Stephanie Doctor
This was made using Tableau. In order to find out which factor (age, years with the company, or gender) was most influential in determining salary, I plotted each individually. I plotted salary against age and years with the company as scatterplots since there are two continuous variables, and added a trendline to see the correlation. The R-squared values don't seem to be very telling (perhaps I need a refresher on stats) but both of these cases look to be fairly similar, with positive correlations which would, I think, be better fits if it weren't for that one young/new CEO (at least that's what I assume her position is). I visualized the gender differences as boxplots to show the distribution of salaries for males and females. I didn't run any stats on it but at a glance gender doesn't appear to explain salaries.
I used boxplots again here since the question asked for distributions. I also tried histograms in small multiples but I didn't think it was as easy to interpret. I guess the main takeaway is that engineers are older? I wonder how they've taken to their 27-year-old boss.
Sales:
I made this visualization in Excel and Illustrator. Since the goal was to see both how the regions' sales AND growth compare with one another, I made a dual axis line graph (since the x-axis is time). The partial axis labels was a design choice I tried on a whim but I think I like it! I was originally trying to do solid lines for sales and dotted lines for percent change but I think this works as well, if not better. With this visualization we can see that a) the South has the highest sales by the end of 2009, b) the South also has the highest growth rates at this time and all other regions have nearly stagnated.
I have neither given nor received aid while working on this assignment. I have completed the graded portion before looking at anyone else's work on this assignment. Stephanie Doctor