ChiSquare Distribution
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Key Questions

by definition a chisquare distribution is the square of a standard normal distribution.
suppose a population follows normal distribution with parameters as mean and variance, will be symmetric in nature and like that a standard normal variate will follow the same property. thus chisquare will be square of that standard normal variate, and will follow the same properties.it will have "n" degrees of freedom,as degrees of freedom is the difference between numbers of observation and limitation or estimated parameters. thus here as there is no estimated parameters are give the chisquare will have "n" degrees of freedom.
view the above link to have a further clear perspective. https://d2gne97vdumgn3.cloudfront.net/api/file/ZTQlvipQRQ2UtM4Y0aVN 
Each chisquare distribution depends on the degrees of freedom parameter,
#k# , specified so it is a family of distributions.The probability distribution for a random variable
#x# that follows a chisquare distribution that has#k# degrees of freedom is#f(x  k) = 1/(2^(k/2) Gamma(k/2)) x^(k/2 1 ) e^(x/2)# for#x>0# and#k=1, 2, 3, ...# Some examples of chisquare distribution shapes follows:

The degrees of freedom
The shape of the chisquared curve is the cumulative density function or probability density function, depending of what you want, of a random variable
#X# with a chi squared distribution with k degrees of freedom(#chi_(k)^2 # ).The only factors that can interfere with the shape of those graphs are the parameters taken by the distribution, the chisquared distribution only takes one parameter, that is, the degrees of freedom, so it is the only one that can interfere in its shape.There is a twist thought , it is possible to have a Noncentral chisquared distribution , this is common in some areas of statistics and in that case you have an extra parameter
Here are some graphs of the central chisquared distribtution:
If you having trouble understanding what the chisquared means, maybe if you use the relation it has with other variables to enlighten you the relation it has with other variables to enlighten yourself