| Course | BU 454 Data Visualization |
|---|---|
| Unit | Unit 5 |
| Paper type | Distribution and relationship charts |
| Length | About 960 words, 6 pages |
| Format | APA 7 student paper |
| School | Herzing University |
| Program | BS in Business Analytics |
| Updated | September 2026 |
Free sample paper for BU 454 Unit 5
An Average of 1,126 That Almost No Day Resembled, and Snow That Matters Most on Weekdays: Distribution and Relationship Charts for a Composite Montana Ski Area
Student Name
Business, Herzing University
BU 454: Data Visualization
Instructor Name
March 11, 2026
An Average of 1,126 That Almost No Day Resembled, and Snow That Matters Most on Weekdays: Distribution and Relationship Charts for a Composite Montana Ski Area
Why a Single Average Misleads Here
Bearpaw Ridge, the composite ski area in western Montana followed through this course, averaged 1,126 skier visits a day over the 114 days it was open in the 2024 to 2025 season. The owners use that figure to plan staffing. Yet very few days had anything close to 1,126 visitors: most weekdays had far fewer and most weekends far more. This unit shows what daily visits actually look like across the season, then puts two measures against each other, new snowfall and visits, and states plainly how much of the pattern the picture can support.
Tukey (1977) argued that the first task in looking at data is to see its shape, including its spread, skew and unusual values, before reducing it to a summary. Anscombe (1973) made the same point with four small data sets that share identical means, variances and correlations yet look entirely different when plotted, which is why the relationship below is shown as a scatter before any number is quoted.
Chart 1: A Histogram of Daily Visits
The histogram groups the 114 open days into intervals of 250 visits, from 0 to 3,500. It shows a large cluster of days between 500 and 1,000 visits, almost all weekdays, a thinner spread from 1,500 to 2,500, mostly weekends, and a short tail of holiday days above 2,750. The median day had 930 visits, below the mean, because the few very busy days pull the average up.
The interval width was chosen by trying three. With intervals of 500, the weekday cluster and the weekend spread merged into one broad hump, hiding the most important fact about the season. With intervals of 100, the chart broke into a noisy comb in which chance variation looked like structure. An interval of 250 kept the two groups distinct without the noise. The choice was checked against a common rule of thumb, which sets the width from the spread of the middle half of the data and the number of observations and suggested roughly 250 to 350 here (Freedman & Diaconis, 1981). The width used is printed in the chart's note, since a different width would change the picture.
Chart 2: Box Plots by Type of Day
Three box plots, drawn side by side on one axis, show the spread of visits for weekdays, weekend days and holiday days. Each box covers the middle half of that group's days, with a line at the median and whiskers to the range excluding outliers, which are drawn as individual points.
| Type of day | Days | Median visits | Middle half of days | Notable points |
|---|---|---|---|---|
| Weekday | 72 | 690 | 560 to 880 | Two powder days above 2,000 drawn as outliers |
| Weekend day | 32 | 1,790 | 1,480 to 2,150 | None |
| Holiday | 10 | 2,480 | 2,210 to 2,900 | Peak of 3,480 on December 28 |
The box plots answer the staffing question directly: plan for about 700 on a typical weekday and about 1,800 on a typical weekend day, with holidays higher still. The overall average of 1,126 describes none of these groups, and staffing to it would leave the mountain short on every weekend and overstaffed on most weekdays.
Chart 3: New Snow and Visits
The scatter plot puts inches of new snow in the 24 hours before opening on the horizontal axis and visits on the vertical axis, one point per day. Weekdays are drawn as open circles and weekend and holiday days as filled triangles, so the two groups can be told apart by shape as well as color.
The picture shows a clear pattern among weekdays: days after large snowfalls drew many more visitors, and the two weekday outliers from the box plot are both mornings after more than a foot of new snow. Among weekend days the pattern is weak, since weekends are busy regardless of snow. Across all days the correlation between new snow and visits is 0.38; within weekdays it is 0.52, and within weekend and holiday days 0.21. Reporting the correlation beside the scatter is useful only because the scatter is shown; a single figure of 0.38 would hide the fact that the relationship lives almost entirely on weekdays.
Design Choices Across the Three Charts
The three charts share a vertical scale for visits wherever visits appear, so a reader moving from the box plots to the scatter sees the same values at the same heights. The histogram's bars touch, as bars in a histogram should, because the intervals are continuous; the box plots are separated, because the day types are distinct groups. In the scatter, points are drawn partly transparent, since several weekdays share similar values and would otherwise hide one another. No trend line is drawn through all the points, because a single line would average the strong weekday pattern with the weak weekend one and describe neither group well. Instead, each group's median is marked at a few snowfall levels, a simpler summary that does not claim a straight-line relationship the data may not have. Each chart carries a one-sentence note stating what one point, bar or box represents, so the reader never has to infer the unit of the display.
How Much the Picture Can Support
The scatter supports a modest claim: in this season, large snowfalls were followed by busier weekdays. It does not support saying that snow causes a set number of extra visitors, for three reasons. Only nine days had ten inches or more of new snow, so the upper part of the chart rests on few points. Big storms cluster in February, when other factors, such as Presidents' Day, also lifted visits. And one season cannot show whether the pattern holds in a dry year. The owners' practical takeaway is narrower but useful: after a big overnight snowfall on a weekday, schedule weekend-level staffing on the lifts that open first. Bearpaw Ridge and its season data are invented for the course.
References
Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27(1), 17-21. https://doi.org/10.1080/00031305.1973.10478966
Freedman, D., & Diaconis, P. (1981). On the histogram as a density estimator: L2 theory. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete, 57(4), 453-476. https://doi.org/10.1007/BF01025868
Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.
The Herzing BU 454 Unit 5 instructions
BU 454 Unit 5 asks students to visualize distributions and relationships. Requirements usually include showing the shape of a measure with a histogram or similar chart, comparing distributions across groups, plotting two measures against each other, and explaining what the picture can and cannot support. Many instructors ask how to choose a histogram's interval width and whether to report a correlation beside a scatter; both should be explained, and a correlation means little without the plot. Using clean data from work is discouraged. The submission typically includes three or more charts with an APA 7 explanation that ends with a limited, clearly stated conclusion.
How this BU 454 Unit 5 example is built
The paper opens by showing why the season average misleads and citing arguments for plotting data before summarizing it. The histogram section describes the two clusters and explains how three interval widths were tried and checked against a published rule. The box plot section presents a table of medians and middle ranges by type of day and draws the staffing conclusion. The scatter section explains the shape coding, describes the weekday pattern and reports correlations overall and by group. A section covers shared design choices such as transparency and the absence of a trend line. The final section limits the conclusion to what one season and nine big-snow days can support.
Reading the BU 454 Unit 5 rubric
Rubrics for distribution and relationship charts usually reward showing shape rather than a single summary, justified binning, meaningful group comparisons, an honest relationship chart and a proportionate conclusion, plus writing and sources. The shape criterion earns full credit when spread and skew are visible. Binning scores highest when the width is tested and stated. Group comparison rewards putting distributions on a shared scale. The relationship criterion is judged on whether the scatter is shown before any statistic and whether groups within it are told apart. The conclusion earns points when it is limited to what the data support, noting small counts and confounding. Instructors often credit students who explain why a single trend line would mislead.
BU 454 Unit 5 help before you submit
Distribution work often loses points by reporting an average and never showing the spread behind it. Another common problem is using the charting tool's default histogram bins without checking how they shape the picture. Students frequently compare groups on different scales. Some report a correlation without showing the scatter, hiding subgroups with different patterns. Others draw one trend line through mixed groups. Many overstate conclusions, treating association as cause or a few extreme points as a pattern. Finally, working with clean data from a job can expose confidential records; supplied data let you show the same judgment without that risk to anyone. Stating the unit that each point or bar represents, one day here, also prevents misreadings that no statistic can fix.
Get a BU 454 Unit 5 example written to your instructions
Upload your Unit 5 instructions together with the BU 454 rubric and the dataset the assignment provides. We write a custom example that draws the shape before any average, declares the bin width, keeps every point on the scatter, defends or omits the fitted line and closes on what the pattern cannot show. First custom sample free, back in 24 to 48 hours.
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BU 454 Unit 5 questions, answered
How do I choose the interval width for a histogram?
By trying several and keeping the one that shows the structure without inventing it, then saying in the note what you tried. Very wide intervals flatten a second peak into one hill; very narrow ones turn ordinary variation into spikes. Where your section specifies a rule, follow it and still comment on what the resulting picture does to the shape.
Should I report a correlation figure alongside the scatter?
Only if the instructions ask, and never as a substitute for looking at the cloud. A single coefficient collapses shapes that are nothing alike into the same number, which is why this unit puts the picture first. Where you do report one, put it beside the plot rather than in place of it, and keep the language descriptive.
The clean data I have access to is at work. Can I use it?
Not for a submission. Record level customer or transaction data is the employer's, and the fact that it is convenient and already tidy is exactly why it is protected. Public microdata files published for research give you real distributions with genuine skew and outliers, and the assignment data will usually carry the shape the unit wants examined anyway.
Where can I find a free BU 454 Unit 5 sample paper?
This page carries the complete BU 454 Unit 5 distribution and relationship charts free, with the histogram reasoning, the box plot table and margin notes. For your own measures, email the prompt and a matching set is drafted free, typically inside two days.
How do I choose histogram intervals for BU 454 Unit 5?
Try several widths and pick one that shows the structure without noise, then check it against a rule of thumb. The sample tried 100, 250 and 500 visits and chose 250, which kept weekday and weekend clusters distinct.