Year of birth is interval level of measurement; age is ratio.
The theory behind estimating time of death, or post mortem interval (PMIfor short) with the help of insects is simple: since insects arrive on the body soon after death,estimatingthe age of the insects will also lead to an estimation of PMI.
When born, age zero.When born, age zero.When born, age zero.When born, age zero.
IQ = mental age / chronological age x 100
he died at age 75.
Ordinal. Though more likely interval or even ratio scale.
Age is none of the items listed. Age is ratio data.
Year of birth is interval level of measurement; age is ratio.
Ordinal
illustrate how you can express the age of group of persons as {1}nominal,{2}ordinal data,{3} interval data,{4}ratio data
Neither, age is at a ratio level of measurement.
I am not sure if I understand your question. I will rephrase it to: Should data collected on the ages of persons in a group be consider as nominal, ordinal, interval or ratio data? It is ratio. Now, let's try another question. A study finds that people with names beginning with the letter "a-k" are older than people with letters "l-z". In this case, the data collected on names in nominal data, but the ages are still ratio data.
Nominal-Genda, religion, post, code, ethnic Ordinal-Satisfaction, exam, grade, position in class Interval-IQ, temperature, score, CGPA Ratio-Height, weight, time, age, grant
A person's age is a ratio scale because we can say person A's age is twice older than person B's. Equal difference ages on a ratio scale all have exactly the same size. Moreover in age, 0 (zero) exists, which is feature of a ratio scale.
Age is typically considered to be at the ordinal level of data, as it represents a certain order or ranking of individuals based on their age, but it does not have a true zero point. However, in some cases, it can also be treated as interval data depending on the context and analysis being conducted.
The data will be interval if you set up the age groups like 20-29; 30-39; 40-49 etc. If it is in groups such as teens, young people, middle aged, baby boomers, Gen X, etc it will be ordinal data.
It depends how we have computed %age. By and large, percentage is a summary statistic. Its a categorical variable (may be nominal or ordinal). That way its a discrete. In case of assay or yield computations it becomes a continuous variable. Naresh K Chawla nkchawla@gmail.com