scatterplot怎么用 slots怎么玩

Scatter (XY) Plots
Scatter Plots
A Scatter (XY) Plot has points
that show the relationship between two sets of data.
In this example, each dot shows one person's weight versus their height.
(The data is plotted
on the graph as
The local ice cream shop keeps track of how much ice cream they sell versus the noon temperature on that day. Here are their figures for the last 12 days:
Ice Cream Sales vs Temperature
Temperature &C
Ice Cream Sales
And here is the same data as a Scatter Plot:
It is now easy to
see that warmer weather leads to more sales, but the relationship is not perfect.
Line of Best Fit
We can also draw a &Line of Best Fit& (also called a &Trend Line&) on our scatter plot:
Try to have the line as close as possible to all points, and
as many points above the line as below.
Example: Sea Level Rise
A Scatter Plot of Sea Level Rise:
And here I have drawn on a &Line of Best Fit&.
Interpolation and Extrapolation
Interpolation is where we find a value inside our set of
data points.
use linear interpolation to estimate the sales at 21 &C.
Extrapolation is where we find a value outside our set of
data points.
use linear extrapolation to estimate the sales at 29 &C (which is higher than any value we have).
Careful: Extrapolation can
give misleading results because we are in &uncharted territory&.
As well as using a graph (like above) we can create a formula to help us.
We can estimate a
graph above
Let's estimate two points on the line near actual values: (12&, $180) and (25&, $610)
First, find the slope:
change in y
change in x
$610 & $180
= 33 (rounded)
Now put the slope and the point (12&, $180) into the :
y & y1 = m(x & x1)
y & 180 = 33(x & 12)
= 33(x & 12) + 180
y = 33x & 396 + 180
y = 33x & 216
Now we can use that equation to interpolate a sales value at 21&:
y = 33&21 & 216 = $477
to extrapolate a sales value at 29&:
y = 33&29 & 216 = $741
The values are close to what we got on the graph. But that doesn't mean they are more (or less) accurate. They are all just estimates.
Don't use extrapolation too far! What sales would you expect at 0& ?
y = 33&0 & 216 = &$216
Hmmm... Minus $216? We
extrapolated too far!
Note: we used linear (based on a line) interpolation and
extrapolation, but there are many other types, for example we could use polynomials to make curvy lines, etc.
Correlation
When the two sets of data are strongly linked together we say they have a High Correlation.
The word Correlation is made of Co- (meaning &together&), and Relation
Correlation is Positive when the values increase together, and
Correlation is Negative when one
value decreases as the other increases
Like this:
Negative CorrelationCorrelations can
be negative, which means there is a correlation but one value goes down as the other value increases.
Example : Birth Rate vs Income
The birth rate tends to be
lower in richer countries.
Below is a scatter plot for about 100 different countries.
Production
per Person
Madagascar
It has a negative correlation (the line slopes down)
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