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Comparison Formula for Estimation-real Value Data Sets

The link is not freely available. And your question is not entirely clear. I will guess you are trying to estimate an unknown parameter. Suppose \$theta\$ is the unknown parameter, and you have an estimator \$T\$ of \$theta\$ based on \$n\$ observations. We say that \$T\$ is an unbiased estimator of \$theta\$ if \$E(T) = theta.\$If you have two unbiased estimators \$T_1\$ and \$T_2,\$ then the estimator with the smaller standard deviation is considered to be better because it is more likely to be near the correct value \$theta.\$Two simple examples: (1) If the data are from a normal distribution with unknown mean \$mu\$, then the sample mean \$bar X\$ and the sample median \$tilde X\$ of the \$n\$ observations are both unbiased. In this case, the sample mean is considered the better estimator because it has smaller standard deviation (or variance).(2) If the data are from a population distributed \$Unif(0, theta),\$ then twice the mean and \$(n1)/n\$ times the maximum are both unbiased, and the latter is preferred because it has the smaller standard deviation.However, if estimators are biased, then the variance or SD is no longer an optimal guide. They can be in error because of the bias or because of sampling variability and it is difficult compare such estimators using the SD or variance alone.For biased estimators one reasonable criterion for 'goodness' is to have a small mean squared error. One can show that \$\$MSE_theta (T) = E[(T-theta)^ = V(T) [b_theta(T)]^2,\$\$ where \$[b_theta (T)]^2 = [E(T)-theta]^2\$ is the square of the bias. In example (2) above, the maximum is biased unless it is multiplied by \$(n1)/n.\$ However, the maximum has smaller MSE than double the mean, and so the maximum is considered the better estimator according to the MSE criterion. â€¢ Other Related Knowledge ofdata sets

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What does it mean to calculate frequencies within a data set?

How many times x happens in a given attribute.Take a look at this pic.In the relational database world columns are called columns.In machine learning they are called feature or attributes. So, in Col 1 the value 1 shows up twice and so does the value 2.The frequency count of value 1 and 2 are 2 in the Col 1 attribute or column.What does it mean to calculate frequencies within a data set?. â€” â€” â€” â€” â€” â€”

Training algorithm for finding relationships within a data set

Given your example, if you want to group phrases based on terms, a simple approach would be to use grep to find rows that contain specific letter sequences (e.g. "art", "ball"). For example, say your table is a csv file (myfile.csv), where each line is a word or phrase (e. g. "Football", "Jazz music"). We can use a UNIX terminal to get all lines that contain "ball":This would yield a file (myresults.txt) that containts the lines numbers of phrases that contain "ball". You can also apply grep to a data frame version of your table using R or Python, so you can loop over different letter sequences and save the found relationships to a separate column of the data frame or a separate data frame or vector.

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What is exactly meant by a "data setâ€?

There are already some good answers here and I do not think I can penetrate any deeper than Nick Cox or Franck Dernoncourt the issue of whether "dataset" refers to the conceptual collection of related data, or to the particular arrangement of those data e.g. into a table/matrix or a computer-readable file. Franck's extract mentions edge cases like continuously-collected data, or data spread across several tables, which are worth bearing in mind if you assumed there was going to be a simple definition. (Not all statistics software can handle it, but it is very easy to imagine a case where data is stored in a relational database with multiple tables. Is the entire database a single "dataset"?)One thing I will add though is that datasets are not generally sets, in the mathematical sense! Sensu stricto either a set contains an object or it does not , but can not contain more than one copy of that object. If I roll a die eight times and score 1, 4, 3, 5, 5, 4, 6, 4 then the set of scores rolled is just 1, 3, 4, 5, 6. Note that the elements could be in any order, I've just written them ascending in value but the set 5, 4, 1, 6, 3 is mathematically equal to it, for instance. This is not what we usually mean by a dataset though!A multiset (or bag) allows entries to be repeated, e.g. 1, 4, 3, 5, 5, 4, 6, 4 though note this still does not include a sense of order, so is equal to 1, 3, 4, 4, 4, 5, 5, 6. Perhaps the "set" in "dataset" might best be read as "multiset". Moreover, if you want order to be preserved, you might instead use a vector: (1, 4, 3, 5, 5, 4, 6, 4) is not the same as (1, 3, 4, 4, 4, 5, 5, 6). The ordering gives us an index which can serve as a kind of identifier - it tells us, for instance, "which four is which?" - and which often serves a purpose for recording observations in their natural temporal or geographic order. When one sees formulae such as \$bar x = frac1n sum_i=1^n x_i\$ this sort of indexing scheme is assumed. In the context of a set or multiset, what would \$x_1\$ or \$x_2\$ mean, given that we can not distinguish a "first" or "second" element due to the lack of ordering?But vectors are only for recording one variable - for several, it may be more convenient to use a matrix to tabulate with order preserved. For more sophisticated situations such as measuring a property of a three-dimensional grid of voxels over time, you might even move up to arranging the data in a tensor (see e.g. this question).But note that conceptually a multiset may suffice in most simple situations, even if it's inconvenient for practical purposes. If I tossed a coin simultaneously with rolling the die, and wanted to record the two results together, then I could use a multiset like (1, H), (3, T), (4, H), (4, H), (4, T), (5, H), (5, T), (6, T) instead of a matrix. An ordinary set will not suffice, as it would not count the multiplicity of the (4, H), for instance.

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