Handbook 1996 : Faculty of Science (Volume 4 page 236)
Statistics subject : Next:619-101 | Search | Help

619-100 "Experimental Design and Statistical Analysis" appears differently in several places - choose the one you want:

1. 619-100 Statistics, Faculty of Science.
2. 619-100 Math. & Stats., Faculty of Educ(Parkville).
3. 619-100 Statistics, Faculty of Arts.
4. 619-100 Geomatics, Faculty of Engineering.

1. Statistics, Faculty of Science (v4, p236) : Next:619-101
4. Geomatics, Faculty of Engineering (v4, p123) : Next:625-101 | Prev:618-200

619-100 Experimental Design and Statistical Analysis

Note:

1. Students may not gain credit for both 619-100 and 617-141 or 617-142.
2. Special Requirement: Electric calculator of an approved kind (refer to Department of Statistics Office)

Credit points: 12.5

Coordinator: Professor T C Brown

Prerequisite: The expected VCE background is Mathematics at level 3/4

Contact: 39 lectures (three a week), 24 hours practical classes (two hours a week) and 12 one-hour tutorials

Timetable: First or second semester

Objectives:

Students completing this subject should:

Comprehend:

• the basic concepts of experimental design, statistical inference and the underlying distribution theory;
• the concept of statistical variation, particularly leading to the notion of sampling distribution;
• the concept of a random variable; and the difference between a discrete random variable and a continuous random variable.
• the concepts of confidence interval and hypothesis test, particularly as applied to proportions and means.
• Have developed the skills:
• to carry out a simple exploratory data analysis; and, in particular, to use simple numerical and graphical methods of summarising data;
• to carry out probability calculations involving the binomial and normal distributions;
• to carry out basic procedures of statistical analysis;
• to recognise and analyse a simple relationship between variables.

Appreciate:

• the elements of experimental design, and the importance of correct design for providing data capable of meaningful analysis;
• the practical applications of the Central Limit Theorem;
• the need to make assumptions and approximations;
• the application of computer software in statistical analysis -- in particular, the use of the package MINITAB.

Content:

Introduction to the concepts of sources of statistical variation, quality improvement. Scientific method and experimental design, including randomisation, blocking, factorial structure. Data description and analysis. Elementary distribution theory: binomial and normal distributions. Random sampling, simulation of random samples. Population parameters and sample statistics. Estimation, confidence intervals and hypothesis testing based on the binomial and normal distributions. Introduction to distribution-free methods. Contingency tables. Introduction to bivariate data, including correlation and linear regression. Application of computer software to data analysis and simulation.

Assessment:

Up to 26 pages of written assignments; project work as required; and up to three hours of written examination.

1. Statistics, Faculty of Science (v4, p236) : Next:619-101
4. Geomatics, Faculty of Engineering (v4, p123) : Next:625-101 | Prev:618-200

2. Math. & Stats., Faculty of Educ(Parkville) (v5, p151) : Next:619-201 | Prev:617-142

619-100 Experimental Design and Statistical Analysis

Note: Special Requirement: Electronic calculator of an approved kind (refer to Department of Statistics Office).

Credit points: 12.5

Coordinator: Professor T C Brown.

Prerequisite: The expected VCE background is Mathematics at level 3/4

Contact: 39 lectures (three each week), 24 hours practical classes (two hours each week) and 12 one-hour tutorials

Timetable: First or second semester.

Objectives:

Students completing this subject should:

Comprehend:

• the basic concepts of experimental design, statistical inference and the underlying distribution theory;
• the concept of statistical variation, particularly leading to the notion of sampling distribution;
• the concept of a random variable; and the difference between a discrete random variable and a continuous random variable.
• the concepts of confidence interval and hypothesis test, particularly as applied to proportions and means.

Have developed the skills:

• to carry out a simple exploratory data analysis; and, in particular, to use simple numerical and graphical methods of summarising data;
• to carry out probability calculations involving the binomial and normal distributions;
• to carry out basic procedures of statistical analysis;
• to recognise and analyse a simple relationship between variables.

Appreciate:

• the elements of experimental design, and the importance of correct design for providing data capable of meaningful analysis;
• the practical applications of the Central Limit Theorem;
• the need to make assumptions and approximations;
• the application of computer software in statistical analysis -- in particular, the use of the package MINITAB.

Content:

Introduction to the concepts of sources of statistical variation, quality improvement. Scientific method and experimental design, including randomisation, blocking, factorial structure. Data description and analysis. Elementary distribution theory: binomial and normal distributions. Random sampling, simulation of random samples. Population parameters and sample statistics. Estimation, confidence intervals and hypothesis testing based on the binomial and normal distributions. Introduction to distribution-free methods. Contingency tables. Introduction to bivariate data, including correlation and linear regression. Application of computer software to data analysis and simulation.

Assessment:

Up to 26 pages of written assignments; project work as required; and up to three hours of written examination.

* Note that CONTACT, NOTE, OBJECTIVES differs from the maintainer's version above. A log of variations is available.

2. Math. & Stats., Faculty of Educ(Parkville) (v5, p151) : Next:619-201 | Prev:617-142

3. Statistics, Faculty of Arts (v3, p170) :

619-100 Experimental Design and Statistical Analysis

Note: Students may not gain credit for both 619-100 and any other first year Statistics unit.

Credit points: 12.5 1st year

Coordinator: Professor T C Brown

Prerequisite: VCE Mathematics at level 3/4.

Contact: 39 lectures (three a week), 24 hours practical classes (two a week) and 12 x 1-hour tutorials (one a week).

Timetable: First or second semester

Objectives:

Students completing this subject should comprehend:

• the basic concepts of experimental design, statistical inference and the underlying distribution theory;
• the concept of statistical variation, particularly leading to the notion of sampling distribution;
• the concept of a random variable; and the difference between a discrete random variable and a continuous random variable.
• the concepts of confidence interval and hypothesis test, particularly as applied to proportions and means.

By the end of this subject the student should have developed the skills:

• to carry out a simple exploratory data analysis; and, in particular, to use simple numerical and graphical methods of summarising data;
• to carry out probability calculations involving the binomial and normal distributions;
• to carry out basic procedures of statistical analysis;
• to recognise and analyse a simple relationship between variables.

Through this subject the student should appreciate:

• the elements of experimental design, and the importance of correct design for providing data capable of meaningful analysis;
• the practical applications of the Central Limit Theorem;
• the need to make assumptions and approximations;
• the application of computer software in statistical analysis ó in particular, the use of the package MINITAB.

Content:

Introduction to the concepts of sources of statistical variation, quality improvement. Scientific method and experimental design, including randomisation, blocking, factorial structure. Data description and analysis. Elementary distribution theory: binomial and normal distributions. Random sampling, simulation of random samples. Population parameters and sample statistics. Estimation, confidence intervals and hypothesis testing based on the binomial and normal distributions. Introduction to distribution-free methods. Contingency tables. Introduction to bivariate data, including correlation and linear regression. Application of computer software to data analysis and simulation.

* Note that CONTACT, NOTE, OBJECTIVES, POINTS, PREREQUISITES differs from the maintainer's version above. A log of variations is available.

3. Statistics, Faculty of Arts (v3, p170) :

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Status:          Official 1996
Date created:    Oct  9 1995
Last modified:   Oct  9 1995
Authorised by:   Academic Registrar
Email enquiries: Course_Information@registrar.unimelb.edu.au

Copyright © University of Melbourne 1995,1996.