161.220 Data Analysis (15 credits)

Understanding, visualising and analysing data in a practical context using R/RStudio. Topics are selected from: data collection including experimental designs, observational studies, and surveys, data cleaning and preparation, exploratory analysis, visualisation of multivariate and time series data, regression, analysis of variance and covariance, autoregressive models and categorical data modelling.

Note(s): Access to a computer and an approved statistics package are required for analysis of data.

Requirements

• Prerequisite(s): 161.101, 161.111, 161.120, 161.122 or 161.130
  General Prerequisite: At least 45 credits from 100 level.
• Restriction(s): 161.250

Expected prior learning

• Students with a high level of attainment in NCEA Level 3 Mathematics with Statistics may apply to enrol in 161.220 directly.

Offerings

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