MATH07026 2019 Health Statistics and Data Analysis

Describe the rationale for and methods of selecting appropriate samples for a study

Graphically display and numerically summarise data using appropriate descriptive statistics

Apply probability and probability distributions to data analysis

Choose and apply appropriate tests of hypotheses based on a research problem and the characteristics of a dataset

Model the relationships between variables using linear regression analysis

Use an appropriate statistical software package to perform statistical analysis of data

The teaching methods used will be a combination of lectures, self-study, labs, tutorials, and any combination of discussion, case study, problem-solving exercises and computer-based learning.

The student will be assessed by means of both summative and formative assessment. The summative assessment will consist of two practical based continuous assessments where the student will be examined on both their theoretical knowledge of statistics and their use of statistical analysis software to apply this knowledge with the emphasis on the practical application of statistics.  They will also take an end of module exam which will concentrate on their theoretical knowledge.

The student will also have access to online self-assessment quizzes as part of the formative assessment. These quizzes will allow the student to monitor their own progress on the module as well as identify any knowledge gaps they may have.

Where the student fails to achieve the pass mark in the module, they may be asked to repeat the final exam or complete a practical laboratory assessment or a combination of both.

The repeat practical laboratory assessment will be held in the first week of September prior to the autumn exam boards taking place.

1. Describe the rationale for and methods of selecting appropriate samples for a study

• Sample selection methods based on study design
• Sample Size
• Sampling Issues

2. Graphically display and numerically summarise data using appropriate descriptive statistics

• Graphical Representation of data including frequency tables and charts
• Measures of Central Tendency, Position and Dispersion.
• Confidence Intervals for Means and Proportion size

3. Apply probability and probability distributions to data analysis

• Probability Experiments
• Probability Trees
• Classical Probability
• Experimental Probability
• Addition and Multiplication Rules of Probability
• Counting Rules
• Discrete Probability Distributions
  • Binomial Distribution
  • Poisson Distribution
• The Normal Distribution
  • Applications of the standard Normal Distribution
  • Assessing Normality
  • The Central Limit Theorem

4. Choose and apply appropriate tests of hypotheses based on a research problem and the characteristics of a dataset

• Introduction to Hypothesis Testing
• Writing hypotheses for statistical tests
• One Sample, Independent Samples and Paired Samples t-tests
• One-Way ANOVA and related Post Hoc Tests
• Repeated Measures ANOVA and related Post Hoc Tests
• z-tests for proportion size
• Non Parametric Tests
  • Introduction to Non-Parametric hypothesis tests
  • Chi-Square test for association and Independence
  • Mann-Whitney test
  • Kruskal Wallis test
  • Wilcoxon signed-rank test

5. Model the relationships between variables using linear regression analysis

• Relationship Modelling
• Pearson's Correlation Co-efficient
• Significance of the correlation co-efficient
• Spearman's Rho

6. Use an appropriate statistical software package to perform statistical analysis of data

• Introduction to SPSS
• Using SPSS to carry out the various statistical tests detailed in the syllabus above.