The 2 Parameter Normal Distribution 7 Formulas

This is part of a short series on the common life data distributions.

The Normal distribution is a continuous distribution widely taught. It is commonly used to describe items, measurements, or time to failure data when there are many additive perturbations that comprise the results. This short article focuses on 7 formulas of the Normal Distribution.

If you want to know more about fitting a set of data to a distribution, well that is in another article.

It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions.

Parameters

The location parameter, μ, is the mean of the distribution. It is the mean, median, and mode, since the distribution is symmetrical about the mean.

The scale parameter is the variance, σ², of the distribution, or the square of the standard deviation.

Probability Density Function (PDF)

With -∞ < t < ∞ then the probability density function formula is:

$$ \displaystyle\large \begin{array}{l}f\left( t \right)=\frac{1}{\sigma \sqrt{2\pi }}\exp \left[ -\frac{1}{2}{{\left( \frac{t-\mu }{\sigma } \right)}^{2}} \right]\\f\left( t \right)=\frac{1}{\sigma }\phi \left[ \frac{t-\mu }{\sigma } \right]\end{array}$$

Where 𝜙 is the standard normal pdf with μ = 0 and σ² = 1

A plot of the PDF provides a histogram-like view of the time-to-failure data.

Cumulative Density Function (CDF)

F(t) is the cumulative probability of failure from time zero till time t. Very handy when estimating the proportion of units that will fail over a warranty period, for example.

$$ \displaystyle\large \begin{array}{l}F\left( t \right)=\frac{1}{\sigma \sqrt{2\pi }}\int_{-\infty }^{t}{\exp \left[ -\frac{1}{2}{{\left( \frac{\theta -\mu }{\sigma } \right)}^{2}} \right]}d\theta \\F\left( t \right)=\Phi \left[ \frac{t-\mu }{\sigma } \right]\end{array}$$

Where 𝚽 is the standard normal cdf with μ = 0 and σ² = 1

Reliability Function

R(t) is the chance of survival from from time zero till time t. Instead of looking for the proportion that will fail the reliability function determine the proportion that are expected to survive.

$$ \displaystyle\large R\left( t \right)=1-\Phi \left[ \frac{t-\mu }{\sigma } \right]$$

Conditional Survivor Function

The m(x) function provides a means to estimate the chance of survival for a duration beyond some known time, t, over which the item(s) have already survived. What the probability of surviving time x given the item has already survived over time t?

$$ \displaystyle\large m\left( x \right)=R\left( x|t \right)=\frac{\Phi \left[ \frac{\mu -x-t}{\sigma } \right]}{\Phi \left[ \frac{\mu -t}{\sigma } \right]}$$

Mean Residual Life

This is the cumulative expected life over time x given survival till time t.

$$ \displaystyle\large u\left( t \right)=\frac{\int_{t}^{\infty }{R\left( x \right)dx}}{R\left( t \right)}$$

Hazard Rate

This is the instantaneous probability of failure per unit time.

$$ \displaystyle\large h\left( t \right)=\frac{\phi \left[ \frac{t-\mu }{\sigma } \right]}{\sigma \left( \Phi \left[ \frac{t-\mu }{\sigma } \right] \right)}$$

Cumulative Hazard Rate

This is the cumulative failure rate from time zero till time t, or the area under the curve described by the hazard rate, h(x).

$$ \displaystyle\large H\left( t \right)=-\ln \left[ \Phi \left[ \frac{\mu -t}{\sigma } \right] \right]$$

Also published on Medium.