Summary
I recorded this session to demonstrate how to turn a raw NCAA basketball win‑percentage dataset into a visual histogram using Google Sheets, a required step for our class assignment. I began by importing the raw data from the "Raw Data Sets" sheet via a QUERY formula, ensuring the win‑percentage column (Y3:Y1759) is available for analysis. After confirming the data import, I calculated the minimum and maximum win percentages using the MIN and MAX functions, discovering a minimum of 0 (San Jose State 2015) and a maximum of 97.5. With those bounds, I explained our goal: to visualize the distribution of team win percentages by dividing the range into 20 equal‑width buckets. I computed the bucket size as (max‑min)/20, then built a "Bucket Range" column where each row represents the upper bound of a bucket (bucket size × 1, 2, …, 20). Using absolute references ($E$3, $F$3) I showed how to lock the bucket‑size cell while dragging formulas down the column. To assign each team's win percentage to a bucket, I applied FLOOR division of the win‑percentage by the bucket size, adjusting the formula to produce integer bucket numbers (e.g., 0.729 falls into bucket 15). I then used the FREQUENCY function, referencing the win‑percentage column and the bucket‑range categories, to count how many teams fall into each bucket. The resulting frequency column feeds a bar chart where the x‑axis shows bucket upper bounds and the y‑axis shows team counts. I highlighted that the histogram roughly follows a normal distribution, with a peak around 50 % (≈184 teams) and very few teams near the extremes (e.g., only one team above 97 %). Finally, I instructed how to add axis labels and a title to the chart, noting that proper labeling is part of the grading rubric, and previewed the next class where we will create a pivot table and graph its results. This walkthrough equips students with the spreadsheet techniques needed to clean, bucket, and visualize large datasets for statistical analysis.