BU 321 Unit 1 descriptive summary set example

Two Hundred Forty Pizza Deliveries, Described: A Descriptive Summary Set for a Composite Six-Store Chain, With Each Number Put Into Plain Words

Student Name

Business, Herzing University

BU 321: Business Statistics

Instructor Name

January 21, 2026

Two Hundred Forty Pizza Deliveries, Described: A Descriptive Summary Set for a Composite Six-Store Chain, With Each Number Put Into Plain Words

This summary set describes one business dataset before any inference is attempted. The data come from Brickoven Express, a composite six-store pizza delivery chain in Columbus, Ohio, built for this course. The dataset holds 240 delivery orders, 40 randomly drawn from each store's records for the two weeks from October 6 to October 19, 2025. The set first classifies every variable by type, then reports center, spread and shape only for the variables where those measures mean something, and finally says in plain words what each figure tells a store manager. All figures were computed in Python with its standard statistics module; the formulas behind each measure are described where the figure first appears.

Classifying the Variables

Stevens (1946) distinguished four scales of measurement, nominal, ordinal, interval and ratio, and argued that the scale determines which statistics are meaningful. A mean of store codes, for example, is arithmetic without meaning. The table classifies the eleven variables in the dataset.

Three consequences follow. The order number is an identifier and receives no summary at all. Store, day part, day type, payment method and on-time status are summarized with counts and percentages. Customer rating, an ordinal scale from 1 to 5, is summarized with its median and distribution rather than a mean, because no evidence says the gap between 3 and 4 stars equals the gap between 4 and 5 (Stevens, 1946).

Summary of the Numeric Variables

The table reports count, mean, median, standard deviation, quartiles, range and skewness for the four numeric variables. The mean is the sum divided by the count; the median is the middle value when the data are sorted; the standard deviation is the square root of the average squared distance from the mean, using n minus 1 in the denominator for a sample; the quartiles mark the 25th and 75th percentiles; and skewness measures the lean of the distribution, with positive values meaning a longer right tail (Anderson et al., 2020; National Institute of Standards and Technology, 2012).

Summary of the Categorical and Ordinal Variables

Each store contributes 40 orders by design, so the store counts describe the sampling plan rather than the business. Dinner accounted for 127 orders (52.9 percent), lunch for 77 (32.1 percent) and late night for 36 (15.0 percent). Weekdays accounted for 140 orders (58.3 percent) and weekends for 100 (41.7 percent). Customers paid by card for 113 orders (47.1 percent), through the app for 98 (40.8 percent) and in cash for 29 (12.1 percent). Of the 240 orders, 217 (90.4 percent) arrived within the chain's 35-minute promise.

Customer ratings were recorded for 159 orders; 81 customers (33.8 percent) did not leave one. Among those who did, 77 gave 5 stars, 68 gave 4, 13 gave 3, one gave 2 and none gave 1. The median rating is 4.

What Each Figure Tells a Manager

Delivery time: a typical order in these two weeks arrived in about 24 minutes, and half of all orders arrived between 19 and 29 minutes. Mean and median sit within a minute of each other, while a mild positive skew reveals a tail of slower deliveries, the longest taking 50 minutes. For a manager, the typical order is comfortably inside the 35-minute promise, and the question worth asking is what the slow tail has in common, which later units examine.

Order value: the typical order was about $30, and the middle half of orders fell between about $23 and $37. Because the mean and median are within 35 cents of each other and the skew is small, the average is a fair description of a typical ticket. A manager planning promotions can treat $30 as the normal order.

Distance: the typical delivery traveled a little under 3 miles, but the distribution leans right, with the mean above the median and a skewness of 0.72. A minority of long trips, up to 7.8 miles, pulls the average upward. For a manager, that means the median is the better description of an ordinary trip, and the long trips deserve separate attention because they may take drivers away from the store for a long time.

Items: most orders contained two to four items, with a median of three. Order size varies less than order value, which suggests that price differences between items, not the number of items, account for much of the variation in ticket size.

Charts, and Why Only Two

Only two charts would add something the tables cannot. A histogram of delivery time would show the shape of the slow tail more clearly than a skewness figure, and a bar chart of the rating distribution would show how heavily ratings cluster at 4 and 5. Charts of order value and items would repeat what the table already shows, so they are left out.

Limits of This Summary

The sample holds 40 orders per store by design, so any figure pooled across stores gives each store equal weight, even though the stores probably handle different volumes. The two weeks in October may not represent the rest of the year. The missing ratings are not random; customers who leave a rating may be those with especially good or bad experiences, so the rating distribution may not describe all customers. These limits do not change the descriptions above, but they matter when later units move from describing these orders to drawing conclusions about all of the chain's deliveries. The chain and every order in the file are invented for teaching.

References

Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J. (2020). Statistics for business and economics (14th ed.). Cengage.

National Institute of Standards and Technology. (2012). NIST/SEMATECH e-handbook of statistical methods. https://www.itl.nist.gov/div898/handbook/

Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677-680. https://doi.org/10.1126/science.103.2684.677