0
Add your answer:
Earn +20 pts
How does the standard normal distribution differ from the t-distribution?
The normal distribution and the t-distribution are both symmetric bell-shaped continuous probability distribution functions. The t-distribution has heavier tails: the probability of observations further from the mean is greater than for the normal distribution. There are other differences in terms of when it is appropriate to use them. Finally, the standard normal distribution is a special case of a normal distribution such that the mean is 0 and the standard deviation is 1.
Why Normal distribution is better then other distributions in statistics?
The normal distribution has two parameters, the mean and the standard deviation Once we know these parameters, we know everything we need to know about a particular normal distribution. This is a very nice feature for a distribution to have. Also, the mean, median and mode are all the same in the normal distribution. Also, the normal distribution is important in the central limit theorem. These and many other facts make the normal distribution a nice distribution to have in statistics.
Why is the normal probability distribution called a family of normal probability distribution?
Because very many variables tend to have the Gaussian distribution. Furthermore, even if the underlying distribution is non-Gaussian, the distribution of the means of repeated samples will be Gaussian. As a result, the Gaussian distributions are also referred to as Normal.
Does a normal probability distribution include a bimodal distribution?
No, the normal distribution is strictly unimodal.
Is normal distribution also a probability distribution?
Yes. When we refer to the normal distribution, we are referring to a probability distribution. When we specify the equation of a continuous distribution, such as the normal distribution, we refer to the equation as a probability density function.
What is the shape of a normal distribution?
It is a symmetrical, "bell-shaped" curve. The tails are infinitely long.
Why two tails of the normal probability distribution extend indefinitely and never touch the horizontal axis?
This is because the normal distribution has a domain that extends to infinity in both directions.
What odes kurtosis mean?
Kurtosis is a measure of the "peakedness" or thickness of the tails of a distribution compared to a normal distribution. A positive kurtosis indicates a distribution with heavier tails and a sharper peak, while a negative kurtosis indicates lighter tails and a flatter peak. Kurtosis helps to understand the shape of a distribution and the likelihood of extreme outcomes.
How does the standard normal distribution differ from the t-distribution?
The normal distribution and the t-distribution are both symmetric bell-shaped continuous probability distribution functions. The t-distribution has heavier tails: the probability of observations further from the mean is greater than for the normal distribution. There are other differences in terms of when it is appropriate to use them. Finally, the standard normal distribution is a special case of a normal distribution such that the mean is 0 and the standard deviation is 1.
If a great many data values cluster to the left of a data distribution which then tails off to the right the distribution is referred to as?
It is a positively skewed distribution.
Are all symmetric distribution are normal?
No. The binomial distribution (discrete) or uniform distribution (discrete or continuous) are symmetrical but they are not normal. There are others.
How many possible outcomes for normal distribution?
Infinitely many. The normal distribution is applicable to a continuous variable whose domain is the whole of the real numbers. Infinitely many. The normal distribution is applicable to a continuous variable whose domain is the whole of the real numbers. Infinitely many. The normal distribution is applicable to a continuous variable whose domain is the whole of the real numbers. Infinitely many. The normal distribution is applicable to a continuous variable whose domain is the whole of the real numbers.
Why Normal distribution is better then other distributions in statistics?
The normal distribution has two parameters, the mean and the standard deviation Once we know these parameters, we know everything we need to know about a particular normal distribution. This is a very nice feature for a distribution to have. Also, the mean, median and mode are all the same in the normal distribution. Also, the normal distribution is important in the central limit theorem. These and many other facts make the normal distribution a nice distribution to have in statistics.
What is the difference between a normal distribution and the standard normal distribution?
The standard normal distribution is a normal distribution with mean 0 and variance 1.
What is the difference of a normal distribution and a stardard normal distribution?
The standard normal distribution is a special case of the normal distribution. The standard normal has mean 0 and variance 1.
Is continuous distribution normal distribution?
le standard normal distribution is a normal distribution who has mean 0 and variance 1
What requirements are necessary for a normal probability distribution to be a standard normal probability distribution?
The normal distribution, also known as the Gaussian distribution, has a familiar "bell curve" shape and approximates many different naturally occurring distributions over real numbers.
