Part A: Course Overview

Course Title: Practical Statistics

Credit Points: 12.00

Terms

Course Code	Campus	Career	School	Learning Mode	Teaching Period(s)
MATH2412	City Campus	Undergraduate	171H School of Science	Face-to-Face	Sem 2 2023, Sem 2 2024

Flexible Terms

Course Code	Campus	Career	School	Learning Mode	Teaching Period(s)
MATH2412	City Campus	Undergraduate	171H School of Science	Face-to-Face	UGRDFlex21 (All)
MATH2412	City Campus	Undergraduate	171H School of Science	Face-to-Face	UGRDFlex22 (All)

Course Coordinator: Assoc. Prof. Melih Ozlen

Course Coordinator Phone: NA

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

Course Coordinator Availability: By appointment; by email

Pre-requisite Courses and Assumed Knowledge and Capabilities

None

Course Description

This course will introduce you to fundamental concepts in statistics and data analytics. You will learn data analysis and modelling, starting with descriptive statistics and data visualisation random variables, normal and binomial probability distributions. You will build upon these and learn how to perform statistical inference including hypothesis testing and confidence intervals. You will learn statistical modelling and analysis techniques including linear regression, analysis of variance categorical data analysis and nonparametric analysis. There will be an emphasis on conceptual understanding and the use of computer software for analytical computations.

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes (PLOs):

PLO1: Apply a broad and coherent set of knowledge and skills for developing data driven solutions for contemporary societal challenges.

PLO2: Apply systematic problem solving and decision making methodologies to identify, design and implement data driven solutions to real world problems, demonstrating the ability to work independently to self-manage processes and projects.

PLO4: Communicate effectively with diverse audiences, employing a range of communication methods in interactions.to both computing and non computing personnel.

On successful completion of this course, you will be able to:

1. Elucidate the concepts of probability and statistics, and identify questions requiring investigation.
2. Carry out statistical data investigation including identifying variables and measures as well as proposing an analysis method that will answer the question posed.
3. Visualise and summarise statistical data for further investigation.
4. Apply statistical methods to explore, analyse, and model data and use these methods for testing statistical hypotheses.
5. Use computer software for visualisation, analysis, and modelling of real-world data. 

Overview of Learning Activities

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both.

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course.

Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course.

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.

Overview of Assessment

Assessment Tasks

Assessment Task 1: Practical Assessments – Descriptive Statistics, Probability, Random variables, Confidence Intervals
Weighting 33%
This assessment task supports CLOs 1, 3 & 5

Assessment Task 2: Practical Assessments – Hypothesis Testing, Analysis of Variance, Simple Linear Regression
Weighting 33%
This assessment task supports CLOs 1, 2, 3, 4 & 5

Assessment Task 3: Practical Assessments – Multiple Regression, Categorical Data Analysis, Nonparametric Analysis
Weighting 34%
This assessment task supports CLOs 1, 2, 3, 4 & 5

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.