(4) Probability

The old habit is to first give the Mind Map of this chapter so that you can have an overall concept. I will not go into details about the basic concepts here, here are some of the easy obfuscation points and key points. The first is the mutex event and the independent event. Many people will confuse the two. An example shows that the two events are not the same: if the two events are mutex events, one of them is

The old habit is to first give the Mind Map of this chapter so that you can have an overall concept. I will not go into details about the basic concepts here, here are some of the easy obfuscation points and key points. The first is the mutex event and the independent event. Many people will confuse the two. An example shows that the two events are not the same: if the two events are mutex events, one of them is

The old habit is to give the Mind Map of this chapter so that you can have a general concept first.

I will not go into details about the basic concepts here. I would like to explain some of the confusing points and key points.

The first is mutex and independent events. Many will confuse the two. An example shows that the two are not the same:

If two events are mutex events, one of which is determined to have occurred, the probability of another event is reduced to 0. Obviously, the two are related. <喎?http: www.2cto.com kf ware vc " target="_blank" class="keylink"> Signature + signature/vKwrz + signature/Signature/gu6W2wMGitcS + signature + 8rCvP61xNOwz + signature/J0tS/tL + signature + icagicagc63lbgx + m/9 s/release/O0sPHvau74bXDtb24/release + NDQ1b3C1L72st/KsaOsu + HU2sihtcPR + bG + release/release + release/s/release + keys/keys + rXEtM7K/keys + ICAgICAgIDxzdHJvbmc + keys/i85MnPysK8/keys + keys /records + vG5Mv7x/i85MnPysK8/srHt/records + records/J08O2/s/records + NDNy + a7 + rHkwb + records/ix8KO6PGJyPgo8L3A + records + a7 + rHkwb/U2sSz0ru4 + Laox/users/K/users + a7/aGjPC9wPgoKPHA + ICAgICAgIDxzdHJvbmc + signature + Signature =" http://www.2cto.com/uploadfile/Collfiles/20140625/2014062509022412.png "Width =" 300 "height =" 200 "alt =" \ ">

Nature:

The normal probability distribution has a complete family. Each particular normal distribution is distinguished by its mean μ and standard deviation σ.
The highest point of a normal curve is the mean, which is also the median and number of distributions.
The mean value of the distribution can be any number: Negative, zero, or positive.
The normal probability distribution is symmetric.
The end of the curve is infinitely stretched in two directions, and theoretically it will never overlap the horizontal axis.

The standard deviation determines the curve width.
The total area under the normal probability distribution curve is 1, which is true for all continuous probability distributions.
The probability of a normal random variable is given by the area under the curve. The probability of some common intervals is 68.26%, 95.44%, 99.72%

Continuous Correction Factor: When continuous normal probability distribution is used to approximate discrete two-term probability distribution, the 0. 5 value is added and subtracted from the x value.

The relationship between the exponential distribution and the Poisson distribution is that if the Poisson distribution gives an appropriate description of the number of occurrences during each interval, the exponential distribution can provide a description of the Interval Length between two occurrences.

PS: the exponential distribution is the severe rightmost distribution with a degree of 2.