A half.
yes
yes
In the majority of Empirical cases the mean will not be equal to the median, so the event is hardly unusual. If the mean is greater, then the distribution is poitivelt skewed (skewed to the right).
The Normal (or Gaussian) distribution is a symmetrical probability function whose shape is determined by two values: the mean and variance (or standard deviation).According to the law of large numbers, if you take repeated independent samples from any distribution, the means of those samples are distributed approximately normally. The greater the size of each sample, or the greater the number of samples, the more closely the results will match the normal distribution. This characteristic makes the Normal distribution central to statistical theory.
less than
The sample distribution of the sample proportion refers to the probability distribution of the proportion of successes in a sample drawn from a population. It is typically approximated by a normal distribution when certain conditions are met, specifically when the sample size is large enough (usually np and n(1-p) both greater than 5). The mean of this distribution is equal to the population proportion (p), and the standard deviation is calculated using the formula √[p(1-p)/n]. This distribution is useful for making inferences about the population proportion based on sample data.
To find the proportion of a normal distribution corresponding to z-scores greater than +1.04, you can use the standard normal distribution table or a calculator. The area to the left of z = 1.04 is approximately 0.8508. Therefore, the proportion of the distribution that corresponds to z-scores greater than +1.04 is 1 - 0.8508, which is approximately 0.1492, or 14.92%.
It provides a good approximation to the binomial distribution when np and n(1-p) are greater than 5 for each population.
In a normal distribution, approximately 76.4% of the data falls below a z score of 1.04. Therefore, the proportion of the distribution that corresponds to z scores greater than 1.04 is about 23.6%. This can be found using standard normal distribution tables or calculators.
The sampling distribution of the sample proportion of adults with credit card debts greater than $2000 can be described using the population proportion, which is 36% (or 0.36). For a simple random sample of 200 adults, the mean of the sampling distribution will be equal to the population proportion (0.36), and the standard error can be calculated using the formula ( \sqrt{\frac{p(1-p)}{n}} ), where ( p ) is the population proportion and ( n ) is the sample size. In this case, the standard error would be approximately ( \sqrt{\frac{0.36(0.64)}{200}} ), leading to a normal distribution centered at 0.36, assuming the sample size is sufficiently large.
Scores on the SAT form a normal distribution with a mean of µ = 500 with σ = 100. What is the probability that a randomly selected college applicant will have a score greater than 640?
The statement that the proportion of defective pieces is not less than 0 nor greater than 1.
If n*p is greater than or equal to 5 AND n*q is greater than or equal to 5, you can use a normal distribution as an estimate for the binomial distribution. Recall, n is the number of trials, p is the probability of success of a trial, and q is 1-p.
The Swaroopâ??s index is the proportion of deaths of people aged 50 years and up. The higher the Swaroopâ??s index of a population, the greater the proportion of the deaths who were able to reach the age of 50 years.
NEw bones.
I assume that "public" simply means population. The simple answer is that Greater London has a population of 8.174 million compared with England's population of 53.012 million, representing 15.4%. However, less than half of London's population (44.9%) is "White British". Removing the Welsh, Irish and Scots people, the English proportion of London's population will be considerably smaller. On the other hand a significant proportion of England's population is not English either.
The probability can be calculated by finding the proportion of the interval where the diameter is greater than 7.4 mm. In this case, the proportion of the interval greater than 7.4 mm is (8.5 - 7.4) / (8.5 - 6.8) = 0.55, so the probability is 55%.