I cut off the age vs income plot at 100k because otherwise it would severely compress the points where it would be really hard to discern anything from it. There are people who reported over 100K.
I wanted to do that, but I'm not proficient enough in excel to do it quickly. Surprisingly enough, even getting the histograms was kind of a pain in the ass. I spent an hour and a half getting those 4 charts done.
Yeah, its always shocked me that Microsoft couldn't be bothered to put in decent histogram fucntionality where you can choose your buckets and it'll put together a histogram.
I didn't even notice, I guess it is some sort of bug (or "feature") with excel, because the data driving that chart is correct. The numbers are in the correct order (increasing in 5K increments), but the second half has a zero clipped for some reason.
Perhaps decreasing the font (I use Numbers so don't know whether it is possible in Excel) of the horiz. axis will allow the full text to appear (if you can't display in 1,000's).
Staring at the Income/Frequency histogram, I am wondering why there is more clusering around the 10s and not the 5s. Ex: more salaries clustered around 60K, 70K, and 80K than around 65K, 75K, and 85K.
I wonder if there is a tendency (intended or otherwise) for employers/managers/etc to push salaries towards these numbers. Do they seem more 'round'?
I have some experience here, but I don't know if its really a trend. I was bumped up a while back from an 'uneven' salary to one of those clustering points. It was a small, strange, increase amount, but it was a welcome increase nonetheless!
I was surprised to see that the number of respondents working "40 hours a week" more than doubled any other entry. I listed 65 and felt sure I would be just average. FML lol
I wasn't sure how to respond to that question. I work 40 hours a week at my day job... but I work another 30+ hours a week on other projects (startup projects, etc).
* Family type v. income
* Number of years in industry v. income
* Hours per week v. family status
* Age v. Employment type
* Level of education v. Employment type
* Marital status v. Employment type
* Age v. Hours per week
If you have any visualisation techniques that are surprising or especially revealing I would be very interested both to look at the data and to learn about data visualisation.