MANU1390 Performance Management Foundations

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Assessment details

Part 1: Data Collection

Collect data from a company of your choice, then briefly summarise the types of real data collected and analyse its comprehensiveness including the number of independent variables used and other details.

Example:

You work in a bank, and you would like to analyse the number of customers, the date/time they arrive, the kinds of services they ask for, the length of time they generally wait, and the service time for each customer.

Part 1 of your report should:

  • Identify the types of real data collected (i.e. is it qualitative or quantitative?)
  • If it is quantitative, indicate whether it is discrete or
  • Assess the comprehensiveness of the data. Part 1 of your report should be less than 300

Part 2: Data Analysis

Anlyse your collected data by constructng a stem and leaf plot, and a box plot on the data. Part 2 of your report should:

  • Comment on its shape noting whether there are any
  • Complete the calculation of basic
  • Check the normal distribution prediction of how many measurements lie between:
    • one standard deviation of the mean,
    • two standard deviations of the mean, and
    • three standard deviations of the

Discuss the result of your data analysis. Part 2 of your report should be less than 600 words.

Part 3: Data Modelling

In the last section of your report, you are required to see how close the data came to the theoretical Normal distribution. Select an appropriate statistical modelling technique (i.e. Discriminant Analysis or linear regression).In this section of the report, you should:

  • Explain why you used this specific statistical modelling
  • Illustrate how you used this
  • Discuss the model result by noting whether there are any
  • Use Microsoft Excel to make Discriminant Analysis or linear regression as explained in the week 2 and week 3
  • Use the appropriate statistical modeling techniques to find the relationship between the data (variables).

Part 3 of your report should be less than 1,000 words.

Part 4: Data forecasting

In the last section of your report, you are required to use that data to build a forecasting model using Microsoft Excel. You will need to determine the most appropriate quantitative forecasting techniques, based on the data and make a prediction for the next 10 years. You will need to:

  • identify the major factors to consider when choosing a forecasting
  • measures of forecast
  • briefly describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems,
  • choose the best forecasting model by comparing different
  • use excel to find the forecasting model, make the comparison between the different techniques and find the forecast Use Mean Squared Error (MSE) and Mean Absolute Deviation (MAD) to compare forecasts.

Part 4 of your report should be less than 1000 words

You are required to submit your report as two components, which include

  • A Microsoft Word document containing the report, which includes details of the model and

the recommendations to the problem.

  • An excel spreadsheet which analyses the

You will need to submit this as a single zip file in Canvas. There is penalty for late submission (10% per day).

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