# Applied Statistics and Research Methods

University of Newcastle

## Course Description

• ### Course Name

Applied Statistics and Research Methods

• ### Host University

University of Newcastle

• ### Location

Newcastle, Australia

• ### Area of Study

Research Methodology, Statistics

• ### Language Level

Taught In English

• ### Prerequisites

Assumed knowledge
STAT1070 or STAT2010

• ### Course Level Recommendations

Upper

ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

### Hours & Credits

• Host University Units

10
• Recommended U.S. Semester Credits
3 - 4
• Recommended U.S. Quarter Units
4 - 6
• ### Overview

Description
This is an applied Statistics course which will meet the needs of practitioners in a wide range of science-related disciplines as well as budding statisticians. STAT2000 builds upon the basic techniques taught in the STAT1070 course. Practical data analysis is experienced including how to design appropriate research studies and collect data.

Learning outcomes
On successful completion of the course students will be able to:

1. Recognise the role of statistical methods in the development of scientific knowledge;

2. Select and implement appropriate standard experimental and observational study designs for collection of valid data in order to solve problems and answer research questions;

3. Select appropriate statistical methods to analyse particular types of data;

4. Understand the concepts of linear statistical models for a single response variable and multiple independent predictor variables

5. Apply a statistical software package to analyse different data types, interpret results, make conclusions and effectively communicate the outcomes of the analysis.

Content
The course will cover the following statistical techniques:

Analysis of Variance (ANOVA)

• One-way ANOVA
• Fixed and Random Effects models
• Hierarchical and factorial ANOVA
• Associated Randomised, Randomised Complete Block and Latin Square study designs

Simple Linear Regression

• Parameter estimation and ANOVA in regression models

Multiple Regression

• Parameter estimation and Analysis of Covariance in regression models
• Model selection

Logistic Regression

• Non-parametric (distribution-free) tests

Data collection and statistical modeling for research

• Study design and data collection concepts and principles
• Assumptions underlying statistical methods
• Power and sample size calculations
• Goodness of fit tests and model validation

Contact hours
Computer Lab
Face to Face On Campus 2 hour(s) per Week for 11 Weeks

Lecture
Face to Face On Campus 2 hour(s) per Week for Full Term

### Course Disclaimer

Courses and course hours of instruction are subject to change.

Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.