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# 記述統計

### 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。

 `numeric::gaussAGM` Gauss' arithmetic geometric mean `stats::correlation` Correlation between data samples `stats::correlationMatrix` Compute the correlation matrix associated with a covariance matrix `stats::covariance` Covariance of data samples `stats::cutoff` Discard outliers `stats::winsorize` Clamp (winsorize) extremal values `stats::frequency` Tally numerical data into classes and count frequencies `stats::geometricMean` Geometric mean of a data sample `stats::harmonicMean` Harmonic mean of a data sample `stats::kurtosis` Kurtosis (excess) of a data sample `stats::mean` Arithmetic mean of a data sample `stats::meandev` Mean deviation of a data sample `stats::median` Median value of a data sample `stats::modal` Modal (most frequent) value(s) in a data sample `stats::moment` The K-th moment of a data sample `stats::obliquity` Obliquity (skewness) of a data sample `stats::quadraticMean` Quadratic mean of a data sample `stats::stdev` Standard deviation of a data sample `stats::variance` Variance 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:

#### Mathematical Modeling with Symbolic Math Toolbox

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