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A Dot Plot is a graphical display of data using dots.
Example: Minutes To Eat Breakfast
A survey of 'How long does it take you to eat breakfast?' has these results:
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| Minutes: | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| People: | 6 | 2 | 3 | 5 | 2 | 5 | 0 | 0 | 2 | 3 | 7 | 4 | 1 |
Which means that 6 people take 0 minutes to eat breakfast (they probably had no breakfast!), 2 people say they only spend 1 minute having breakfast, etc.
And here is the dot plot:
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Another version of the dot plot has just one dot for each data point like this:
Example: (continued)
This has the same data as above:
But notice that we need numbers on the side so we can see what the dots mean.
Grouping
Example: Access to Electricity across the World
Some people don't have access to electricity (they live in remote or poorly served areas). A survey of many countries had these results:
| Country | Access to Electricity (% of population) |
|---|---|
| Algeria | 99.4 |
| Angola | 37.8 |
| Argentina | 97.2 |
| Bahrain | 99.4 |
| Bangladesh | 59.6 |
| ... | ... etc |
But hang on! How do we make a dot plot of that? There might be only one '59.6' and one '37.8', etc. Nearly all values will have just one dot.
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The answer is to group the data (put it into 'bins').
In this case let's try rounding every value to the nearest 10%:
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| Country | Access to Electricity (% of population, nearest 10%) |
|---|---|
| Algeria | 100 |
| Angola | 40 |
| Argentina | 100 |
| Bahrain | 100 |
| Bangladesh | 60 |
| ... | ... etc |
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Now we count how many of each 10% grouping and these are the results:
| Access to Electricity (% of population, nearest 10%) | Number of Countries |
|---|---|
| 10 | 5 |
| 20 | 6 |
| 30 | 12 |
| 40 | 5 |
| 50 | 4 |
| 60 | 5 |
| 70 | 6 |
| 80 | 10 |
| 90 | 15 |
| 100 | 34 |
So there were 5 countries where only 10% of the people had access to electricity, 6 countries where 20% of the people had access to electricity, etc
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Here is the dot plot:
Percent of Population with Access to Electricity
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And that is a good plot, it shows the data nicely.