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lollersauce914 t1_iuhohyn wrote

Could you provide some context? I don't think your question provides enough information to give you a meaningful answer.

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bascalie OP t1_iuhoyt3 wrote

Well I am learning about types of data more specifically qualitative and categorical data. One of the categories is Nominal data that splits into Polinominal Data and Binominal data. Both have no hierarchy and are used for naming and labeling variables without a quantitative value...

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lollersauce914 t1_iuhrgjz wrote

Categorical data are data that track a characteristic in which people are put into non-quantifiable, mutually-exclusive categories.

If I want to measure if you're from New York, you either are or you aren't. You can't be 0.7 New Yorks. Everyone exists in one of two categories, from New York and not from New York.

We can split categorical data into two subtypes, ordinal (order matters) and nominal (order doesn't matter). The "from New York/not from New York" is an example of nominal data. If I wanted to put people into categories based on their income (e.g., $0-$10000, $10001-$20000, etc.) it would be ordinal. The information being tracked is still not quantitative (we're just tracking your membership in a category), but there is an order to the categories.

The terms "polynomial" and "binomial" do not make sense to use in the context of categorical data. It sounds like you may just be using them to refer to nominal data that track 2 categories vs. more than 2 categories. The former is often referred to as a "binary variable" because it has two states (e.g., "In New York" and "Not in New York").

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artrald-7083 t1_iuia5m4 wrote

I believe that what you are calling 'polynominal' might be more widely known as 'multinomial', that is, a nominal variable with lots of categories as seen in a multinomial logistic regression.

The distinction then makes sense: your standard logistic regression is modelling the probability of seeing the two values of a binary variable at different values of a continuous variable, while the multinomial logistic regression generalises this to a nominal variable with multiple values.

The reason a search engine would get confused (I ended up going via a dictionary) is that as you may or may not know polynomial without the N is a word for a sort of equation (and indeed, one that makes sense to see in a statistical context).

I could be wrong, but I do not think this term 'polynomiNal' is a common one, and in my own work as an industrial data scientist I tend to call your 'binominal' variables 'binary', and 'polynominal' simply 'nominal'.

There is always the possibility that I missed something.

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jd158ug t1_iuidhi4 wrote

Agree - statistician here. Binomial= 2 categories: yes/no, positive/negative, etc. Multinomial= more than 2 eg race, hair color etc . I think OP's source is not using 'polynomial' in the appropriate way.

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[deleted] t1_iui3taf wrote

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