-
Input:
chisquaret([57,54])
Output:
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
|
- Input:
chisquaret([1,1,1,1,1,0,0,1,0,1,1],[.4,.6])
Output:
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
|
- Input:
chisquaret(ranv(1000,binomial,10,.5),binomial)
Output:
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
|
- Input:
chisquaret(ranv(1000,binomial,10,.5),binomial,11,.5)
Output:
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
|
- As an example using class_min and class_size:
Input:
L:= ranv(1000,normald,0,.2) |
chisquaret(L,normald,classes,-2,.25)
|
or (setting class_min to −2 and class_size to −0.25 in the graphical configuration):
chisquaret(L,normald,classes)
Output:
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 d2 of the statistic
D2 = ∑j=1k (nj − ej)/ej, where k is the number of
sample classes for classes(L,-2,0.25) (or
classes(L)), nj is the size of the jth class, and ej =
n pj where n is the size of L and pj is the
probability of the jth class interval assuming a normal distribution
with the mean and population standard deviation of L.