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Yes. The normal distribution is used to approximate a binomial distribution when the sample size (n) times the probability of success (p), and the probability of failure (q) are both greater than or equal to 5. The mean of the normal approximation is n*p and the standard deviation is the square root of n*p*q.
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What is a normal data set?
A normal data set is a set of observations from a Gaussian distribution, which is also called the Normal distribution.
What Percent of data is below the mean in a normal distribution?
In the normal distribution, the mean and median coincide, and 50% of the data are below the mean.
What percentage of the data in a normal distribution is represented by 1 SD of a sample?
Assuming that we have a Normal Distribution of Data, approx. 65% of the data will fall within One Sigma.
What is a example of discrete data?
Discrete data are observations on a variable that which take values from a discrete set.
What is the mean of the sampling distribution of the sample mean?
Frequently it's impossible or impractical to test the entire universe of data to determine probabilities. So we test a small sub-set of the universal database and we call that the sample. Then using that sub-set of data we calculate its distribution, which is called the sample distribution. Normally we find the sample distribution has a bell shape, which we actually call the "normal distribution." When the data reflect the normal distribution of a sample, we call it the Student's t distribution to distinguish it from the normal distribution of a universe of data. The Student's t distribution is useful because with it and the small number of data we test, we can infer the probability distribution of the entire universal data set with some degree of confidence.
Can normal distridution apply on discrete data?
Yes, If you have a large data set, you can approximate the discrete data by Normal distribution (which is continuous). An example would be, "A coin is tossed 1000 times. What is the probability of rolling between 300 and 400 heads?" This problem, usually solved by Binomial distribution (which is a discrete distribution), is very difficult to solve because of the large data set and can be approximated by the Normal distribution.
When do you use goodness of fit in statistical analysis?
statistical goodness of fit test used for categorical data to test if a sample of data came from a population with a specific distribution. It can be applied for discrete distributions.
What is a normal data set?
A normal data set is a set of observations from a Gaussian distribution, which is also called the Normal distribution.
What Percent of data is below the mean in a normal distribution?
In the normal distribution, the mean and median coincide, and 50% of the data are below the mean.
Why you prefer normal distribution over other distributions in statistics?
we prefer normal distribution over other distribution in statistics because most of the data around us is continuous. So, for continuous data normal distribution is used.
What percentage of the data in a normal distribution is represented by 1 SD of a sample?
Assuming that we have a Normal Distribution of Data, approx. 65% of the data will fall within One Sigma.
Can normal distribution be used if the data is not normal?
You can use a normal distribution to approximate a binomial distribution if conditions are met such as n*p and n*q is > or = to 5 & n >30.
What is a example of discrete data?
Discrete data are observations on a variable that which take values from a discrete set.
What does it mean when data is normally distributed?
It means that the data are distributed according to a probability distribution function known as the normal distribution. This site is useless for showing most mathematical functions but you can Google "normal distribution" to get more details.
Are weight of motorcycles discrete data or continuous data?
The weight of the motorcycles is discrete and not the continuous data.
What is the mean of the sampling distribution of the sample mean?
Frequently it's impossible or impractical to test the entire universe of data to determine probabilities. So we test a small sub-set of the universal database and we call that the sample. Then using that sub-set of data we calculate its distribution, which is called the sample distribution. Normally we find the sample distribution has a bell shape, which we actually call the "normal distribution." When the data reflect the normal distribution of a sample, we call it the Student's t distribution to distinguish it from the normal distribution of a universe of data. The Student's t distribution is useful because with it and the small number of data we test, we can infer the probability distribution of the entire universal data set with some degree of confidence.
What is the sampling distribution of sample means and why is it useful?
Frequently it's impossible or impractical to test the entire universe of data to determine probabilities. So we test a small sub-set of the universal database and we call that the sample. Then using that sub-set of data we calculate its distribution, which is called the sample distribution. Normally we find the sample distribution has a bell shape, which we actually call the "normal distribution." When the data reflect the normal distribution of a sample, we call it the Student's t distribution to distinguish it from the normal distribution of a universe of data. The Student's t distribution is useful because with it and the small number of data we test, we can infer the probability distribution of the entire universal data set with some degree of confidence.
