The chisquaret command will use the χ^{2} test to compare
sample data to a specified distribution.
You need to provide chisquaret with the following arguments:

- A list of sample data.
- The name of a distribution, or another list of sample data. If this is omitted, a uniform distribution will be used.
- The parameters of the distribution, if a name is given as the previous argument, or the parameter class followed by class_min and class_dim (or the default values will be used).

The chisquaret command will return the result of the χ^{2}
test between the sample data and the named distribution or the two
sample data.

For example, if you enter

chisquaret([57,54])

you will get

Guessing data is the list of number of elements in each class, adequation to uniform distribution Sample adequation to a finite discrete probability distribution Chi2 test result 0.0810810810811, reject adequation if superior to chisquare_icdf(1,0.95)=3.84145882069 or chisquare_icdf(1,1-alpha) if alpha!=5% 0.0810810810811

If you enter

chisquaret([1,1,1,1,1,0,0,1,0,1,1],[.4,.6])

you will get

Sample adequation to a finite discrete probability distribution Chi2 test result 0.742424242424, reject adequation if superior to chisquare_icdf(1,0.95)=3.84145882069 or chisquare_icdf(1,1-alpha) if alpha!=5% 0.742424242424

If you enter

chisquaret(ranv(1000,binomial,10,.5),binomial)

you will get

Binomial: estimating n and p from data 10 0.5055 Sample adequation to binomial(10,0.5055,.), Chi2 test result 7.77825189838, reject adequation if superior to chisquare_icdf(7,0.95)=14.0671404493 or chisquare_icdf(7,1-alpha) if alpha!=5% 7.77825189838

and if you enter

chisquaret(ranv(1000,binomial,10,.5),binomial,11,.5)

you will get

Sample adequation to binomial(11,0.5,.), Chi2 test result 125.617374161, reject adequation if superior to chisquare_icdf(10,0.95)=18.3070380533 or chisquare_icdf(10,1-alpha) if alpha!=5% 125.617374161

For an example using class_min and class_dim, let

L := ranv(1000,normald,0,.2)

If you then enter

chisquaret(L,normald,classes,-2,.25)

or equivalently set class_min to −2 and class_dim to −0.25 in the graphical configuration and enter

chisquaret(L,normald,classes)

you will get

Normal density, estimating mean and stddev from data -0.00345919752912 0.201708100832 Sample adequation to normald_cdf(-0.00345919752912,0.201708100832,.), Chi2 test result 2.11405080381, reject adequation if superior to chisquare_icdf(4,0.95)=9.48772903678 or chisquare_icdf(4,1-alpha) if alpha!=5% 2.11405080381

In this last case, you are given the value of d^{2} of the statistic
D^{2} = ∑_{j=1}^{k} (n_{j} − e_{j})/e_{j}, where k is the number of
sample classes for classes(L,-2,0.25) (or
classes(L)), n_{j} is the size of the jth class, and e_{j} =
n p_{j} where n is the size of L and p_{j} is the
probability of the jth class interval assuming a normal distribution
with the mean and population standard deviation of L.