ETC1000 Business and Economic Statistics Group Project Sem 1 2025

ETC1000 The Analysis Part A: 

Parkrun Data Parkrun is a weekly volunteer-run 5km event held on many venues throughout the world. The results of each week's runs are found on the Parkrunwebsite, along with basic information about each participant. In this section of the project, you will look at the results for one particular event / venue from two points of view. 

First, choose your parkrun event / venue to analyse. It can be chosen from any of the venues in Australia. However, to make your analysis reliable, choose a venue where there are more than 300 runners in a particular week, and one that has been going for at least 8 years. 

1. For your chosen venue, first analyse results for one particular week. Make sure there are at least 300 runners in that week.

This will involve: 

• For the week you choose, you will need to "scrape" the data from the website into Excel. 
• You also need to clean the data file once it is imported, to make it useful. Cleaning will include dealing with the important variable of "Time". Excel is likely to format this incorrectly, so you will need to work out how to transform that variable into something useful. 
• Do some analysis of the relationship between time and the other characteristics of people - age and gender. For this analysis, you should use pivot tables, and any other relevant analysis, including graphs to aid presentation.
• Develop a model that can predict how a person's performance is likely to change as they get older, and show how it works for different people of different ages. E.g. Imagine a male Monash professor who is 64 years old, and can run 5km in 28 minutes. What time do you predict he will be running by the time he is 74? Make sure you talk about how reliable your predictions are likely to be.

2. For the same venue, analyse the event history data. 

• First, Scrape the data from the website into Excel, then do the cleaning as in the previous case. 
• Now do some analysis of the data. You are interested in trends over time in three measures: the number of finishers, the first-place finishing times of females and of males. Also analyse the time gap between first-place finishing times of males and females: is there any evidence of the gap getting smaller over time? For this analysis, you might do some graphs, and a regression analysis.

ETC1000 Part B: Life Expectancy Models 

In this part, you will develop models that try to explain a country's average life expectancy, based on a few key characteristics of that country. This section of your presentation should include some background to the issue, definitions of the variables and why they are relevant, some descriptions of the data that motivates the analysis, and based on the results, some possible policy implications. 

Go to Our World in Data, and download data on 3 variables across as many years and countries as possible: 

• Life Expectancy 
• Gross Domestic Product (GDP) per capita 
• Infant Mortality Rate

You should analyse two subsets of the data:

• A "cross section" analysis with all countries for which data is available, for the most recent year. •A "time series" analysis for just one country that you choose, using all the years for which data is available. Be sure to choose a country where there is at least 20 years of data on all the variables. 

Before you can estimate the models, you will need to work out how to merge the three datasets for the three variables into one dataset, matching by country (for the cross-section analysis) or year (for the time series analysis). One function you can use in Excel is VLOOKUP. We will produce a short video to show how this function works. You will also need to make sure you deal with any missing datapoints, and beware, some of the rows of the dataset are not countries, so should not be included. 

• Calculate some descriptive statistics and graphs on the variables for each of the two datasets, and describe key characteristics of the variables.
• Estimate multiple regression models with life expectancy as the dependent variable, and the two other variables as the explanatory variables. You should estimate a model with the cross-section data, and a model with the time series data, and comment on what we learn about the keys to improving life expectancy. You should also comment on how similar or different the two models are, and why you think the differences exist.

ETC1000 The Presentation 

Create a 12-16 minute presentation showing the results of your analysis. Every member of the group needs to take part in the presentation.We will penalise you if your presentation exceeds 16 minutes.