Mathematics (Statistics)

Course summary

Data and Probability - Students will develop an understanding of: ∙ Numerical measures, graphs, and diagrams ∙ Probability ∙ Population and samples ∙ Introduction to probability distributions ∙ Binomial distribution ∙ Normal distribution ∙ Correlation and linear regression ∙ Bayes’ theorem ∙ Probability distributions ∙ Experimental design ∙ Exponential and Poisson distributions. Statistical Inference - Students will develop an understanding of: ∙ Correlation and regression ∙ Introduction to hypothesis testing ∙ Contingency tables ∙ One and two sample non-parametric tests ∙ Experimental design ∙ Sampling, estimates, and resampling ∙ Hypothesis testing, significance testing, confidence intervals and power ∙ Hypothesis testing for 1 and 2 samples ∙ Paired tests ∙ Goodness of fit ∙ Analysis of variance ∙ Effect size Statistics in Practice - Students will develop a deeper understanding of all of the aforementioned topics as all could be examined on the final paper.