age is a categorical variable?
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Examples of categorical variables are race, sex, age group, and educational level. While the latter two variables may also be considered in a numerical manner by using exact values for age and highest grade completed, it is often more informative to categorize such variables into a relatively small number of groups.
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Consider the variable age. Age is frequently collected as ratio data, but can also be collected as ordinal data.
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An ordinal variable is similar to a categorical variable. The difference between the two is that there is a clear ordering of the variables. ... If these categories were equally spaced, then the variable would be an numerical variable.
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Age is not necessarily just a number. But it will always be represented as a number because it is the length of time that a person has lived or a thing has existed in the physical world. ... Likewise a 50 year old person can have the maturity level of a 10 year old. But they are still 50.
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Psykolord1989 . Answer: Continuous if looking for exact age, discrete if going by number of years. If a data set is continuous, then the associated random variable could take on any value within the range.
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Multinomial logistic regression is used when the dependent variable in question is nominal (equivalently categorical, meaning that it falls into any one of a set of categories that cannot be ordered in any meaningful way) and for which there are more than two categories.
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Types. Categorical data is divided into two types, namely; nominal and ordinal data while numerical data is categorised into discrete and continuous data. Continuous data is now further divided into interval data and ratio data. Although they are both of 2 types, these data types are not similar.
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A nominal variable has no intrinsic ordering to its categories. For example, gender is a categorical variable having two categories (male and female) with no intrinsic ordering to the categories. An ordinal variable has a clear ordering.
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It is a variable that stands alone and isn't changed by the other variables you are trying to measure. For example, someone's age might be an independent variable. Other factors (such as what they eat, how much they go to school, how much television they watch) aren't going to change a person's age.
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Numerical Value Both numerical and categorical data can take numerical values. Categorical data can take values like identification number, postal code, phone number, etc. The only difference is that arithmetic operations cannot be performed on the values taken by categorical data.
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For example, you could be: 25 years, 10 months, 2 days, 5 hours, 4 seconds, 4 milliseconds, 8 nanoseconds, 99 picosends…and so on. Time is a continuous variable. You could turn age into a discrete variable and then you could count it.
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[Ratio] Age is at the ratio level of measurement because it has an absolute zero value and the difference between values is meaningful. For example, a person who is 20 years old has lived (since birth) half as long as a person who is 40 years old.
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Age or AgeGroup is normally its own dimension. So you would have an Age dimension that would have ages from say 1 to 150 and it would have an attribute for AgeGroup (eg 20-25). When it comes to deciding between dimension attribute or measures, you need to meet your requirements.
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1 Answer. Categorical data are not from a normal distribution. The normal distribution only makes sense if you're dealing with at least interval data, and the normal distribution is continuous and on the whole real line.
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Age distribution, also called Age Composition, in population studies, the proportionate numbers of persons in successive age categories in a given population. Age distributions differ among countries mainly because of differences in the levels and trends of fertility.
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Variable Type Vs. Consider the variable age. Age is frequently collected as ratio data, but can also be collected as ordinal data. ... Variables that are naturally ordinal can't be captured as interval or ratio data, but can be captured as nominal.
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Nominal variables allow a researcher to distinguish between 2 or more classes of an attribute by name. There is no order associated with values on nominal variables. 5. Age. [Ratio] Age is at the ratio level of measurement because it has an absolute zero value and the difference between values is meaningful.