Course Title: Statistics for Chemical Engineers

Part A: Course Overview

Course Title: Statistics for Chemical Engineers

Credit Points: 6.00

Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 1 2006,
Sem 2 2007

Course Coordinator: A/Prof. Gary Fitz-Gerald

Course Coordinator Phone: +61 3 9925 2278

Course Coordinator Email:

Pre-requisite Courses and Assumed Knowledge and Capabilities

This course assumes knowledge of first year tertiary level calculus. Extensive use is made of the MINITAB software during the course therefore some minimal computer literacy is expected.

Course Description

Statistics for Chemical Engineers is an introduction to statistical techniques in data analysis required in chemical engineering. It includes introduction to useful statistical distributions, basic probability theory, statistical inferences (including confidence intervals technique and hypothesis testing), analysis of variances, regression analysis, analysis of categorical data and non-parametric statistics.

Objectives/Learning Outcomes/Capability Development

On completion of this subject you will be able to:

* Construct appropriate graphical displays of data (stem and leaf plots, boxplots, etc) and to understand the role of such displays in data analysis;

* Understand the nature of random variables and probability distributions (binomial, Poisson, exponential, normal) through direct calculation and computer simulation;

* Perform statistical inference tasks using software (t-test, hypothesis tests, confidence intervals, linear regression) and to understand the calculations involved in such tasks and to be aware of assumptions necessary for the validity of results.

*Implement the multiple variable linear regression analysis and understand its assumptions and limitations. Implement several types of nalysis of variances.

*Perform statistical inferences for categorical data and implement the non-parametric statistical tests.

Overview of Learning Activities

You will attend 2 hours of lectures per week and a 1 hour lab each week. Weeks 4 and 9 are for tutorial work - doing problems in class without new lecture material. Students will use the MINITAB statistical software to perform data analysis. Sample questions may be attempted in WebLearn. Students experiencing difficulty in understanding lecture material may seek free help from tutors at the School’s Drop in Centre and the access times will be announced in class. The Drop in Centre is not compulsory but offered as an immediate help desk.

Overview of Learning Resources

Learning resources comprise of the prescribed text book, which is available in the RMIT library and bookshop, and a set of on-line learning resources placed on the course web-site. These resources will include lecture notes on selected topics, example of problems and assignments with solutions. You will be provided with access code to this website during the first week of semester.

Overview of Assessment

The course will be assessed mainly through the assignments, a mid-semester test and a final examination. Assignments will be marked and returned promptly to you with detailed solutions in order to provide you the opportunity to be informed about your marks and assessment criteria. While attendance is not compulsory, you will find that regular presence in the class is necessary for achieving the resulted required to pass the course.