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MIS171 – Business Analytics

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MIS171 – Business Analytics – Trimester 2, 2022 Assignment 3 – Individual

DUE DATE AND TIME:                     Due by 8 p.m. AEST on Monday 3 October 2022

PERCENTAGE OF FINAL GRADE:    40%

SUBMISSION                                   You will submit, via file upload to Cloud Deakin:

·  one Excel file, with your analysis, and

  • one Word file, with your written report (1,000 words)

Description:

This assignment requires you to analyse a data set (including performing a multiple regression analysis), interpret, and draw conclusions from your analysis. You are required to explain the Summary Output from your multiple regression analysis in a written report, which must also include TWO recommendations to the RACV Board.

The assignment must be completed individually and submitted electronically in CloudDeakin by the due date. When submitting electronically, you must check that you have submitted the work correctly by following the instructions provided in CloudDeakin. Hard copies or assignments submitted via email will NOT be accepted.

The assignment uses the file 2022 T2 RACV Solar Data Set A3.xlsx which can be downloaded from CloudDeakin. The assignment focuses on materials presented up to and including Week 11.

The data set is based on actual data provided by RACV Solar. For confidentiality and anonymity reasons actual data has been manipulated in the assessment task. Following is an introduction to this scenario and detailed guidelines.

RACV Solar

Logo  Description automatically generatedRACV Solar is a wholly-owned subsidiary of the RACV (Royal Automobile Club of Victoria). While the RACV is best known as a provider of car and road-user services, the organisation has been active in expanding the range of services it offers to its members, including insurance and travel services.

RACV Solar is one of the biggest solar companies in Victoria. RACV Solar has

designed and installed more than 10,000 solar installations, with over 50 MW (megawatts) of solar

power across multiple sectors, including residential homes, businesses, and community projects. RACV Solar holds the record for the largest installations on a school and a hospital in Australia.

Scenario: RACV Community Solar Installation Promotion

In 2021, RACV Solar partnered with Geelong Sustainability to offer solar power generating and battery systems to households in the Geelong, Surf Coast and Otway region.

Key data from 250 clients who participated in the promotion is included in the data set in the Excel file which forms part of this assignment (2022 T2 RACV Solar Data Set).

The promotion with Geelong Sustainability provided potential clients with multiple points of contact. The main pathways for Customer Introduction included coming into an RACV Store or via an RACV Solar digital platform, including the website or one of RACV Solar’s social media platforms (e.g. Facebook and Instagram). Clients also made enquiries via an event at which RACV Solar was present (e.g. the launch of the Geelong Sustainability promotion). Other clients were referred by customers who had previously installed an RACV solar PV system.

Every client enquiry was managed by one of RACV Solar’s three Sales People – Andrew, Claris or Dinah. The performance of RACV Solar’s sales people is measured according to number of deals closed, revenue generated, and client satisfaction.

RACV Solar offered clients three Installation Types. All clients in the Geelong Sustainability promotion remained connected to the national electricity grid. This enabled a client to import (and pay their electricity supplier for) electricity from the national grid in the evenings and during daylight hours where a solar installation did not generate enough electricity to meet their needs. The basic installation type was where a client only had solar PV panels installed. The aim was to reduce the amount of electricity imported from the national grid. Hybrid installations combined a solar PV array with a battery that would store surplus electricity which could be used when the installation did not meet the demands of the client. Other clients installed a system that only generated hot water (i.e. no electricity was produced).

Electricity generated by solar panels is Direct Current (DC). DC must be changed (inverted) to Alternating Current (AC). Inverter size depends on the size of the solar PV installation.

The size of a solar electricity system is measured in kilowatts (kW). System Size represents the capacity of a solar installation at maximum efficiency. Larger systems have greater capacity to generate electricity than smaller systems. The amount of electricity generated depends on several factors, including system size, orientation (e.g. if the system faces north or west), and shading (i.e. if any shade, for example from nearby trees, falls on the solar panels during the day). Electricity generated is measured as kilowatt hours (kWh), the number of hours the system will power appliances with a requirement of 1,000 watts (i.e. one kilowatt).

The cost of the installation for the client is revenue for RACV Solar. System Cost depends on several factors, including the size of the system, the complexity of the system (e.g. one bank of panels facing north and another bank of panels facing west), access to the site, and other factors (e.g. clients are offered different models of solar panels and extended warranties).

To track the benefit they receive, RACV Solar’s clients were asked to provide details of the amount of electricity consumed in the year prior to the new system being installed (i.e. Current Consumption). Based on details of the installation (size, orientation, shading, etc) RACV Solar can estimate the amount of electricity expected to be generated by the installation (i.e. Expected Production). RACV Solar’s clients will not have to purchase this from their electricity supplier, generating a saving.

Where a solar installation generates more electricity than is required the surplus can be exported to the electricity grid (i.e. Surplus Production). Electricity customers will be paid for the amount of electricity exported to the grid. This revenue is based on the kWhs exported multiplied by the “feed- in tariff” (price per kW) offered by the customer’s electricity supplier.

By adding the annual surplus revenue to the annual electricity saving an annual payoff can be calculated. This financial benefit to an RACV Solar client can be divided into the cost of the installation to calculate the number of years it will take for the payoff (savings and surplus revenue) to recover the cost of installing the solar PV system (i.e. Payback Period).

All electricity (or hot water) generated by the installation will reduce the amount of electricity drawn from the national grid. This means less electricity needs to be generated by large power plants that

burn fossil fuels to generate electricity. The resulting reduction in greenhouse gas emissions, measured in tonnes per year) can be calculated as Climate Change Impact.

At the conclusion of each installation RACV Solar asks their clients to rate their experience, on a scale of zero (completely unsatisfied) to 100 (completely satisfied). Using this feedback RACV Solar sorts clients into four groups: Unhappy, Acceptable, Satisfied, and Delighted.

Assume that you are a business analyst recruited by RACV Solar. You have received an email from James Wright, RACV Solar’s Regional General Manager. James’s email asks you to analyse the 2021 client data. Your response will be used as part of a report to the RACV Solar Board of Directors. James’s email together with guidelines (shown in blue) are presented below:

Email from James Wright

To:                   You

From:               James Wright, Regional General Manager, RACV Solar

Subject:           Analysis of the RACV Solar’s Promotion with Geelong Sustainability

Hi …,

We are very happy with the strong interest and business generated from the promotion with Geelong Sustainability. The Board wants a detailed understanding of some of the key aspects of the promotion. I have attached an Excel file with key data from the promotion and included some guidelines (shown in blue) to direct your work.

Please provide answers to the following questions. Return the Excel file to me. As I have training in business analysis I am comfortable with technical language.

I also need a two-section report:

  1. The first section will be read by me, and will provide an explanation of your analysis. As I am training in business analytics I understand technical terms,
  2. The second section will be presented to the RACV Solar Board. They have requested you to make two recommendations that RACV Solar could consider to maximise Client Satisfaction. As the Board members do not have the benefit of training in business analytics your report must be phrased in plain, straight-forward language.
  1. Hypothesis Testing (consider α = 5%)

The Board is concerned that average client satisfaction from the sample of Geelong Sustainability clients is less than 70. It has been suggested at a recent Board meeting that the average Client Satisfaction for every Client Installation, is now less than 70. Does the data confirm this hypothesis?

To answer this question, you will need to conduct an appropriate hypothesis test for Client Satisfaction for each Installation Type.

  • Multiple Linear Regression Modelling (consider α = 5%)

Client Satisfaction is an important measure for RACV Solar, as it represents a major element of the company’s marketing strategy. Build a multiple regression model to predict Client Satisfaction. Your model should provide insights into which factors have a significant influence on client satisfaction, as well as the ability to predict Client Satisfaction for various scenarios.

For this analysis, you will need to build a multiple regression model using Client Satisfaction as the dependent variable. All other variables in the RACV Solar data-set should be included in the model, except Satisfaction Type (i.e. exclude Satisfaction Type from your regression model).

Follow the model building process introduced in the lecture and seminars. Carefully consider the following:

  • Transform categorical variables into suitable dummy variables

(i.e. Customer Introduction, Sales Person, Location, Installation Type and Inverter Capacity).

Copy the RACV Solar Data Set (excluding Satisfaction Type) to the “Correlation” spreadsheet in the Excel file that has been provided (no earlier than Column W – be careful not to overwrite the Conclusion, Correlation Table and Scatter Diagram frames). Delete the Client ID column.

  1. When transforming Customer Introduction into dummy variables, consider Referral as the baseline category; meaning the created dummy variables for Method of Enquiry should only include Digital (Yes and No), RACV Store (Yes and No) and Event (Yes and No)
    1. When transforming Sales Person into dummy variables, consider Dinah as the baseline category; meaning the created dummy variables for Sales Person should only include Andrew (Yes and No), and Claris (Yes and No).
    1. When transforming Location into dummy variables, consider Otway as the baseline category; meaning the created dummy variables for Location should only include Geelong (Yes and No), and Surf Coast (Yes and No).
    1. When transforming Installation Type into dummy variables, consider Solar Hot Water as the baseline category; meaning the created dummy variables for Installation Type should only include Electricity Panels (Yes and No), and Hybrid (Yes and No).
    1. When transforming Inverter Type into dummy variables, consider Large as the baseline category; meaning the created dummy variables for Inverter Type should only include Small (Yes and No), and Medium (Yes and No).

Complete the Dummy Variables Summary table which is in the Conclusion section of the Correlation worksheet. The table summarises the results of your transformation of categorical variables into dummy variables.

  • Using the RACV Solar data set (which now includes transformed dummy variables) as your reference, complete the following steps:
    • Correlation – in the section marked “Correlation Table” (below the Conclusion section on the “Correlation” worksheet) generate a correlation table.

Use the “Correlation” option in Excel’s Data Analysis ToolPak.

  1. On the correlation table, identify and clearly indicate the Independent Variables which are (virtually) uncorrelated with the Dependent Variable (i.e. all IVs which have a correlation coefficient with the DV of between -0.05 and 0.05).

These IVs are to be removed from the model prior to running the first iteration of the regression model.

  1. Complete the Uncorrelated Independent Variables summary table which is in the Conclusion section of the Correlation worksheet. This table summarises which Independent Variables are to be eliminated from the regression model due to being (virtually) uncorrelated with Client Satisfaction (DV).
    1. Multi-collinearity – review the correlation table for instances of multi-collinearity between Independent Variables (IV). In cases of multicollinearity, identify and clearly indicate the IVs with the weakest correlation with the Dependent Variable.

These IVs are to be removed from the model prior to running the first iteration of the regression model.

  • Complete the Multi-Collinearity summary table which is in the Conclusion section of the Correlation worksheet. This table summarises which Independent Variables are to be eliminated from the regression model due to multi-collinearity.
    • Scatter diagrams – in the section marked “Scatter Diagrams” (below the Correlation Table section on the “Correlation” worksheet) generate three scatter diagrams, for:
      • Client Satisfaction (Dependent Variable, DV) and the numerical (not categorical) Independent Variable (IV) which has the highest correlation with the DV. Include a calculation of the correlation coefficient.

Format the diagram, and include a linear trendline,

  • Client Satisfaction (DV) and the numerical (not categorical) Independent Variable

(IV) which has the lowest (i.e. most negative) correlation with the DV. Include a calculation of the correlation coefficient.

Format the diagram, and include a linear trendline, and

  • Client Satisfaction (DV) the Independent Variable (IV) that is closest to being uncorrelated with the DV (i.e. correlation coefficient closest to zero). Include a calculation of the correlation coefficient.

Format the diagram, and include a linear trendline.

  • Using the RACV Solar data set as your reference complete the following steps, on the “Model” spreadsheet in the Excel file that has been provided (the data set includes the dummy variables you have created and excludes the Independent Variables which have been eliminated due to multi-collinearity or being uncorrelated with the Dependent Variable):
    • Using the “Regression” option in Excel’s Data Analysis ToolPak build a multiple regression model.
      • Assess the model for overall significance

(F test with alpha set at 0.05, i.e. Confidence Level = 95%).

  1. If your first iteration of the overall model is found to be significant, in a step-wise fashion, sequentially (one at a time) remove the Independent Variables that are least likely to be contributing to any significant change in the Dependent Variable.
    1. You will need to conduct t-tests with alpha set at 0.05 to determine the significance of the various IVs you exclude and include in your model.
  • Once you have created a regression model where all the remaining Independent Variables are contributing significantly to a change in Client Satisfaction copy the Summary Output of your final multiple regression model and paste it into the Output section of the “Model” spreadsheet in the Excel file that has been provided,
    • In the Conclusion section of the “Model” spreadsheet,
      • Write the (final) multiple regression equation. Use the format: Ŷ = ƅ0 + ƅ1X1 + ƅ2X2…
      • Explain (interpret) the (final) multiple regression equation/model.
  • Using the final multiple regression equation (from step (d)(i)),
    • In the Predictions section of the “Model” spreadsheet in the Excel file that has been provided, for the scenario outlined below:
      • Calculate a Point Estimate for Client Satisfaction (DV),
      • Calculate a Prediction Interval for Client Satisfaction (DV),
      • Calculate a Confidence Interval for Client Satisfaction (DV),
    • In the Conclusion section of the “Model” spreadsheet in the Excel file that has been provided, for the scenario outlined below:
      • Interpret the Point Estimate calculation
      • Interpret the Prediction Interval calculation
  • Interpret the Confidence Interval calculation

Independent Variables                         Scenario

Customer Introduction                             Digital

Sales Person                                            Andrew

Location                                                 Surf Coast

Installation Type                              Electricity Panels Inverter Capacity                                                     Small

System Size                                               6.5kW

System Cost                                              $8,000

Current Consumption                        6,500 kW (p.a.)

Expected Production                         8,200 kW (p.a.)

Surplus Production                            1,800 kW (p.a.) Payback Period                                                    5.35 years Climate Change Impact                                18.4 (tonnes p.a.)

I look forward to receiving details of your analysis, and your report, by Monday 3 October, 2022. Sincerely,

James                                                                                                 

Data description

The provided data file includes multiple sheets, labelled “Data Description”, “RACV Solar Data Set” and worksheets for your analysis. The “Data Description” sheet describes all the variables used in the “RACV Solar Data Set” and is copied below for your convenience.

Campaign Sheet:

Client ID:                            Numbered list from Client 1 to Client 250

Customer Introduction:     The pathway chosen by the client to make their first enquiry::

  • Digital, the client submitted an enquiry via the RACV Solar website or one of RACV Solar’s social media platforms
    • Event, the client enquiry came via a contact at an event attended by RACV Solar
      • RACV Store, the client came into an RACV Store to enquire about installing a solar electricity system
      • Referral, the client enquiry came via a referral from a previous RACV Solar client

Sales Person (CAT):           Client enquiries were managed by one of RACV Solar’s three sales people:

  • Andrew
    • Claris
      • Dinah

Location (CAT):                  Clients fall into one of three broad locations:

  • Geelong, the City of Greater Geelong
    • Surf Coast, includes locations on the Bellarine Peninsula and along the coast line to Apollo Bay.
      • Otway, includes inland locations to the west of Geelong, including Colac and Camperdown.

Installation Type (CAT):     Solar PV installations were typically of three different types:

  • Electricity Panels, where the installation was only electricity generating solar PV panels
    • Hybrid, where a solar PV system was installed with battery back-up
      • Solar Hot Water, where the installation only produced hot water (and no electricity)

Inverter Capacity (CAT):    Inverter size (capacity) depends on the size of the solar PV installation:

  • Small, where the PV System Size is less than 5kW
    • Medium, where the PV System Size is greater than 5kW, and less than 10kW
      • Large, where the PV System Size is greater than 10kW

System Size:                       The capacity of the solar installation to generate electricity, measured in kilowatts (kW).

System Cost:                      Revenue    generated    by    RACV    Solar    for    installing    the    system (cost to the client).

Current                              The amount of electricity consumed by the client in the year prior to the Consumption:                    installation (measured as kWh)

Expected                            The amount of electricity expected to be generated by the PV system Production:                        in the year following the installation (measured as kWhs).

Surplus Production:           The amount of surplus electricity expected to be exported to the national grid in the year following the installation

Payback Period:                 The number of years required for reduced expenditure on electricity to pay for the cost of the solar PV installation.

Climate Change                 The annual reduction in greenhouse gas emissions (tonnes per year) Impact:                                           due to installing the solar PV system

Client Satisfaction:            The experience of RACV Solar clients on a scale from zero (completely unsatisfied) to 100 (completely satisfied).

Satisfaction Type:              The experience of RACV Solar clients is classified into four different (CAT)                                               Satisfaction Types:

  • Unhappy, Satisfaction Rating <50
    • Acceptable, Satisfaction Rating between 50 and 69
      • Satisfied, Satisfaction Rating between 70 and 79
      • Delighted, Satisfaction Rating between 80 and 100 (max)

Assignment instructions

The assignment consists of two parts.

Part 1: Data Analysis

Your data analysis must be performed on the Assignment 3 Excel file. The file includes tabs (spreadsheets) for:

  • Data Description
  • RACV Solar Data Set
  • Analysis for Question 1
  • Correlation, which includes:
    • creating Dummy variables,
    • creating a correlation table,
    • eliminating uncorrelated Independent Variables (IVs), and
    • eliminating IVs where multi-collinearity is present
  • Model – building the regression model, including multiple iterations, and
    • reporting the Summary Output of the final regression model,
    • identifying the final equation, and explaining/interpreting the final equation, and
    • calculating and explaining the Point Estimate, Prediction Interval, and Confidence Interval for the scenario provided.

When conducting the hypothesis tests you need to follow the steps outlined in the lecture and seminars, including appropriate conclusions.

When performing the multiple regression carefully follow the steps outlined in the lecture and seminars, and set out in the steps (in blue) in James’s email.

The only worksheets in the Excel file provided by James that will be marked are Q.1, Correlation, and Model. Follow the steps (in blue) in James’s email for what to include in the reporting sections that will be marked (e.g. Output and Conclusion sections in the different worksheets). There should be no need to create additional worksheets.

Make sure your analysis and process complete, clear, and easy to follow. You may need to add (or widen/narrow) rows or columns to present your analysis clearly and completely. Poorly presented, disorganised analysis or excessive output will be penalised.

Part 2: Report

Your report is to be addressed to James Wright. The report is to be split into two sections.

In the first section it is expected that you will use technical terms such as “hypothesis test”, “coefficient of determination”, “standard error”, and “adjusted R square”, etc. You can assume that James is familiar with all these terms.

In the second section please assume that James will send Section 2 of your report directly to the RACV Solar Board. Your language must be appropriate for a professional audience that has no formal training in business analytics. This means that you must use plain, straight-forward, and easily understood language.

Section 1

(less than 400 words)

Section 1 will only be read by James Wright. James has training in business analytics and understands technical terms.

Having completed the hypothesis test and your regression analysis of the RACV Solar data set and created your final regression model, the first section of your report requires an explanation of the Summary Output of your final regression analysis, including (but not limited to) the different values in the Regression Statistics, the significant variables, the regression coefficients, Significance F, the different P-values. Include an overall practical interpretation of the Summary Output (a geometric or algebraic interpretation is not required).

Section 2

(more than 600 words)

Make TWO (2) recommendations that the Board of RACV Solar could consider to maximise Client Satisfaction.

Your recommendations can be based on analysis in this assignment, analysis from previous assignments and any other analysis that you consider is relevant and adds impact to your recommendations.

Thoughts to consider in framing your recommendations include:

  • Specific actions RACV Solar could take to maximise Client Satisfaction based on the outcomes of your regression model
  • Specific actions RACV Solar could take to maximise Client Satisfaction based on the outcomes of your analysis from Assignment 1 and Assignment 2
  • Specific actions RACV Solar could take to maximise Client Satisfaction based on the outcomes of any additional analysis you perform
  • Recommending targeting a group that RACV Solar could pursue that maximises Client Satisfaction
  • The impact of maximising Client Satisfaction on the other corporate KPIs of Revenue, System Size and Climate Change Impact
  • The impact on Client Satisfaction arising from pursuing the other corporate KPIs of Revenue, System Size and Climate Change Impact
  • Considering the impact on Client Satisfaction of the variables not specifically included in your regression model.
  • Recommending targeting a group that RACV Solar could pursue that provides an optimal balance in terms of all RACV Solar’s corporate KPIs: including Client Revenue, System Size, Climate Change Impact and Client Satisfaction

Make sure that all your recommendations are directly informed by your data analysis. Do not include any commentary that is not supported by your data analysis.

Highest marks will be awarded to students who draft distinct (i.e. different) recommendations, and whose recommendations take into account a broad range of (data-supported) considerations.

In a previous trimester, where the dependent variable was membership of an elite sporting team, one recommendation was, “The data analysis suggests that a group that would be responsive to an invitation to take up membership of the club would be females in their 20s who are members of a sporting team”. From a clear statement of recommendation the discussion then moved to explaining why females were the prime target group (and not males or the Prefer Not to Say group), etc. The discussion presented the analysis of Females in the survey data, and age analysis, and whether or not survey respondents were a member of a sporting club. Key analysis of other variables in the survey, and any relevant combinations of variables, was also included in the recommendation. Analysis from the multiple regression, other assignments, and additional analysis were all considered when drafting the recommendation.

When exploring data, we often produce more results than we eventually use in the final report, but by investigating the data from different angles, we can develop a much deeper understanding of the data. This will be valuable when drafting your written report.

It is useful to produce both numerical and graphical statistical summaries. Sometimes something is revealed in one that is not obvious in the other.

The word limit for your report is 1,000 words (900 to 1,100 words). This includes Section 1 and Section

  • Remember you should use font size 11 and leave margins of 2.54 cm. A template is provided for your convenience.

Carefully consider the following points:

  • Your report is to be written as a stand-alone document. Assume that your Excel file is for James’s use only and that James will only pass (Section 2 of) your written report directly to the Board.
    • The goal in Section 1 is to express yourself in the language of a business analyst to a data- literate professional audience.
    • The goal in Section 2 is to keep the English simple and the explanations clear. Avoid the use of technical statistical jargon. Your task is to express your analysis into plain, simple, easy to understand language.
    • Follow the format of the template when writing your report. Delete the report template instructions (in purple) when drafting your report.
    • Do not include any charts, graphs or tables into your Report.
    • Include a succinct introduction at the start of your report, and a conclusion that identifies how your recommendations will benefit RACV Solar.
    • Marks will be deducted for the inclusion of irrelevant material, poor presentation, poor organisation, poor formatting, and reports that exceed the word limit.

When you have completed drafting your report, it is a useful exercise to leave it for a day, and then return to it and re-read it as if you knew nothing about the analysis. Does it flow easily? Does it make sense? Can someone without prior knowledge follow your written conclusions? Often when re- reading, you become aware that you can edit the report to make it more direct and clearer.

Learning Outcomes

This task allows you to demonstrate achievement towards the unit learning outcomes (ULOs). The ULOs are aligned with specific graduate learning outcomes – that is, the skills and knowledge graduates are expected to have upon completion of their studies – and this assessment task is an important tool in determining achievement of those outcomes. If you do not demonstrate achievement of the unit learning outcomes, you will not be successful in this assignment.

It is good practice to familiarise yourself with the ULOs and GLOs as they provide guidance on the knowledge, understanding and skills you are expected to demonstrate upon completion of the unit. In this way they can be used to guide your study.

Unit Learning Outcomes (ULO)Graduate Learning Outcomes (GLO)
ULO1: Apply quantitative reasoning skills to analyse business problems.GLO1: Discipline-specific knowledge and capabilities
ULO2: Create data-driven/fact-based solutions to complex business scenarios.GLO5: Problem solving
ULO3: Analyse business performance by implementing contemporary data analysis tools.GLO3: Digital literacy
ULO4: Interpret findings and effectively communicate solutions to business problemsGLO2: Communication

Submission

Your submission will comprise of two files:

  1. A Microsoft Excel workbook file containing your Data Analysis (Part 1), on the relevant tabs, and
  • A Microsoft Word document containing your report (Part 2) to James (and the RACV Solar Board).

Your submission files should be named:

Word file: MIS171_T2_YOURStudentID.doc (or .docx), and Excel file: MIS171_T2_YOURStudentID.xls (or .xlsx).

Enter YOUR student ID in the appropriate place when naming your submission files. This assessment is to be submitted ONLINE ONLY into the Assessment 3 Dropbox available on the MIS171 Cloud Deakin site.

All work you submit is checked electronically or by other means for the purposes of detecting collusion and/or plagiarism.

When you are required to submit an assignment through your CloudDeakin unit site, you will receive an email to your Deakin email address confirming that it has been submitted. You should check that you can see your assignment in the Submissions view of the Assignment Dropbox folder after upload, and check for, and keep, the email receipt for the submission.

Marking and feedback

The marking rubric for this task is available in the MIS171 CloudDeakin unit site – in the Assessment folder (under Assessment Resources).

It is always a useful exercise to familiarise yourself with the criteria before completing any assessment task. Criteria act as a boundary around the task and help identify what assessors are specifically looking for in your submission. The criteria are drawn from the unit’s learning outcomes ensuring they align with appropriate graduate attribute/s.

Identifying the standard you aim to achieve is also a useful strategy for success and to that end, familiarising yourself with the descriptor for that standard is highly recommended.

Students who submit their work by the due date will receive their marks and feedback on CloudDeakin 15 working days after the submission date. In order to understand areas where improvement is required, students are encouraged to refer to the suggested solutions, comparing their answers with the ‘model’ answers provided, which will be released after the test closes for all students.

Extensions

Extensions will only be granted for exceptional and/or unavoidable circumstances outside the

student’s control.

Students seeking an extension for an assignment prior to the due date should apply directly on the Unit Site (Content > Assessment Resources > Extension Request). When requesting an extension, you need to provide evidence to support your request (e.g. attach a medical certificate). You must also attach a draft of the work that you have completed on the assessment to date. You must be able to demonstrate exceptional circumstances. Applications for special consideration after this time must be submitted via StudentConnect.

Late submission

The following marking penalties will apply if you submit an assessment task after the due date without an approved extension: 5% will be deducted from available marks for each day up to five days, and work that is submitted more than five days after the due date will not be marked and will receive 0% for the task. ‘Day’ means working day for paper submissions and calendar day for electronic submissions. The Unit Chair may refuse to accept a late submission where it is unreasonable or impracticable to assess the task after the due date.

Calculation of the late penalty is as follows:

  • 1 day late (5% penalty):

submitted after Monday (3 Oct 2022) 11:59 pm and before Tuesday (4 Oct) 11:59 pm

  • 2 days late (10% penalty):

submitted after Tuesday 11:59 pm and before Wednesday 11:59 pm

  • 3 days late (15% penalty):

submitted after Wednesday 11:59 pm and before Thursday 11:59 pm

  • 4 days late (20% penalty):

submitted after Thursday 11:59 pm and before Friday 11:59 pm

  • 5 days late (25% penalty):

submitted after Friday 11:59 pm and before Saturday 11:59 pm The Dropbox closes after 11:59 pm AEST on Saturday 7 May 2022.

Support

The Division of Student Life (see link below) provides all students with editing assistance. Students who wish to take advantage of this service must be organized and plan ahead and contact the Division of Student Life in order to schedule a booking, well in advance of the due date of this assignment. http://www.deakin.edu.au/about-deakin/administrative-divisions/student-life

Referencing

Any material used in this assignment that is not your original work must be acknowledged as such and appropriately referenced. You can find information about plagiarism and other study support resources at the following website: http://www.deakin.edu.au/students/study-support

Academic misconduct

For information about academic misconduct, special consideration, extensions, and assessment feedback, please refer to the document Your rights and responsibilities as a student in this Unit in the first folder next to the Unit Guide in the Resources area of the CloudDeakin unit site.

Good luck everyone. The Unit Team