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Why is the Normal Distribution called normal?
Suppose you could call it the Gaussian Distribution or the Laplace-Gauss (not to be confused with the Laplace distribution which takes an absolute difference from the mean rather than a squared error)... however the Brits had no one to name this distribution after (not the German and French names) and because it is the ubiquitous distribution they just called it... well the NORMAL!!
What is a skewed right?
A distribution or set of observations is said to be skewed right or positively skewed if it has a longer "tail" of numbers on the right. The mass of the distribution is more towards the left of the figure rather than the middle.
A complete probability distribution is always an objective listing of all possible events Since it is impossible to list all the possible outcomes from a single event probability distributions are o?
Your question is not clear, but I will attempt to interpret it as best I can. When you first learn about probability, you are taught to list out the possible outcomes. If all outcomes are equally probable, then the probability is easy to calculate. Probability distributions are functions which provide probabilities of events or outcomes. A probability distribution may be discrete or continuous. The range of both must cover all possible outcomes. In the discrete distribution, the sum of probabilities must add to 1 and in the continuous distribtion, the area under the curve must sum to 1. In both the discrete and continuous distributions, a range (or domain) can be described without a listing of all possible outcomes. For example, the domain of the normal distribution (a continuous distribution is minus infinity to positive infinity. The domain for the Poisson distribution (a discrete distribution) is 0 to infinity. You will learn in math that certain series can have infinite number of terms, yet have finite results. Thus, a probability distribution can have an infinite number of events and sum to 1. For a continuous distribution, the probability of an event are stated as a range, for example, the probability of a phone call is between 4 to 10 minutes is 10% or probability of a phone call greater than 10 minutes is 60%, rather than as a single event.
Can two events be both mutually exclusive and independent at the same time?
They can't. If they are ME, then if you get one, you know that the other will not occur. By def of Indep. , knowing the outcome of an event cannot tell you info about the other. Actually, that is not entirely true - in the (rather trivial) case that the probability of one event is zero - both conditions are met. It is false
What is kurtosis?
I will answer your question in a couple of ways. First as a concept: Kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution. That is, data sets with high kurtosis tend to have a distinct peak near the mean, decline rather rapidly, and have heavy tails. Data sets with low kurtosis tend to have a flat top near the mean rather than a sharp peak. A uniform distribution would be the extreme case. Now as a mathematical formula: For univariate data Y1, Y2, ..., YN, the formula for kurtosis is:where is the mean, is the standard deviation, and N is the number of data points. You may find more information at this website: http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm
