RäKNA MED VARIATION STUDIEMATERIAL I
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What is the molarity of the KOH solution? (Molecular… Description. THR = wpbmpen(T,SIGMA,ALPHA) returns a global threshold THR for denoising. THR is obtained by a wavelet packet coefficients selection rule using a penalization method provided by Birgé-Massart..
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wname = 'sym6'; lev = 5; tree = wpdec(x,lev,wname); % Estimate the noise standard deviation from the % detail coefficients at level 1, % corresponding to the node index 2. det1 = wpcoef(tree,2); sigma = median(abs(det1))/0.6745; % Use wpbmpen for selecting global threshold % for signal denoising, using the recommended parameter. alpha = 2; thr = wpbmpen(tree is the sample median.
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11 0.6745.
_ |…| is the absolute value operator.
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I grafen över den empiriska Beräkna dess median. 3.49 µ − 0.9945 · σ = 53 µ + 0.6745 · σ = 63. ( ⇔. µ ≈ 58.96 σ data_summary <- function(x) { median <- median(x) sigma1 <- median-0.6745*mad(x) sigma2 <- median+0.6745*mad(x) return(c(y=median,ymin=sigma1,ymax=sigma2)) } The scaling factor 0.6745 adjusts the MAD to constant = 1 (1 / 1.4826 = 0.6745).
MAD = Median (Abs (values - Median (Values))) As per Iglewicz & Hoaglin article, it suggests Modified Z-Score > 3.5 as a outlier. When i apply that rule, it suggests my data has no outliers
Mi=0.6745 * (Xi -Median (Xi)) / MAD, where MAD stands for Median Absolute Deviation.
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SweFreq
load noischir; x = noischir; % Perform a wavelet packet decomposition of the signal % at level 5 using sym6. wname = 'sym6'; lev = 5; tree = wpdec(x,lev,wname); % Estimate the noise standard deviation from the % detail coefficients at level 1, % corresponding to the node index 2. det1 = wpcoef(tree,2); sigma = median(abs(det1))/0.6745; % Use wpbmpen for selecting global threshold % for signal denoising, using the recommended parameter.