Below you will find complete descriptions and links to 11 different analytics calculators for computing normal distribution-related values.

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Compute the cumulative distribution function (CDF) for the normal distribution, given the mean, the standard deviation, and the upper limit of integration x. The normal distribution CDF yields the area under the normal distribution from negative infinity to x, which is very useful for assessing probabilities in analytics studies that rely on the normal distribution.

Compute the probability density function (PDF) for the normal distribution, given the point at which to evaluate the function x, the mean, and the standard deviation. The normal distribution PDF identifies the relative likelihood that an associated random variable will have a particular value, and is very useful for analytics studies that are concerned with normal distribution probabilities.

Compute the 90%, 95%, and 99% confidence intervals for the mean of a normal population when the population standard deviation is known, given the population standard deviation, the sample mean, and the sample size. Knowing the confidence interval for the mean of a normal population can be very useful for assessing the true nature of the population in analytics studies that rely on normally distributed sample data.

Compute the 90%, 95%, and 99% confidence intervals for the mean of a normal population, given the sample standard deviation, the sample mean, and the sample size. Knowing the confidence interval for the mean of a normal population can be very useful for assessing the true nature of a population variable in analytics studies that use normally distributed sample data.

Compute the cumulative area under the standard normal distribution, given a z-score. The standard normal distribution is used frequently in analytics, and it is often very useful to know the cumulative probability for the distribution from minus infinity to the z-score.

Compute the cumulative distribution function (CDF) for the standard normal distribution, given the upper limit of integration x. The standard normal distribution CDF yields the area under the standard normal distribution from negative infinity to x, which is very useful for assessing probabilities in analytics studies that rely on the standard normal distribution.

Compute the one-tailed (right-tail) area under the standard normal distribution associated with a given z-score. Knowing the one-tailed probability for a particular z-score can be useful in a wide variety of analytics techniques.

Compute the probability density function (PDF) for the standard normal distribution, given the point at which to evaluate the function x. The standard normal distribution PDF identifies the relative likelihood that an associated random variable will have a particular value, and is very useful for analytics studies that involve standard normal distribution probabilities.

Compute the two-tailed area under the standard normal distribution that is associated with +/- a given z-score. Knowing the two-tailed probability for a particular z-score can be useful in a wide variety of analytics techniques.

Compute the z-score associated with a given cumulative probability level for the standard normal distribution. Knowing the z-score associated with a particular standard normal probability can be very useful for analytics studies that use z-scores for comparative or descriptive purposes.

Compute a z-score (or standard normal score), given an unstandardized raw value, the population mean, and the population standard deviation. Computing z-scores can be very useful for analytics studies that want to compare values or observations from different categories or populations.