Is there a PCF way to calculate (or approximate) the average of multiple Linear Regression values. I'm interested in knowing the average of LR's from a duration of 10 to 200 in ten increments. 

Such that n equals:      (LR10+LR20+LR30+LR40+LR50+LR60+LR70+LR80+LR90+

LR100+LR110+RL120+LR130+LR140+LR150+LR160+LR170+LR180+LR190+LR200) / 20 = n

thanks,

jynkin

Please try the following:

.06749693 * C + .06303906 * C1 + .0585812 * C2 + .05412333 * C3 + .04966547 * C4 + .0452076 * C5 + .04074974 * C6 + .03629187 * C7 + .03183401 * C8 + .02737614 * C9 + .03291828 * C10 + .03118769 * C11 + .0294571 * C12 + .0277265 * C13 + .02599591 * C14 + .02426532 * C15 + .02253473 * C16 + .02080414 * C17 + .01907354 * C18 + .01734295 * C19 + .02061236 * C20 + .019596053 * C21 + .01857975 * C22 + .01756344 * C23 + .01654713 * C24 + .01553083 * C25 + .01451452 * C26 + .01349821 * C27 + .01248191 * C28 + .0114656 * C29 + .01378263 * C30 + .0130889 * C31 + .01239518 * C32 + .01170145 * C33 + .01100773 * C34 + .010314 * C35 + .009620274 * C36 + .008926548 * C37 + .008232823 * C38 + .007539097 * C39 + .009345371 * C40 + .008834572 * C41 + .008323773 * C42 + .007812974 * C43 + .007302175 * C44 + .006791376 * C45 + .006280578 * C46 + .005769779 * C47 + .00525898 * C48 + .004748181 * C49 + .006237382 * C50 + .00584423 * C51 + .005451078 * C52 + .005057926 * C53 + .004664774 * C54 + .004271623 * C55 + .003878471 * C56 + .003485319 * C57 + .003092167 * C58 + .002699015 * C59 + .00397253 * C60 + .003661345 * C61 + .003350161 * C62 + .003038976 * C63 + .002727791 * C64 + .002416607 * C65 + .002105422 * C66 + .001794238 * C67 + .001483053 * C68 + .001171868 * C69 + .002289255 * C70 + .002038433 * C71 + .00178761 * C72 + .001536788 * C73 + .001285965 * C74 + .001035143 * C75 + .0007843202 * C76 + .0005334978 * C77 + .0002826753 * C78 + .00003185283 * C79 + .00103103 * C80 + .0008265042 * C81 + .000621978 * C82 + .0004174518 * C83 + .0002129257 * C84 + .0004174519 * C85 - .0002129257 * C86 - .0004006528 * C87 - .000605179 * C88 - .0008097052 * C89 + .00009687976 * C90 - .00007101638 * C91 - .0002389125 * C92 - .0004068086 * C93 - .0005747048 * C94 - .0007426009 * C95 - .0009104971 * C96 - .001078393 * C97 - .001246289 * C98 - .001414185 * C99 - .0005820816 * C100 - .0007202748 * C101 - .0008584679 * C102 - .000996661 * C103 - .001134854 * C104 - .001273047 * C105 - .001411241 * C106 - .001549434 * C107 - .001687627 * C108 - .00182582 * C109 - .001054922 * C110 - .001168545 * C111 - .001282169 * C112 - .001395792 * C113 - .001509415 * C114 - .001623038 * C115 - .001736661 * C116 - .001850284 * C117 - .001963907 * C118 - .002077531 * C119 - .00135782 * C120 - .001450782 * C121 - .001543744 * C122 - .001636706 * C123 - .001729668 * C124 - .00182263 * C125 - .001915592 * C126 - .002008554 * C127 - .002101516 * C128 - .002194478 * C129 - .001518209 * C130 - .001593555 * C131 - .001668901 * C132 - .001744248 * C133 - .001819594 * C134 - .00189494 * C135 - .001970286 * C136 - .002045632 * C137 - .002120978 * C138 - .002196324 * C139 - .001557384 * C140 - .001617532 * C141 - .001677681 * C142 - .001737829 * C143 - .001797978 * C144 - .001858126 * C145 - .001918275 * C146 - .001978423 * C147 - .002038571 * C148 - .00209872 * C149 - .001492202 * C150 - .001539105 * C151 - .001586008 * C152 - .001632912 * C153 - .001679815 * C154 - .001726719 * C155 - .001773622 * C156 - .001820526 * C157 - .001867429 * C158 - .001914332 * C159 - .001336236 * C160 - .001371493 * C161 - .001406751 * C162 - .001442008 * C163 - .001477266 * C164 - .001512523 * C165 - .00154778 * C166 - .001583038 * C167 - .001618295 * C168 - .001653553 * C169 - .001100575 * C170 - .001125512 * C171 - .00115045 * C172 - .001175388 * C173 - .001200325 * C174 - .001225263 * C175 - .0012502 * C176 - .001275138 * C177 - .001300075 * C178 - .001325013 * C179 - .0007943947 * C180 - .0008101241 * C181 - .0008258535 * C182 - .0008415829 * C183 - .0008573124 * C184 - .0008730418 * C185 - .0008887712 * C186 - .0009045006 * C187 - .0009202301 * C188 - .0009359595 * C189 - .0004253731 * C190 - .0004328358 * C191 - .0004402985 * C192 - .0004477612 * C193 - .0004552239 * C194 - .0004626866 * C195 - .0004701493 * C196 - .0004776119 * C197 - .0004850746 * C198 - .0004925373 * C199

Using Linear Regression vs Classical Peaks/Valleys for Divergence Analysis
PCF Formula Descriptions
Handy PCF example formulas to help you learn the syntax of PCFs!

Wow....that's awesomely close to my spreadsheet calculations.  And very useable.  Thanks! jynkin

You're welcome.