Organizations collect data from multiple sources to analyze their operations and support decision-making processes. Once the data is collected, it is inspected and cleaned so that it is usable in an analysis, and exploratory activities are taken for the purposes of familiarization with the data and determining how it may support the organization’s needs.
In this assessment, you will use the raw data found in the attached “Raw Data” and “Linear Regression” files to execute a planned analysis, preparing the dataset for analysis, create a visualization, interpret the provided linear regression, and use that analysis to assess if a local police department is eligible for a special funding incentive.
Note: To draw the graph, you may use one or a combination of the following:
– A spreadsheet program, such as Excel (*.xls)
– A graphics program, such as Paint (*.jpeg, *.gif)
– A word-processing program, such as Word (*.rtf)
– A scanned hand-drawn graph (*.jpeg, *.gif)
Note: This assessment requires you to submit pictures, graphics, and/or diagrams. Each file must be an attachment no larger than 30 MB in size. Diagrams must be original and may be hand-drawn or drawn using a graphics program. Do not use CAD programs because attachments will be too large.
A local police department is interested in discovering the behavior, trends, and needs of the department based on data that has been collected. As a data analyst, you have been recruited to do consulting work for the department.
Your submission must be your original work. No more than a combined total of 30% of the submission and no more than a 10% match to any one individual source can be directly quoted or closely paraphrased from sources, even if cited correctly. An originality report is provided when you submit your task that can be used as a guide.
You must use the rubric to direct the creation of your submission because it provides detailed criteria that will be used to evaluate your work. Each requirement below may be evaluated by more than one rubric aspect. The rubric aspect titles may contain hyperlinks to relevant portions of the course.
The police chief asks you to analyze the logs from emergency 911 calls in the city and then provide a summary of that data.
A. Prepare a dataset from the data provided in the “Raw Data” spreadsheet, attached below. Remove any potential errors or outliers, duplicate records, or data that are not necessary. Provide a clean copy of the data in your submission.
B. Explain why you removed each column and row from the “Raw Data” spreadsheet, or why you imputed data in empty fields as you prepared the data for analysis.
C. Create data sheets using your cleaned dataset, provide each of the following to represent the requested aggregated data.
• table: date and number of events
• bar graph: date and number of events
• table: number of incident occurrences by event type
• bar graph: number of incident occurrences by event type
• table: sectors and number of events
• bar graph: sectors and number of events
D. Summarize your observations from reviewing the data sheets you have created
The state governor has offered an additional funding incentive for police departments that are able to meet the standard of having a minimum of 2.5 officers onsite per incident. The police department has asked you to analyze their data to determine if the department will be eligible for additional funding, using the attached linear regression.
E. Describe the fit of the linear regression line to the data, providing graphical representations or tables as evidence to support your description.
F. Describe the impact of the outliers on the regression model, providing graphical representations or tables as evidence to support your description.
G. Create a residual plot and explain how to improve the linear regression model based on your interpretation of the plot.
H. Using the linear regression analysis, explain if the department qualifies for additional state funding, including any limitations posed by the available data to the assessment of the department’s current funding eligibility.
I. Describe the precautions or behaviors that should be exercised when working with and communicating about the sensitive data in this scenario.
J. Acknowledge sources, using in-text citations and references, for content that is quoted, paraphrased, or summarized.
K. Demonstrate professional communication in the content and presentation of your submission.
File RestrictionsFile name may contain only letters, numbers, spaces, and these symbols: ! – _ . * ‘ ( )
File size limit: 200 MB
File types allowed: doc, docx, rtf, xls, xlsx, ppt, pptx, odt, pdf, txt, qt, mov, mpg, avi, mp3, wav, mp4, wma, flv, asf, mpeg, wmv, m4v, svg, tif, tiff, jpeg, jpg, gif, png, zip, rar, tar, 7z
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