MIS171 - Business Analytics and Data Visualisation Assessment 3 - Deakin University

Assignment Task

Description

The assignment requires that you analyse a data set, interpret, and draw conclusions from your analysis, and then convey your conclusions in a written report. The assignment must be completed individually and must be 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 a data set which can be downloaded from CloudDeakin. The assignment focuses on materials presented up to and including Week 11. Following is an introduction to this scenario and detailed guidelines.

Context/Scenario

Data-Driven Insights for Quality Education and Gender Equality

In an increasingly interconnected world, the power of data analytics has become undeniable, serving as a crucial tool in identifying gaps, measuring progress, and strategizing interventions for global challenges. The Sustainable Development Goals (SDGs) , particularly SDG 4 (Quality Education) and SDG 5 (Gender Equality) , stand at the forefront of the global agenda to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, alongside achieving gender equality and empowering everyone.

The Sustainable Development Goals (SDGs) are a universal call to action, adopted by all United Nations Member States in 2015, to “end poverty and inequality, protect the planet, and ensure that all people enjoy health, justice and prosperity by 2030. It is critical that no one is left behind (WHO1).”

Among these goals, SDG 4 and SDG 5 stand out for their commitment to transforming the global landscape of quality education and gender equality. SDG 4 aims to “Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all” (UN2), targeting significant advances in literacy, access to quality education, and lifelong learning opportunities. SDG 5's objective is to “Achieve gender equality and empower all women and girls” (UN3) focusing on ending discrimination, violence, and any harmful practices against women and girls in all spheres of life. These goals highlight the critical intersection of education and gender equality as fundamental to achieving broader economic development, social inclusion, and environmental sustainability. For those interested in exploring these goals further and understanding their targets, indicators, and progress, the United Nations' official SDG website provides comprehensive resources and updates. Studying these resources will enrich your understanding of the global efforts underway to secure a better and more equitable future 

This assignment is designed to engage your critical thinking, problem-solving, and analytical skills through the use of predictive analytics on the given dataset. The objective is to conduct a multivariate linear regression analysis to explore the factors that potentially contribute to a higher Human Development Index (HDI). Building upon Assignment 1's interactive dashboard/data visualization and Assignment 2’s descriptive analytics, your challenge is to explore the dataset to uncover meaningful insights and patterns that illustrate the progress made and challenges faced in achieving the relevant Sustainable Development Goals (SDGs).

A question, accompanied by guidelines highlighted in blue, are presented below. You are required to submit your Excel file containing your data analysis, along with a report that explains the outcomes of your analysis and two recommendations. Given that your audience may not have training in business analytics, your report must present the results in plain, straightforward language. A template has been provided for your use.

Multiple Linear Regression Modelling

As Human Development Index (HDI) is crucial for evaluating the effectiveness of national development strategies, build a multiple regression model to predict HDI. Your model should provide insights into which factors significantly influence HDI, and also offer the ability to predict HDI levels under various scenarios. This analysis will help understand the driving forces behind human development and assist in strategic planning to improve HDI scores.

Data description

The provided Excel file includes multiple sheets, labelled “Data Description”, “SDG Data” and a worksheet for your dashboard. The “Data Description” sheet describes all the variables used in the “SDG Data” and is copied below for your convenience. You also need to calculate the total population from urban and rural figures and creating a new HDI group variable, categorizing countries into HDI tiers as described in the data description sheet.

The dataset you will be working with in this assignment is compiled from publicly available sources, offering authentic data and insights directly relevant to the Sustainable Development Goals (SDGs). It is specifically curated by Reza Kachouie at Deakin Business School to be used for educational purposes in MIS171: Business Analytics unit. This dataset provides a rich basis for analysis, primarily focusing on data from 2022. Where data for 2022 was not accessible, I have included the most recent data available, with the understanding that some figures might be lagged, originating from 2020. For a deeper understanding of this data, a ZIP file containing all dataset sources is available for your review. It's important to note that due to instances of missing data, certain countries have been omitted from the dataset to ensure the reliability of your analysis.

Part 1 - Data Analysis

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

• Data Description
• SDG Data
• Analysis for Correlation and Regression Model

When conducting the analysis, you need to apply techniques from multivariate linear regression analysis.

The analysis section you submit should be limited to the Correlation and Regression Model worksheets of the Excel file. These are the only worksheets which will be marked. Your analysis should be clearly labelled and grouped around each question. Poorly presented, unorganised analysis or excessive output will be penalised.

In the Conclusion section of each worksheet there is space allocated for you to write a succinct response to the questions. When drafting your Conclusion, make sure that you directly answer the questions asked. State the important features of the analysis in your Output section. Responses in the Conclusion section will be marked .

Use the Output section for your analysis to complete the analysis as directed and supports your response to the questions (which you will write in the Conclusion section). Analysis in the Output section will be marked , please make sure your analysis is complete, clear, and easy to follow. You may need to add rows or columns to present your analysis clearly and completely.

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

Use the Workings section for calculations and workings that support your analysis. The Workings section will not be marked .

Part 2 - Report

Having analysed the data, including answers (in technical terms) to the Data Analysis questions from Part 1 you are required to provide a formal report. Given that your audience may not have training in business analytics, your report must present the results in plain, straightforward language. The audience will only be familiar with broad generally understood terms (e.g., average, correlation,

proportion, and probability). They will need you to explain more technical terms, such as quartile, mode, standard deviation, coefficient of variation, correlation coefficient, and confidence interval, etc.

In section 1 of the report, provide a brief interpretation of your findings of the Correlation and Regression analyses. In section 2 of the report, provide TWO (2) recommendations that could help countries improve their Human Development Index (HDI) scores. Your recommendations should be based on the analysis conducted in this assignment, insights from previous assignments, and any additional relevant analysis that enhances the impact of your recommendations.

Consider the following in framing your recommendations:

• Specific actions countries could take to enhance HDI based on the outcomes of your regression
• Specific actions countries could take to improve HDI based on the outcomes of your analysis from Assignment 1 and Assignment 2.
• Specific actions countries could take to enhance HDI based on any additional analysis you
• Recommending strategies for targeting specific demographic or economic groups that could significantly improve HDI.
• The impact of other important measures such as GII and GDI on
• Considering the impact on HDI of variables not specifically included in your regression

Ensure that all your recommendations are directly informed by your data analysis. Avoid including any commentary 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.

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.

You are allowed approximately 1,000 words (950 to 1,050 words) for your report. 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
• Keep the English simple and the explanations Avoid the use of technical statistical jargon. Your task is to convert your analysis into plain, simple, easy to understand language.
• Follow the format of the template when writing your 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 clearly summarises your findings.
• Marks will be deducted for the inclusion of irrelevant material, poor presentation, poor organisation, poor formatting, and reports that exceed the word limit.