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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
Translation license Creative Commons Attribution Share Alike 4.0 International
Translation process
  • Translations can be made directly.
  • Translation suggestions can be made.
  • Any authenticated user can contribute.
  • The translation uses bilingual files.
Source code repository git@github.com:sjentsch/lsjDocs-i18n.git
Repository branch main
Last remote commit Translated using Weblate (Chinese (Traditional Han script)) b2f6c0ae
hugoalh authored a month ago
Last commit in Weblate Translated using Weblate (Chinese (Traditional Han script)) b2f6c0ae
hugoalh authored a month ago
Weblate repository https://hosted.weblate.org/git/lsjdocs/start/
File mask */LC_MESSAGES/Ch04.po
3 weeks ago

String statistics

Strings percent Hosted strings Words percent Hosted words Characters percent Hosted characters
Total 5,565 222,591 1,308,718
Source 242 9,682 56,927
Translated 30% 1,724 14% 32,837 14% 193,640
Needs editing 0% 0 0% 0 0% 0
Read-only 4% 242 4% 9,682 4% 56,927
Failing checks 0% 0 0% 0 0% 0
Strings with suggestions 0% 0 0% 0 0% 0
Untranslated strings 69% 3,841 85% 189,754 85% 1,115,078
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222 k
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0
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User avatar None

Resource updated
The “zh_TW/LC_MESSAGES/Ch04.po” file was changed. 4 months ago
User avatar None

String added in the repository
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.
 
4 months ago
User avatar None

String added in the repository
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:
 
4 months ago
User avatar None

String added in the repository
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:
 
4 months ago
User avatar None

Resource updated
The “zh_CN/LC_MESSAGES/Ch04.po” file was changed. 4 months ago
User avatar None

String added in the repository
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.
 
4 months ago
User avatar None

String added in the repository
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:
 
4 months ago
User avatar None

String added in the repository
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:
 
4 months ago
User avatar None

Resource updated
The “uk/LC_MESSAGES/Ch04.po” file was changed. 4 months ago
User avatar None

String added in the repository
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.
 
4 months ago
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