Median running

Running mean Running median Running polynomial Fourier filtering... 

Map-making completed Number of runs 1177 Number of cliques 46614 Mean run length 39.6041 Median run length 19 Max run length 513 Min run length 1... 

Before run ti in g a molecu lar dyn am ics sim ulatioti with solvent and a m olccular median ics meth od, choose the appropriate dielectric con Stan i. You specify th e type an d value of th c dielectric con slari t in thehorce hield Option s dialog box. ITi e dielectric con star t defines the screen irig effect of solvent molecules on nonbonded (electrostalic) in teraction s. 

H.L. Stalcup (Ref 28) evaluated the instru- -ment for the particle size determination of HMX used in rocket formulations. He found that Coulter Counter distribution was similar to that obtained with the Micromerograph except at the large particle end, where the Micromerograph indicated particles over lOOp compared to 32 p for the Coulter Counter. Median values were 13.5p by Micromerograph and 16 p for the Coulter Counter. The samples for the Coulter Counter were run on an as received basis, ie, while still water-isopropanol wet ... 

Report the iteration at which the reaction A + B C + D occurs for each run. Determine the average and median reaction iterations for the set of 10 results. 

In one set of ten runs the following reaction times were observed (placed in numerical order) 35, 185, 204, 346, 444,454, 780, 843, 925, and a long value of 2771. The median time for this set of runs was 449 itn., and the average time for reaction was 699 itn. A second collection of ten runs would very likely yield a quite different set of results. 

Change the reaction probability Pr(AB) to 1.0, and let the simulation run for 1000 iterations. At what time (what iteration) does reaction occur Repeat this simulation nine more times and tabulate the results. Find the average time and its standard deviation for your results, as well as the median time. Next change Pr(AB) to 0.05, increase the number of iterations for each run to 5000, and tabulate the results for 10 trial runs. Repeat the averaging process above. This study reveals the influence of the reaction probability on the course of the reaction. 

Figure 6.15 Chart in which the Reference Center (RC) and participant (PART) data sets are compared. This chart shows HER2/Chl7 ratio results from Runs 4-6 of the UK NEQAS ICC and ISH assessments. The heavy bar indicates the median, the limits of the shaded box the inter-quartiles, and the extending lines the minimum and maximum for the range.

After eight hours, the median objective was —17.33, the worst out of five runs was -17.25. 

Figure 3.5. Results after applying a running median srrioother with varying window widths to the same data found in Figure 3.4.

A CSF sample was analyzed 11-fold. The within-run variation coefficient ranged from 1 to 3.5% with two exceptions tryptophan (5%) and methionine (7%), which partially coeluted. The interassay coefficients of variation were calculated from a series of 11 analyses over a 7-month period. The median CV was 8% only taurine, arginine, and glutamate had CVs slightly in excess of 10%. The recovery of added amino acids to three CSF samples ranged form 83% (taurine) to 101% (isoleucine). Most recoveries were between 90 and 100%. At the lower end of the concentration range for CSF, a level of 1 pmol/1 can be safely detected. 

Melissa runs the 50-yard dash five times, with times of 5.4 seconds, 5.6 seconds, 5.4 seconds, 6.3 seconds, and 5.3 seconds. If she runs a sixth dash, which of the following would change the mean and mode of her scores, but not the median ... 

The data fluctuate because of uncontrolled variables and measurement error. Suppose we want the best single value of the yield, where best means the yield that we can expect in future runs at the same conditions. We could calculate the sample mean X=58.4, the median m=60 and the mode 63. But perhaps the 32% yield was a run involving some error of which we are unaware. We cannot arbitrarily drop the run without knowing the cause of the low value, but the mean places undue weight on it. 

The B score (Brideau et al., 2003) is a robust analog of the Z score after median polish it is more resistant to outliers and also more robust to row- and column-position related systematic errors (Table 14.1). The iterative median polish procedure followed by a smoothing algorithm over nearby plates is used to compute estimates for row and column (in addition to plate) effects that are subtracted from the measured value and then divided by the median absolute deviation (MAD) of the corrected measures to robustly standardize for the plate-to-plate variability of random noise. A similar approach uses a robust linear model to obtain robust estimates of row and column effects. After adjustment, the corrected measures are standardized by the scale estimate of the robust linear model fit to generate a Z statistic referred to as the R score (Wu, Liu, and Sui, 2008). In a related approach to detect and eliminate systematic position-dependent errors, the distribution of Z score-normalized data for each well position over a screening run or subset is fitted to a statistical model as a function of the plate the resulting trend is used to correct the data (Makarenkov et al., 2007). 

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