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Stat > Basic Statistics > Normality Test

Generates a normal probability plot and performs a hypothesis test to examine whether or not the observations follow a normal distribution. For the normality test, the hypotheses are,

H0: data follow a normal distribution vs. H1: data do not follow a normal distribution

The vertical scale on the graph resembles the vertical scale found on normal probability paper. The horizontal axis is a linear scale. The line forms an estimate of the cumulative distribution function for the population from which data are drawn. Numerical estimates of the population parameters, m and s, the normality test value, and the associated p-value are displayed with the plot.

Variable: Enter the column to use for the x-axis. Minitab calculates the probability of occurrence for each observation in the column (assuming a normal distribution) and uses the log of the calculated probabilities as y-values.

Percentile lines: Minitab marks each percent in the column with a horizontal reference line on the plot, and marks each line with the percent value. Minitab draws a vertical reference line where the horizontal reference line intersects the line fit to the data, and marks this line with the estimated data value.

None: Choose to display no percentile line.

At Y values: Choose to enter y-scale values for placing percentile lines. Enter values between 0 and 100 when percents are used as the y-scale type or 0 to 1 when probability is the y-scale type.

At data values: Choose to enter data values for placing percentile lines.

Tests for Normality: See [7] and [27] for discussions of tests for normality.

Anderson-Darling: Choose to perform an Anderson-Darling test for normality, an ECDF (empirical cumulative distribution function) based test.

Ryan-Joiner: Choose to perform a Ryan-Joiner test, similar to the Shapiro-Wilk test. The Ryan-Joiner test is a correlation based test.

Kolmogorov-Smirnov: Choose to perform a Kolmogorov-Smirnov test for normality, an ECDF based test.

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