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STA6200 Statistics for Quantitative Researchers

Semester 2, 2023 Online
Units : 1
School or Department : School of Mathematics, Physics & Computing
Grading basis : Graded
Course fee schedule : /current-students/administration/fees/fee-schedules

Staffing

Course Coordinator:

Requisites

Enrolment is not permitted in STA6200 if STA2300 or STA1003 or STA1004 has been previously completed

Overview

This course aims to provide post graduate students with no or limited knowledge of statistics, the fundamental statistical concepts, methods and data analysis skills necessary to undertake quantitative research and interpret subsequent results. The course is aimed at developing statistical thinking and literacy for students from diverse disciplines and is a pre-requisite for most high-level statistics courses.

This course focuses primarily on the graphical and numerical methods of data analysis including descriptive and inferential statistics for post-graduate students. Emphasis is on the understanding of the concepts and principles associated with statistical methods and skills required for the analysis of real-life for quantitative studies. Core components of the course include the use of a popular statistical software package (SPSS) and the development of problem-solving skills relevant to many disciplines. However, the mathematical underpinnings of the methods are not covered in detail in this course; other statistics courses cover this aspect.

Course learning outcomes

On Completion of this course students should be able to:

  1. Recognise types of data and describe them using appropriate graphical and numerical analyses including exploring relationships in data;
  2. Distinguish between different methods of data generation, collection and analysis;
  3. Select and implement appropriate statistical methods to analyse quantitative data to answer an underlying research question;
  4. Prepare, manage, format and analyse data using a popular statistical computer package in order to apply appropriate statistical inferencing procedure;
  5. Apply professional skills to effectively present and communicate results of statistical analyses including interpretation and justification of statistical decisions to a diverse scholarly audience.

Topics

Description Weighting(%)
1. Exploring and understanding data: variables and values; types of data; introduction to SPSS; Describing distributions: quantitative data; graphs of distributions; summary statistics. 10.00
2. Using the normal model: standardising; unstandardising; standard normal curve. 10.00
3. Exploring relationships between variables: scatterplots; correlation and regression; residuals. 10.00
4. Gathering data: Observational and experimental studies; surveys; sampling methods; principles of good design; causation and confounding. 10.00
5. Hypothesis testing and confidence interval for one mean; sample distribution of the mean, CLT, one sample t-procedure for a mean; and dependent/paired samples t procedure; confidence intervals; determination of sample size; Type I and Type II errors; P-value; significance level, power of test; use of t table. 10.00
6. Comparing means: two sample t-procedures and paired procedure; confidence intervals and hypothesis testing. 10.00
7. One-way Analysis of Variance (ANOVA); pairwise comparison; multifactor analysis. 10.00
8. Categorical variables; joint, marginal, and conditional distributions. Non-parametric hypothesis test: chi-square test of independence; chi-square goodness-of-fit test; follow-up residual analysis. 10.00
9. Linear Regression Analysis: Inference for simple regression model; introduction and inference for multiple regression model – test and confidence interval for regression parameters. 10.00
10. Synthesis and consolidation, ethical collection, and use of data. 10.00

Text and materials required to be purchased or accessed

De Veaux, R.D., Vellerman, P.F. & Brock, D.E 2021, Stats: data and models, 5th Global edn, Pearson Education, Harlow, United Kingdom.
All additional study material will be provided on the course ¾«¶«´«Ã½appDesk.
IBM SPSS STATISTICS BASE GRAD PACK VERSION 26.0 (SPSS Version 20.0 or later is acceptable) (Note: This software can be accessed when on campus in computer laboratories and the library.).

Student workload expectations

To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.

Assessment details

Approach Type Description Group
Assessment
Weighting (%) Course learning outcomes
Assignments Written Quiz No 10 1,4
Assignments Written Problem Solving 1 No 25 1,2,3,4,5
Assignments Written Problem Solving 2 No 25 1,2,3,4,5
Assignments Written Report No 40 1,2,3,4,5
Date printed 9 February 2024