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Polynomial regression: Difference between revisions
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3x**2+2x+1
1.0848x**2+10.3552x-0.6164
</pre>
=={{header|BBC BASIC}}==
{{works with|BBC BASIC for Windows}}
The code listed below is good for up to 10000 data points and fits an order-5 polynomial, so the test data for this task is hardly challenging!
<lang bbcbasic> INSTALL @lib$+"ARRAYLIB"
Max% = 10000
DIM vector(5), matrix(5,5)
DIM x(Max%), x2(Max%), x3(Max%), x4(Max%), x5(Max%)
DIM x6(Max%), x7(Max%), x8(Max%), x9(Max%), x10(Max%)
DIM y(Max%), xy(Max%), x2y(Max%), x3y(Max%), x4y(Max%), x5y(Max%)
npts% = 11
x() = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
y() = 1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321
sum_x = SUM(x())
x2() = x() * x() : sum_x2 = SUM(x2())
x3() = x() * x2() : sum_x3 = SUM(x3())
x4() = x2() * x2() : sum_x4 = SUM(x4())
x5() = x2() * x3() : sum_x5 = SUM(x5())
x6() = x3() * x3() : sum_x6 = SUM(x6())
x7() = x3() * x4() : sum_x7 = SUM(x7())
x8() = x4() * x4() : sum_x8 = SUM(x8())
x9() = x4() * x5() : sum_x9 = SUM(x9())
x10() = x5() * x5() : sum_x10 = SUM(x10())
sum_y = SUM(y())
xy() = x() * y() : sum_xy = SUM(xy())
x2y() = x2() * y() : sum_x2y = SUM(x2y())
x3y() = x3() * y() : sum_x3y = SUM(x3y())
x4y() = x4() * y() : sum_x4y = SUM(x4y())
x5y() = x5() * y() : sum_x5y = SUM(x5y())
matrix() = \
\ npts%, sum_x, sum_x2, sum_x3, sum_x4, sum_x5, \
\ sum_x, sum_x2, sum_x3, sum_x4, sum_x5, sum_x6, \
\ sum_x2, sum_x3, sum_x4, sum_x5, sum_x6, sum_x7, \
\ sum_x3, sum_x4, sum_x5, sum_x6, sum_x7, sum_x8, \
\ sum_x4, sum_x5, sum_x6, sum_x7, sum_x8, sum_x9, \
\ sum_x5, sum_x6, sum_x7, sum_x8, sum_x9, sum_x10
vector() = \
\ sum_y, sum_xy, sum_x2y, sum_x3y, sum_x4y, sum_x5y
PROC_invert(matrix())
vector() = matrix().vector()
@% = &2040A
PRINT "Polynomial coefficients = "
FOR term% = 5 TO 0 STEP -1
PRINT ;vector(term%) " * x^" STR$(term%)
NEXT</lang>
Output:
<pre>
Polynomial coefficients =
0.0000 * x^5
-0.0000 * x^4
0.0002 * x^3
2.9993 * x^2
2.0012 * x^1
0.9998 * x^0
</pre>
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