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Object[Analysis, StandardCurve]

Analysis for fitting and applying a standard curve to input data.

Organizational Information

    Name

    Name of this Object.
    Format: Single
    Class: String
    Programmatic Pattern: _String
    Pattern Description: A string.

    ID

    ID of this Object.
    Format: Single
    Class: String
    Programmatic Pattern: _String
    Pattern Description: The ID of this object.

    Object

    Object of this Object.
    Format: Single
    Class: Expression
    Programmatic Pattern: Object[Analysis, StandardCurve, _String]
    Pattern Description: The object reference of this object.

    Type

    Type of this Object.
    Format: Single
    Class: Expression
    Programmatic Pattern: Object[Analysis, StandardCurve]
    Pattern Description: Object[Analysis, StandardCurve]

    Notebook

    Notebook this object belongs to.
    Format: Single
    Class: Link
    Programmatic Pattern: _Link
    Pattern Description: An object of that matches ObjectP[Object[LaboratoryNotebook]].
    Relation: Object[LaboratoryNotebook][Objects]

    Author

    The person who ran the analysis.
    Format: Single
    Class: Link
    Programmatic Pattern: _Link
    Relation: Object[User]

General

Analysis & Reports

    InputDataPoints

    List of numerical values in each dataset which the standard curve will be applied to.
    Format: Multiple
    Class: Expression
    Programmatic Pattern: {(UnitsP[] | DistributionP[])..} | _?QuantityVectorQ

    InputDataUnits

    Physical units associated with the data which the standard curve will be applied to.
    Format: Single
    Class: Expression
    Programmatic Pattern: UnitsP[]

    StandardDataPoints

    List of {x,y} data points used to fit the standard curve.
    Format: Single
    Class: Compressed
    Unit: ['None', 'None']
    Programmatic Pattern: MatrixP[NumericP] | MatrixP[UnitsP[]] | MatrixP[NumericP | _?DistributionParameterQ] | _?QuantityMatrixQ

    StandardDataUnits

    Physical units associated with the data points used for fitting the standard curve.
    Format: Multiple
    Class: Expression
    Programmatic Pattern: UnitsP[]

    PredictedValues

    List of numerical values obtained from applying the standard curve to each input dataset.
    Format: Multiple
    Class: Expression
    Programmatic Pattern: {(UnitsP[] | DistributionP[])..} | _?QuantityVectorQ

Standard Curve

    StandardCurveFit

    The Analysis object representing the fitted standard curve.
    Format: Single
    Class: Link
    Programmatic Pattern: _Link
    Relation: Object[Analysis, Fit]

    StandardCurveDomain

    The minimum and maximum x-value in the data points used to fit the standard curve.
    Format: Multiple
    Class: Expression
    Programmatic Pattern: UnitsP[]

    StandardCurveRange

    The minimum and maximum y-value in the data points used to fit the standard curve.
    Format: Multiple
    Class: Expression
    Programmatic Pattern: UnitsP[]

    SymbolicExpression

    The symbolic expression representing the function used to fit the standard curve, with best fit parameters represented as symbolic variables.
    Format: Single
    Class: Expression
    Programmatic Pattern: Except[_String]

    ExpressionType

    The mathematical function used to fit the standard curve.
    Format: Single
    Class: Expression
    Programmatic Pattern: FitExpressionP

    BestFitFunction

    Pure function that calculates the expected Y as a function of X.
    Format: Single
    Class: Expression
    Programmatic Pattern: _Function | _QuantityFunction

    BestFitExpression

    The symbolic standard curve expression with the best fit parameters replaced by their fitted values.
    Format: Single
    Class: Expression
    Programmatic Pattern: Except[_String]

    BestFitParameters

    The unknown parameters from the SymbolicExpression along with their fitted values and standard deviations.
    Format: Multiple

    [[1]] Parameter Name

      Header: Parameter Name
      Class: Expression
      Programmatic Pattern: _Symbol

    [[2]] Fitted Value

      Header: Fitted Value
      Class: Real
      Programmatic Pattern: NumericP

    [[3]] Standard Deviation

      Header: Standard Deviation
      Class: Real
      Programmatic Pattern: NumericP?NonNegative

    BestFitParametersDistribution

    The multivariate distribution describing the best fit parameters of the standard curve.
    Format: Single
    Class: Compressed
    Programmatic Pattern: _MultinormalDistribution | _DataDistribution

    MarginalBestFitDistribution

    The marginal distributions describing the best fit parameters for the standard curve.
    Format: Multiple

    [[1]] Parameter Name

      Header: Parameter Name
      Class: Expression
      Programmatic Pattern: _Symbol

    [[2]] Fitted Distribution

      Header: Fitted Distribution
      Class: Expression
      Programmatic Pattern: DistributionP[]

    BestFitVariables

    A list of the variables present in the BestFitExpression of the standard curve.
    Format: Multiple
    Class: Expression
    Programmatic Pattern: _Symbol

Standard Curve Statistics

    ANOVATable

    The statistics table generated by performing an ANOVA analysis.
    Format: Single
    Class: Expression
    Programmatic Pattern: _Pane

    ANOVAOfModel

    The ANOVA results when source of variation is from the regression model.
    Format: Multiple

    [[1]] DF

      Header: DF
      Class: Real
      Programmatic Pattern: NumericP

    [[2]] Sum of Squares

      Header: Sum of Squares
      Class: Real
      Programmatic Pattern: NumericP

    [[3]] Mean Squares

      Header: Mean Squares
      Class: Real
      Programmatic Pattern: NumericP

    [[4]] F-Statistic

      Header: F-Statistic
      Class: Real
      Programmatic Pattern: NumericP

    [[5]] F-Critical (95%)

      Header: F-Critical (95%)
      Class: Real
      Programmatic Pattern: NumericP

    [[6]] P-Value

      Header: P-Value
      Class: Real
      Programmatic Pattern: NumericP

    ANOVAOfError

    The ANOVA results when source of variation is from the residual error.
    Format: Multiple

    [[1]] DF

      Header: DF
      Class: Real
      Programmatic Pattern: NumericP

    [[2]] Sum of Squares

      Header: Sum of Squares
      Class: Real
      Programmatic Pattern: NumericP

    [[3]] Mean Squares

      Header: Mean Squares
      Class: Real
      Programmatic Pattern: NumericP

    ANOVAOfTotal

    The ANOVA results when source of variation is from the original data.
    Format: Multiple

    [[1]] DF

      Header: DF
      Class: Real
      Programmatic Pattern: NumericP

    [[2]] Sum of Squares

      Header: Sum of Squares
      Class: Real
      Programmatic Pattern: NumericP

    FStatistic

    F Statistic is calculated by performing a F hypothesis test, following the equation MSR/MSE where MSR is the regression mean square, MSE is the mean square error.
    Format: Single
    Class: Real
    Programmatic Pattern: NumericP

    FCritical

    By default, it is the 95% critical value for the F-ratio distribution determined by the degrees of freedom in this object.
    Format: Single
    Class: Real
    Programmatic Pattern: NumericP

    FTestPValue

    The cumulative probability beyond FStatistic for the F-ratio distribution.
    Format: Single
    Class: Real
    Programmatic Pattern: NumericP

    RSquared

    R^2 value, also known as coefficient of determination, is a measure of how well the generated model fits its data.
    Format: Single
    Class: Real
    Programmatic Pattern: NumericP

    AdjustedRSquared

    An adjustment to the R^2 value that penalizes additional complexity in the model.
    Format: Single
    Class: Real
    Programmatic Pattern: NumericP

    AIC

    Akaike information criterion, a measure of the fit model's quality compared with other models.
    Format: Single
    Class: Real
    Programmatic Pattern: _?NumericQ

    AICc

    Corrected Akaike information criterion, a measure of the fit model's quality compared with other models, corrected for small sample sizes.
    Format: Single
    Class: Real
    Programmatic Pattern: _?NumericQ

    BIC

    Bayesian information criterion, a measure of a fit model's quality relative to other fit models, which penalizes model complexity more strongly than AIC.
    Format: Single
    Class: Real
    Programmatic Pattern: _?NumericQ

    EstimatedVariance

    Estimate of the error variance, calculated by dividing the sum squared error by the degrees of freedom (difference between the number of data points and number of model parameters).
    Format: Single
    Class: Real
    Programmatic Pattern: GreaterEqualP[0]

    SumSquaredError

    Sum of squared errors between fit-predicted and actual y-values.
    Format: Single
    Class: Real
    Programmatic Pattern: GreaterEqualP[0]

    StandardDeviation

    StandardDeviation of the fit error, which is equal to the squre root of the EstimatedVariance.
    Format: Single
    Class: Real
    Programmatic Pattern: GreaterEqualP[0]
Last modified on Mon 26 Sep 2022 15:43:43
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