on the left and when it is skewed left it is on the right
4
A Gaussian distribution has the mean at the highest value. Sum all the values and divide by the number of values. * * * * * A very partial answer and one that does not address the question which was in the context of a frequency distribution table. If the frequencies are for grouped data, replace the range of each group by its midpoint. This, then, comprises the set of values, x, for the random variable. For each x there is an associated frequency, f. Multiply each x by its frequency and add these together. Divide the answer by the sum of the f values. That is the mean.
denoting or relating to a value or quantity lying at the midpoint of a frequency distribution of observed values or quantities, such that there is an equal probability of falling above or below it.
The median in a set of data, would be the middle item of the data string... such as: 1,2,3,4,5,6,7 the Median of this set of data would be: 4
Data validation.
Neither the left or the right but the middle
It is a positively skewed distribution.
positively skewed
A positively skewed or right skewed distribution means that the mean of the data falls to the right of the median. Picturewise, most of the frequency would occur to the left of the graph.
The distribution is unbalanced, because the right tail is larger than it would be if the distribution were balanced (symmetrical). Also called positive skew. See related link with diagrams that clarify this term.
1. The typical distribution of data in a bell curve shows that variations occur rarely and the majority of data is clustered around a mean or average. 2. The distribution of funds by the board of directors will be decided based on several factors that affect the organizations needs. 3. After the earthquake, the aid relief was quick to respond with distribution of water, food and medical supplies
frequency distribution contain qualitative data
A normal data set is a set of observations from a Gaussian distribution, which is also called the Normal distribution.
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.
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.
Assuming that we have a Normal Distribution of Data, approx. 65% of the data will fall within One Sigma.
In the normal distribution, the mean and median coincide, and 50% of the data are below the mean.