For Assignment One, you will analyse a data set which relates to the performance of an engineering enterprise. Using the data you collect, you will use a statistical model of your choice to find the predicted value of your depenedent variable yi. You will need to use the tools available in Microsoft Excel to analyse the data, and present it in the form of a report. Your report will include four parts:In Part 1, you will collect data from a company of your choice, then briefly summarise the types of real data collected and analyse its comprehensiveness.In Part 2, you need to further analyse the data by constructing a stem and leaf plot and a box plot.In Part 3, you need to use the appropriate statistical modelling techniques to find the relationship between the data (variables).In Part 4, Use the data you collected to build a forecasting model using Microsoft Excel, and apply a suitable forecasting technique to make a set of predictions for the next 10 years.This assignment will include two components – a word file for the report, and an excel file.
School of Engineering — MANU2469 Performance Management Foundations Assessment 1: Data Collection, analysis and modelling Assessment Type: Report Word limit: 3000 (+/– 10%) Due date: Sunday of Week 3, 23:59 (Melbourne time) Weighting: 30% Overview For Assignment One, you will analyse a data set which relates to the performance of an engineering enterprise. Using the data you collect, you will use a statistical model of your choice to find the predicted value of your depenedent variable yi. You will need to use the tools available in Microsoft Excel to analyse the data, and present it in the form of a report. Your report will include four parts: In Part 1, you will collect data from a company of your choice, then briefly summarise the types of real data collected and analyse its comprehensiveness. In Part 2, you need to further analyse the data by constructing a stem and leaf plot and a box plot. In Part 3, you need to use the appropriate statistical modelling techniques to find the relationship between the data (variables). In Part 4, Use the data you collected to build a forecasting model using Microsoft Excel, and apply a suitable forecasting technique to make a set of predictions for the next 10 years. This assignment will include two components – a word file for the report, and an excel file. Learning Outcomes This assessment is relevant to the following course learning outcomes: CLO1 Perform a thorough data analysis of the performance data set and summarise the findings using Microsoft Excel. CLO2 Recognise situations and apply the appropriate forecasting models to represent the trend of the business. CLO3 Fit some parts of the data set to a regression or discriminant analysis model and interpret the implication of the model in terms of the enterprise’s past and future performance. CLO4 Define and apply the Monte Carlo technique to a number of different business modelling situations. After completion of this assessment you should be able to: − Describe the kinds of real data collected from a company and analyse its comprehensiveness. − Construct a stem cell and leaf plot and a box plot on the data and define its shape. − Calculate basic statistics and analyse the results. − Apply approproriate statistical modelling techniques. − Apply forecasting techniques to make valid predictions. − Analyse the results and note whether there are any outliers. − Use excel software to analyse the data and find the statistical model. 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 compreheniveness. 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 continuous. − Assess the comprehensiveness of the data. Part 1 of your report should be 300 words. 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 outliers. − Complete the calculation of basic statistics. − 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 mean. Discuss the result of your data analysis. Part 2 of your report should be 700 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 statistical modelling technique. − Illustrate how you used this technique. − Discuss the model result by noting whether there are any outliers. − Use Microsoft Excel to make Discriminant Analysis or linear regression as explained in the week 2 and week 3 topic. − Use the appropriate statistical modelling techniques to find the relationship between the data (variables). Part 3 of your report should be 1,000 words. Page 3 of 8 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 forecasting techniques, based on the data and make a predicition for the next 10 years. You will need to: • identify the major factors to consider when choosing a forecasting technique. • measures of forecast accuracy. • briefly describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems, • choose the best forecasting model by comparing different technique. • use excel to find the forecasting model, make the comparing between the different technique and find the forecast accuracy. Part 4 of your report should be 1000 words You are requifred to submit your report as two components, which include − A Microsoft Word document containing the report, which includes details of the model and recommendations to the problem. − An excel spreadsheet which analyses the data. You will need to submit this as a single zip file in Canvas. Page 4 of 8 Referencing guidelines Use RMIT Harvard referencing style for this assessment. You must acknowledge all the courses of information you have used in your assessments. Refer to the RMIT Easy Cite referencing tool to see examples and tips on how to reference in the appropriated style. You can also refer to the library referencing page for more tools such as EndNote, referencing tutorials and referencing guides for printing. Submission format You will zip the Word Document and Excel file and upload as one (1) single file via Canvas. Academic integrity and plagiarism Academic integrity is about honest presentation of your academic work. It means acknowledging the work of others while developing your own insights, knowledge and ideas. You should take extreme care that you have: • Acknowledged words, data, diagrams, models, frameworks and/or ideas of others you have quoted (i.e. directly copied), summarised, paraphrased, discussed or mentioned in your assessment through the appropriate referencing methods • Provided a reference list of the publication details so your reader can locate the source if necessary. This includes material taken from Internet sites If you do not acknowledge the sources of your material, you may be accused of plagiarism because you have passed off the work and ideas of another person without appropriate referencing, as if they were your own. RMIT University treats plagiarism as a very serious offence constituting misconduct. Plagiarism covers a variety of inappropriate behaviours, including: • Failure to properly document a source • Copyright material from the internet or databases • Collusion between students For further information on our policies and procedures, please refer to the University website. Assessment declaration When you submit work electronically, you agree to the assessment declaration. Page 5 of 8 https://www.rmit.edu.au/library/study/referencing/referencing-guides-for-printing https://www.lib.rmit.edu.au/easy-cite/ https://www.rmit.edu.au/students/student-essentials/rights-and-responsibilities/academic-integrity https://www.rmit.edu.au/students/student-essentials/assessment-and-exams/assessment/assessment-declaration Assessment Criteria Criteria Ratings Pts HD D C P N Criterion 1 Describe kind of real data collected from the company and analyse its comprehensiveness. The report describes what kind of real data was collected from the company and demonstrates the comprehensiveness of the data well. The report describes what kind of real data was collected from the company and demonstrates the comprehensiveness of the data. The report describes what kind of real data was collected from the company and demonstrates the comprehensiveness of the data well, but there are some unclear areas. The report describes what kind of real data was collected from the company and demonstrates the comprehensiveness of the data. The report does not, or hardly, describes what kind of real data was collected from the company and demonstrates the comprehensiveness of the data. 5.0 to >3.99 3.99 to > 3.49 3.49 to > 2.99 2.99 to > 2.49 2.49 to > 0 5.0 Criterion 2 Construct a stem cell and leaf plot and a box plot on the data and define its shape. The report shows a stem and leaf plot and a box plot on the data and demonstrates a professional level of understanding of the result. The report shows a stem and leaf plot and a box plot on the data with logical extrapolations from research and analysis. The report shows basic understanding of a