Sentences with phrase «probability of dying»

The main points that effect the cost of life cover are: Age The probability of dying increases as we get older therefore Life companies asses this risk by looking at mortality rates showing the number of people per thousand that die in each age group.

The Insurance Institute for Highway Safety notes that teenagers have a higher crash rate despite driving fewer miles than adults; the probability of dying in a crash is higher for teenagers than it is for drivers just a few years older.

This is because the probability of you dying increases every year.

Is it because he has a degree in statistics and can describe accurately your statistical probability of dying from a procedure he is recommending?

If you assume that the probability of mortality (1 - survival) is «p» and that some proportion «c» of that is due to cannibalism, then the probability of dying due to cannibalism is p * c.

Black men in the United States still have nearly twice the lifetime probability of dying from prostate cancer as white men.

The probability of dying earlier is also much higher — around 40 per cent for men in this age group.

Siblings may have the same probability of dying in a given year, for example, but only one may be lost to, say, an accidental drowning or other chance event.

They focus too much on the probability of dying early and ignore the consequences of living longer.

Not exact matches

These concerns are further inter-related to the diminishing state of food stocks, farmlands, oceans, climate, bio-diversity and more that are adding to an overall decline of the planet's wealth thus making the probability associated with a massive die - off in some form much more likely.

In the cost - effectiveness analysis (GiveWell estimate of Living Goods cost effectiveness (November 2014)-RRB-, in all Sheets except for «U5MR (Jake's assumptions),» we use 5q0, or the probability of a child dying before his or her 5th birthday expressed in deaths per 1,000 live births assuming constant mortality rates throughout childhood, instead of the under - 5 mortality rate (under 5 deaths per person per year), because the original report on the RCT we received from Living Goods reported outcomes in terms of 5q0.

Thus we support a reduction in the speed limit because it will «save 7,466 lives every year,» though we would not do so if we recognized that such a law merely reduces the probability that an individual will die in a car accident from.0005 to.0004, a benefit too trivial to be noticed and, for any individual faced with the choice, far below the value of the additional driving time it entails.

We have already seen that Whitehead says that «in all probability» electrons and protons are structured societies — this warns us both that (1) he is not ready to die in the last ditch over this issue, but (2) he is pretty certain that at the level of electrons and protons we have not yet gotten down to personally ordered, serial strands of actual occasions.

Pagels (1984 p. 101) tells a story of a teacher in post-revolutionary Iran who began a lecture on probability theory by holding up a die which he was going to use in a demonstration.

The probability of a baby dying from a home birth is approximately twice the probability of a child dying in a car accident at any point from birth to age 25, and ten times as high as the risk of dying in a car accident between birth and age 10.

Although though this figure may sound high, in fact this is a small fraction of all babies and the probability of your baby dying from it is very low.

The 0.5 % death rate of a higher - risk home birth is the same as the probability of a child dying between the ages of 1 and 18 from any cause at all.

Each year, each simulated woman had a possibility of giving birth, after which she had a possibility of breastfeeding her child for 0 — 18 months; each year, each simulated woman also had a probability of developing one of the five health conditions of interest or of dying (Fig. 1).

In fact, calculating the probability of a particle of Caesar's dying breath appearing in any given liter of air (the volume of a deep breath) has become a classic exercise for chemistry and physics students.

According to the institute's research, the probability of adult men dying early from traffic accidents or cerebrovascular disease more than halved between 1970 and 2006, while death by suicide held relatively steady through the years.

At left, a stream graph depicts the shifting probability that a 15 - year - old male in the United States will die before reaching the age of 60, broken down by cause.

As scientists and as a society, we accept the low probability that a handful of people may become accidentally infected and even die doing necessary science.

Spotted hyena cubs of high - ranking mothers have a lower probability of infection with and are less likely to die from canine distemper virus (CDV) than cubs of low - ranking mothers.

A major study published in the New England Journal of Medicine analyzed 80,000 pregnancies and found that the probability of a baby dying during a home birth is 2.4 times greater than in a planned hospital delivery.

Therefore if more people with a personal history of diabetes (as is reported) or if the above analysis on the probability of family history of diabetes holds true, we really shouldn't be all the surprised that those people are more likely to subsequently die of diabetes.

A Monte Carlo analysis is essentially plugging in a range of possible values (a probability function) for yearly values of pretty much anything involved in your financial life: salary growth, investment rate of return, expected life span, etc, etc, etc.... and then running thousands of simulations on those values to give you the probability that your money will last until you die.

However Flannery et al claim we have now loaded the dice which in gaming parlance means that you weight the dice in a particular way so as to change the chance of probability and skew the results by artificially creating an imbalance in the die itself causing the same number to be rolled over and over again.

So we have two die and if we roll the dice we can get any combination from 2 to 12 and by the chance of probability and combination could occur on any roll.

In the die example it is justified to assume that the properties of the die do not change in time, so the probabilities resulting by ME will be constant.

Applying the principle of maximum entropy (ME), we can (very easily) obtain a simple result, that the probability for each face is 1/6, provided that nothing tells us that the die is not fair.

All die are loaded, they are no completely symmetrical, so the probability of it coming down on each of its faces will never be precisely equal.

Given the same results (viz., rolling four sixes in 10 throws), we have two probability estimates: one (Bayesian) based on an initial prior, which gives the probability that the die is fair, given the results, and another (frequentist) that gives the probability of the results, given the die is fair.

That is, the Bayesian probability that the hypothesis (viz., that the die is fair) is true, given the experimental results, is estimated with reference to the prior probability, i.e., the probability of the hypothesis being true before the study was undertaken (so, before any results are known).

Not only is the probability of re-incarceration a risk, so is the chance the felon will die during the relevant crime.

The premium paid is then based on the expected probability of the insured dying in that one year.

This is done using aggregate data and actuarial tables — which basically display the probability a person will die at each age — to see how likely it is they'll die over the term of the policy.

This is what's known as the underwriting process; the carrier is finding out your risk level — the probability that you'll die over the term of your policy — and setting your premiums accordingly.

It doesn't make sense to allow someone with a greater probability of death to pay the same as someone who likely won't die for another handful of decades.

The more healthy and physically fit a person is; the lesser is his likelihood of his dying early, and hence, a lower probability of the insurance companies charging extra premium.

The purpose of underwriting is to attempt to assess the probability that the insured will die at a normal or premature age, and what the life expectancy of the insured person is given large scale probabilities for people with similar health risks.

If there's a high probability you'll die during the term of your policy, your premiums will be more expensive.

Since it covers two people, the policy will most likely last longer due to the lower probability of two people dying early as opposed to just one.

It is indeed an important aspect to consider, for it is actually a measure of the probability that one's dear and near ones would be able to easily claim the insurance amount if the policy holder was to die all of a sudden; which is the very reason why one buys an insurance policy!

This, of course, is a theory and not reality, but we can save significant premiums by assessing mortality prospects and minimizing the cash value buildup when mortality probabilities are more in favor of dying around, say, age 85 than age 100.

We all are tempted to push the ethical / moral / legal rules «just this once» because the probability of being discovered / caught and punished is low (particularly if everyone else is working on the honour system), the reward is great and the time to act «is now»... do or die.