The first data-type constraint I found was found by searching. The answers were simply incorrect. The user answered 1 for every linear scale question, then typed out, "012345" for every free form question. I decided of course to delete the entire row. A mandatory constraint that appeared in my answers was in the age column. The user answered, "-_" for their age, they then answered, "_---/_/_-" for every open ended question. I deleted the row of this user. A range constraint that I was searching for appeared in my writing involved a user answering 95 for their age. Then answered using chatgpt replied answers that did not relate to the question being asked and were also 5 pages long. I deleted this entire row. In total I needed to fix 3 data records.
Google Sheet
I created a chart using my summary table to give a more clear representation of my data. This summary table asks the question of do certain positions require more skill compared to the count of answers people selected. As you can see from the chart 30% of answers strongly felt that certain positions require more skill. As well as 47% pretty strongly feel they require more skill. This chart serves as strong evidence to my topic because it shows how that users feel NBA positions overall require more skill.
This summary table shows the sum of steals and blocks collected from each NBA position. Furthermore, we can see how certain positions prevail in different statistics. I chose steals and blocks related to positions because it is important to see which positions bring the most steals and blocks. This summary table serves as supporting evidence to my topic because it help answer one of my questions which is how changing positions affects a players performance.