Translation components API.

See the Weblate's Web API documentation for detailed description of the API.

GET /api/translations/libo_help-7-6/textscalc01/ast/units/?format=api&page=164
HTTP 200 OK
Allow: GET, POST, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

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            "source": [
                "<variable id=\"sam01\">Choose <menuitem>Data - Statistics - Regression</menuitem></variable>"
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            "source": [
                "For more information on regression analysis, refer to the <link href=\"https://en.wikipedia.org/wiki/Regression_analysis\">corresponding Wikipedia article</link>."
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            "source": [
                "Data"
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                "Datos"
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            "source": [
                "Independent variable(s) (X) range:"
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                "Enter a single range that contains multiple independent variable observations (along columns or rows). All X variable observations need to be entered adjacent to each other in the same table."
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                "Dependent variable (Y) range:"
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            "source": [
                "Enter the range that contains the dependent variable whose regression is to be calculated."
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            "source": [
                "Both X and Y ranges have labels"
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            "source": [
                "Check to use the first line (or column) of the data sets as variable names in the output range."
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            "source": [
                "Results to:"
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            "source": [
                "The reference of the top left cell of the range where the results will be displayed."
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                ""
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            "source": [
                "Output Regression Types"
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            "source": [
                "Set the regression type. Three types are available:"
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            "source": [
                "<emph>Linear Regression</emph>: finds a linear function in the form of y = b + a<sub>1</sub>.[x<sub>1</sub>] + a<sub>2</sub>.[x<sub>2</sub>] + a<sub>3</sub>.[x<sub>3</sub>] ..., where a<sub>i</sub> is the i-th slope, [x<sub>i</sub>] is the i-th independent variable, and b is the intercept that best fits the data."
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            "id": 99535714,
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            "source": [
                "<emph>Logarithmic regression</emph>: finds a logarithmic curve in the form of y = b + a<sub>1</sub>.ln[x<sub>1</sub>] + a<sub>2</sub>.ln[x<sub>2</sub>] + a<sub>3</sub>.ln[x<sub>3</sub>] ..., where a<sub>i</sub> is the i-th coefficient, b is the intercept and ln[x<sub>i</sub>] is the natural logarithm of the i-th independent variable, that best fits the data."
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                "<emph>Power regression</emph>: finds a power curve in the form of y = exp( b + a<sub>1</sub>.ln[x<sub>1</sub>] + a<sub>2</sub>.ln[x<sub>2</sub>] + a<sub>3</sub>.ln[x<sub>3</sub>] ...), where a<sub>i</sub> is the i-th power, [x<sub>i</sub>] is the i-th independent variable, and b is intercept that best fits the data."
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