{"id":61799,"date":"2026-01-13T13:32:05","date_gmt":"2026-01-13T13:32:05","guid":{"rendered":"https:\/\/www.dewetron.com\/?p=61799"},"modified":"2026-01-22T13:15:06","modified_gmt":"2026-01-22T13:15:06","slug":"statistische-datenanalyse-in-oxygen","status":"publish","type":"post","link":"https:\/\/www.dewetron.com\/de\/news\/statistische-datenanalyse-in-oxygen\/","title":{"rendered":"Was ist statistische Datenanalyse?"},"content":{"rendered":"<div id=\"\" class=\"wp-block-text-columns margin-bottom-small\n      \">\n  <div class=\"container\">\n    <div class=\"row justify-content-center\">\n              <div class=\"col-12\">\n          <h2><\/h2>\n          <div class=\"content-size-normal\">\n            <p>Moderne Messaufbauten erzeugen enorme Datenmengen \u2013 doch reine Rohdaten erz\u00e4hlen selten die ganze Geschichte. Statistische Analyse hilft dabei, diese Daten in ein klares, aussagekr\u00e4ftiges Bild zu verwandeln: Sie fasst Ergebnisse sinnvoll zusammen, deckt Muster auf und erm\u00f6glicht sogar Vorhersagen \u00fcber zuk\u00fcnftiges Verhalten. Im Folgenden erkl\u00e4ren wir die Grundlagen der statistischen Datenanalyse und werfen einen Blick auf die statistischen Werkzeuge in unserer Messsoftware OXYGEN.<\/p>\n<p>&nbsp;<\/p>\n<h3>Was versteht man unter statistischer Datenanalyse?<\/h3>\n<p>Statistische Datenanalyse bezeichnet den Prozess, Messdaten zu untersuchen, zu transformieren und zu interpretieren, um Muster zu erkennen, Schwankungen zu quantifizieren und fundierte Entscheidungen zu treffen. Statt sich auf Einzelwerte oder eine rein visuelle Beurteilung zu verlassen, liefert die Statistik eine mathematische Grundlage daf\u00fcr, wie sich Systeme tats\u00e4chlich verhalten.<\/p>\n<p>Obwohl es unz\u00e4hlige statistische Methoden gibt, lassen sich die meisten zwei grundlegenden Kategorien zuordnen:<\/p>\n<ul>\n<li>Deskriptive Statistik:\u00a0Sie beschreibt und fasst die wichtigsten Merkmale eines Datensatzes zusammen. Dazu geh\u00f6ren z. B. Mittelwerte, Streuungen, Bereiche, Perzentile oder Verteilungen \u2013 also alles, was zeigt, was die Daten widerspiegeln.<\/li>\n<li>Inferenzstatistik: Sie geht einen Schritt weiter: Auf Basis von Wahrscheinlichkeiten, Modellen und Stichproben werden R\u00fcckschl\u00fcsse \u00fcber ein gr\u00f6\u00dferes System gezogen. Damit lassen sich Vorhersagen treffen, Parameter sch\u00e4tzen oder Hypothesen pr\u00fcfen.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<table width=\"1100\" cellpadding=\"10\" align=\"center\">\n<tbody>\n<tr>\n<td style=\"border-bottom: 1px solid #cbcbcb; text-align: left;\"><strong>Aspekt<\/strong><\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb; text-align: left;\"><strong>Deskriptive Statistik<\/strong><\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb; text-align: left;\"><strong>Inferenzstatistik<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"border-bottom: 1px solid #cbcbcb; color: #0095db;\">Ziel<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb;\">Beschreibt beobachtete Daten<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb;\">Zieht Schlussfolgerungen oder macht Vorhersagen \u00fcber beobachtete Daten hinaus.<\/td>\n<\/tr>\n<tr>\n<td style=\"border-bottom: 1px solid #cbcbcb; color: #0095db;\">Typische Ergebnisse<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb; text-align: left;\">Mittelwert, Median, Min\/Max, Standardabweichung, Perzentile, Verteilungen<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb; text-align: left;\">Konfidenzintervalle, Hypothesentests, Wahrscheinlichkeiten, Regressionsmodelle<\/td>\n<\/tr>\n<tr>\n<td style=\"border-bottom: 1px solid #cbcbcb; color: #0095db;\">Beispiele<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb; text-align: left;\">Histogramme, Korrelationen, Boxplots, einfache Regressionen<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb; text-align: left;\">Bayessche Methoden, t-Tests, Chi-Quadrat, komplexe Regressionen, Vorhersagemodelle<\/td>\n<\/tr>\n<tr>\n<td style=\"border-bottom: 1px solid #cbcbcb; color: #0095db; text-align: left;\">Datengrundlage<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb; text-align: left;\">Gesamter Datensatz<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb; text-align: left;\">Stichprobe gen\u00fcgt<\/td>\n<\/tr>\n<tr>\n<td style=\"border-bottom: 1px solid #cbcbcb; color: #0095db;\">Risiko<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb;\">Gering \u2013 rein beschreibend<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb;\">H\u00f6her \u2013 basiert auf Wahrscheinlichkeiten<\/td>\n<\/tr>\n<tr>\n<td style=\"border-bottom: 1px solid #cbcbcb; color: #0095db;\">Annahmen<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb; text-align: left;\">Wenige, datengetrieben<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb; text-align: left;\">Abh\u00e4ngig von statistischen Modellen<\/td>\n<\/tr>\n<tr>\n<td style=\"border-bottom: 1px solid #cbcbcb; color: #0095db;\">Typische Anwendungen<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb; text-align: left;\">Trends erkennen, Signale zusammenfassen<\/td>\n<td style=\"border-bottom: 1px solid #cbcbcb; text-align: left;\">Systemverhalten vorhersagen, Leistungsgrenzen sch\u00e4tzen<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: center;\"><em>Tab. 1: Grundlegender Vergleich zwischen deskriptiver und inferenzieller Statistik<\/em><\/p>\n<p>&nbsp;<\/p>\n<h3>Warum und wo wird statistische Analyse eingesetzt?<\/h3>\n<p>Immer dann, wenn Daten interpretiert, verglichen oder zur Entscheidungsfindung genutzt werden m\u00fcssen, ist statistische Analyse hilfreich. W\u00e4hrend Prozesse in der Theorie stabil und st\u00f6rungsfrei sein sollten, zeigt die Realit\u00e4t etwas anderes: Rauschen, Schwankungen, Umwelteinfl\u00fcsse und dynamisches Verhalten beeinflussen jede Messung. Statistik hilft, diese Komplexit\u00e4t verst\u00e4ndlich zu machen.<\/p>\n<p>Je nach Branche kommen unterschiedliche Methoden zum Einsatz:<\/p>\n<p>&nbsp;<\/p>\n<h5>Automotive Testing<\/h5>\n<ul>\n<li>Vergleich wiederholter Man\u00f6ver mithilfe von Perzentilen<\/li>\n<li>Bewertung von NVH- und Vibrationsverhalten \u00fcber RMS und Standardabweichung<\/li>\n<li>Analyse von Bremsverhalten mit Min\/Max- und Zeitreihenstatistiken<\/li>\n<\/ul>\n<h5>Power &amp; Energy<\/h5>\n<ul>\n<li>Langzeitstabilit\u00e4t von Stromnetzen \u00fcber gleitende Mittelwerte<\/li>\n<li>Auswertung von Lastzyklen (Min\/Max\/Avg pro Zyklus)<\/li>\n<li>Erkennen ungew\u00f6hnlicher Zust\u00e4nde \u00fcber Histogramme und Verteilungen<\/li>\n<\/ul>\n<h5>Aerospace &amp; Defense<\/h5>\n<ul>\n<li>Bewertung von Strukturbelastungen mit Spitzenwerten<\/li>\n<li>Bayessche Absch\u00e4tzung von Ausfallwahrscheinlichkeiten<\/li>\n<li>Analyse von Vibrationen mit spektralen und Varianz-basierten Kennwerten<\/li>\n<\/ul>\n<h5>Manufacturing &amp; Quality Control<\/h5>\n<ul>\n<li>\u00dcberwachung der Prozessstabilit\u00e4t \u00fcber Standardabweichungen und Kontrollgrenzen<\/li>\n<li>Erkennen von Ausrei\u00dfern via Boxplots oder Perzentilbereichen<\/li>\n<li>Chargenvergleiche \u00fcber Korrelationen oder einfache Hypothesentests<\/li>\n<\/ul>\n<h5>Research &amp; Development<\/h5>\n<ul>\n<li>Untersuchung von Parameterbeziehungen mittels Regression und Korrelation<\/li>\n<li>Identifikation von Rauschmustern \u00fcber Verteilungen oder RMS-Werte<\/li>\n<li>Validierung theoretischer Annahmen durch Hypothesentests<\/li>\n<\/ul>\n<h5>Langzeit- und Felddatenanalyse<\/h5>\n<ul>\n<li>Zusammenfassung riesiger Datenmengen zu aussagekr\u00e4ftigen Kennzahlen<\/li>\n<li>Erkennen langsamer Degradationsprozesse \u00fcber gleitende Mittelwerte<\/li>\n<li>Identifikation seltener Ereignisse via Histogramme oder Grenzwert\u00fcberwachung<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><img decoding=\"async\" class=\"aligncenter\" src=\"https:\/\/www.dewetron.com\/app\/uploads\/2025\/12\/Statistics_OXY_Screen1.png\" alt=\"\" width=\"800\" \/><\/p>\n<p style=\"text-align: center;\"><em>Abb. 1: Statistische Auswertung der Verz\u00f6gerung bei der Netzwerk\u00fcbertragung mittels OXYGEN<\/em><\/p>\n<p>&nbsp;<\/p>\n<h3>Welche Optionen bietet OXYGEN zur statistischen Datenanalyse?<\/h3>\n<p>Unsere Messsoftware OXYGEN stellt eine breite Palette an Analysewerkzeugen bereit \u2013 von einfachen Frequenzbewertungen bis hin zu anspruchsvollen Leistungsanalysen oder Shock-Response-Berechnungen.<br \/>\nIm Bereich der Statistik bietet OXYGEN umfassende Funktionen, insbesondere f\u00fcr deskriptive und zusammenfassende Auswertungen. Dazu geh\u00f6ren:<\/p>\n<ul>\n<li>Basiskennwerte (Mean, Min, Max, Standardabweichung usw.)<\/li>\n<li>Block- und gleitende Statistik<\/li>\n<li>Getriggerte Statistik f\u00fcr ereignisbasierte Auswertung<\/li>\n<li>Statistik \u00fcber gesamte Messungen<\/li>\n<li>Array-Statistik<\/li>\n<li>Histogramme<\/li>\n<li>Korrelation<\/li>\n<li>\u2026 sowie weitere Funktionen f\u00fcr professionelle Test- und Messabl\u00e4ufe<\/li>\n<\/ul>\n\n          <\/div>\n        <\/div>\n          <\/div>\n  <\/div>\n<\/div>\n\n\n<div id=\"\" class=\"wp-block-claim-links-gray margin-bottom-none\">\n  <div class=\"container-fluid bg-container py-7 bg-darkgray\">\n    <div class=\"container position-relative\">\n      <div class=\"claim col-12 col-lg-8 offset-lg-2 text-center text-light\">\n                  <h3 class=\"h3 text-uppercase\">DOWNLOAD<\/h3>\n                          <h4 class=\"mb-0\">\n            F\u00fcr eine vollst\u00e4ndige \u00dcbersicht aller Statistikfunktionen in OXYGEN empfehlen 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