How to represent the data using graphical tools, as well as numerical summaries

Description of assessment task

This first milestone aims to give you a better understanding of the datasets, variables, and questions investigated by PwC. This exploratory data analysis seeks to get the necessary insights so that a development plan can be formulated to address the following key points of the distribution project:

(1) Correlation analysis: a cross-sectional analysis of correlation patterns between the profitability proxy variables and the local jurisdiction's key economic factors like GDP.

(2) Covid-19 effect analysis of cross-sectional patterns across the years may capture the effect of Covid on profitability (by country).

Approach to the assessment task

In week 1, we learnt how to represent the data using graphical tools, as well as numerical summaries. All these tools are meant to give us an idea of what the data is trying to tell us'. Can we make sense of the large numbers of observations and tell a simple story or pick up a trend? This is what you will do in this milestone: understand the data and what PwC is trying to find out from the data.

(A) Expected Tasks

(i) Attend the presentation session by PwC. This will give you important information about the importance of this analysis for PwC and will inform your formulation of the development plan for the analysis. You must understand the problem, PwC's expectations to inform your analysis.

(ii) Download your allocated data' spreadsheets. This is individual work, and you have been randomly assigned countries for Milestone 1 analysis. Please check the file 'Country Allocation' to find out which comp-anies you must analyse.

(iii) Data preparation. Please check the document,'Data Preparation and Data Cleaning' to prepare your data for analysis. The main data issue here is the missing points. These appear as 'n.a.' but sometimes also as '0'. In the latter case, you should double-check whether the variable in question can have a 'real' O value or whether '0', in fact, represents an 'n.a.' missing value. Some strategies for dealing with missing points appear in the document and will be discussed in your weekly SEM. You must explain any data manipulations you perform and provide a rationale for them.

(iv) Variables of interest. While preparing the data, think about (1) and (2) above and ask what information you need to be able to address these points. For instance, the columns in the data files have many variables that may proxy for profitability, like:

1) Operating Revenue (Turnover) US$
2) Gross Profits US$
3) Gross Profits Mark-up (%)
4) Gross Profit/ Total Operating Costs (%)
5) Gross Profit / Total Assets (%)

You must decide which variable(s) to address PwC profitability questions.

(v) Data structure. An essential aspect of the analysis is understanding the dataset's structure. The dataset provided by PwC has three dimensions: country, company and year. For each country, we have a panel dataset. Each panel is a cross-section of companies observed over six years. In this first milestone, we will fix the country dimension and analyse the profitability variation across companies and years.

(vi) You may not have all the information needed in the data file to address (1) and (2). For example, PwC is interested in understanding the effect of a country's key economic indicators (like GDP) on a company's profitability. At this point, you may want to ask how to use additional data to answer this question.

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