欢迎来到51Due,请先 | 注册
关注我们: 51due论文代写二维码 51due论文代写平台微博
英国论文代写,英国essay代写知名品牌微信

Report代写范文

为您解决留学中生活、学习、工作的困难、疑惑
释放自我

英国report代写范文:商业预测与仿真

2017-03-22 | 来源:51教员组 | 类别:Report代写范文

这篇范文主要写的是英国report——商业预测与仿真的一项作业,这项任务将分为两部分。 第一部分将被加权40%,而第二部分将被加权60%。 第一部分是书面报告,第二部分是一个实用的内容,其中包括使用Excel的预测任务。


Business Forecasting and Simulation 


Assignment One – Forecasting (weighted 50%)

Issue Date: Monday 22nd October 2012

This is an individual assignment. 个人assignment

This assignment will be submitted into two parts. The first part will be weighted 40% whilst the second part will be weighted 60%.  The first part is a written report and the second a practical element, which will comprise a forecasting task using Excel.

The data for the assignment and instructions on how to download it are available via the unit BREO site.


Part One - Written Report 写作报告


Write a report (maximum 2,000 words) explaining, with appropriate referenced theory, the methodology that you intend to use in order to produce a four-step ahead forecast for your dataset.  The report should cover the following areas:-


1. An examination of the data and description of the nature of the dataset with which you have been supplied. (40%)


2. Discussion and justification of the three techniques you have decided to use; (30%)


3. Your reasons for rejecting other techniques that you consider inappropriate; (20%)


4. Some discussion of other forecasting issues that should be considered when attempting to produce medium to long term forecasts (10%)


Note: Your report must include a printout of the Excel graph of your data.

Make sure that you submit a Microsoft Word 2003 file.

Part Two - Practical Excel assignment 实践 Excel Asssignment

Using the data supplied, carry out the tasks detailed below and submit a single Excel file with your spreadsheet work as required by the instructions for each task.  Where comments are required these should be included on the sheets.


Task 1 (5%)  


Graph the data – remembering everything which should be included on a good chart or graph.


Task 2 (80%)


• Using three different and appropriate forecasting techniques, produce forecasting models for the data set.


• Test the models using three measures of goodness of fit (error tests).


• Plot the fitted models (your forecasts) and the original data on the same chart and comment on the goodness of fit of your models.


• Plot the residuals (errors) from each model and comment on the nature of the residuals.


Task 3 (15%)


Produce a forecast for the next four periods using the most accurate of your models.

As well as the tasks above, you will be assessed on your presentation and use of Excel.


NOTE – Although you may include in your file the models that you have tried but rejected after testing, as well as the model which is your final choice, the rejected models may not be considered when marking.  You may only ask for assistance in respect of the operation of the spreadsheet, not in the choice of technique.

Submission Dates:


Part One – Written Report:  10.00 a.m. Monday 26th November 2012

Part Two – Practical Excel models: 10.00 a.m. Monday 14th January 2013

Both parts must be submitted through BREO.

Note: You will see from the schedule that time has been set aside in Weeks 8 and 11 for working on the assignment.  In addition, there will be plenty of opportunities to ask for help during class times.  I will be happy to provide appropriate support to students who have made a reasonable attempt.  Please don’t leave the work till the last minute; I may not be available (in person or via e-mail) over the Christmas break.


Return Dates: I will aim to provide feedback (including the grade) through BREO by the following dates:


Part One:  Monday 17th December 2012.

Part Two: Monday 4th February 2013.

You will receive feedback through Turnitin on BREO.  If you require further, more detailed feedback, then please speak to me.


Learning Outcomes covered in this assignment:


1 Select appropriate forecasting approaches in a range of commonly occurring business situations. Select and justify a forecasting approach relevant to the given problem by examining the strengths and weaknesses of different models.


2 Prepare and evaluate data for use in forecasting applications. Analyse data for both regular patterns and abnormal behaviour and make appropriate selections of, and adjustments to, the data for detailed analysis.


3 Participate in a range of forecasting activities using comport-based models where appropriate and report on the results Work either individually or as part of a group to build forecasting model, produce a written report explaining the techniques used and the results obtained and commenting on any operational and implementation issues which must be borne in mind.


4 Evaluate the performance of forecasts using appropriate techniques Carry out statistical analysis of errors.

The following is a guide as to how Part One of the assignment will be marked:

Examination of Data (40%) F/F- E D (Range) C (Range) B (Range) A (Range)

Examination of the data and description of the nature of the dataset. Work is of little or no merit whatsoever Poor Charts. Little evidence of examination of the data. Some errors in the charting of the data.  Poor examination of the patterns in the data supported by little evidence. Some errors in the charting of the data.  Some examination of the patterns in the data supported by little evidence. Accurate charting of the data.  Thorough examination of the patterns in the data supported by some evidence. Accurate charting of the data.  Thorough examination of the patterns in the data supported by comprehensive evidence.

Selection of Techniques (30%) F/F- E D (Range) C (Range) B (Range) A (Range)

Discussion and justification of the three techniques you have decided to use.   Work is of little or no merit whatsoever Selection of inappropriate techniques with no real justification.  Selection of inappropriate techniques but some attempt at justification. A satisfactory discussion and limited identification of problems you foresee and/or limitations, of the techniques when applied to YOUR data. A comprehensive and CRITICAL discussion, including any problems you foresee and/or limitations, of the techniques when applied to YOUR data, although there may be some weaknesses and omissions. A comprehensive and CRITICAL discussion, including any problems you foresee and/or limitations, of the techniques when applied to YOUR data.

Rejection of Techniques (20 %) F/F- E D (Range) C (Range) B (Range) A (Range)

Your reasons for rejecting other techniques that you consider inappropriate. Work is of little or no merit whatsoever Rejection of techniques with no real justification.  Rejection of inappropriate techniques and some attempt at justification. A satisfactory discussion, giving clear reasons as to the unsuitability of the rejected techniques, when applied to YOUR data. A comprehensive and CRITICAL discussion, giving detailed reasons as to the unsuitability of the rejected techniques, when applied to YOUR data, although there may be some weaknesses and omissions. A comprehensive and CRITICAL discussion, giving detailed reasons as to the unsuitability of the rejected techniques, when applied to YOUR data.


Other Issues (10%) F/F- E D (Range) C (Range) B (Range) A (Range)

Discussion of other forecasting issues that should be considered when attempting to produce medium to long term forecasts. Work is of little or no merit  Shows little understanding of the issues. Poor referencing. Some relevant points made, but no examples. Limited evidence of understanding of the main issues.  Maybe some errors in referencing. Some relevant points made with limited examples. Evidence of some understanding of the main issues.  Maybe some errors in referencing. Interesting points with examples provided.  Evidence of some understanding of the main issues. Accurate referencing. Interesting points with examples provided.  Evidence of clear understanding of the main issues. Creativity evidenced in the discussion.  Accurate referencing.


The following is a guide as to how Part Two of the assignment will be marked:

Task 1 (5%)

Graph of the data You MUST include a graph of the data.  You cannot pass this assignment if you do not include a graph of your data.

You must make sure that your graph includes all the features we discussed in the Forecasting exercises.  These include (all the features are not listed here) an appropriate title, labelling of axes, source and date, appropriate annotation where necessary, and appropriate scale.


Task 2  (80%) E D (Range) C (Range) B (Range) A (Range)

Forecast  models (18%) 1. Only 1 model is appropriate for the dataset and able to forecast 4 periods into the future.

2.  Application of the models has significant errors.

 1. At least 2 models are appropriate for the dataset and able to forecast 4 periods into the future.

2.  Application of the models has major errors.

 1. At least 2 models are appropriate for the dataset and able to forecast 4 periods into the future.

2.  Application of the models has minor errors.

 1. At least 2 models are appropriate for the dataset and able to forecast 4 periods into the future.

2.  Application of the models has no errors.

 1.  All 3 models are appropriate for the dataset and able to forecast 4 periods into the future.

2. Application of the models has no errors.

 E D (Range) C (Range) B (Range) A (Range)

Testing the models (22%) Poor attempt at testing all 3 models.  Poor presentation, insignificant comments on the results.

 All 3 models accurately tested (with significant errors) using appropriate error tests.  Shallow comments on the results, poorly presented.

 All 3 models accurately tested (with minor errors) using appropriate error tests.  Adequate presentation, shallow comments on the results. All 3 models accurately tested (with minor errors) using appropriate error tests and a clear presentation of meaningful comments on the results.


要想成绩好,英国论文得写好,51due英国论文代写平台为你提供英国留学专题,留学知识,专业辅导,还为你提供专业英国essay代写,paper代写,report代写,需要找英国论文代写的话快来联系我们51due工作客服QQ:800020041吧。

我们的优势

  • 05年成立,已帮助上万人
  • 24小时专业客服
  • 团队成员都毕业于全球著名高校
  • 保证原创,支持检测

英国站