Suppose you compare the mean of raw data and the mean of the same raw data grouped into a frequency distribution. These two means will be
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.
Median.
It is the set of values that a variable can take together with the probability or frequency distribution for those values.
it is used to find mean<median and mode of grouped data
In the normal distribution, the mean and median coincide, and 50% of the data are below the mean.
Suppose you compare the mean of raw data and the mean of the same raw data grouped into a frequency distribution. These two means will be
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.
It is the average of all the numbers in the distribution. If you chose a random data point of the distribution, there would be a 50% chance that it is above the mean, and a 50% chance that it is below the 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.
Median.
When the data distribution is negatively skewed.
It is the set of values that a variable can take together with the probability or frequency distribution for those values.
No, not always. It depends on the type of data you collect. If it is quantitative data, you will be able to calculate a mean. If it is qualitative data, a mean can't be calculated but you can describe the data in terms of a mode.
A bit of data that is very distant from the normal distribution of data and its mean. An unusual value.
The normal distribution allows you to measure the distribution of a set of data points. It helps to determine the average (mean) of the data and how spread out the data is (standard deviation). By using the normal distribution, you can make predictions about the likelihood of certain values occurring within the data set.
Mean is the average, sum total divided by total number of data entries. Standard deviation is the square root of the sum total of the data values divided by the total number of data values. The standard normal distribution is a distribution that closely resembles a bell curve.