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A boxplot is a 1-dimensional plot. The data points are jittered along the x-axis to make them less crowded.
More on boxplots here:
➡️ https://labplot.kde.org/2021/08/11/box-plot/
➡️ https://userbase.kde.org/LabPlot/2DPlotting/BoxPlot
We used #LabPlot, a free, open source and cross-platform data visualization and analysis software.
LabPlot’s homepage:
➡️ https://labplot.kde.org/
Video tutorials:
➡️ https://www.youtube.com/@LabPlot/videos
Great question! And what’s _your_ answer?
Any exploratory plot forms a question and your comment shows how to look for answers. Thanks!
The points are jittered along the x-axis, otherwise the data points could overlap.
Australia is the next country after Ethiopia, but it’s not outlier in this case.
You can read more on boxplots here:
Our Christmas tree includes a test to check if our readers are humans 😉
Let’s just assume that the Christmas tree includes a test to check if our readers are humans 🙂
The process average X and control limits are added to the plot for men. The average is 63.4. The upper control limit (UCL) is 64.8 and the lower control limit (LCL) is 62.0.
The UCL represents the largest value you would expect if you only have common causes of variation present. The LCL represents the smallest value you would expect if you only have common causes of variation present.
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@dataisbeautiful !health@lemmy.world !labplot@lemmy.kde.social
@Generous1146
Actually, it looks more like the disability-free life expectancy has increased in 2015 and since then stays at about the same level.
#DataViz #Statistics #Visualization #Health #Disability #LabPlot #OpenSource #FOSS
@dataisbeautiful @health @labplot@lemmy.kde.social
According to the Eurostat, in 2021, the number of healthy life years at birth was estimated at 64.2 years for women and 63.1 years for men in the EU, this represented approximately 77.4 % and 81.7 % of the total life expectancy for women and men.
Is the act of distinguishing a question from an answer as difficult as recognizing spurious correlations?
The question has been raised earlier by others,. See for example this paper from 2021 (Measuring the effect of energy consumption on the epidemic
of overweight in Latin America and Caribbean countries):
If you are interested, please see also this thread on the importance of visualizing data (the Anscombe’s quartet, Simpson’s paradox are also included in @LabPlot):
https://mstdn.social/@onemoment/109692198312380103
#Anscombe #SimpsonsParadox #DatasaurusDozen #Visualization #DataViz
We agree. But still, a question is just a question, and you can always refine your questions.
Matejka, J., & Fitzmaurice, G. (2017). Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing.
BTW, the Datasaurus Dozen example is already available in @LabPlot via File > Open Example.
For example: Tonga, Samoa, Kiribati, Nauru with electricity consumption per capita (the median) 548 kWh.
@dataisbeautiful
Thank you for all your comments. A jittering of data points along the x-axis was used to avoid over-plotting. But yes, a scatter plot with a boxplot attached along the y-axis (to show outliers) may be more informative in this case.