Data Analysis and Findings for Spacely Space Sprockets

ProdnTime.csv - Production Times Summary and Analysis

Spacely Space Sprockets — traditionally, Spacely Space Sprockets (SSS) sold space sprockets and other rocket parts to government agencies (e.g., NASA, the Canadian Space Agency, et al) and various militaries for use on missiles and ICBMs. However, with all of the billionaires rushing into space these days, their business is really “taking off”! 

The CEO of SSS — Cosmo Spacely — usually relies on George Jetson to analyze corporate data, but George is taking a well-deserved vacation so Mr. Spacely has called upon your services.

 

Here is a description of the data you will need to analyze:

ProdnTime.csv — before George left on vacation, he gathered 50 data points from the production line — sprocket production times (in minutes). Provide Mr. Spacely with a summary
of this data and a very short explanation of what the data reveals.

X10.csv — this dataset shows a sample of the weights of 350 X10 Space Sprockets. Provide Mr. Spacely with a summary of this data and a very short explanation of what the data reveals.

 

CustSurvey.csv — SSS surveyed 200 customers across four dimensions: Quality, Ease (ease of use), Price, and Service (customer service). The Region column denotes where the customer is located: NA (North America), SA (South America), Eur (Europe and African nations), Pac (Pacific — India, Australia, Japan). As China is an emerging market for SSS, it has been separated from the Pac data — it is the only country data where the others are grouped in regions. In this data set, customer responses run from SD (Strong Disagree) to SA (Strongly Agree), with Disagree, Neutral, and Agree as points in between. SSS is using a new customer survey company to get the results. In the past the data has been coded from 1 (strongly disagree) to 5 (strongly agree) so that numeric statistics like the mean can be calculated. You should convert the SD…SA scale to a 1…5 scale. Provide Mr. Spacely with a summary of the data and a very short explanation of where he should focus his efforts.

EmpData.csv — The Human Resources department at SSS downloaded an anonymized a list of employees so that you could prepare a short analysis of employee demographic data. The column headers in this data set are: YearsSSS (# of years at Spacely Space Sprockets), YearsEDU (number of years of education), Age (employee’s age), Gender (self-identified gender; M or F), TechGrad (Y or N — if they graduated with a technical degree (e.g., engineering) or diploma (e.g., trade school)), and 10KM (Y or N if they live within 10 kilometers of the SSS factory or not). You may find it useful to use PivotTables™ to analyze this data. Provide Mr. Spacely with a summary of the data analyzed a few different ways and a very short explanation of what the data reveals.

X10.csv - X10 Space Sprockets Weights Summary and Analysis

 

This same dataset can be used to develop an employee retention regression model. Retention is measured as the number of years an employee has worked at SSS. Do the analysis and provide Mr. Spacely with your findings. How long could SSS expect a 25-year-old female with 16 years of education, who walks to work, and who graduated from a welding program to work at Spacely Space Sprockets?

fueltypesall.csv — finally, Mr. Spacely is wondering what the future will bring in terms of the cost of Regular Unleaded Gasoline to power his fleet of delivery trucks. Since SSS is based in Ottawa, George Jetson visited the Ontario government’s Data Catalogue at: https://data.ontario.ca/dataset/fuels-price-survey-information to download the weekly fuel prices summary. Since this data may change over time, DO NOT use the https://ontario.ca/v1/files/fuel-prices/fueltypesall.csv link — use the downloaded CSV file available at Moodle. Use the last 20 weeks of 2021 data to forecast the next three weeks of prices. Use two different approaches and include information about the accuracy of your forecast. Do the analysis and provide Mr. Spacely with your findings.

With all of your analysis done — prepare a professional memo detailing your findings to Mr. Spacely. What is a professional memo? Include the date, to: & from: fields, titles, a subject line, an opening, your findings — in the same order as above, and a conclusion. The tone should be professional. No spelling mistakes. No grammatical errors. State the facts. Be concise. Page numbers. Paragraphs are fine; bullet points are too. Include relevant charts / graphs / tables to support your analysis. Your final document MUST be in PDF. Why? Because I can read a PDF document in Moodle and grade it at the same time without having to download anything.

 

How to create the document? Your choice. You can use Word, paste in the relevant bits from Excel or Tableau or R and save as a PDF. You can use R Markdown and do the analysis in R and use knitr to create a PDF from that. Please keep a copy of your spreadsheets or R scripts or whatever you used to do your analysis in case I need to see it — you don’t need to upload those.

Questions — please post ‘em at Moodle.

You will be graded on your analysis and your presentation. If your analysis is correct and your presentation of the data is professional — you’ll hit the target grade. If your analysis is weak or incorrect but your presentation is fine — or vice-versa — expect a grade hit on one category but not both. If your analysis is top-notch and your communication matches — expect an “outstanding” level of grade.