The probability density function of a normal (Gaussian) random variable X is given by:į x = 1 σ ⋅ 2 ⋅ π ⋅ &ExponentialE − x − μ 2 2 ⋅ σ 2 The square root of the variance, &sigma, is called the standard deviation. The variance, &sigma 2, is the expected value of the square of the difference between the value of the X and its mean. For any distribution X, the mean, denoted &mu, is the expected value of X. The values contained in the standard normal distribution table can also be calculated by hand. Once this z-score is known, its respective probability can be looked up in the standard normal distribution table. For more on standardizing data samples, see the Scale command. Z-scores are calculated by first subtracting the mean of the data set from every observation, then dividing by the standard deviation, such that every standardized observation is a measure of how many standard deviations a given observation is from the sample mean. It is common practice to convert any normally distributed data to the standard normal distribution as the standard normal distribution table contains a value for every standardized z-score. More specifically, the table contains values for the cumulative distribution function of the standard normal distribution at a given value, x. A standard normal distribution table, also known as the unit normal table or Z table, is used to find the probability that a statistic is observed below, above, or between values in the standard normal distribution, the so-called p-value.
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