TASK 1: Scalable Vector Graphics (SVG)

Scalable Vector Graphics (SVG) are not made of pixels, but lines,
ellipses, and curves, that can be scaled to any size without any loss
of quality. If you have ever tried to resize a small JPG or PNG, you
know what I mean by 'loss of quality'! What many people do not know
about SVG files is, they are simply XML files, so they can easily be
generated programmatically.

For this task, you may use external library, such as Perl's SVG library,
maintained in recent years by our very own Mohammad S Anwar. You can
instead generate the XML yourself; it's actually quite simple. The
source for the example image for Task #2 might be instructive.

Your task is to accept a series of points and lines in the following format, one per line, in arbitrary order:

Point: x,y
Line: x1,y1,x2,y2

Example:

53,10
53,10,23,30
23,30

Then, generate an SVG file plotting all points, and all lines. If done
correctly, you can view the output .svg file in your browser.

MY NOTES: ok.  Having read the SVG Wikipedia page, it seems very easy (although I hate XML).

GUEST LANGUAGE: As a bonus, I also had a go at translating ch-1.pl
into C (look in the C directory for that).


Task 2: Line of Best Fit

When you have a scatter plot of points, a line of best fit is the line
that best describes the relationship between the points, and is very
useful in statistics. Otherwise known as linear regression, here is an
example of what such a line might look like:

[image showing red best fit line on graph of black data points]

The method most often used is known as the least squares method, as
it is straightforward and efficient, but you may use any method that
generates the correct result.

Calculate the line of best fit for the following 48 points:

333,129  39,189 140,156 292,134 393,52  160,166 362,122  13,193
341,104 320,113 109,177 203,152 343,100 225,110  23,186 282,102
284,98  205,133 297,114 292,126 339,112 327,79  253,136  61,169
128,176 346,72  316,103 124,162  65,181 159,137 212,116 337,86
215,136 153,137 390,104 100,180  76,188  77,181  69,195  92,186
275,96  250,147  34,174 213,134 186,129 189,154 361,82  363,89

Using your rudimentary graphing engine from Task #1, graph all points, as well as the line of best fit.

MY NOTES: seems pretty simple, https://www.mathsisfun.com/data/least-squares-regression.html has a good
explanation of the method.  I've put the above data, one point per line, in "bestfitdata" in the
top-level directory.

GUEST LANGUAGE: As a bonus, I also had a go at translating ch-2.pl
into C (look in the C directory for that).