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Overview

Project website lsj.readthedocs.io
Instructions for translators

Dear translators,

welcome and thank you for your willingness to contribute to the translation of the “learning statistics with jamovi”-text book!

There are a couple of things that I would like to ask you, in order to make your and my life a bit easier (manual corrections need time, so that what you translated won't appear until I fixed it): [1] There are buttons underneath the “Translation” box. One of them is “Automatic suggestions” which provides you with a machine translation of the text to be translated. This can just be copied into the target field for the translation and adjusted. [2] There are a couple of text elements that should be treated different from the rest of the text (please check: the automatic suggestions might translate this “wrong”): - placeholders (marked by “|”) should not be translated: e.g., |placeholder| or |placeholder|_, among them is |continuous| which produces an icon denoting a continuous level of measurement - double backticks : they typically are used for variable or analysis names – the variable names shouldn't be changed, for the analysis names, it is up to your discretion whether to translate them or not (I decided to rather go with the English analysis since the feature to set the UI language in jamovi is fairly new and it is relatively simple to figure out the translated name from the English one) - be careful that :doc: does not contain spaces at the begin of the string, also ensure to leave the part enclosed by <> as it is (it points to a file; therefore, please double-check that the automatic translation did not translate it otherwise it will end up as a broken link) - figure references, e.g., :numref:fig-normAreaSD should not to be translated - (language-specific) quotation marks: there are buttons above the text field for the target translation that can be used to replace "'s by your language specific quotation marks [3] markdown-formattings for italicsor bold: please ensure that the stars are placed around the same word / meaning in the translation as in the original [4] if there doesn't exist a good translation of a particular word or technical term, either try "translation (original word / phrase)" or "original word / phrase" (this shows the original English term in italics either with a translation that you may not be completely happy with or without a translation

Finally, I would like to suggest that you install LanguageTool (browser add-on) as an additional check that your translations are correct (however, LanguageTool might not be available for every language).

Don't hesitate to contact me (@sjentsch) if you have questions or suggestions.
Project maintainers User avatar sjentsch
Language German
Language code de
Text direction Left to right
Number of speakers 141,444,441
2 days ago

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Strings percent Hosted strings Words percent Hosted words Characters percent Hosted characters
Total 4,301 182,767 1,089,732
Translated 100% 4,301 100% 182,767 100% 1,089,732
Needs editing 0% 0 0% 0 0% 0
Read-only 0% 0 0% 0 0% 0
Failing checks 0% 0 0% 0 0% 0
Strings with suggestions 0% 0 0% 0 0% 0
Untranslated strings 0% 0 0% 0 0% 0
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Changes committed 4 months ago
User avatar sjentsch

Translation changed
The way we calculate these is to divide the ordinary residual by an estimate of the (population) standard deviation of these residuals. For technical reasons, mumble mumble, the formula for this is:
Die Art und Weise, wie wir diese berechnen, besteht darin, das gewöhnlichen Residuum durch eine Schätzung der Standardabweichung dieser Residuen in der Grundgesamtheit zu dividieren. Aus technischen Gründen… unverständlich… lautet die Formel dafür:
4 months ago
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Resource updated
The “de/LC_MESSAGES/Ch08.po” file was changed. 4 months ago
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String added in the repository
This is an unbiased estimator of the population variance σ². Moreover, this finally answers the question we raised in :doc:`../Ch04/Ch04_Descriptives_2`. Why did jamovi give us slightly different answers for variance? It’s because jamovi calculates :math:`\hat\sigma^2` not *s*\², that’s why. A similar story applies for the standard deviation. If we divide by *N* - 1 rather than *N* our estimate of the population standard deviation becomes:
Dies ist ein unverzerrter Schätzer der Varianz der Grundgesamtheit σ. Außerdem beantwortet dies endlich die Frage, die wir in :doc:`../Ch04/Ch04_Descriptives_2` gestellt haben. Warum hat uns jamovi ein leicht unterschiedliches Resultat für die Varianz ausgegeben? Das liegt daran, dass jamovi :math:`\hat\sigma^2` berechnet und nicht *s*\². Ähnlich verhält es sich mit der Standardabweichung. Wenn wir durch *N* - 1 statt durch *N* dividieren, ergibt sich eine Schätzung der Standardabweichung der Grundgesamtheit:
4 months ago
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Resource updated
The “de/LC_MESSAGES/Ch04.po” file was changed. 4 months ago
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Evidently there were three drugs: a ``placebo``, something called ``anxifree`` and something called ``joyzepam``, and there were 6 people administered each drug. There were 9 people treated using cognitive behavioural therapy (``CBT``) and 9 people who received no psychological treatment (``no.therapy``). And we can see from looking at the ``Descriptives`` of the ``mood.gain`` variable that most people did show a mood gain (mean = 0.88), though without knowing what the scale is here it’s hard to say much more than that. Still, that’s not too bad. Overall I feel that I learned something from that.
Offensichtlich gab es drei Medikamente: ein ``placebo``, ein Medikament namens ``anxifree`` und ein Medikament namens ``joyzepam``. Jedes Medikament wurde 6 Personen verabreicht. Es gab 9 Personen, die mit kognitiver Verhaltenstherapie (``CBT``) behandelt wurden, und 9 Personen, die keine psychologische Behandlung erhielten (``no.therapy``). Und wenn man sich die ``Descriptives`` der ``mood.gain`` Variable ansieht, kann man erkennen, dass die meisten Personen eine Verbesserung der Stimmung zeigten (Mittelwert = 0,88), obwohl es schwer ist, mehr als das zu sagen, wenn man nicht weiß, wie die Skala hier aussieht. Dennoch ist das nicht allzu schlecht. Insgesamt habe ich das Gefühl, dass ich daraus etwas gelernt habe.
4 months ago
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Since the previous paragraph might sound a little abstract, let’s go through the **mean absolute deviation** from the mean a little more slowly. One useful thing about this measure is that the name actually tells you exactly how to calculate it. Let’s think about our AFL winning margins data, and once again we’ll start by pretending that there are only 5 games in total, with winning margins of 56, 31, 56, 8 and 32. Since our calculations rely on an examination of the deviation from some reference point (in this case the mean), the first thing we need to calculate is the mean, *X̄*. For these five observations, our mean is *X̄* = 36.6. The next step is to convert each of our observations *X*\ :sub:`i` into a deviation score. We do this by calculating the difference between the observation *X*\ :sub:`i` and the mean *X̄*. That is, the deviation score is defined to be *X*\ :sub:`i` - *X̄*. For the first observation in our sample, this is equal to 56 - 36.6 = 19.4. Okay, that’s simple enough. The next step in the process is to convert these deviations to absolute deviations, and we do this by converting any negative values to positive ones. Mathematically, we would denote the absolute value of -3 as \|-3\|, and so we say that \|-3\| = 3. We use the absolute value here because we don’t really care whether the value is higher than the mean or lower than the mean, we’re just interested in how *close* it is to the mean. To help make this process as obvious as possible, the table below shows these calculations for all five observations:
Da der vorige Absatz etwas abstrakt geklungen haben mag, wollen wir die **mittlere absolute Abweichung** vom Mittelwert schrittweise durchgehen. Das Nützliche an dieser Kennzahl ist, dass der Name genau angibt, wie sie zu berechnen ist. Betrachten wir unsere AFL-Gewinnspannen-Daten und nehmen wir wieder einmal an, dass es insgesamt nur 5 Spiele mit Gewinnspannen von 56, 31, 56, 8 und 32 gibt. Da unsere Berechnungen auf einer Untersuchung der Abweichung von einem Bezugspunkt (in diesem Fall dem Mittelwert) beruhen, müssen wir zunächst den Mittelwert *X̄* berechnen. Für diese fünf Beobachtungen beträgt unser Mittelwert *X̄* = 36,6. Der nächste Schritt besteht darin, jede unserer Beobachtungen *X*\ :sub:`i` in einen Abweichungswert umzuwandeln. Dazu berechnen wir die Differenz zwischen der Beobachtung *X*\ :sub:`i` und dem Mittelwert *X̄*. Das heißt, der Abweichungswert ist definiert als *X*\ :sub:`i` - *X̄*. Für die erste Beobachtung in unserer Stichprobe ist dies gleich 56 - 36,6 = 19,4. Okay, das ist einfach genug. Der nächste Schritt besteht darin, diese Abweichungen in absolute Abweichungen umzuwandeln, und zwar indem wir alle negativen Werte in positive umwandeln. Mathematisch gesehen würden wir den absoluten Wert von -3 als \|-3\| bezeichnen, und so sagen wir, dass \|-3\| = 3. Wir verwenden hier den absoluten Wert, weil es uns nicht wirklich interessiert, ob der Wert höher oder niedriger als der Mittelwert ist, sondern nur, wie *nah* er am Mittelwert liegt. Um diesen Vorgang so deutlich wie möglich zu machen, zeigt die folgende Tabelle diese Berechnungen für alle fünf Beobachtungen:
4 months ago
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The first piece of notation to introduce is *N*, which we’ll use to refer to the number of observations that we’re averaging (in this case *N* = 5). Next, we need to attach a label to the observations themselves. It’s traditional to use *X* for this, and to use subscripts to indicate which observation we’re actually talking about. That is, we’ll use *X*\ :sub:`1` to refer to the first observation, *X*\ :sub:`2` to refer to the second observation, and so on all the way up to *X*\ :sub:`N` for the last one. Or, to say the same thing in a slightly more abstract way, we use *X*\ :sub:`i` to refer to the *i*-th observation. Just to make sure we’re clear on the notation, the following table lists the 5 observations in the ``afl.margins`` variable, along with the mathematical symbol used to refer to it and the actual value that the observation corresponds to:
Die erste Notation, die wir einführen müssen, ist *N*, die wir verwenden werden, um die Anzahl der Beobachtungen zu bezeichnen, über die wir den Durchschnitt bilden (in diesem Fall *N* = 5). Als Nächstes müssen wir den Beobachtungen selbst eine Bezeichnung zuweisen. Traditionell wird dafür *X* verwendet, und mit tiefgestellten Indizes wird angegeben, um welche Beobachtung es sich tatsächlich handelt. Das heißt, wir verwenden *X*\ :sub:`1` für die erste Beobachtung, *X*\ :sub:`2` für die zweite Beobachtung, und so weiter bis hin zu *X*\ :sub:`N` für die letzte Beobachtung. Oder, um das Gleiche etwas abstrakter auszudrücken, verwenden wir *X*\ :sub:`i` für die *i*-te Beobachtung. Um die Notation zu verdeutlichen, sind in der folgenden Tabelle die 5 Beobachtungen in der Variablen ``afl.margins`` aufgelistet, zusammen mit dem mathematischen Symbol, das sich auf sie bezieht, und dem tatsächlichen Wert, dem die Beobachtung entspricht:
4 months ago
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Resource updated
The “de/LC_MESSAGES/Ch14.po” file was changed. 4 months ago
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