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In the analysis of data, a correlogram is an image of correlation statistics. For example, in time series analysis, a correlogram, also known as an autocorrelation plot, is a plot of the sample autocorrelations versus (the time lags). If cross-correlation is used, the result is called a ''cross-correlogram''. The correlogram is a commonly used tool for checking randomness in a data set. This randomness is ascertained by computing autocorrelations for data values at varying time lags. If random, such autocorrelations should be near zero for any and all time-lag separations. If non-random, then one or more of the autocorrelations will be significantly non-zero. In addition, correlograms are used in the model identification stage for Box–Jenkins autoregressive moving average time series models. Autocorrelations should be near-zero for randomness; if the analyst does not check for randomness, then the validity of many of the statistical conclusions becomes suspect. The correlogram is an excellent way of checking for such randomness. Sometimes, corrgrams, color-mapped matrices of correlation strengths in multivariate analysis, are also called correlograms. ==Applications== The correlogram can help provide answers to the following questions: * Are the data random? * Is an observation related to an adjacent observation? * Is an observation related to an observation twice-removed? (etc.) * Is the observed time series white noise? * Is the observed time series sinusoidal? * Is the observed time series autoregressive? * What is an appropriate model for the observed time series? * Is the model : valid and sufficient? * Is the formula valid? 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Correlogram」の詳細全文を読む スポンサード リンク
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