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# STA1003 Fundamental Statistics

 Semester 3, 2022 Online Units : 1 Faculty or Section : Faculty of Health, Engineering and Sciences School or Department : School of Mathematics, Physics & Computing Student contribution band : Band 1 Grading basis : Graded Version produced : 17 May 2022

## Requisites

Enrolment is not permitted in STA1003 if STA2300 or STA8170 has been previously completed.

## Overview

This course aims to provide students who have limited knowledge of statistics, with the fundamental statistical concepts, methods and skills necessary in order to undertake or critically appraise quantitative methods and the interpretation of subsequent results. The course is aimed at developing statistical literacy and a strong foundation in threshold statistical competencies in students from a variety of disciplines, including science, psychology, the physical sciences, business, commerce, and IT and is a pre-requisite for most high-level statistics courses.

This course focuses primarily on the appropriate application, interpretation and communication of foundational descriptive and inferential statistical methods. Emphasis is placed on understanding the concepts and principles associated with dealing with data, in particular descriptive and inferential statistics. Data sets from a range of disciplines are included as well as examples of statistics presented in popular media. Core components of the course include the use of statistical software and the development of problem solving and quantitative skills relevant to many disciplines of study. Note: The mathematical underpinnings of the methods used are not covered; other statistics courses cover this aspect.

## Course learning outcomes

On successful completion of this course students should be able to:

1. Explore relationships in data and distinguish between different methods of data collection and analysis;
2. Evaluate and apply a variety of statistical inferential methods to real life situations;
3. Use a statistical computer package to enter, summarise and analyse data;
4. Interpret and communicate the results of statistical analyses for a diverse audience.

## Topics

Description Weighting(%)
1. Exploring and understanding data: variables and values; types of data; introduction to SPSS; categorical variables; contingency tables; sampling methods; surveys. 10.00
2. Describing distributions: quantitative data; graphs of distributions; summary statistics; Experimental design: principles of good design; causation and confounding. 10.00
3. Using the normal model: standardising; unstandardising; standard normal curve; using Standard Normal Probabilities. 8.00
4. Exploring relationships between quantitative variables: scatterplots; correlation and regression; boxplots. 12.00
5. Randomness and probability: probability rules; events; probability models; means and standard deviation; introduction to statistical inference. 10.00
6. Non-parametric hypothesis test: chi-square test of independence; chi-square goodness-of-fit test. 10.00
7. Sampling distribution models: proportions and means; standard error; the central limit theorem. 5.00
8. Statistical inference about a proportion; introduction to parametric tests; confidence intervals for proportion; z-test for proportion; sample size determination. 14.00
9. Statistical inference about mean: one sample t-procedure for a mean; confidence intervals for a mean; level of significance; type I and type II errors. 8.00
10. Comparing means: two sample t-procedures; independent and dependent samples; confidence intervals and hypothesis testing. 8.00
11. Synthesis and consolidation, ethical collection and use of data; the binomial model. 5.00
Date printed 17 May 2022