I'm trying to calculate standard deviation & variance. My code loads a file of 100 integers and puts them into an array, counts them, calculates the mean, sum, variance and SD. But I'm having a little trouble with the variance. I keep getting a huge number - I have a feeling it's to do with its calculation. My mean and sum are ok. NB:
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Where is the information about the 'new' PCs and how much variance they account for? This might come in useful if, for example, I am using preProcess(<SOME_MATRIX>, method ="pca", thresh = 0.8) and this returns 6 PCs, but I find that the first 5 PCs explain a total of 79.5% of the variance. Then I might be inclined not to include all 6 PCs.
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Well, there are two ways for defining the variance. You have the variance n that you use when you have a full set, and the variance n-1 that you use when you have a sample. The difference between the 2 is whether the value m = sum(xi) / n is the real average or whether it is just an approximation of what the average should be.
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I have a data frame with these columns: Date, ID, and Value. And I need to perform mean, median and variance on Value and I used .agg like this: df = dataset\\ .groupby(['ID', pd.Grouper(key='D...
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In standard statistical practice, ddof=1 provides an unbiased estimator of the variance of a hypothetical infinite population. ddof=0 provides a maximum likelihood estimate of the variance for normally distributed variables. Statistical libraries like numpy use the variance n for what they call var or variance and the standard deviation
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I've run across this problem as well. There are some great posts out there in computing the running cumulative variance such as John Cooke's Accurately computing running variance post and the post from Digital explorations, Python code for computing sample and population variances, covariance and correlation coefficient.
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Thus pca.explained_variance_ratio_[i] gives the variance explained solely by the i+1st dimension. You probably want to do pca.explained_variance_ratio_.cumsum(). That will return a vector x such that x[i] returns the cumulative variance explained by the first i+1 dimensions.
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The problem is I don't understand conceptually how do I compute variance of an image. Every pixel has 4 values for every color channel, therefore I can compute the variance of every channel, but then I get 4 values, or even 16 by computing variance-covariance matrix, but according to the OpenCV example, they have only 1 number.
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Variance is a measure of the"variability" of the data you have. Potentially the number of components is infinite (actually, after numerization it is at most equal to the rank of the matrix, as @jazibjamil pointed out), so you want to"squeeze" the most information in each component of the finite set you build.
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Sorry it was off topic - haven't used this site before :). I mean the actual variance statistic that is in turn used to calculate the SE and so on. It's easy to calculate, I just wondered if there was a simple call for it. I'll do it by hand though, no matter. Cheers :) –
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