What kinda of Variable is this?

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The discussion centers on classifying a dataset of monthly animal feed values as "quantitative ratio variables." Participants note that while the data represents continuous measurements, the mixed precision of values complicates proper analysis. There is a suggestion that the data may not fit the standard definition of ratio variables due to inconsistencies in formatting. The intent behind analyzing the data is to determine if production can be planned based on expected orders. Overall, the conversation highlights the importance of variable classification and data precision in statistical analysis.
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The following values are tonnes of a specific animal feed for a specific consumer.

Jan - 13.97
Feb - 12
Mar - 11
Apr - 5.87
May - 10
Jun - 13.95
Jul - 15.96

Are these variables best described as "quantitative ratio variables" and why?

If not what would be the most accurate class for these variables and why?

I pretty green when it comes to stats :smile:, hopefully this isn't too easy to be worth a reply.
 
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nitsuj said:
Are these variables best described as "quantitative ratio variables" and why?

My personal opinion is that such data isn't obviously a "ratio" of any kind, but you should list all the variable types we have to pick from.

Such classifications of variable types are not standard terminology across all statistics texts. Perhaps you are studying statistics in a specialized field.
 
Mathematically, there are discrete and continuous variables. In applications, variables are sometimes broken down into ordered categorical variables, unordered categorical variables and indicator variables. Since virtually all data are discrete, the question becomes: "What kind of distribution do the data represent?" In your case, I would say that the observations come from a continuous distribution of measure, limited by the precision of measurement and the convenience of rounding. By the this I mean that, in principle, uncountably many possible values lie between the values you actually observed.

EDIT: Your particular data set has some problems in that you have numbers like 10 and others like 13.97. At the very least a value like "10" should be written as 10.00. However, you really can't properly analyze data with data points having such mixed levels of precision.
 
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Thanks for the replies guys!

SW Vandecarr,
Thanks for the reply Stephen,

The intent is to try and see if the production is linear enough to assume a feed order will be placed and production could be planned accordingly. i.e. in Jan the customer will likely order 13 tonnes of feed, so in Dec we produce the feed in advance.

Distribution would be what ever is between the high & low. Said different, this is all the data there is. I think that's what you were asking. i.e. the consumer would never order 20 tonnes or 2 tonnes of feed in a month.

Not sure if that helps,

I think I might need to take a course or two in this.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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