In this lecture we discuss how to compute the values of the Chi-square distribution function, using Chi-square distribution tables or computer programs (in particular Matlab and Excel).
Let
be a Chi-square random variable with
degrees of freedom and denote its
distribution function by
As we have discussed in the lecture entitled
Chi-square distribution, there is no simple
analytical expression for
and its values are usually looked up in a table or computed with a computer
algorithm. The next sections discuss these alternatives in detail.
In the past, when computers were not widely available, people used to look up
the values of
in Chi-square distribution tables, where some critical values of
were tabulated for several values of the degrees of freedom parameter
.
A Chi-square distribution table looks something like this:
| Degrees of freedom / Probability | 0.01 | 0.05 | 0.10 | 0.90 | 0.95 | 0.99 |
|---|---|---|---|---|---|---|
| 1 | 0.00 | 0.00 | 0.02 | 2.71 | 3.84 | 6.63 |
| 2 | 0.02 | 0.10 | 0.21 | 4.61 | 5.99 | 9.21 |
| 3 | 0.11 | 0.35 | 0.58 | 6.25 | 7.81 | 11.34 |
| 4 | 0.30 | 0.71 | 1.06 | 7.78 | 9.49 | 13.28 |
| 5 | 0.55 | 1.15 | 1.61 | 9.24 | 11.07 | 15.09 |
| 6 | 0.87 | 1.64 | 2.20 | 10.64 | 12.59 | 16.81 |
| 7 | 1.24 | 2.17 | 2.83 | 12.02 | 14.07 | 18.48 |
| 8 | 1.65 | 2.73 | 3.49 | 13.36 | 15.51 | 20.09 |
| 9 | 2.09 | 3.33 | 4.17 | 14.68 | 16.92 | 21.67 |
| 10 | 2.56 | 3.94 | 4.87 | 15.99 | 18.31 | 23.21 |
| 11 | 3.05 | 4.57 | 5.58 | 17.28 | 19.68 | 24.72 |
| 12 | 3.57 | 5.23 | 6.30 | 18.55 | 21.03 | 26.22 |
| 13 | 4.11 | 5.89 | 7.04 | 19.81 | 22.36 | 27.69 |
| 14 | 4.66 | 6.57 | 7.79 | 21.06 | 23.68 | 29.14 |
| 15 | 5.23 | 7.26 | 8.55 | 22.31 | 25.00 | 30.58 |
| 16 | 5.81 | 7.96 | 9.31 | 23.54 | 26.30 | 32.00 |
| 17 | 6.41 | 8.67 | 10.09 | 24.77 | 27.59 | 33.41 |
| 18 | 7.01 | 9.39 | 10.86 | 25.99 | 28.87 | 34.81 |
| 19 | 7.63 | 10.12 | 11.65 | 27.20 | 30.14 | 36.19 |
| 20 | 8.26 | 10.85 | 12.44 | 28.41 | 31.41 | 37.57 |
| 21 | 8.90 | 11.59 | 13.24 | 29.62 | 32.67 | 38.93 |
| 22 | 9.54 | 12.34 | 14.04 | 30.81 | 33.92 | 40.29 |
| 23 | 10.20 | 13.09 | 14.85 | 32.01 | 35.17 | 41.64 |
| 24 | 10.86 | 13.85 | 15.66 | 33.20 | 36.42 | 42.98 |
| 25 | 11.52 | 14.61 | 16.47 | 34.38 | 37.65 | 44.31 |
| 26 | 12.20 | 15.38 | 17.29 | 35.56 | 38.89 | 45.64 |
| 27 | 12.88 | 16.15 | 18.11 | 36.74 | 40.11 | 46.96 |
| 28 | 13.56 | 16.93 | 18.94 | 37.92 | 41.34 | 48.28 |
| 29 | 14.26 | 17.71 | 19.77 | 39.09 | 42.56 | 49.59 |
| 30 | 14.95 | 18.49 | 20.60 | 40.26 | 43.77 | 50.89 |
| 31 | 15.66 | 19.28 | 21.43 | 41.42 | 44.99 | 52.19 |
| 32 | 16.36 | 20.07 | 22.27 | 42.58 | 46.19 | 53.49 |
| 33 | 17.07 | 20.87 | 23.11 | 43.75 | 47.40 | 54.78 |
| 34 | 17.79 | 21.66 | 23.95 | 44.90 | 48.60 | 56.06 |
| 35 | 18.51 | 22.47 | 24.80 | 46.06 | 49.80 | 57.34 |
| 36 | 19.23 | 23.27 | 25.64 | 47.21 | 51.00 | 58.62 |
| 37 | 19.96 | 24.07 | 26.49 | 48.36 | 52.19 | 59.89 |
| 38 | 20.69 | 24.88 | 27.34 | 49.51 | 53.38 | 61.16 |
| 39 | 21.43 | 25.70 | 28.20 | 50.66 | 54.57 | 62.43 |
| 40 | 22.16 | 26.51 | 29.05 | 51.81 | 55.76 | 63.69 |
| 41 | 22.91 | 27.33 | 29.91 | 52.95 | 56.94 | 64.95 |
| 42 | 23.65 | 28.14 | 30.77 | 54.09 | 58.12 | 66.21 |
| 43 | 24.40 | 28.96 | 31.63 | 55.23 | 59.30 | 67.46 |
| 44 | 25.15 | 29.79 | 32.49 | 56.37 | 60.48 | 68.71 |
| 45 | 25.90 | 30.61 | 33.35 | 57.51 | 61.66 | 69.96 |
| 46 | 26.66 | 31.44 | 34.22 | 58.64 | 62.83 | 71.20 |
| 47 | 27.42 | 32.27 | 35.08 | 59.77 | 64.00 | 72.44 |
| 48 | 28.18 | 33.10 | 35.95 | 60.91 | 65.17 | 73.68 |
| 49 | 28.94 | 33.93 | 36.82 | 62.04 | 66.34 | 74.92 |
| 50 | 29.71 | 34.76 | 37.69 | 63.17 | 67.50 | 76.15 |
For example, at the intersection of the row corresponding to 5 degrees of
freedom and the column corresponding to a value of the distribution function
of 0.95, we read the value 11.07. This means
that
In
other words, the realization of a Chi-square random variable with 5 degrees of
freedom will be less than 11.07 with probability 0.95.
If we are searching for a value of
that does not correspond to one of the critical values in the first row, then
a Chi-square distribution table is not of any help. In this case, we need to
use a computer algorithm (see below).
To compute the values of the Chi-square distribution function
,
we can use the built-in Excel function CHISQ.DIST().
For example, if we need to compute
and the value
is stored in cell
A1, we can type in another cell:
=CHISQ.DIST(A1,5)
To compute the values of the Chi-square distribution function
,
we can use the Matlab function chi2cdf(), which takes
the value
as its first argument and the number of degrees of freedom
as its second argument. For example, if we need to compute
,
we can input the following command:
chi2cdf(1,5)
At the end of the lecture entitled Chi-square distribution, you can find some solved exercises that also require the computation of Chi-square distribution values.
Please cite as:
Taboga, Marco (2021). "Values of the Chi-square distribution", Lectures on probability theory and mathematical statistics. Kindle Direct Publishing. Online appendix. https://www.statlect.com/probability-distributions/chi-square-distribution-values.
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