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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