Sentences with phrase «curve fitting equations»

I use the same curve fitting equations as before.

Not exact matches

The list of accomplishments is far too large to fit within one article, but they include: the first search for extraterrestrial intelligence; creation of the Drake equation; discovery of flat galactic rotation curves; first pulsar discovered in a supernova remnant; first organic polyatomic molecule detected in interstellar space; black hole detected at the center of the Milky Way; determination of the Tully - Fisher relationship; detection of the first interstellar anion; measurement of the most massive neutron star known; first high angular resolution image of the Sunyaev - Zel» Dovich Effect; discovery of only known millisecond pulsar in a stellar triple system; discovery of pebble - sized proto - planets in Orion, and the first detection of a chiral molecule in space.

We analyze the effects of serial position on forgetting and investigate what mathematical equations present a good fit to the Ebbinghaus forgetting curve and its replications.

The student is asked to find the equation of the best fit curve and justify their answer.

The student will collect and analyze data, determine the equation of the curve of best fit in order to make predictions, and solve real - world problems, using mathematical models.

I used Excel's plotting capability to determine regression equations (i.e., linear curve fits).

I used Excel plots to determine regression equations (i.e., linear curve fits) of balances versus the percentage earnings yield 100E10 / P.

I used Excel's curve fitting capability to fit straight lines to the data and to report the equations (i.e., regression equations) and goodness of fit (R - squared).

Return0 = y and HSWR50 = x. Years R - Squared Equation 1... 0.0908 y = 2.647 x - 10.053 2... 0.1704 y = 2.6542 x - 10.527 3... 0.2519 y = 2.506 x - 9.8705 4... 0.3386 y = 2.5412 x - 10.112 5... 0.4022 y = 2.4671 x - 9.7509 6... 0.4710 y = 2.6452 x - 9.7732 7... 0.5370 y = 2.4858 x - 9.8994 8... 0.6018 y = 2.4853 x - 9.9486 9... 0.6775 y = 2.4061 x - 9.5746 10... 0.7381 y = 2.341 x - 9.2439 11... 0.8002 y = 2.3113 x - 9.107 12... 0.8643 y = 2.33 x - 9.2065 13... 0.8949 y = 2.2709 x - 8.8728 14... 0.9027 y = 2.1245 x - 8.0652 15... 0.8964 y = 1.9819 x - 7.2668 16... 0.8773 y = 1.8006 x -6.2552 17... 0.8665 y = 1.6398 x - 5.3589 18... 0.8509 y = 1.5422 x - 4.7903 19... 0.8036 y = 1.412 x - 4.0325 20... 0.7286 y = 1.2552 x - 3.1208 21... 0.6626 y = 1.1094 x - 2.2726 22... 0.6045 y = 0.9825 x - 1.5285 23... 0.5293 y = 0.8381 x - 0.6976 24... 0.4287 y = 0.6779 x + 0.2077 25... 0.3156 y = 0.5041 x + 1.1781 26... 0.2079 y = 0.3336 x + 2.1127 27... 0.1100 y = 0.2031 x + 2.829 28... 0.0380 y = 0.1084 x + 3.3421 29... 0.0109 y = 0.0603 x + 3.583 30... 0.0004 y = 0.0116 x + 3.8169 Return0 = y and HSWR80 = x. Years R - Squared Equation 1... 0.1258 y = 2.9412 x - 12.533 2... 0.2332 y = 2.9192 x - 13.189 3... 0.3294 y = 2.6717 x - 11.883 4... 0.4193 y = 2.616 x - 11.611 5... 0.4715 y = 2.4141 x - 10.377 6... 0.5241 y = 2.2778 x - 9.5372 7... 0.5778 y = 2.2064 x - 9.0847 8... 0.6309 y = 2.1431 x - 8.7466 9... 0.6951 y = 2.0267 x - 8.0839 10... 0.7478 y = 1.9526 x - 7.6448 11... 0.8085 y = 1.9398 x - 7.5947 12... 0.8703 y = 1.9776 x - 7.8088 13... 0.9001 y = 1.9485 x - 7.584 14... 0.9045 y = 1.8432 x - 6.8875 15... 0.8910 y = 1.7278 x - 6.112 16... 0.8566 y = 1.5774 x - 5.1143 17... 0.8395 y = 1.4588 x - 4.3331 18... 0.8151 y = 1.3856 x - 3.8072 19... 0.7602 y = 1.2813 x - 3.0685 20... 0.6819 y = 1.1517 x - 2.1588 21... 0.6170 y = 1.0346 x - 1.3363 22... 0.5699 y = 0.9358 x - 0.6359 23... 0.5099 y = 0.8232 x + 0.1334 24... 0.4265 y = 0.6936 x + 0.9921 25... 0.3218 y = 0.5417 x + 1.9799 26... 0.2284 y = 0.4031 x + 2.852 27... 0.1451 y = 0.2931 x + 3.542 28... 0.0826 y = 0.2118 x + 4.0356 29... 0.0551 y = 0.1774 x + 4.2041 30... 0.0318 y = 0.1399 x + 4.3784 Notice that the curve fit is especially good from years 11 through 19 for HSWR50 (with 50 % stocks) and 11 through 18 for HSWR80 (with 80 % stocks).

I used Excel's curve fitting capability to fit straight lines to the data and report the equations (i.e., regression equations) and goodness of fit (R - squared).

I used Excel to determine regression equations (i.e., straight - line, linear curve fits).

An exponential curve fit of the dividend amount has the equation: Dividend amount = 2E - 47 * exp (0.055 * Year) with R - squared = 0.989.

I used Excel's plotting function to calculate regression equations (i.e., linear, straight - line curve fits) of the dividend amount at Year 10 and at Year 20 versus the percentage earnings yield 100E10 / P.

From what he's said I think he's using his longer equation that he got from curve fitting to model output.

I think, but am not sure, that he is trying to say that his «lagged linear equation» (ie his fudged curve fit from his previous article) is the «functional equivalent» of ∆ Ts = λ.

Also when you do a least squares curve fit using the normal equations (the usual method), there is an implicit weighting of the data points proportional to their distance from the center.

The precision of the best - fitting equations was then evaluated using receiver operating characteristic (ROC) curve analysis.