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Course

Introduction to Data Analysis - ZPEM7301

Faculty: UNSW Canberra at ADFA

School: School of Physical, Environmental and Mathematical Sciences @ UNSW Canberra at ADFA

Course Outline: ZPEM7301 Course Outline

Campus: UNSW Canberra at ADFA

Career: Postgraduate

Units of Credit: 6

EFTSL: 0.12500 (more info)

Indicative Contact Hours per Week: 3

Enrolment Requirements:

Restricted to students enrolled in Graduate Certificate programs.

CSS Contribution Charge: 2 (more info)

Tuition Fee: See Tuition Fee Schedule

Further Information: See Class Timetable

View course information for previous years.

Description

The course provides a foundation for further studies in statistics, management or any other area requiring some proficiency in data analysis. It gives an introduction to data analysis, with emphasis on the analysis of experiments. It teaches the principle of good experimental design, and focuses on a project where you design and analyse your own experiment. The course introduces a simple statistical computer package that is used for data exploration and presentation, and the analysis of data from simple experimental and observational studies.

Learning Outcomes

By the end of this course students should be able to:
  1. summarise and present data clearly in numerical and graphical forms
  2. relate population distributions (such as the Normal and Binomial distributions), and sampling distributions to real-life problems
  3. design a simple experiment (allowing for and controlling variability)
  4. calculate confidence intervals and perform hypothesis tests for population means and proportions for one or two populations via z and t-tests
  5. carry out one- and two-way analyses of variance
  6. determine the relationships between two or more variables, via correlation and regression analysis
  7. draw conclusions from statistical analyses and present these conclusions in clear non-technical language

Delivery Mode

Distance

Topics

  • Describing Data
  • Population Distributions
  • Random variables, expectations, assessing normality
  • Sampling, experimental design
  • Sampling Distributions
  • Confidence intervals
  • Hypothesis tests
  • Comparing two populations
  • Comparing several populations: one-way ANOVA
  • Comparing several populations: two-way ANOVA
  • Correlation and regression
  • Inference for regression
  • Project work
Aerial View

Study Levels

Undergraduate Study Postgraudate Study Postgraduate Research Study Non-Award Study

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