Therefore the chances of being a fit man and having a resting heart rate greater than or equal to 90/min is 0.0013 or 0.13%. Consequently the probability of getting a value greater than or equal to 3.0 is represented by area under the curve to the right of the shaded area. As the curve is symmetrical, the area under each half is 0.5. The area under the curve represents the total probability and is equal to 1.0. Using the z statistic tables (fig 3), the area demarcated by z = 0 (that is, the mean) and z = 3 is 0.4987. They have resting heart rates of 90 and 40/min respectively.Įstimating a single value from a population with known μ and σ THE CHANCE OF GETTING AN ELEMENT GREATER THAN OR EQUAL TO A PARTICULAR VALUEĬonsider “Pot belly” Pete, what would be the chance of getting a resting heart rate equal or greater than 90 and still be part of the fit male population? Two of the staff members he finds are Charge Nurse “Pot belly” Pete the hospital's darts and wine tasting expert and “Skier” Sphen the locum SHO from Sweden. He finds in a physiology textbook that resting heart rate for fit men (μ) is 60/min with a standard deviation (σ) of 10 (fig 1). He therefore decides to explore this further by looking at the resting heart rate. Following his previous work, he suspects that the men in the department could be unfit. To demonstrate this consider the continuing work of Egbert Everard in the Emergency Department of Deathstar General. When a normal distribution is plotted as a standard normal distribution, the original values on the horizontal axis are converted to their equivalent z statistic. Therefore for any element the z statistic is equal to /σ The Greek letters μ and σ are often used to respectively represent the population's mean and standard deviation It is possible to convert any normal distribution to a standard normal distribution Central to this is converting the original data to a standard normal distribution so that these estimations can be made. We can however work out the probability of a particular value based upon information from either the sample or population.
![how to use a standard normal table how to use a standard normal table](https://0.academia-photos.com/attachment_thumbnails/49035195/mini_magick20190201-18317-lwy0eb.png)
In both calculations the values obtained are only estimations because of the normal variation that occurs. This is not commonly done because it requires the population's mean and standard deviation to be known and this is rarely the case. It can also be used to do the opposite-that is, estimate a sample statistic from a population's parameter. For example, inferential statistics would be used to estimate a population's mean from a sample's mean. 1 This form of numeric manipulation is often used to estimate a population's parameter from a sample's statistic. In the previous article the term inferential statistic was introduced. In covering these objectives we will introduce the following terms: