此插件繪製並比較各種概率密度函數的尾部區域。在解釋頻率論 p 值或貝葉斯單邊後驗概率時，這種比較會很有幫助。該加載項也是了解概率和統計中最常用密度的形狀和形式的便捷方式。
通過將 .jmpaddin 文件拖到 JMP 窗口來安裝插件後，您必須創建一個小的 JMP 表來描述要繪製的密度。每一行對應一個不同的密度，需要幾個具有特定名稱的列來完全指定每個密度和尾部切割點。設置好表格後，確保它處於焦點，然後單擊加載項 > 概率密度尾區。
附加了幾個示例表，名為 pdta_normal.jmp 的表生成以下輸出。 它說明了單邊 p 值和貝葉斯後驗概率的等價性，即在對均值進行正常推斷的情況下具有錯誤符號。
您可以通過三種不同的方式動態更改截止值：1。 單擊並拖動垂直虛線參考線。 2. 在剪切框中輸入值。 3. 單擊並拖動滑塊條中的菱形。
輸入表必須包含以下列：
- 密度（用作圖中的標籤）
- 形式（密度種類的關鍵字，見下表）
- 尾巴（右或左）
- cut（繪製初始切割點的數值）
此外，您必須指定與所需密度參數相對應的數字列。以下摘自加載項的 JSL 代碼顯示了當前可用的密度以及隨附的參數名稱：
required = associativearray();
required["beta"] = {"alpha","beta","theta","sigma"};
required["betabinomial"] = {"p","n","delta"};
required["binomial"] = {"p","n"};
required["cauchy"] = {"mu","sigma"};
required["chisquare"] = {"df","noncentrality"};
required["exp"] = {"theta"};
required["f"] = {"dfnum","dfden","noncentrality"};
required["frechet"] = {"mu","sigma"};
required["gamma"] = {"alpha","scale","threshold"};
required["gengamma"] = {"mu","sigma","lambda"};
required["gammapoisson"] = {"lambda","sigma"};
required["glog"] = {"mu","sigma","lambda"};
required["hypergeometric"] = {"s","k","n"};
required["johnsonsb"] = {"gamma","delta","theta","sigma"};
required["johnsonsl"] = {"gamma","delta","theta","sigma"};
required["johnsonsu"] = {"gamma","delta","theta","sigma"};
required["lev"] = {"mu","sigma"};
required["loggengamma"] = {"mu","sigma","lambda"};
required["logistic"] = {"mu","sigma"};
required["loglogistic"] = {"mu","sigma"};
required["lognormal"] = {"mu","sigma"};
required["negbinomial"] = {"p","n"};
required["normal"] = {"mu","sigma"};
required["poisson"] = {"lambda"};
required["sev"] = {"mu","sigma"};
required["shash"] = {"gamma","delta","theta","sigma"};
required["t"] = {"mu","sigma","df"};
required["weibull"] = {"shape","scale","threshold"};
有關這些密度和參數的更多詳細信息，請參閱 JSL 文檔。
