Variables in Statistics, Screen - 4 & Comparing Frequency Distribution, Screen- 2

  1. Variables in Statistics Mission, Screen-4 : In the Nominal table, it is given that we can measure qualitative variables. How.

  2. Comparing Frequency Distribution Mission , Screen- 2. It is mentioned in this para that :
    Comparing the five distributions is now easier, and we can make a couple of observations:

  • There’s only one rookie playing on a combined position ( F/C ). This is significantly less compared to more experienced players, which suggests that combined positions ( F/C and G/F ) may require more complex skills on the field that rookies rarely have.

But, there is only one veteran playing on a combined position ( F/C ) also. What this tells us.

Hi @sharathnandalike. Thanks for the question and the comment. I’ll respond to each below:

Variables in Statistics Mission, Screen-4 : In the Nominal table, it is given that we can measure qualitative variables. How.

Measuring a variable using a nominal scale means classifying individuals of that variable into different categories. So measuring is synonymous with “classifying” or “categorizing”. If a variable is quantitative, the numbers do have mathematical meaning and we can measure that, it’s just that the nominal scale is not good to measure these kind of variables.

Essentially, if a variable is quantitative, the individual values are related in systematic ways: for instance, one value is greater than the other; another value is equal to another value; etc. However, if a variable is qualitative, the individual values are not related in a systematic way: the values are just labels/categories, and there’s no mathematical relation between the categories (you can’t say eye color “green” is greater than eye color “blue”). So, in this context, measuring is synonymous with “classifying” or “categorizing”.

Comparing Frequency Distribution Mission , Screen- 2. It is mentioned in this para that :
Comparing the five distributions is now easier, and we can make a couple of observations:

  • There’s only one rookie playing on a combined position ( F/C ). This is significantly less compared to more experienced players, which suggests that combined positions ( F/C and G/F ) may require more complex skills on the field that rookies rarely have.
    But, there is only one veteran playing on a combined position ( F/C ) also. What this tells us.

I think you make a good observation about the F/C position that there is only one veteran playing on a combined position F/C. So a more accurate answer might be:

There are no rookies playing on the combined position, G/F. This suggests that combined position, G/F, may require more complex skills on the field that rookies rarely have.

I’ll consider this when I perform my next round of optimizations to this course. Thanks for pointing this out.

Thank you very much Casey for all your kind & prompt replies. They surely helped me out.
Allasame, don’t get me wrong if my acknowledgement to your responses is a tad late. You see, I am just in a terrible hurry to finish this Analyst in R course. I guess, its a wrong way though,since you gotta have patience to learn Analytics . However, this is due to unavoidable situation .

Anyways, the 1st query on “measuring qualitative vars.” I guess its just the word ‘measure’ that confused me, though I had understood the concept. Its fine now.

The 2nd query: G/F pos comparison is obvious from the graphs , but how the rookies & veterans compare with F/C pos. ( I am no basket ball fan)

Best Wishes,

Sharath

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Nice to hear from you @sharathnandalike. Best wishes on finishing up the Data Analyst in R path. Please let us know of other questions that come up along the way. Best,
-Casey