# Course Title: Mathematics and Statistics

## Part A: Course Overview

Course Title: Mathematics and Statistics

Credit Points: 12.00

## Terms

### Teaching Period(s)

MATH2123

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 1 2006,
Sem 2 2006,
Sem 1 2007,
Sem 2 2007,
Sem 1 2008,
Sem 2 2008,
Sem 1 2009,
Sem 2 2009,
Sem 1 2010,
Sem 2 2010,
Sem 1 2011,
Sem 2 2011,
Sem 1 2012,
Sem 2 2012,
Sem 1 2013,
Sem 2 2013,
Sem 1 2014,
Sem 2 2014,
Sem 1 2015,
Sem 2 2015,
Sem 1 2016,
Sem 2 2016

MATH2123

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 1 2017,
Sem 2 2017,
Sem 1 2018,
Sem 2 2018,
Sem 1 2019,
Sem 2 2019,
Sem 1 2020,
Sem 2 2020,
Sem 2 2021,
Sem 1 2022,
Sem 2 2022

MATH2123

City Campus

Undergraduate

171H School of Science

Internet

Sem 1 2021

Course Coordinator: Assoc. Prof. Melih Ozlen

Course Coordinator Phone: +61 3 9925 3007

Course Coordinator Email: melih.ozlen@rmit.edu.au

Course Coordinator Availability: By appointment, by email

Pre-requisite Courses and Assumed Knowledge and Capabilities

VCE Further Mathematics (or equivalent)

Course Description

MATH 2123 - Mathematics and Statistics will introduce you to a number of mathematical and statistical procedures often used in science. The course aims to provide the theoretical foundations that any scientist will require to meet community and regulatory requirements. The learning of statistics will integrate theory and applications using a problem-based approach aided by the use of the Minitab statistical package. The course will focus on developing your abilities in critical analysis, decision making and reflection.

Objectives/Learning Outcomes/Capability Development

On successful completion of this course you will be able to

1. perform appropriate graphical displays of data and understand the role of such displays in data analysis;

2. calculate probabilities of binomial and normal distributions;

3. construct point and interval estimation for a mean and a proportion;

4. conduct hypothesis tests for a mean and a proportion, and compare two-sample means and proportions;

5. carry out linear regression, analysis of variance and categorical data analysis;

6. use a statistical package to carry out descriptive and simple inferential statistical analyses.

Overview of Learning Activities

The prerecorded lectures, and tutorials will focus on problem-based activities encountered in science disciplines. These problems will be used to assist you to construct appropriate graphical displays of data, understand the nature of random variables through direct calculation and computer simulation, perform quality assessment tasks using software packages and understand the calculations involved in such tasks and to be aware of assumptions that are necessary for the validation of results. You will get an insight into the mental processes one goes through when mapping real life problems to abstract mathematical and statistical procedures and the use of Minitab. Learn the problem identification-solution process by “thinking aloud”. From this, you will be able to compare your own problem solving approaches to these problems. You will have a highlight of the main solution techniques and understand how the solution relates to the original model or data. Each interactive sessions will provide opportunities for you to receive feedback while attempting to determine solutions using suitable statistical packages.

Overview of Learning Resources

You will be able to access course information and learning materials through the course Canvas page.  Lists of relevant reference texts and resources in the library will be provided. You will also use computer software available online through RMIT myDesktop.

Overview of Assessment

Note:

This course has no hurdle requirements

Assessment Task 1: Practical Assessments – Descriptive Statistics, Probability and Distributions
Weighting 34%
This assessment task supports CLOs 1, 2 & 6.

Assessment Task 2: Practical Assessments – Interval Estimation, Hypothesis Testing
Weighting 34%
This assessment task supports CLOs 3, 4 & 6.

Assessment Task 3: Practical Assessments – Analysis of Variance
Weighting 8%
This assessment task supports CLOs 5 & 6.

Assessment Task 4: Practical Assessments – Linear Regression and Categorical Data Analysis
Weighting 24%
This assessment task supports CLOs 5 & 6.