����app

A normal distribution is a continuous probability distribution characterized by its bell-shaped curve, defined by its mean and standard deviation. In a normal distribution, data points are symmetrically distributed around the mean, with most observations clustering near the center and fewer observations appearing as you move away from the mean. Understanding this concept is crucial for interpreting normal quantile plots, as deviations from this shape indicate non-normality.

A quantile plot, specifically a normal quantile plot, is a graphical tool used to assess if a dataset follows a normal distribution. It plots the quantiles of the dataset against the quantiles of a standard normal distribution. If the points in the plot closely follow a straight line, it suggests that the data is normally distributed; deviations from this line indicate departures from normality.

Z-scores are standardized scores that indicate how many standard deviations an element is from the mean of the dataset. In the context of normal quantile plots, z-scores are used to transform the data points so that they can be compared to the standard normal distribution. This transformation is essential for determining how well the data fits a normal distribution, as it allows for a direct comparison of the data's distribution to the theoretical normal distribution.