Human beings are expected to live on average 3040 years in Swaziland and 82.6 years in Japan, but the latter's recorded life expectancy may have been very slightly increased by counting many infant deaths as stillborn. An analysis published in 2011 in The Lancet attributes Japanese life expectancy to equal opportunities and public health as well as diet.
The oldest confirmed recorded age for any human is 122 years, reached by Jeanne Calment who lived between 18751997. This is referred to as the "maximum life span", which is the upper boundary of life, the maximum number of years any human is known to have lived. Theoretical study shows that the maximum life expectancy at birth is limited by the human life characteristic value δ, which is around 104 years. According to a study by biologists Bryan G. Hughes and Siegfried Hekimi, there is no evidence for limit on human lifespan.
Ageing or aging is the process of becoming older.
In humans, aging represents the accumulation of changes in a human being over time, encompassing physical, psychological, and social changes. Reaction time, for example, may slow with age, while knowledge of world events and wisdom may expand. Ageing is among the greatest known risk factors for most human diseases: of the roughly 150,000 people who die each day across the globe, about twothirds die from agerelated causes.
The causes of aging are uncertain; current theories are assigned to the damage concept, whereby the accumulation of damage (such as DNA oxidation) may cause biological systems to fail, or to the programmed aging concept, whereby internal processes (such as DNA methylation) may cause aging. Programmed aging should not be confused with programmed cell death (apoptosis).
The discovery, in 1934, that calorie restriction can extend lifespan by 50% in rats has motivated research into delaying and preventing aging.
By Fobos92 (Own work) [CC BYSA 3.0 (https://creativecommons.org/licenses/bysa/3.0)], via Wikimedia Commons

The starting point for calculating life expectancy is the agespecific death rates of the population members. If a large number of data is available, a statistical population can be created that allow the agespecific death rates to be simply taken as the mortality rates actually experienced at each age (the number of deaths divided by the number of years "exposed to risk" in each data cell). However, it is customary to apply smoothing to iron out, as much as possible, the random statistical fluctuations from one year of age to the next. In the past, a very simple model used for this purpose was the Gompertz function, but more sophisticated methods are now used.
These are the most common methods now used for that purpose:
 to fit a mathematical formula, such as an extension of the Gompertz function, to the data,
 for relatively small amounts of data, to look at an established mortality table that was previously derived for a larger population and make a simple adjustment to it (as multiply by a constant factor) to fit the data.
 with a large number of data, one looks at the mortality rates actually experienced at each age, and applies smoothing (as by cubic splines).
By Cmglee  Own work, CC BYSA 3.0, Link
While the data required are easily identified in the case of humans, the computation of life expectancy of industrial products and wild animals involves more indirect techniques. The life expectancy and demography of wild animals are often estimated by capturing, marking, and recapturing them. The life of a product, more often termed shelf life, is also computed using similar methods. In the case of longlived components, such as those used in critical applications: in aircraft, methods like accelerated aging are used to model the life expectancy of a component.
The agespecific death rates are calculated separately for separate groups of data that are believed to have different mortality rates (such as males and females, and perhaps smokers and nonsmokers if data are available separately for those groups) and are then used to calculate a life table from which one can calculate the probability of surviving to each age. In actuarial notation, the probability of surviving from age to age is denoted and the probability of dying during age (between ages and ) is denoted . For example, if 10% of a group of people alive at their 90th birthday die before their 91st birthday, the agespecific death probability at 90 would be 10%. That is a probability, not a mortality rate.
The expected future lifetime of a life age in whole years (the curtate expected lifetime of (x)) is denoted by the symbol . It is the conditional expected future lifetime (in whole years), assuming survival to age . If denotes the curtate future lifetime at ,
Substituting in the sum and simplifying gives the equivalent formula: If the assumption is made that on average, people live a half year in the year of death, the complete expectation of future lifetime at age is .
Life expectancy is by definition an arithmetic mean. It can also be calculated by integrating the survival curve from 0 to positive infinity (or equivalently to the maximum lifespan, sometimes called 'omega'). For an extinct or completed cohort (all people born in year 1850, for example), it can of course simply be calculated by averaging the ages at death. For cohorts with some survivors, it is estimated by using mortality experience in recent years. The estimates are called period cohort life expectancies.
It is important to note that the statistic is usually based on past mortality experience and assumes that the same agespecific mortality rates will continue into the future. Thus, such life expectancy figures need to be adjusted for temporal trends before calculating how long a currently living individual of a particular age is expected to live. Period life expectancy remains a commonly used statistic to summarize the current health status of a population.
However, for some purposes, such as pensions calculations, it is usual to adjust the life table used by assuming that agespecific death rates will continue to decrease over the years, as they have usually done in the past. That is often done by simply extrapolating past trends; but some models exist to account for the evolution of mortality like the Lee–Carter model.
As discussed above, on an individual basis, a number of factors correlate with a longer life. Factors that are associated with variations in life expectancy include family history, marital status, economic status, physique, exercise, diet, drug use including smoking and alcohol consumption, disposition, education, environment, sleep, climate, and health care.
Thanks to Wikipedia: Life Expectancy Ageing