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Identify appropriate techniques to implement forecasting methods employing the SPSS software package.
ASSIGNMENT FRONT SHEET
ONL719 Business Analytics for Project Management
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Certification: I certify that the whole of this work is the result of my individual effort and that all quotations from books, periodicals etc. have been acknowledged
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Programme : MBA (online)
Level: Post Graduate
Academic Year: 20/21
Semester: NA
Module title: Business Analytics for Project Management
Assignment no. 2
Module code: ONL719
Word guide: 1000
Percentage Weighting of this assignment for the module: 35%
Issue date:
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Lecturer:
Second marker:
Notes for students:
Electronic copy of assignment must be submitted through Turnitin.
Students should ensure that they comply with Glyndwr University’s plagiarism policy.
Students should make correct use of the Harvard referencing method.
Learning Outcomes Tested in this Assignment:
Identify appropriate techniques to implement forecasting methods employing the SPSS software package. Critically evaluate several measures of prediction accuracy of a forecasting method in project management.
Overall Comment:
Mark (%)
Would students please note that achievement of the learning outcomes for this assessment is demonstrated against the assessment criteria shown below (which are not necessarily weighted equally). All marks/grades remain indicative until they have been considered and confirmed by the Assessment Boa
Assessment Criteria
Marks Awarded
Marks Available
1
MBA (Online) - Business Analytics for Project Management - Assignment 2 - November 2020
Submission: Assignment 2: Econometric forecasting models
Your second assignment will be on Econometric forecasting models. In this assignment you will asked to write a report using SPSS to carry out an analysis of data of your choice.
Task
Using a project and data of your choice, produce a report examining how you would use one of the econometric forecasting models covered so far to forecast trends in your chosen project. Projects can be found online, in textbooks, academic journals, or can be a project that you are currently working on.
1. You must include an introduction to your project and identify what the data is aiming to illustrate
2. An overview of the econometric method used.
3. You must illustrate your method using SPSS with explanation and analysis. This will require you to find data for your project. You may put this in an appendix.
4. A prediction of the future trends for your project in line with what your data is trying to illustrate.
Please note that many examples of data can be found in the core textbook.
Masters level Marking Criteria
No work has been submitted in the time allowed, or the work submitted demonstrates little or no understanding of the task or the subject matter. This may be evident where the work is substantially incoherent, irrelevant or lacking in factual content, or where these shortcomings are present in combination such that the work as a whole is unsound. Major errors of fact, or evidence of substantially poor cognitive or other relevant skills will also lead to a fail.
Fail:
Marks below 30%
The work shows some knowledge and required skills are present to a degree. There may be appreciable error or omission of facts, poor structure, misdirection to the task, or poor conceptualisation or illustration of the work. Evidence of analysis and evaluation is weak. There will be indications in the work that the candidate is capable of improving it by further application to the task
Fail:
Marks in the range
30% – 39%
The work contains sufficient descriptive information. There is some analysis and explanation with appropriate illustration and example, and some attempt to evaluate. The work will generally be coherent and relevant, it will contain some useful proposals or solutions related to familiar solutions and there will be some attempt at originality. It will be communicated clearly.
Pass:
Marks in the range of
40% – 49%
The work contains all the necessary contextual information. There will be adequate analysis, explanation and conceptualisation, with appropriate illustration and example, and sound attempts to evaluate and judge. The work will be substantially coherent and will contain relevant and feasible proposals or solutions related to familiar situations, some responses to uncertainty or ambiguity and some acknowledgements of the implications of change.
Pass:
Marks in the range of
50% – 59%
The work will contain complete explanations using most available information. There will be substantial analysis; the ability to recognise evidence, use ideas, conceptualise, evaluate and judge in familiar situations will be clearly demonstrated. Proposals or solutions will be contextually relevant and useful, with substantial evidence of the skill necessary to operationalize them in a variety of situations, including those in which uncertainty, ambiguity or change are present. The work will provide evidence of originality and of useful knowledge transfer to novel situations. It will be coherent and convincing.
Pass:
Marks in the range of
60% - 69%
The work will clearly demonstrate the ability to analyse accurately, reliably and fully, all relevant information; to use evidence; to conceptualise, evaluate and judge; to propose and operationalise effective solutions, and to show substantial originality and creativity in a variety of familiar situations or in the face of ambiguity, uncertainty or change. It will demonstrate valuable knowledge transfer and propose feasible solutions for a wide range of situations. Evidence of the ability to innovate will be present.
Pass:
Marks in the range of 70% and above
Example Answer - Plagiarised
Report Title: Forecasting Sales Trends for the New Eco-Friendly Beverage Using Econometric Models in SPSS
Identify appropriate techniques to implement forecasting methods employing the SPSS software package. Critically evaluate several measures of prediction accuracy of a forecasting method in project management.
1. Introduction
Project Overview
Data Aims
2. Overview of the Econometric Method
Chosen Method: Autoregressive Integrated Moving Average (ARIMA)
Theoretical Basis : The ARIMA model assumes that the current value of the series can be explained as a linear combination of its previous values (autoregressive part) and previous error terms (moving average part).Application Context : This model will help forecast future sales of the eco-friendly beverage by analyzing the historical sales data and the influence of marketing efforts.Assumptions : Key assumptions include the data being stationary after differencing, linearity in relationships, and independence of errors.
Continued...
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