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How does the number of repetitions effect the shape of the normal distribution?
Wiki User
∙ 14y ago
When we discuss a sample drawn from a population, the larger the sample, or the large the number of repetitions of the event, the more certain we are of the mean value.
So, when the normal distribution is considered the sampling distribution of the mean, then more repetitions lead to smaller values of the variance of the distribution.
Wiki User
∙ 14y ago
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What properties distinguishes standard normal distribution from other normal distribution?
The normal distribution can have any real number as mean and any positive number as variance. The mean of the standard normal distribution is 0 and its variance is 1.
The distribution of sample means is not always a normal distribution Under what circumstances will the distribution of sample means not be normal?
The distribution of sample means will not be normal if the number of samples does not reach 30.
When does normal distribution occur?
The normal distribution occurs when a number of random variables, with independent distributions, are added together. No matter what the underlying probability distribution of the individual variables, their sum tends to the normal as their number increases. Many everyday measures are composed of the sums of small components and so they follow the normal distribution.
What discrete probability distribution can be approximated by a normal dsitribution?
If X has any discrete probability distribution then the sum of a number of observations for X will be normal.
What is the number of repetitions of an event?
The number of repetitions of an event depends on the type of event and its individual structure, which can range from 2 to 10.
An area of a normal probability distribution represents?
The number 1. The area of any probability distribution equals 1.
What is normal distribution in statstics?
The normal distribution is a statistical distribution. Many naturally occurring variables follow the normal distribution: examples are peoples' height, weights. The sum of independent, identically distributed variables - whatever their own underlying distribution - will tend towards the normal distribution as the number in the sum increases. This means that the mean of repeated measures of ANY variable will approach the normal distribution. Furthermore, some distributions that are not normal to start with, can be converted to normality through simple transformations of the variable. These characteristics make the normal distribution very important in statistics. See attached link for more.
Why the normal distribution can be used as an approximation to the binomial distribution?
The central limit theorem basically states that for any distribution, the distribution of the sample means approaches a normal distribution as the sample size gets larger and larger. This allows us to use the normal distribution as an approximation to binomial, as long as the number of trials times the probability of success is greater than or equal to 5 and if you use the normal distribution as an approximation, you apply the continuity correction factor.
Is normal distribution symmetric with a single peak?
the normal distribution are a very important class of statistical distributions.all normal distributions are symmetric and have bell- shaped density curves with a single peak.both the normal and symmetrical distributions are u-shape and equal from both sides. the normal distribution is considered the most prominent probability distribution in statistics.There are several reasons for this first, the normal distribution is very tractable analytically. that is a large number of results involving this distribution can be derived in explicit from.Second, the normal distribution arises as the outcome of the central limit theorem, which states that under mild conditions the large number of variables is distributed approximately normally.finally, the "bell" shape of the normal distribution marks it is a convenient choice for modeling a large variety of random variables encountered in practices.
When can a binomial situation be approxiamted with a normal distribution?
The binomial distribution can be approximated with a normal distribution when np > 5 and np(1-p) > 5 where p is the proportion (probability) of success of an event and n is the total number of independent trials.
What is the difference between a general normal curve and a standard normal curve?
A standard normal distribution has a mean of zero and a standard deviation of 1. A normal distribution can have any real number as a mean and the standard deviation must be greater than zero.
When you do a number of repetitions of the same movement it is called?
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