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stats_tables.c

/*
 *  Copyright (c) by Ramu Ramanathan and Allin Cottrell
 *
 *   This program is free software; you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation; either version 2 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program; if not, write to the Free Software
 *   Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
 *
 */

/* statistical tables for gretl */

#include "libgretl.h"

#define NDVAL 12
#define NCRIT  5

typedef struct {
    int n;
    double dval[NDVAL];
} dw_t;

typedef struct {
    int df;
    double crit[NCRIT];
} dfstat_t;

dw_t dw_vals[] = {
    { 15,{1.08,1.36, 0.95,1.54, 0.82,1.75, 0.69,1.97, 0.56,2.21, 0.00,0.00}},
    { 16,{1.10,1.37, 0.98,1.54, 0.86,1.73, 0.74,1.93, 0.62,2.15, 0.16,3.30}},
    { 17,{1.13,1.38, 1.02,1.54, 0.90,1.71, 0.78,1.90, 0.67,2.10, 0.20,3.18}},
    { 18,{1.16,1.39, 1.05,1.53, 0.93,1.69, 0.82,1.87, 0.71,2.06, 0.24,3.07}},
    { 19,{1.18,1.40, 1.08,1.53, 0.97,1.68, 0.86,1.85, 0.75,2.02, 0.29,2.97}},
    { 20,{1.20,1.41, 1.10,1.54, 1.00,1.68, 0.90,1.83, 0.79,1.99, 0.34,2.89}},
    { 21,{1.22,1.42, 1.13,1.54, 1.03,1.67, 0.93,1.81, 0.83,1.96, 0.38,2.81}},
    { 22,{1.24,1.43, 1.15,1.54, 1.05,1.66, 0.96,1.80, 0.86,1.94, 0.42,2.73}},
    { 23,{1.26,1.44, 1.17,1.54, 1.08,1.66, 0.99,1.79, 0.90,1.92, 0.47,2.67}},
    { 24,{1.27,1.45, 1.19,1.55, 1.10,1.66, 1.01,1.78, 0.93,1.90, 0.51,2.61}},
    { 25,{1.29,1.45, 1.21,1.55, 1.12,1.66, 1.04,1.77, 0.95,1.89, 0.54,2.57}},
    { 26,{1.30,1.46, 1.22,1.55, 1.14,1.65, 1.06,1.76, 0.98,1.88, 0.58,2.51}},
    { 27,{1.32,1.47, 1.24,1.56, 1.16,1.65, 1.08,1.76, 1.01,1.86, 0.62,2.47}},
    { 28,{1.33,1.48, 1.26,1.56, 1.18,1.65, 1.10,1.75, 1.03,1.85, 0.65,2.43}},
    { 29,{1.34,1.48, 1.27,1.56, 1.20,1.65, 1.12,1.74, 1.05,1.84, 0.68,2.40}},
    { 30,{1.35,1.49, 1.28,1.57, 1.21,1.65, 1.14,1.74, 1.07,1.83, 0.71,2.36}},
    { 31,{1.36,1.50, 1.30,1.57, 1.23,1.65, 1.16,1.74, 1.09,1.83, 0.74,2.33}},
    { 32,{1.37,1.50, 1.31,1.57, 1.24,1.65, 1.18,1.73, 1.11,1.82, 0.77,2.31}},
    { 33,{1.38,1.51, 1.32,1.58, 1.26,1.65, 1.19,1.73, 1.13,1.81, 0.80,2.28}},
    { 34,{1.39,1.51, 1.33,1.58, 1.27,1.65, 1.21,1.73, 1.15,1.81, 0.82,2.26}},
    { 35,{1.40,1.52, 1.34,1.53, 1.28,1.65, 1.22,1.73, 1.16,1.80, 0.85,2.24}},
    { 36,{1.41,1.52, 1.35,1.59, 1.29,1.65, 1.24,1.73, 1.18,1.80, 0.87,2.22}},
    { 37,{1.42,1.53, 1.36,1.59, 1.31,1.66, 1.25,1.72, 1.19,1.80, 0.89,2.20}},
    { 38,{1.43,1.54, 1.37,1.59, 1.32,1.66, 1.26,1.72, 1.21,1.79, 0.91,2.18}},
    { 39,{1.43,1.54, 1.38,1.60, 1.33,1.66, 1.27,1.72, 1.22,1.79, 0.93,2.16}},
    { 40,{1.44,1.54, 1.39,1.60, 1.34,1.66, 1.29,1.72, 1.23,1.79, 0.95,2.15}},
    { 45,{1.48,1.57, 1.43,1.62, 1.38,1.67, 1.34,1.72, 1.29,1.78, 1.04,2.09}},
    { 50,{1.50,1.59, 1.46,1.63, 1.42,1.67, 1.38,1.72, 1.34,1.77, 1.11,2.04}},
    { 55,{1.53,1.60, 1.49,1.64, 1.45,1.68, 1.41,1.72, 1.38,1.77, 1.17,2.01}},
    { 60,{1.55,1.62, 1.51,1.65, 1.48,1.69, 1.44,1.73, 1.41,1.77, 1.22,1.98}},
    { 65,{1.57,1.63, 1.54,1.66, 1.50,1.70, 1.47,1.73, 1.44,1.77, 1.27,1.96}},
    { 70,{1.58,1.64, 1.55,1.67, 1.52,1.70, 1.49,1.74, 1.46,1.77, 1.30,1.95}},
    { 75,{1.60,1.65, 1.57,1.68, 1.54,1.71, 1.51,1.74, 1.49,1.77, 1.34,1.94}},
    { 80,{1.61,1.66, 1.59,1.69, 1.56,1.72, 1.53,1.74, 1.51,1.77, 1.37,1.93}},
    { 85,{1.62,1.67, 1.60,1.70, 1.57,1.72, 1.55,1.75, 1.52,1.77, 1.40,1.92}},
    { 90,{1.63,1.68, 1.61,1.70, 1.59,1.73, 1.57,1.75, 1.54,1.78, 1.42,1.91}},
    { 95,{1.64,1.69, 1.62,1.71, 1.60,1.73, 1.58,1.75, 1.56,1.78, 1.44,1.90}},
    {100,{1.65,1.69, 1.63,1.72, 1.61,1.74, 1.59,1.76, 1.57,1.78, 1.46,1.90}}
};

/* source for t-dist table:
   http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm
*/

dfstat_t t_vals[] = {
      /* 0.10  0.05  0.025  0.01  0.001 */
    {  1,{3.078,6.314,12.706,31.821,318.313}},
    {  2,{1.886,2.920,4.303,6.965,22.327}},
    {  3,{1.638,2.353,3.182,4.541,10.215}},
    {  4,{1.533,2.132,2.776,3.747,7.173}},
    {  5,{1.476,2.015,2.571,3.365,5.893}},
    {  6,{1.440,1.943,2.447,3.143,5.208}},
    {  7,{1.415,1.895,2.365,2.998,4.782}},
    {  8,{1.397,1.860,2.306,2.896,4.499}},
    {  9,{1.383,1.833,2.262,2.821,4.296}},
    { 10,{1.372,1.812,2.228,2.764,4.143}},
    { 11,{1.363,1.796,2.201,2.718,4.024}},
    { 12,{1.356,1.782,2.179,2.681,3.929}},
    { 13,{1.350,1.771,2.160,2.650,3.852}},
    { 14,{1.345,1.761,2.145,2.624,3.787}},
    { 15,{1.341,1.753,2.131,2.602,3.733}},
    { 16,{1.337,1.746,2.120,2.583,3.686}},
    { 17,{1.333,1.740,2.110,2.567,3.646}},
    { 18,{1.330,1.734,2.101,2.552,3.610}},
    { 19,{1.328,1.729,2.093,2.539,3.579}},
    { 20,{1.325,1.725,2.086,2.528,3.552}},
    { 21,{1.323,1.721,2.080,2.518,3.527}},
    { 22,{1.321,1.717,2.074,2.508,3.505}},
    { 23,{1.319,1.714,2.069,2.500,3.485}},
    { 24,{1.318,1.711,2.064,2.492,3.467}},
    { 25,{1.316,1.708,2.060,2.485,3.450}},
    { 26,{1.315,1.706,2.056,2.479,3.435}},
    { 27,{1.314,1.703,2.052,2.473,3.421}},
    { 28,{1.313,1.701,2.048,2.467,3.408}},
    { 29,{1.311,1.699,2.045,2.462,3.396}},
    { 30,{1.310,1.697,2.042,2.457,3.385}},
    { 31,{1.309,1.696,2.040,2.453,3.375}},
    { 32,{1.309,1.694,2.037,2.449,3.365}},
    { 33,{1.308,1.692,2.035,2.445,3.356}},
    { 34,{1.307,1.691,2.032,2.441,3.348}},
    { 35,{1.306,1.690,2.030,2.438,3.340}},
    { 36,{1.306,1.688,2.028,2.434,3.333}},
    { 37,{1.305,1.687,2.026,2.431,3.326}},
    { 38,{1.304,1.686,2.024,2.429,3.319}},
    { 39,{1.304,1.685,2.023,2.426,3.313}},
    { 40,{1.303,1.684,2.021,2.423,3.307}},
    { 41,{1.303,1.683,2.020,2.421,3.301}},
    { 42,{1.302,1.682,2.018,2.418,3.296}},
    { 43,{1.302,1.681,2.017,2.416,3.291}},
    { 44,{1.301,1.680,2.015,2.414,3.286}},
    { 45,{1.301,1.679,2.014,2.412,3.281}},
    { 46,{1.300,1.679,2.013,2.410,3.277}},
    { 47,{1.300,1.678,2.012,2.408,3.273}},
    { 48,{1.299,1.677,2.011,2.407,3.269}},
    { 49,{1.299,1.677,2.010,2.405,3.265}},
    { 50,{1.299,1.676,2.009,2.403,3.261}},
    { 51,{1.298,1.675,2.008,2.402,3.258}},
    { 52,{1.298,1.675,2.007,2.400,3.255}},
    { 53,{1.298,1.674,2.006,2.399,3.251}},
    { 54,{1.297,1.674,2.005,2.397,3.248}},
    { 55,{1.297,1.673,2.004,2.396,3.245}},
    { 56,{1.297,1.673,2.003,2.395,3.242}},
    { 57,{1.297,1.672,2.002,2.394,3.239}},
    { 58,{1.296,1.672,2.002,2.392,3.237}},
    { 59,{1.296,1.671,2.001,2.391,3.234}},
    { 60,{1.296,1.671,2.000,2.390,3.232}},
    { 61,{1.296,1.670,2.000,2.389,3.229}},
    { 62,{1.295,1.670,1.999,2.388,3.227}},
    { 63,{1.295,1.669,1.998,2.387,3.225}},
    { 64,{1.295,1.669,1.998,2.386,3.223}},
    { 65,{1.295,1.669,1.997,2.385,3.220}},
    { 66,{1.295,1.668,1.997,2.384,3.218}},
    { 67,{1.294,1.668,1.996,2.383,3.216}},
    { 68,{1.294,1.668,1.995,2.382,3.214}},
    { 69,{1.294,1.667,1.995,2.382,3.213}},
    { 70,{1.294,1.667,1.994,2.381,3.211}},
    { 71,{1.294,1.667,1.994,2.380,3.209}},
    { 72,{1.293,1.666,1.993,2.379,3.207}},
    { 73,{1.293,1.666,1.993,2.379,3.206}},
    { 74,{1.293,1.666,1.993,2.378,3.204}},
    { 75,{1.293,1.665,1.992,2.377,3.202}},
    { 76,{1.293,1.665,1.992,2.376,3.201}},
    { 77,{1.293,1.665,1.991,2.376,3.199}},
    { 78,{1.292,1.665,1.991,2.375,3.198}},
    { 79,{1.292,1.664,1.990,2.374,3.197}},
    { 80,{1.292,1.664,1.990,2.374,3.195}},
    { 81,{1.292,1.664,1.990,2.373,3.194}},
    { 82,{1.292,1.664,1.989,2.373,3.193}},
    { 83,{1.292,1.663,1.989,2.372,3.191}},
    { 84,{1.292,1.663,1.989,2.372,3.190}},
    { 85,{1.292,1.663,1.988,2.371,3.189}},
    { 86,{1.291,1.663,1.988,2.370,3.188}},
    { 87,{1.291,1.663,1.988,2.370,3.187}},
    { 88,{1.291,1.662,1.987,2.369,3.185}},
    { 89,{1.291,1.662,1.987,2.369,3.184}},
    { 90,{1.291,1.662,1.987,2.368,3.183}},
    { 91,{1.291,1.662,1.986,2.368,3.182}},
    { 92,{1.291,1.662,1.986,2.368,3.181}},
    { 93,{1.291,1.661,1.986,2.367,3.180}},
    { 94,{1.291,1.661,1.986,2.367,3.179}},
    { 95,{1.291,1.661,1.985,2.366,3.178}},
    { 96,{1.290,1.661,1.985,2.366,3.177}},
    { 97,{1.290,1.661,1.985,2.365,3.176}},
    { 98,{1.290,1.661,1.984,2.365,3.175}},
    { 99,{1.290,1.660,1.984,2.365,3.175}},
    {100,{1.290,1.660,1.984,2.364,3.174}},
    {999,{1.282,1.645,1.960,2.326,3.090}}
};

/* source for chi-square table:
   http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm
*/

dfstat_t chi_vals[] = {
    /* 0.10  0.05  0.025  0.01  0.001 */
    {1,{2.706,3.841,5.024,6.635,10.828}},
    {2,{4.605,5.991,7.378,9.210,13.816}},
    {3,{6.251,7.815,9.348,11.345,16.266}},
    {4,{7.779,9.488,11.143,13.277,18.467}},
    {5,{9.236,11.070,12.833,15.086,20.515}},
    {6,{10.645,12.592,14.449,16.812,22.458}},
    {7,{12.017,14.067,16.013,18.475,24.322}},
    {8,{13.362,15.507,17.535,20.090,26.125}},
    {9,{14.684,16.919,19.023,21.666,27.877}},
    {10,{15.987,18.307,20.483,23.209,29.588}},
    {11,{17.275,19.675,21.920,24.725,31.264}},
    {12,{18.549,21.026,23.337,26.217,32.910}},
    {13,{19.812,22.362,24.736,27.688,34.528}},
    {14,{21.064,23.685,26.119,29.141,36.123}},
    {15,{22.307,24.996,27.488,30.578,37.697}},
    {16,{23.542,26.296,28.845,32.000,39.252}},
    {17,{24.769,27.587,30.191,33.409,40.790}},
    {18,{25.989,28.869,31.526,34.805,42.312}},
    {19,{27.204,30.144,32.852,36.191,43.820}},
    {20,{28.412,31.410,34.170,37.566,45.315}},
    {21,{29.615,32.671,35.479,38.932,46.797}},
    {22,{30.813,33.924,36.781,40.289,48.268}},
    {23,{32.007,35.172,38.076,41.638,49.728}},
    {24,{33.196,36.415,39.364,42.980,51.179}},
    {25,{34.382,37.652,40.646,44.314,52.620}},
    {26,{35.563,38.885,41.923,45.642,54.052}},
    {27,{36.741,40.113,43.195,46.963,55.476}},
    {28,{37.916,41.337,44.461,48.278,56.892}},
    {29,{39.087,42.557,45.722,49.588,58.301}},
    {30,{40.256,43.773,46.979,50.892,59.703}},
    {31,{41.422,44.985,48.232,52.191,61.098}},
    {32,{42.585,46.194,49.480,53.486,62.487}},
    {33,{43.745,47.400,50.725,54.776,63.870}},
    {34,{44.903,48.602,51.966,56.061,65.247}},
    {35,{46.059,49.802,53.203,57.342,66.619}},
    {36,{47.212,50.998,54.437,58.619,67.985}},
    {37,{48.363,52.192,55.668,59.893,69.347}},
    {38,{49.513,53.384,56.896,61.162,70.703}},
    {39,{50.660,54.572,58.120,62.428,72.055}},
    {40,{51.805,55.758,59.342,63.691,73.402}},
    {41,{52.949,56.942,60.561,64.950,74.745}},
    {42,{54.090,58.124,61.777,66.206,76.084}},
    {43,{55.230,59.304,62.990,67.459,77.419}},
    {44,{56.369,60.481,64.201,68.710,78.750}},
    {45,{57.505,61.656,65.410,69.957,80.077}},
    {46,{58.641,62.830,66.617,71.201,81.400}},
    {47,{59.774,64.001,67.821,72.443,82.720}},
    {48,{60.907,65.171,69.023,73.683,84.037}},
    {49,{62.038,66.339,70.222,74.919,85.351}},
    {50,{63.167,67.505,71.420,76.154,86.661}},
    {51,{64.295,68.669,72.616,77.386,87.968}},
    {52,{65.422,69.832,73.810,78.616,89.272}},
    {53,{66.548,70.993,75.002,79.843,90.573}},
    {54,{67.673,72.153,76.192,81.069,91.872}},
    {55,{68.796,73.311,77.380,82.292,93.168}},
    {56,{69.919,74.468,78.567,83.513,94.461}},
    {57,{71.040,75.624,79.752,84.733,95.751}},
    {58,{72.160,76.778,80.936,85.950,97.039}},
    {59,{73.279,77.931,82.117,87.166,98.324}},
    {60,{74.397,79.082,83.298,88.379,99.607}},
    {61,{75.514,80.232,84.476,89.591,100.888}},
    {62,{76.630,81.381,85.654,90.802,102.166}},
    {63,{77.745,82.529,86.830,92.010,103.442}},
    {64,{78.860,83.675,88.004,93.217,104.716}},
    {65,{79.973,84.821,89.177,94.422,105.988}},
    {66,{81.085,85.965,90.349,95.626,107.258}},
    {67,{82.197,87.108,91.519,96.828,108.526}},
    {68,{83.308,88.250,92.689,98.028,109.791}},
    {69,{84.418,89.391,93.856,99.228,111.055}},
    {70,{85.527,90.531,95.023,100.425,112.317}},
    {71,{86.635,91.670,96.189,101.621,113.577}},
    {72,{87.743,92.808,97.353,102.816,114.835}},
    {73,{88.850,93.945,98.516,104.010,116.092}},
    {74,{89.956,95.081,99.678,105.202,117.346}},
    {75,{91.061,96.217,100.839,106.393,118.599}},
    {76,{92.166,97.351,101.999,107.583,119.850}},
    {77,{93.270,98.484,103.158,108.771,121.100}},
    {78,{94.374,99.617,104.316,109.958,122.348}},
    {79,{95.476,100.749,105.473,111.144,123.594}},
    {80,{96.578,101.879,106.629,112.329,124.839}},
    {81,{97.680,103.010,107.783,113.512,126.083}},
    {82,{98.780,104.139,108.937,114.695,127.324}},
    {83,{99.880,105.267,110.090,115.876,128.565}},
    {84,{100.980,106.395,111.242,117.057,129.804}},
    {85,{102.079,107.522,112.393,118.236,131.041}},
    {86,{103.177,108.648,113.544,119.414,132.277}},
    {87,{104.275,109.773,114.693,120.591,133.512}},
    {88,{105.372,110.898,115.841,121.767,134.746}},
    {89,{106.469,112.022,116.989,122.942,135.978}},
    {90,{107.565,113.145,118.136,124.116,137.208}},
    {91,{108.661,114.268,119.282,125.289,138.438}},
    {92,{109.756,115.390,120.427,126.462,139.666}},
    {93,{110.850,116.511,121.571,127.633,140.893}},
    {94,{111.944,117.632,122.715,128.803,142.119}},
    {95,{113.038,118.752,123.858,129.973,143.344}},
    {96,{114.131,119.871,125.000,131.141,144.567}},
    {97,{115.223,120.990,126.141,132.309,145.789}},
    {98,{116.315,122.108,127.282,133.476,147.010}},
    {99,{117.407,123.225,128.422,134.642,148.230}},
    {100,{118.498,124.342,129.561,135.807,149.449}}
};

                    
static void other_tables (PRN *prn)
{
    pputs(prn, _("\nFor more comprehensive statistical tables, please consult "
             "a statistics or\neconometrics text, e.g. Ramanathan's "
             "Introductory Econometrics.\n"));
}

void dw_lookup (int n, PRN *prn)
{
    int ndw = sizeof dw_vals / sizeof dw_vals[0];
    int dist, mindist = 1000;
    int row = 0;
    int i, j;

    if (n < 15) {
      n = 15;
    } else if (n > 100) {
      n = 100;
    }

    for (i=0; i<ndw; i++) {
      dist = abs(dw_vals[i].n - n);
      if (dist == 0) {
          row = i;
          break;
      } else if (dist < mindist) {
          mindist = dist;
          row = i;
      } else if (dist >= mindist) {
          break;
      }
    }

    pprintf(prn, "%s, n = %d\n\n",
          /* xgettext:no-c-format */
          _("5% critical values for Durbin-Watson statistic"), 
          dw_vals[row].n);

    pprintf(prn, "%s:\n\n", 
          _("       Number of explanatory variables (excluding the "
             "constant)"));
    pputs(prn, "      1           2           3           4"
        "           5          10\n");
    pputs(prn, "   dL   dU     dL   dU     dL   dU     dL   dU"
        "     dL   dU     dL   dU\n\n");

    for (j=0; j<NDVAL; j++) {
      if (dw_vals[row].dval[j] == 0.0) {
          break;
      }
      if (j % 2 == 0) {
          pprintf(prn, "%6.2f ", dw_vals[row].dval[j]);
      } else {
          pprintf(prn, "%4.2f ", dw_vals[row].dval[j]);
      }
    }

    pputc(prn, '\n');

    other_tables(prn);
}

void norm_lookup (PRN *prn, int gui)
{
    pputs(prn, _("Critical values for standard normal distribution\n\n"));
    pputs(prn, _("Column headings show alpha (significance level) for "
             "a one-tailed test.\n"));
    pputs(prn, _("For a two-tailed test, select the column heading "
             "showing half the desired\nalpha level.  "));
    /* xgettext:no-c-format */ 
    pputs(prn, _("(For example, for a two-tailed test using the 10% "
             "significance\nlevel, use the 0.05 column.)\n\n"));

    pprintf(prn, "      %.2f     %.2f    %.3f     %.2f    %.3f    %.3f\n\n",
          0.10, 0.05, 0.025, 0.01, 0.005, 0.001); 
    pprintf(prn, "  %8.3f %8.3f %8.3f %8.3f %8.3f %8.3f\n",
          1.282, 1.645, 1.960, 2.326, 2.576, 3.090);

    if (gui) {
      other_tables(prn);
    }
}

static void t_chi_pvals (PRN *prn)
{
    pprintf(prn, "             %.2f     %.2f    %.3f     %.2f    %.3f\n\n",
          0.10, 0.05, 0.025, 0.01, 0.001); 
}    

void t_lookup (int df, PRN *prn, int gui)
{
    int row, j;

    if (df < 1) {
      row = 0;
    } else if (df > 200) {
      row = 100;
    } else if (df > 100) {
      row = 99;
    } else {
      row = df - 1;
    }

    pputs(prn, _("Critical values for Student's t distribution\n\n"));
    pputs(prn, _("Column headings show alpha (significance level) for "
             "a one-tailed test.\n"));
    pputs(prn, _("For a two-tailed test, select the column heading "
             "showing half the desired\nalpha level.  "));
    /* xgettext:no-c-format */
    pputs(prn, _("(For example, for a two-tailed test using the 10% "
             "significance\nlevel, use the 0.05 column.)\n\n"));

    t_chi_pvals(prn);

    pprintf(prn, "%s = ", _("df"));
    if (row > 99) {
      pputs(prn, _("inf."));
    } else {
      pprintf(prn, "%3d ", t_vals[row].df);
    }
    for (j=0; j<NCRIT; j++) {
      pprintf(prn, "%8.3f ", t_vals[row].crit[j]);
    }
    pputc(prn, '\n');

    if (gui) {
      other_tables(prn);
    }
}

void chisq_lookup (int df, PRN *prn, int gui)
{
    int i, j;

    if (df < 1) {
      df = 1;
    } else if (df > 100) {
      df = 100;
    }

    i = df - 1;

    pputs(prn, _("Critical values for Chi-square distribution\n\n"));
    pputs(prn, _("Column headings show alpha (significance level) for "
             "a one-tailed test.\n\n"));

    t_chi_pvals(prn);

    pprintf(prn, "%s = %3d ", _("df"), df);
    for (j=0; j<NCRIT; j++) {
      pprintf(prn, "%8.3f ", chi_vals[i].crit[j]);
    }
    pputc(prn, '\n');

    if (gui) {
      other_tables(prn);
    }
}




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