs, and chi-squared tests.
- Activities:
- In Test 2 and the Exam, students are required to choose and use appropriate statistical techniques to analyse and discuss given data sets or summary of data and to make decisions based on those analyses.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Quantitative Literacy)
05. Use simple linear regression and correlation to describe data and make predictions based on that data.
- Activities:
- In Test 2 and the Exam, students are required to use linear regression and the ideas of correlation to explain trends and make predictions.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Quantitative Literacy)
06. Perform basic statistical analyses using the software SPSS as a tool.
- Activities:
- Students are required to interpret the results of sample SPSS outputs in the tests and the exam, to carry out the statistical analyses of ILOs 1-5.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Quantitative Literacy)
07. Communicate effectively in both technical and non-technical written language the results of statistical analyses.
- Activities:
- Approximately half of the total marks available in the tests and exam are for effective written communication. This is mostly made up of the interpretation of the results of statistical analyses in non-technical language relating to the specific context of a given problem. It also includes the correct use of symbols and technical terms and the clear setting out of statistical analyses.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Quantitative Literacy)
Subject options
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Bendigo, 2019, Semester 2, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorChristopher Lenard
Class requirements
LectureWeek: 31 - 43
Three 1.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
WorkShopWeek: 31 - 43
One 1.0 hours workshop per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| One 3-hour examination (3000 words equivalent) | Hurdle requirement: To pass the subject, a pass in the examination is mandatory. | 60 | 01, 07, 06, 05, 04, 03, 02 |
| One 30 minute computer lab test (500 words equivalent) | 10 | 07, 06 | |
| Two 30 minute written tests (Test 1-10%, Test 2-20%) (500 words equivalent each, 1000 words total) | 30 | 07, 03, 02, 01, 05, 06, 04 |