the midpoint of the data set
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.
In parametric statistical analysis we always have some probability distributions such as Normal, Binomial, Poisson uniform etc.In statistics we always work with data. So Probability distribution means "from which distribution the data are?
A sampling distribution refers to the distribution from which data relating to a population follows. Information about the sampling distribution plus other information about the population can be inferred by appropriate analysis of samples taken from a distribution.
A bimodal distribution.
It is not necessary that all symetric distribution may be normal.
on the left and when it is skewed left it is on the right
positively skewed
yes
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
It is a positively skewed distribution.
Many line of
its graph is symetric x-axises
aorta
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.