Griffith University - 1014SCG: Statistics

This course provides an introduction to the basic concepts and practices of data analysis and inferential statistics. It will provide and understanding of several techniques including testing one or more means from sampled populations (t-Tests and ANOVAs), relationships between scale variables (correlations and regressions), and contingency table analysis (Chi-square tests). The course places emphasis on understanding the use of appropriate data for these tests, the use of exploratory data analysis and the underlying distributions and assumptions associated with these tests. Students will learn how to carry out these tests by hand, via the use of statistical software (R), check assumptions, interpret results and report findings in plain English. Assumed prior knowledge: Grade 12 Maths A or B or equivalent. Incompatible: 1003ENV Statistics, 2012MSC Biostatistics, 2012HSC, Intro to Biomedical Data Analysis, Bioinformatics, 1003PSY Research Methods and Statistics in Psychology, 1002MSC Introduction to Biomedical Data Analysis NOTE: this course is only offered on-campus, and students are expected to attend exams and weekly classes on campus, in person.

✔ After successfully completing this course you should be able to:

• Produce, evaluate and interpret summary statistics and graphics appropriate to a specific research question and the characteristics of a given data set.
• Be familiar with various population probability distributions and be able to recognise the appropriate underlying distribution for particular methods of analyses and hypothesis testing
• Apply the appropriate statistical test for a given experimental design (e.g. t-test, ANOVA, regression, etc.)
• Apply the appropriate statistical test for categorical data sets (e.g. chi-squared test)
• Demonstrate a working knowledge of the basics of The Research Method and its relationship to statistical inference
• Understand the framework of statistical inference (estimation and hypothesis testing) based on random samples
• Understand experimental design concepts, as they relate to the statistical models and hypotheses;
• Demonstrate an ability to effectively interpret and communicate statistical output from commonly used statistical software
• Determine appropriate statistical summaries and analytical methods and apply these to a given research problem; perform analyses using statistical software; interpret results of the analyses.
• Synthesise and communicate the aims, methods, results and interpretation in the format of a statistical report.