Computer Assignments

In Class Test

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&#39;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)

Statistical Methods in R - 7011DATSCI

Statistical Methods in R

Critically evaluate the appropriateness of various techniques for a particular problem.

Critically apply a variety of statistical techniques to problems in data science, using the R computer language.

Synthesize a combination of statistical methods to test a complex problem.

Critically analyse the outcomes of hypothesis testing in an informed fashion