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Notes/Semester 3/STS - Stochastik und Statistik/Boxplot.png
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Notes/Semester 3/STS - Stochastik und Statistik/Histogram.png
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Notes/Semester 3/STS - Stochastik und Statistik/Quantiles.png
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@ -1,9 +1,22 @@
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<style>
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img {
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max-height: 300px;
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}
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</style>
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# Stochastik und Statistik
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- [Stochastik und Statistik](#stochastik-und-statistik)
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- [Deskriptive Statistik](#deskriptive-statistik)
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- [Merkmals-Typen](#merkmals-typen)
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- [Häufigkeiten](#häufigkeiten)
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- [Klassierte Stichproben im Histogramm](#klassierte-stichproben-im-histogramm)
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- [Kennwerte](#kennwerte)
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- [Quantile](#quantile)
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- [Boxplot](#boxplot)
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- [Lage-Kennwerte](#lage-kennwerte)
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- [Streuungs-Kennwerte](#streuungs-kennwerte)
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- [Lagewerte in Klassierten Daten](#lagewerte-in-klassierten-daten)
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- [Glossar](#glossar)
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## Deskriptive Statistik
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@ -66,7 +79,7 @@ Frage: Wieviel kostet ein Mars-Riegel?
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| ----------------------- | ----------- | ------------------------------------------------------------------- |
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| Beliebiger Preis in CHF | Diskret | Beträge, die nicht durch 5 Rappen teilbar sind, sind nicht möglich. |
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## Häufigkeiten
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### Häufigkeiten
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Eine Häufigkeit ist die Anzahl Male, die ein Merkmalsträger in der Stichprobe eine bestimmte Eigenschaft erfüllt.
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Diese kann auf verschiedene Weisen dargestellt werden.
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@ -84,6 +97,14 @@ Die Funktion für die Häufigkeitsfunktion (auch genannt: Dichtefunktion) hat fo
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- Für diskrete Merkmale: _PMF_ (probability mass function)
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- Für stetige Merkmale: _PDF_ (probability density function)
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Zudem gibt es folgende Verteilungsfunktionen:
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- $H(x)$ Absolute Summenhäufigkeit: Anzahl Merkmalträger mit Merkmal $x_i$ mit $x_i < x$
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- $F(x)$ Kummulative Verteilungsfunktion _CDF_: Relative Häufigkeit der Merkmalträger mit Merkmal $x_i$ mit $x_i < x$
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Beispiele der wichtigsten Häufigkeits-Funktionen:
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![](./Probabilities.png)
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<div class="letters">
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- $f_i$: Relative Häufigkeit
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@ -92,5 +113,112 @@ Die Funktion für die Häufigkeitsfunktion (auch genannt: Dichtefunktion) hat fo
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</div>
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#### Klassierte Stichproben im Histogramm
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Klassierte Stellen werden durch Grösse der Klasse geteilt, um diese zu berücksichtigen.
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Daraus gewonnene Daten können in einem Histogramm dargestellt werden.
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Beispiel:
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| Klasse | $[100,200[$ | $[200,500[$ | $[500,800[$ | $[800,1000[$ | $[1000,2000[$ | Total |
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| ---------------------------- | -------------------------- | --------------------------- | --------------------------- | -------------------------- | ---------------------------- | ----- |
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| Absolute Häufigkeit | $35$ | $182$ | $317$ | $84$ | $132$ | $750$ |
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| Relative Häufigkeit | $\frac{35}{750}$ | $\frac{182}{750}$ | $\frac{317}{750}$ | $\frac{84}{750}$ | $\frac{132}{750}$ | $1$ |
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| Klassen-Grösse | $100$ | $300$ | $300$ | $200$ | $1000$ |
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| Säulenhöhe für Absolut | $\frac{35}{100}$ | $\frac{182}{300}$ | $\frac{317}{300}$ | $\frac{84}{200}$ | $\frac{132}{1000}$ | |
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| Säulenhöhe für Relativ (PDF) | $\frac{35}{750 \cdot 100}$ | $\frac{182}{750 \cdot 300}$ | $\frac{317}{750 \cdot 300}$ | $\frac{84}{750 \cdot 200}$ | $\frac{132}{750 \cdot 1000}$ | |
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Histogramm der genannten Daten:
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![](./Histogram.png)
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### Kennwerte
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#### Quantile
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Ein $q$-Quantil definiert den Wert des $\lceil n \cdot q \rceil$-te Element.
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Folgende bekannte $q$-Quantile gibt es:
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- $0.25$-Quantil: Das 1. Quartil
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- $0.50$-Quantil: Das 2. Quartil auch "Median" oder "Zentralwert"
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- $0.75$-Quantil: Das 3. Quartil
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Beispiel einer Statistik mit eingezeichneten Quartilen:
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![](./Quantiles.png)
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#### Boxplot
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- Boxplots zeigen folgende Informationen
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- Das 1. Quartil $Q_1$
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- Den Median $Q_2$
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- Das 3. Quartil $Q_3$
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- Den Minimalwert (min. $1.5 \cdot (Q_3 - Q_1)$)
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- Den Maximalwert (max. $1.5 \cdot (Q_3 - Q_1)$)
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![](Boxplot.png)
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#### Lage-Kennwerte
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- Arithmetisches Mittel $\overline{x}$: Mittelwert der Stichprobenwerte
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- Median $x_\text{med}$: Wert des 2. Quartil $Q_2$
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- Modus $x_\text{mod}$: Der häufigste Wert in der Stichprobe
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#### Streuungs-Kennwerte
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<div class="formula">
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***Varianz $\tilde{s}^2$:***
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$$\tilde{s}^2 = \left(\frac{1}{n} \cdot \sum_{i = 1}^m{a_i^2 \cdot h_i}\right) - \tilde{x}^2$$
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alternative Schreibweisen:
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$$\begin{aligned}
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\tilde{s}^2 &= \frac{1}{n} \cdot \sum_{i = 1}^n(x_i - \overline{x})^2 = \frac{1}{n} \cdot \sum_{i = 1}^m{h_i \cdot (a_i - \overline{x})^2} \\
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&= \left(\frac{1}{n} \cdot \sum_{i = 1}^n x_i^2\right) - \overline{x}^2 = \left(\sum_{i = 1}^m{a_i^2 \cdot f_i}\right) - \overline{x}^2
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\end{aligned}
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$$
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***Standardabweichung $\tilde{s}$:***
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$$\tilde{s} = \sqrt{\tilde{s}^2}$$
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***Korrigierte Varianz $s^2$:***
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$$s^2 = \frac{n}{n - 1} \tilde{s}^2$$
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***Korrigierte Standardabweichung $s$:***
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$$s = \sqrt{s^2} = \sqrt{\frac{n}{n - 1}} \cdot \tilde{s}$$
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***Interquartilsabstand $IQR$:***
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$$IQR = Q_3 - Q_1$$
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</div>
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<div class="letters">
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- $a_i$: $i$-te Merkmals-Ausprägung
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- $m$: Anzahl unterschiedlicher Merkmals-Ausprägungen (oder Klassen)
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</div>
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#### Lagewerte in Klassierten Daten
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Einige Werte berechnen sich speziell in klassierten Daten.
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***Quantile:***
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Der Wert $R_q$ eines $q$-Quantils berechnet sich, indem folgendes durchgeführt wird:
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1. Erste Klasse $K$ mit einem $CDF > q$ finden
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2. $K_0$ auf untere und $K_1$ auf obere Grenze der Klasse $K$ setzen
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3. Folgendes berechnen:
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$$R_q = K_0 + \frac{(K_1 - K_0) \cdot (q - CDF(K_0))}{CDF(K_1) - CDF(K_0)}$$
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4. $R_q$ entspricht nun dem $q$-Quartil
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***Modus:***
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1. Klasse $K$ mit grösster relativer Häufigkeit bestimmen
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2. $K_0$ auf untere und $K_1$ auf obere Grenze der Klasse $K$ setzen
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3. Folgendes berechnen:
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$$x_\text{mod} = K_0 + \frac{K_1 - K_0}{2}$$
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# Glossar
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- Univariate Daten: Daten, welche nur ein Merkmal haben
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Pipfile
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@ -0,0 +1,15 @@
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[[source]]
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url = "https://pypi.org/simple"
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verify_ssl = true
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name = "pypi"
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[packages]
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numpy = "*"
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matplotlib = "*"
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ipykernel = "*"
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scipy = "*"
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[dev-packages]
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[requires]
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python_version = "3.10"
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generated
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@ -0,0 +1,736 @@
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{
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"pipfile-spec": 6,
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"requires": {
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"sources": [
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"backcall": {
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
"sha256:9e20e5a1908e18aaa60d9077a6d8753090e3f85ca25da6e25d30dc0a9e84c2c6",
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||||
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||||
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||||
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||||
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||||
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||||
"sha256:fd7dc0e6812b799a34f6d12fcb1000539098c249c8da54f3566c6a6461d0dbad"
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||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==1.0.7"
|
||||
},
|
||||
"cycler": {
|
||||
"hashes": [
|
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"sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3",
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||||
"sha256:ba09ef14ca9893954244fd872798b4ccb2367c165946ce2dd7376aebdde8e3ac",
|
||||
"sha256:d3a2f5999215a3a06a4fc218026cd84c61b8b2b40ac5296a6db1f1451ef04c1e",
|
||||
"sha256:e5f923aa6a47e133d1cf87d60700889d7eae68988704e20c75fb2d65677a8e4b"
|
||||
],
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==6.2"
|
||||
},
|
||||
"traitlets": {
|
||||
"hashes": [
|
||||
"sha256:32500888f5ff7bbf3b9267ea31748fa657aaf34d56d85e60f91dda7dc7f5785b",
|
||||
"sha256:a1ca5df6414f8b5760f7c5f256e326ee21b581742114545b462b35ffe3f04861"
|
||||
],
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==5.8.1"
|
||||
},
|
||||
"wcwidth": {
|
||||
"hashes": [
|
||||
"sha256:795b138f6875577cd91bba52baf9e445cd5118fd32723b460e30a0af30ea230e",
|
||||
"sha256:a5220780a404dbe3353789870978e472cfe477761f06ee55077256e509b156d0"
|
||||
],
|
||||
"version": "==0.2.6"
|
||||
}
|
||||
},
|
||||
"develop": {}
|
||||
}
|
Loading…
Reference in a new issue