Teaching Responsibility

LJMU Schools involved in Delivery:

Astrophysics Research Institute

Learning Methods

Lecture

Practical

Module Offerings

7011DATSCI-SEP-CTY

Aims

The module aims to provide an introduction to students from differing academic disciplines to key concepts in statistics and statistical computing using the R programming language, with an emphasis on the informed interpretation of the results of statistical testing.

Learning Outcomes

1.
Critically apply a variety of statistical techniques to problems in data science, using the R computer language.
2.
Critically evaluate the appropriateness of various techniques for a particular problem.
3.
Critically analyse the outcomes of hypothesis testing in an informed fashion
4.
Synthesize a combination of statistical methods to test a complex problem.

Module Content

Outline Syllabus:1. Statistical fundamentals (significance, inference and hypotheses, replication, models, confidence intervals) 2. Introduction to the R language (dataframes, functions, loops, I/O, plotting, packages) 3. Descriptive statistics (measures of central tendency and variability) 4. Probability density, central limit theorem, normal distribution 5. Single sample tests (Z-test, t-test, testing for normality, nonparametric tests, jackknife/bootstrap) 6. Two sample testing (F-test, ECDF-based tests, Fisher's exact test, Chi-squared test, correlation and covariance) 7. Regression (maximum likelihood, least-squares, linear and nonlinear regression, nonparametric curve-fitting) 8. Analysis of variance (ANOVA inference, post-hoc tests, multifactor analysis, structured error, goodness of fit / model comparison) 9. Multiple regression (ANCOVA, stepwise regression, cross-validation) 10. Count data (Binomial/Poisson errors, logistic regression, generalized linear models) 11. Survival analysis (Hazard, censorship, distribution fitting) 12. Bayesean inference (Likelihood, priors and posteriors, credibility intervals)
Module Overview:
This module provides an introduction to key concepts in statistics and statistical computing using the R programming language, with an emphasis on the informed interpretation of the results of statistical testing.

Assessments

Exam

Report