# I have a set of data with 10 variables. The first variable is categorical and/or ordinal. I am not 100% sure because my data are categorised into 4 groups (i.e. C, Y, A, R). C, Y, A, R represents “less than 5%”, “5%-9.99%”, “10%-14.99%” and “15% & above” respectively.

I have a set of data with 10 variables. The first variable is categorical and/or ordinal. I am not 100% sure because my data are categorised into 4 groups (i.e. C, Y, A, R). C, Y, A, R represents “less than 5%”, “5%-9.99%”, “10%-14.99%” and “15% & above” respectively.

The other 9 variables are independent of each other, but each of them is there for comparison to the first variable. Each of these variables is categorised into 7 groups i.e. “-15%”, “-10%”, “-5%”, “0%”, “+5%”, “+10%” and “+15%”.

I am trying to prove that each of these 9 variables matches the first variable. For example, if Row No. 1 data is categorised into group R (“15% & above”), I am expecting the category of each of the 9 variables are also “+15%” (but in my dataset, not all variables are matched exactly like this. For example, some data that is categorised into R doesn’t hit “+15%”, instead they hit “+10%” or “0%”, etc).

So I am stuck here even after reading a lot of explanations, I am not sure whether my data and aim make sense at all?

In the other 9 variables columns, the empty cells means “0%”.

When it’s negative, it means the price was lower than its SMA calculation. Shall I convert all the negative category as 0% in this case? This seems to make more sense, because C,Y,A,R are 0/5/10/15%. The negative categories were there because previously I was looking at “dump”. But the whole project technically, looks at the “pump” (which are the 0/5/10/15%). What do you think?

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