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  1. specifies a numeric constant, variable, or expression. At least one non-missing argument is required. Otherwise, the function returns a missing value.

    • MEDIAN Function

      The MEDIAN function returns the median of nonmissing values,...

    • MDY Function

      MDY Function Returns a SAS date value from month, day, and...

    • GEOMEAN Function

      If you do not want SAS to fuzz the extremely small values,...

  2. specifies a numeric constant, variable, or expression. At least one non-missing argument is required. Otherwise, the function returns a missing value.

  3. 27 maj 2017 · PROC MEANS is one of the most common SAS procedures used for data analysis. It is mainly used to calculate descriptive statistics such as mean, median, count, sum etc. It can also be used to calculate several other metrics such as percentiles, quartiles, standard deviation, variance and sample t-test.

  4. 4 paź 2024 · MEAN Function. Returns the arithmetic mean (average) of the non-null or nonmissing arguments. Table of Contents. Syntax. Arguments. Comparisons. Example. See Also. Syntax. MEAN ( expression-1 [, ... expression-n]) Arguments. expression. specifies any valid expression that evaluates to a numeric value. Comparisons.

  5. 12 cze 2024 · MEAN Function. Returns the arithmetic mean (average). Category: Descriptive Statistics. Table of Contents. Syntax. Required Argument. Details. Example. See Also. Syntax. MEAN ( argument-1 <, ... argument-n >) Required Argument. argument. specifies a numeric constant, variable, or expression. At least one nonmissing argument is required.

  6. PROC MEANS Statement. ALPHA=value. specifies the confidence level to compute the confidence limits for the mean. The percentage for the confidence limits is (1- value)×100. For example, ALPHA=.05 results in a 95% confidence limit. CHARTYPE.

  7. 5 mar 2012 · The MEAN function was introduced in SAS/IML 9.22. The expression mean (x) computes the arithmetic mean of each column of a matrix. It is equivalent to x [:,]. The MEAN function also supports trimmed and Winsorized means, which are robust estimators of location.

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