f5e38ea3-bfd4-4e2e-a582-23b2b7cb2429 5 3 12 decimal

&[DATE] &[TIME] - &[FILENAME]

XY 001 2.6 3 23.16 4 27.57 5 24.26 6 16.63 8 30.41 11 47.2 12 50.03 13 60.33 14 59.89 16 71.18 17 84.27 19 77.69 142 mat :

data char size clr plot k 1 data rows range r3 k el char : r4 k el size : r5 k el clr : 13 mat for data r3 r4 r5 augment 21 line :

X XY 1 col : Y XY 2 col : 12 mat data XY + 5 black plot : 21 sys

x1 X 14 range 11 range el : x2 X 59 range 11 range el : x3 X 1014 range 11 range el : 31 sys

y1 Y 14 range 11 range el : y2 Y 59 range 11 range el : y3 Y 1014 range 11 range el : 31 sys

• medfit(vx, vy) Returns a vector containing the coefficients a and b for a line of the form a + b·x that best approximates the data in vectors vx and vy using median-median regression. The median-median algorithm divides data sets into 3 smaller sets of approximately the same size. Each set is then represented by a single point determined by the medians of the x and y data in the set. A line is then fit to these 3 points. The intercept is chosen so that the line passes through the point which is the average of the medians among the three groups:

S y1 Median y2 Median y3 Median 31 mat Mean y1 Median y2 Median y3 Median 31 mat Median - 2 / : 3.873

I x1 Median x2 Median x3 Median 31 mat Mean : 8.667

a X Y Intercept : b X Y Slope : 21 mat 1.587 4.287 21 mat

I S x * + a b x * + data 3 1 sys