Module proposal information

Syllabus:

Underlying Key Core Topics:

1. The importance of statistics and probability in quantitative scientific (life sciences) research: Introduction and motivation
2. Basic notions of Probability: Events and probabilities; Intersections, unions and independence; Conditional probabilities; Bayes theorem; Combinatorics
3. Summarising data: Types of data; Exploratory data analysis - Graphical tools, Summary statistics; Common statistical distributions and their properties - Normal distribution (including central limit theorem), Binomial distribution, Poisson distribution; Estimates and confidence intervals – point estimates for Normal, Binomial and Poisson distributions, confidence intervals for the mean and for the difference between two means (including the t-distribution)
4. Statistical computing: Statistics with spreadsheets such as Excel; Choice of statistical packages - GenStat, R and Minitab
5. Testing hypotheses: Concept and language; Construction of a simple likelihood ratio test; Student’s t-test for comparing means – one-sample, two-sample, paired sample, power; F-test for comparing variances; Chi-square test for association
6. Simple analyses of continuous data: From a two-sample t-test to one-way analysis of variance (ANOVA); Finding the best fitting line; Comparison of the approaches

More Specialised Core Topics:

1. The basics of experimental design: Main principles of good experimental design – Replication, Randomisation, Blocking and Representativeness; Separation of plot and treatment structure; Choice of treatments and treatment structure
2. Analysing designed experiments: Analysis of Variance (ANOVA) and testing assumptions;
3. Relationships between variables: Calibration and regression; Finding the best fitting line; Comparison of regression lines; Multiple linear regression; Common non-linear regression models 
4. Analyses for non-Normal data – counts and proportions: log-linear models for counts; log-linear models for contingency tables; logit or probit analysis for proportions
5. Multivariate analysis: Data structure - the basic data matrix; principal component analysis; discriminant analysis, canonical variates and multivariate analysis of variance; principal coordinates and cluster analysis; multidimensional scaling

Specialised Topics:

1. The nature of measurement: Random and systematic errors; repeatability and reproducibility; detectin limits; blank correction; propagation of eerror
2. Sequence comparisons: Pairwise alignment – substitution matrices, global and local alignments; Multiway alignments – BLAST and FASTA.
3. Sampling and quality control: Basic concepts of sampling; random and stratified sampling; Shewhart and cusum charts
4. More experiemntal design: More on factorial structure - 2 to the n and 3 to the n designs; response surfaces; Taguchi
5. Statistical approaches for microarray experiments: Experimental design; Data collection; Image analysis using standard software; 1-colour or 2-colour arrays? Normalisation and Variance-Mean relationships; Analysis approaches; Clustering methods; Other multivariate tools
6. Multivariate calibration and regression: more on multiple regression - principal componet regression, partia least squares and other multivariate methods

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