- Visualization Methods
- for EDA: visualize patterns, trends, anomalies in data
- for model diagnostic methods: visualize to assess violations of assumptions
- for summary methods: visualize to provide an interpretable summary of data
- apply theory to practice
- conert research questions into statistical hypotheses and models
- look into the difference between non-parametric (ex. fisher exact test) vs parametric (ex. \(\chi^2 test for independence\)) vs model-based methods (ex. logistic regression)
- for summary methods: visualize to provide an interpretable summary of data
- categorical (or frequency) data consist of a discrete set of categories, which may be ordered or unordered.
- unordered
- gener: {male, female, transgender}
- marital status: {never married, married, separated, divorced, widowed}
- party preference: {NDP, liberal, conservative, green}
- treatment improvement: {none, some, marked}
- ordered
- age group: {0s,10s,20s,30s, …}
- number of children: {0, 1 , 2 ,3, …} ## Structures
- unordered
1 Goal
2 Definition of Categorical Data
Categorical data appears in various forms like: