ドキュメンテーション

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記述統計

中心傾向、分散、形状および相関の測定値

メモ

MuPAD® Notebook は将来のリリースでは削除される予定です。代わりに MATLAB® ライブ スクリプトを使用してください。

MuPAD Notebook ファイルを MATLAB ライブ スクリプト ファイルに変換するには、convertMuPADNotebook を参照してください。MATLAB ライブ スクリプトは、多少の違いはありますが、MuPAD 機能の大半をサポートします。詳細は、MuPAD Notebook を MATLAB ライブ スクリプトに変換を参照してください。

MuPAD 関数

numeric::gaussAGMGauss' arithmetic geometric mean
stats::correlationCorrelation between data samples
stats::correlationMatrixCompute the correlation matrix associated with a covariance matrix
stats::covarianceCovariance of data samples
stats::cutoffDiscard outliers
stats::winsorizeClamp (winsorize) extremal values
stats::frequencyTally numerical data into classes and count frequencies
stats::geometricMeanGeometric mean of a data sample
stats::harmonicMeanHarmonic mean of a data sample
stats::kurtosisKurtosis (excess) of a data sample
stats::meanArithmetic mean of a data sample
stats::meandevMean deviation of a data sample
stats::medianMedian value of a data sample
stats::modalModal (most frequent) value(s) in a data sample
stats::momentThe K-th moment of a data sample
stats::obliquityObliquity (skewness) of a data sample
stats::quadraticMeanQuadratic mean of a data sample
stats::stdevStandard deviation of a data sample
stats::varianceVariance of a data sample

トピック

Store Statistical Data

MuPAD offers various data containers, such as lists, arrays, tables, and so on, to store and organize data. For details about the MuPAD data structures, see データ構造体. Although, you can use any of these data containers to store statistical data, the following containers serve best. The reason is that many functions of the 統計 library accept these data containers as input parameters:

Compute Measures of Central Tendency

Measures of central tendency locate a distribution of data along an appropriate scale. There are several standard measures of central tendency. Knowing the properties of a particular data sample (such as the origin of the data sample and possible outliers and their values) can help you choose the most useful measure of central tendency for that data sample. MuPAD provides the following functions for calculating the measures of central tendency:

Compute Measures of Dispersion

The measures of dispersion summarize how spread out (or scattered) the data values are on the number line. MuPAD provides the following functions for calculating the measures of dispersion. These functions describe the deviation from the arithmetic average (mean) of a data sample:

Compute Measures of Shape

The measures of shape indicate the symmetry and flatness of the distribution of a data sample. MuPAD provides the following functions for calculating the measures of shape:

Compute Covariance and Correlation

If you have two or more data samples with an equal number of elements, you can estimate how similar these data samples are. The most common measures of similarity of two data samples are the covariance and the correlation. MuPAD provides the following functions for computing the covariance and the correlation of two data samples:

Handle Outliers

The outliers are data points located far outside the range of the majority of the data. Glitches, data-entry errors, and inaccurate measurements can produce outliers in real data samples. The outliers can significantly affect the analysis of data samples. If you suspect that the data that you want to analyze contains outliers, you can discard the outliers or replace them with the values typical for that data sample.

Bin Data

The stats::frequency function categorizes the numerical data into a number of bins given by semiopen intervals (ai, bi]. This function returns a table with the entries (rows) corresponding to the bins. Each entry shows the following information: