Nominal data have no inherent order or ranking, while ordinal data have categories that have a natural order or ranking.
Nominal and ordinal are two sorts of information that are utilized in measurements and information examination. The primary distinction between them is in the idea of the information.
Nominal information are all out information where the qualities address discrete classes or gatherings, however there is no inborn request or positioning among them. Instances of ostensible information incorporate tones, orientation, conjugal status, or sorts of natural product.
Ordinal information, then again, have a characteristic request or positioning to their qualities. They are straight out information, yet the qualities address a particular request or progressive system. Instances of ordinal information incorporate degrees of training, grades, or rankings of sports groups.
To sum up, ostensible information are portrayed by classes that have no intrinsic request or positioning, while ordinal information have classifications that have a characteristic request or positioning.
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When can a correlation coefficient based on an observational study be used to support a claim of cause and effect? Never When the correlation coefficient is close to -1 or +1. When the correlation coefficient is equal to -1 or +1. When the scatterplot of the data has little vertical variation.
Never. Correlation coefficients are used to measure the strength of a linear relationship between two variables, not to prove cause and effect. To determine causation, it is necessary to conduct an experiment or study in which the independent variable is manipulated and the dependent variable is measured.
Correlation coefficients are used to measure the strength of a linear relationship between two variables. They can measure the degree to which variables move together, but they cannot be used to prove cause and effect. To determine causation, it is necessary to perform an experiment or study in which one variable is manipulated and the other is measured. This allows researchers to control for confounding variables and to determine if the manipulation of the independent variable had a direct effect on the dependent variable. Therefore, correlation coefficients cannot be used to support a claim of cause and effect.
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