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 7. 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 (Sustainable development goals:
This assignment is designed to engage your critical thinking, problem-solving, and analytical skills through the use of descriptive analytics on the given dataset. You are tasked with conducting univariate and bivariate analyses, calculating some probabilities, performing hypothesis testing, and building confidence intervals to analyse and comprehend the status of quality education and gender equality across various countries and regions. Building upon Assignment 1`s interactive dashboard/data visualization, your challenge is to dig deeper into the dataset to extract meaningful insights and patterns that illustrate the advancements and obstacles in meeting these global objectives.
A series of questions, 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. 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.
1. Univariate Analysis:
Categorical Variables
Our assumption is that income groups are evenly distributed. If the distribution is uneven, identify the most (and least) frequent income group.
You will need to create a suitable table that includes the number and proportion of income groups.
Create an appropriate graph to illustrate your analysis.
Numerical Variables
2. Bivariate Analysis
Categorical/Categorical Variables
Categorical/Numerical Variables
Numerical/Numerical Variables
3. Probability
4. Confidence Intervals:
The Human Development Index (HDI) is a vital measure for assessing the level of human development across countries. Provide an overall estimate of the average HDI for countries of each Income Group. Which Income Group appears to have the highest (average) HDI? Which Income Group appears to have the lowest (average) HDI?
You will need to produce a comparative table of descriptive/summary statistics of the HDI for each Income Group. Then, you will need to calculate a 95% confidence interval for the average HDI for each Income Group.
Create an appropriate graph to illustrate your analysis.
Hypothesis Testing (consider α = 5%)
It is suggested that the average Human Development Index (HDI) for countries within every Income Group may now be above a specific threshold. For instance, a threshold might be set at an HDI of 0.7. Does the data confirm this hypothesis?
To address this question, you will need to conduct an appropriate hypothesis test for the HDI values for each Income Group.
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.
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