| Course | DATA 5003 Data Analytics |
|---|---|
| Module | Module 4 |
| Paper type | Patterns, trends and relationships analysis |
| Length | 1,230 words, about 4 pages plus title and reference pages |
| Format | APA 7 student paper |
| School | American College of Education |
| Program | M.S. in Organizational Leadership |
| Updated | October 2026 |
Free sample paper for DATA 5003 Module 4
Pests Quadruple the Odds: Which Violations Travel With Failed Chicago Restaurant Inspections
Student Name
American College of Education
DATA5003: Data Analytics
Module 4 Assignment
Instructor Name
March 27, 2028
Introduction
Module 3 described the 11,426 Chicago restaurant inspections in 2025 recorded in the city's open data portal (City of Chicago, 2026) that ended in a pass, a conditional pass or a fail, and found an overall failure rate of 21.2 percent, higher for complaint inspections and in late summer. It also listed the most frequent violations, but frequency does not show which problems matter most for failing. This paper tests relationships. For each common violation, it compares the failure rate of inspections that cited it with those that did not, measures the size and reliability of the difference and examines combinations and seasonal patterns. It closes by explaining what these associations mean for the restaurant group's decisions and what they cannot show.
Method
For each violation, inspections were split into two groups: those citing the violation and those not citing it. The analysis reports each group's failure rate; the relative risk, the first group's failure rate divided by the second's; the range within which the true gap between the two rates probably lies, at 95 percent confidence; and a chi-square test of independence for the two-by-two table of group and result, a standard tool for categorical data (Agresti, 2013). With more than 11,000 inspections, almost any difference will be statistically significant, so the size of the relative risk matters more than the test. The goal is predictive in the sense Shmueli (2010) describes: identifying which observed conditions signal a higher chance of failure, rather than estimating the causal effect of each one.
The Strongest Signals
Evidence of insects, rodents or animals, violation 38, was the strongest signal. Inspections citing it failed 58.1 percent of the time, against 13.7 percent for those that did not, a relative risk of 4.24; the difference in rates had a 95 percent confidence interval of 42.1 to 46.7 percentage points. Problems with handwashing sinks, violation 10, came next: 46.4 percent against 16.5 percent, a relative risk of 2.82. Improper cold holding temperatures, violation 22, showed 47.0 against 19.4 percent, and the absence of a required city food service sanitation certificate, violation 2, showed 45.9 against 18.7 percent, each with a relative risk near 2.4. All four chi-square statistics exceeded 300 with one degree of freedom, far beyond chance.
Weaker Signals From Common Violations
The most frequently cited violations were much weaker signals. Physical facilities not maintained and clean, violation 55, appeared in 5,248 inspections, and those inspections failed 27.5 percent of the time against 15.9 percent without it, a relative risk of 1.73. Surfaces that were not cleanable or not clean, violations 47 and 49, had relative risks of 1.41 and 1.59. Missing allergen training, violation 58, had a relative risk of 1.48, and missing food handler training, violation 57, 1.79. These problems are common at passing and failing inspections alike. They matter, but they are not what separates a failed kitchen from a passing one.
Combinations
Because failed inspections average four violations, combinations matter. Inspections that cited both pests and handwashing sink problems, 482 in all, failed 76.1 percent of the time, against 18.8 percent for all others. That pattern makes practical sense: a kitchen where pests are found and handwashing is compromised has lost control of two basic barriers to contamination. Inspections were also grouped by the number of distinct violations cited, as in Module 3; failure rates rose from 4.2 percent with none to 67.3 percent with six or more. The relationship is steady rather than sudden, which suggests that the cumulative condition of a kitchen matters as well as any single problem.
Inspection Type and Season
Two context factors were also tested. Complaint inspections failed 35.3 percent of the time against 19.3 percent for all other types, a relative risk of 1.83, and a chi-square test comparing complaint and canvass inspections gave a value of 85.7, again far beyond chance. Inspections from July through September failed 23.6 percent of the time against 20.5 percent in other months, a smaller relative risk of 1.15. A seasonal pattern also appears in pest citations: the share of inspections citing pests rose from about 13 to 15 percent in January through April to between 18.5 and 20.6 percent from May through October, suggesting that warmer months bring more pest activity and, with it, more failures.
What These Associations Mean
The associations must be read carefully. Some reflect how inspections are graded: the city treats certain violations, such as evidence of pests or lack of handwashing, as serious enough that their presence often leads directly to a failing result. In that sense, the analysis partly rediscovers the grading rules. That is still useful to a restaurant operator, because it shows which conditions most reliably turn an inspection into a failure. Other associations, such as missing certificates, may signal broader management problems rather than cause failure themselves. Shmueli (2010) distinguishes explanatory models, which test causal theories, from predictive ones, which aim to forecast outcomes; this analysis is the second kind.
A Check on Consistency
One way to test whether a pattern is robust is to see whether it holds across subgroups. The pest finding passed that test: among routine canvass inspections alone, where inspectors arrive without a complaint, inspections citing pests failed 60.7 percent of the time against 15.3 percent without, and within high-risk restaurants, which make up most of the data, the figures were 57.7 and 13.7 percent. The handwashing sink finding also held across inspection types. These checks do not remove every alternative explanation, but they show that the strongest relationships are not artifacts of one kind of inspection or one group of restaurants. A fuller analysis would repeat the comparisons for 2024 and for 2026 as data become available.
Limits
Several limits apply. The analysis uses only the city's data, so it cannot test the practices behind each violation; that requires the group's internal records, which will be linked as described in Module 2. Relative risks compare groups without adjusting for other factors, so a violation's apparent effect may partly reflect others that occur with it; a multivariable model would address that, and is a natural next step. Inspection patterns may also differ across neighborhoods in ways the data do not record. And one year of data may not represent other years, particularly if enforcement priorities change.
Implications for the Group
Even with these limits, the findings point to clear priorities. Pest control and handwashing sinks are the conditions most strongly tied to failure, and their combination is especially dangerous. Cold holding and current sanitation certificates come next. The group's two failed kitchens in 2025 were both cited for pests and handwashing sinks in August, consistent with the citywide pattern. Common cleanliness and surface violations deserve routine attention, but spending on them is less likely to prevent failures than spending on pest prevention, sink access and refrigeration. Module 5 will turn these findings into a recommendation.
Conclusion
Comparing failure rates across more than 11,000 Chicago restaurant inspections shows that pests, handwashing sink problems, cold holding failures and missing certificates are the strongest signals of a failed inspection, with relative risks from about 2.4 to 4.2, while the most common violations are weaker signals. Complaints and warmer months raise the odds as well. The associations predict failure without proving cause, but they give the restaurant group a clear basis for focusing its food safety effort.
References
Agresti, A. (2013). Categorical data analysis (3rd ed.). Wiley.
City of Chicago. (2026). Food inspections [Data set]. Chicago Data Portal. https://data.cityofchicago.org/Health-Human-Services/Food-Inspections/4ijn-s7e5
Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289-310. https://doi.org/10.1214/10-STS330
DATA 5003 Module 4 instructions, in plain terms
The fourth DATA 5003 paper, in many sections, asks you to look for patterns, trends and relationships in your data. Expect to choose comparisons that answer your business question, such as outcomes across groups or over time, and to measure both the size and the reliability of each difference. Most prompts want an appropriate statistical method explained and applied, with results reported clearly. Many also ask you to discuss what the relationships mean and what they cannot show, especially the difference between association and cause. Close with implications for the decision your analysis supports, and cite the data set and research in APA. Report uncertainty, not only point estimates. Check that key findings hold in subgroups.
How this DATA 5003 Module 4 example is built
A method section opens the sample, defining the with-and-without comparison, relative risk, confidence intervals and chi-square tests, and explaining why effect size matters more than significance in a large data set. The strongest signals, pests, handwashing sinks, cold holding and missing certificates, are reported with rates and intervals. The most common violations are then shown to be weaker signals. A combinations section finds a 76.1 percent failure rate when pests and sink problems appear together. Context factors cover complaints and a seasonal rise in pest citations. A section on meaning notes that grading rules shape the results, followed by limits and implications. A consistency check repeats the main comparison within subgroups.
Where the points sit in the DATA 5003 Module 4 rubric
Relationship analyses are graded on method, interpretation and honesty about limits. Instructors look for comparisons chosen to answer the question, an appropriate statistical approach explained in plain terms and results reported with effect sizes and measures of uncertainty. Distinguishing strong from weak relationships, and practical from merely statistical significance, earns credit. The strongest papers explain what the associations mean and do not mean, including how the data were produced. Clear implications for decisions tie the analysis together. Correlation presented as cause, tests without effect sizes and comparisons unrelated to the question tend to lose marks; references should follow APA 7. Plain-language explanations of each statistic help managers follow the argument.
DATA 5003 Module 4 help: mistakes that cost points
Relationship analysis is where many analytics students worry about choosing the wrong test or overstating results. If you are unsure which comparisons to make, how to report effect sizes and confidence intervals or how to explain the limits of association, we can help. Send a description of your data set, the question it serves and your instructor's guidelines; our writer then runs suitable comparisons, report the results clearly and explain what they mean for the decision. Public, internal and survey data all work for this assignment. Comparisons like these are usually written up within two days. Every statistic is explained in plain words.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official American College of Education document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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DATA 5003 Module 4 questions, answered
What does DATA5003 Module 4 usually ask for?
In many sections, the fourth DATA5003 module asks you to look for patterns, trends and relationships in your data set and explain what they mean for the business question.
What is a relative risk?
The rate of an outcome in one group divided by the rate in another; a relative risk of 4 means the outcome is four times as common in the first group.
Why do effect sizes matter more than significance in large data sets?
With thousands of observations, even trivial differences become statistically significant, so the size of the difference tells you whether it matters in practice.
Where can I find a free DATA 5003 Module 4 sample paper?
This page includes one: an analysis of 11,426 Chicago restaurant inspections showing that pest findings raise the failure rate from 13.7 to 58.1 percent, a relative risk of 4.24.
Can association show what causes an outcome?
Not by itself. Associations can predict outcomes, but causal claims need designs or models that account for other factors and for how the data were produced.