Business Analytics Assignment

 

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Assignment Details:

• Words: 2500

 

The UK government through the Home Office has committed to decreasing the levels of crime in young people across England. In the last ten years, most areas have seen a decrease in the levels of crime committed by 18–25-year-olds. However, in some areas the levels have actually started to increase again.

The Home Office has commissioned Buxton Analytics to undertake a descriptive and the start of a predictive analysis to determine what specific factors can be used to influence the crime rate and ultimately provide suggestions on how based on these statistics the crime rate can be decreased.

 

The Home Office has provided Buxton Analytics with a data set MN-2520CrimeRateData2010-2020.xlsx. The spreadsheet contains the crime data for counties across England from 2010 and 2020. They would like you to undertake the necessary background research and create a comparison between the two snapshots to determine what has changed. You will then be expected to include your findings in a report to be approved by the executive and submitted to the client.

The following variables are included within the MN-2520CrimeRateData2010-2020.xlsx file:

• Variable

• Year

• Crime Rate

• Youth

• North (0) South (1) Midlands (2)

• Education

• ExpenditureYear0

• Labour Force

• Males

• More Males

• County Size

• Youth Unemployment

• Mature Unemployment

• High Youth Unemploy

• Wage

• Below Wage

For further explanation of each variable see the glossary provided in the MN-2520CrimeRateData2010-2020.xlsx file

 

The Home Office requires the following questions to be answered:

 

1. According to the Home Office liaison, there may be some errors in the data set. They suggest that before conducting any analysis that the data is cleaned and checked for duplicates. Initial analysis shows that there should be 92 individual records (46 for 2010 and 46 for 2020). Use appropriate methods to remove the data and show that the correct duplicates have been removed. (5 marks)

 

2. Using the Glossary table within the provided excel file determine the type of data used by each variable: Time, Discrete Numerical, Continuous Numerical, Ordinal Categorical, Binary, Nominal Categorical (8 marks)

 

3. Using appropriate descriptive analytics methods create descriptive analytics to compare crime rate with the other independent variables from 2010 and 2020. Your answer is not limited to descriptive analytics statistics but can include revenant data visualisations such as graphs and plot. Please remember to discuss how each variable has changed in relation to the crime rate between 2010 and 2020. (20 marks)

 

4. Create dummy variables for the categorical variables so that you can use them as independent variables within your regression. You will be able to determine these from your answer to question 2. (5 marks)
   • Also provide a short explanation of why categorical variables need to be converted to a dummy variable before including in the analysis.

 

5. Undertake a correlation comparing crime rate with all variables. You may wish to create separate tables for the numerical and dummy variables (previously categorical variables). (20 marks)
   1. To what degree are the independent numerical variables correlated with crime rate?
   2. To what degree are the independent variables correlated with each other?
   3. If two independent variables were highly correlated with each other what does this suggest and what impact would this have on a regression model?
   4. What would be the best way to resolve the issue? Remove the variable completely or combining the variables (interaction variables). Remember to explain your answer.

 

6. Conduct a multiple regression using crime rate as the dependent variable and just the numerical variables as determined in question 2. (20 marks)

• Discuss your findings using the data outputs from the regression model including the relative significance of individual independent variables?
• Are there variables that can be eliminated to improve the model?
• Are there any other variables in the data file not currently used that you feel could be included in the regression to improve the model?
• Remember to also discuss the VIF scores in relation to the multicollinearity discussed in question 5.
• If there are any variables with high VIF scores you may wish to rerun the regression and either remove the relevant variables or create interaction variables as discussed in question 5.
• Please show your original and final regression outputs, however you only need to provide an interpretation for the final regression output.

 

 

7. Based on the analysis conducted above, what conclusions and recommendations would you make to the home office in relation to the current data set?
   1. Are there any numerical variables that have an impact on crime rate? Please explain your answer and support using statistical evidence. (4 marks)
   2. Please include 3 reference to three additional variables that you feel the Home Office should collect data on and would have an impact on crime rate? (3 marks)
   3. Discuss the changes in crime rate between 2010 and 2020, what are the significant changes? (4 marks)

 

The managerial report should contain

 

• Abstract – this should be brief no more than 200 words and answer the following question
  • What did I do in a nutshell?
• Introduction
  • What is the problem? – no more than 200 words
• Methods
  • How did I solve the problem? – no more than 200 words
• Results & Discussion – around 1400 words for this section
  • What did I find out? – describe the results
  • What do the results mean? – interpret the results
  • Tables/graphs/figures are not included in the word count
  • This is where you answer questions 1-6
• Conclusion – around 500 words for this section
  • Bringing everything together – what does it all mean?
  • Make sure you discuss the overall aim of the assignment in this section.
  • This is where you answer question 7
• References
  • Whose work did I refer to?
  • Remember to include any textbooks that you may have used to help with analysis and interpretation.
  • Not included in word count
• Appendices
  • Extra information
  • Not included in word count

 

 

Marking breakdown

 

• Abstract – 3 marks
• Introduction – 4 marks
• Methods – 4 marks
• Results & Discussion – overall 78 marks
  • Q1 – 5 marks
  • Q2 – 8 marks
  • Q3 – 20 marks
  • Q4 – 5 marks
  • Q5 – 20 marks
  • Q6 – 20 marks
• Q7 Recommendations and Conclusions – 11 marks

 

For question 3 – 10 marks will be awarded just for analysis with no interpretation

For question 4 – 2 marks will be awarded just for analysis with no interpretation

For question 5 – 10 marks will be awarded just for analysis with no interpretation

For question 6 – 10 marks will be awarded just for analysis with no interpretation (5 for the original regression and 5 for the final regression)

 

 

Key Marking criteria will include:

 

 

• Assignment Structure: clarity of aims, objective, structure and presentation
• Quality of Writing: Readability and ability to convey key message(s) concisely
• Methodology: has the correct method been followed and documented
• Analysis: has the analysis been conducted and documented appropriately
• Insightfulness of Analysis: Interest and usefulness of findings, relevant conclusions drawn.
• Initiative: originality, innovativeness of answer and response to problem
• Overall Quality of Assignment and ability to develop a professional report

 

A rubric containing the marking criteria is also available at the end of this handbook.