query: Treating Categorical Data as nominal or ordinal?
positive_passages.docid: 147544
positive_passages.text: Continuous dependent variable with ordinal independent variable
negative_passages.docid: 130037
negative_passages.text: correlation between categorical variables
negative_passages.docid: 65293
negative_passages.text: Dealing with Categorical variables in Multiple Regression
negative_passages.docid: 151975
negative_passages.text: Appropriate data transformation
negative_passages.docid: 111321
negative_passages.text: When should we discretize/bin continuous independent variables/features and when should not?
negative_passages.docid: 79483
negative_passages.text: How do I sample from a discrete (categorical) distribution in log space?
negative_passages.docid: 43821
negative_passages.text: MS excel - assigning "categories" based on keywords
negative_passages.docid: 25399
negative_passages.text: Predicting with both continuous and categorical features
negative_passages.docid: 146768
negative_passages.text: How to correctly assess the correlation between ordinal and a continuous variable?
negative_passages.docid: 5301
negative_passages.text: F test and categorical variables
negative_passages.docid: 78658
negative_passages.text: How to statistically prove if a column has categorical data or not using Python
negative_passages.docid: 112942
negative_passages.text: Checking Normality of Numerical, and Categorical Data
negative_passages.docid: 31068
negative_passages.text: How to measure correlation between categorical variable?
negative_passages.docid: 95969
negative_passages.text: How to choose the split in Random forest for categorical predictors (features)?
negative_passages.docid: 33206
negative_passages.text: Derivation of Mode of grouped data
negative_passages.docid: 113009
negative_passages.text: Which test should I use for ordinal data?
negative_passages.docid: 71272
negative_passages.text: Scoring predictions of an ordinal variable