Possible Data Problems and Rules for Handling Each:

Invalid Weight $-$ Pay attention to the maximum single piece weights for various FedEx service types. Identify any shipments heavier than the maximum weight for all FedEx Express ${?}^{}($comprised of $4$ Service Types$)$ and FedEx Ground ${?}^{}(2$ Service Types$)$ small parcel shipments.

Note: FedEx Home Delivery ${?}^{}$ has a maximum weight per package, and residential shipments over $70$ lbs$.$ must be shipped via FedEx Ground or one of the FedEx Express services.

Invalid Service Types $-$ If a particular service type provided in the data set does not exist in the Service Attributes Table or rates for that service are not included in Rate Sheets, the transaction should be identified as an error.

Zip Code Discrepancies $-$ Origin and Destination zip codes not found in a transit table or from outside the contiguous $48$ states should be identified as an error. Origin zip codes that do not match one of the existing DCs or stores should also be identified as an error.

Summarize your changes in a brief one$-$page report. Your report must include: $($a$)$ a table listing the number of errors found by type and showing the total number of errors found and $($b$)$ a one$-$paragraph summary explanation of why data clean$-$up is an important part of an effective data analysis. This explanation must be supported by at least one citation using APA citation style.

A clean version of the Shipping History data set that eliminates or modifies all transactions with errors will be provided once Part $1$ is submitted. You should not proceed to Part $2$ until you have received the Clean Shipping History data set.