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  2. Estimated date of delivery - Wikipedia

    en.wikipedia.org/wiki/Estimated_date_of_delivery

    Here's the formula to calculate your Estimated Due Date using Naegele's rule : Date of Last Menstrual Period + 7 Days + 9 Calendar Months = Date of Estimated Date of Delivery. Example: LMP = 8 May 2020. +1 year = 8 May 2021. −3 months = 8 February 2021.

  3. Gestational age - Wikipedia

    en.wikipedia.org/wiki/Gestational_age

    Gestational age. In obstetrics, gestational age is a measure of the age of a pregnancy taken from the beginning of the woman's last menstrual period (LMP), [1] or the corresponding age of the gestation as estimated by a more accurate method, if available. Such methods include adding 14 days to a known duration since fertilization (as is ...

  4. Residual sum of squares - Wikipedia

    en.wikipedia.org/wiki/Residual_sum_of_squares

    Residual sum of squares. In statistics, the residual sum of squares ( RSS ), also known as the sum of squared residuals ( SSR) or the sum of squared estimate of errors ( SSE ), is the sum of the squares of residuals (deviations predicted from actual empirical values of data). It is a measure of the discrepancy between the data and an estimation ...

  5. Explained sum of squares - Wikipedia

    en.wikipedia.org/wiki/Explained_sum_of_squares

    The explained sum of squares, defined as the sum of squared deviations of the predicted values from the observed mean of y, is. Using in this, and simplifying to obtain , gives the result that TSS = ESS + RSS if and only if . The left side of this is times the sum of the elements of y, and the right side is times the sum of the elements of , so ...

  6. en.wikipedia.org

    en.wikipedia.org/wiki/Baby-birth-date-calculator

    en.wikipedia.org

  7. Partition of sums of squares - Wikipedia

    en.wikipedia.org/wiki/Partition_of_sums_of_squares

    If the sum of squares were not normalized, its value would always be larger for the sample of 100 people than for the sample of 20 people. To scale the sum of squares, we divide it by the degrees of freedom, i.e., calculate the sum of squares per degree of freedom, or variance. Standard deviation, in turn, is the square root of the variance.

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