A while ago I created this page on life expectancies: https://excelmaster.co/life-expectancy/ and that page alone is well worth visiting because of the Excel techniques I have used in it. This page concerns the same topic but from a different point of view.
Obviously I don’t know how your mind works but sometimes I get an idea that stays with me for a while and I just have to work through it until it runs out of steam or I get tired of it. This page relates to such an idea but I am not at the end of the road with it yet.
A couple of months ago I received an email from a web site that I use and it said, take a look at life expectancies by country: that got me thinking about what is it in a country’s health regime that led to a high or low life expectancy. Now I know there are public health specialists who know all of this but I am just a hack teacher who had an idea.
What follows, then, is an image of a large correlation matrix that contains all of the variables that I have gathered to date in my quest to explore life expectancies. The purpose of the page is to ask you to consider which of the variables seem to explain why the people in some countries live longer than people in other countries.
To try to focus my thinking, I have used conditional formatting on the matrix in this way:
- if a correlation coefficient is greater than 0.75 or less than -0.75 it is coloured green
- if a correlation coefficient is between 0.50 and 0.74999 or between -0.5 and -0.74999 it is coloured yellow
Those cut off points are my arbitrary cut off points and you might consider different ones. Here is the matrix:
Click on the image to enlarge it!
At this stage I am not sharing my Excel file unless you ask me for it as it is a work in progress and I am not sure at this stage that it is completely error free.
I should say that the list of countries in my file covers only those countries monitored by the web site I use and that means that some countries/territories are not included. However, 172 countries are included so it a reasonably comprehensive list!
I would appreciate any feedback on this as I have found it generates excellent discussion from a statistical and public health point of view when I use the data in my face to face training courses.