F检验临界值表(α=0.05(a))

F检验临界值表(α=0.05(a))
F检验临界值表(α=0.05(a))

自由度(df)1

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5

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8

9

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n -m -11161.448199.500215.707224.583230.162233.986236.768238.883240.543241.882218.51319.00019.16419.24719.29619.33019.35319.37119.38519.396310.1289.5529.2779.1179.0138.9418.8878.8458.8128.78647.709 6.944 6.591 6.388 6.256 6.163 6.094 6.041 5.999 5.9645 6.608 5.786 5.409 5.192 5.050 4.950 4.876 4.818 4.772 4.7356 5.987 5.143 4.757 4.534 4.387 4.284 4.207 4.147 4.099 4.0607 5.591 4.737 4.347 4.120 3.972 3.866 3.787 3.726 3.677 3.6378 5.318 4.459 4.066 3.838 3.687 3.581 3.500 3.438 3.388 3.3479 5.117 4.256 3.863 3.633 3.482 3.374 3.293 3.230 3.179 3.13710 4.965 4.103 3.708 3.478 3.326 3.217 3.135 3.072 3.020 2.97811 4.844 3.982 3.587 3.357 3.204 3.095 3.012 2.948 2.896 2.85412 4.747 3.885 3.490 3.259 3.106 2.996 2.913 2.849 2.796 2.75313 4.667 3.806 3.411 3.179 3.025 2.915 2.832 2.767 2.714 2.67114 4.600 3.739 3.344 3.112 2.958 2.848 2.764 2.699 2.646 2.60215 4.543 3.682 3.287 3.056 2.901 2.790 2.707 2.641 2.588 2.54416 4.494 3.634 3.239 3.007 2.852 2.741 2.657 2.591 2.538 2.49417 4.451 3.592 3.197 2.965 2.810 2.699 2.614 2.548 2.494 2.45018 4.414 3.555 3.160 2.928 2.773 2.661 2.577 2.510 2.456 2.41219 4.381 3.522 3.127 2.895 2.740 2.628 2.544 2.477 2.423 2.37820 4.351 3.493 3.098 2.866 2.711 2.599 2.514 2.447 2.393 2.34821 4.325 3.467 3.072 2.840 2.685 2.573 2.488 2.420 2.366 2.32122 4.301 3.443 3.049 2.817 2.661 2.549 2.464 2.397 2.342 2.29723 4.279 3.422 3.028 2.796 2.640 2.528 2.442 2.375 2.320 2.27524 4.260 3.403 3.009 2.776 2.621 2.508 2.423 2.355 2.300 2.25525 4.242 3.385 2.991 2.759 2.603 2.490 2.405 2.337 2.282 2.23626 4.225 3.369 2.975 2.743 2.587 2.474 2.388 2.321 2.265 2.22027 4.210 3.354 2.960 2.728 2.572 2.459 2.373 2.305 2.250 2.20428 4.196 3.340 2.947 2.714 2.558 2.445 2.359 2.291 2.236 2.19029 4.183 3.328 2.934 2.701 2.545 2.432 2.346 2.278 2.223 2.17730 4.171 3.316 2.922 2.690 2.534 2.421 2.334 2.266 2.211 2.16531 4.160 3.305 2.911 2.679 2.523 2.409 2.323 2.255 2.199 2.15332 4.149 3.295 2.901 2.668 2.512 2.399 2.313 2.244 2.189 2.14233 4.139 3.285 2.892 2.659 2.503 2.389 2.303 2.235 2.179 2.13334 4.130 3.276 2.883 2.650 2.494 2.380 2.294 2.225 2.170 2.12335 4.121 3.267 2.874 2.641 2.485 2.372 2.285 2.217 2.161 2.11436 4.113 3.259 2.866 2.634 2.477 2.364 2.277 2.209 2.153 2.10637 4.105 3.252 2.859 2.626 2.470 2.356 2.270 2.201 2.145 2.09838 4.098 3.245 2.852 2.619 2.463 2.349 2.262 2.194 2.138 2.09139 4.091 3.238 2.845 2.612 2.456 2.342 2.255 2.187 2.131 2.08440 4.085 3.232 2.839 2.606 2.449 2.336 2.249 2.180 2.124 2.07741

4.079 3.226 2.833 2.600 2.443 2.330 2.243 2.174 2.118 2.071

自变量数目(m ) 显著性水平:α=0.05

F 检验临界值表(α=0.05(a))

42 4.073 3.220 2.827 2.594 2.438 2.324 2.237 2.168 2.112 2.065

43 4.067 3.214 2.822 2.589 2.432 2.318 2.232 2.163 2.106 2.059

44 4.062 3.209 2.816 2.584 2.427 2.313 2.226 2.157 2.101 2.054

45 4.057 3.204 2.812 2.579 2.422 2.308 2.221 2.152 2.096 2.049

46 4.052 3.200 2.807 2.574 2.417 2.304 2.216 2.147 2.091 2.044

47 4.047 3.195 2.802 2.570 2.413 2.299 2.212 2.143 2.086 2.039

48 4.043 3.191 2.798 2.565 2.409 2.295 2.207 2.138 2.082 2.035

49 4.038 3.187 2.794 2.561 2.404 2.290 2.203 2.134 2.077 2.030

50 4.034 3.183 2.790 2.557 2.400 2.286 2.199 2.130 2.073 2.026

51 4.030 3.179 2.786 2.553 2.397 2.283 2.195 2.126 2.069 2.022

52 4.027 3.175 2.783 2.550 2.393 2.279 2.192 2.122 2.066 2.018

53 4.023 3.172 2.779 2.546 2.389 2.275 2.188 2.119 2.062 2.015

54 4.020 3.168 2.776 2.543 2.386 2.272 2.185 2.115 2.059 2.011

55 4.016 3.165 2.773 2.540 2.383 2.269 2.181 2.112 2.055 2.008

56 4.013 3.162 2.769 2.537 2.380 2.266 2.178 2.109 2.052 2.005

57 4.010 3.159 2.766 2.534 2.377 2.263 2.175 2.106 2.049 2.001

58 4.007 3.156 2.764 2.531 2.374 2.260 2.172 2.103 2.046 1.998

59 4.004 3.153 2.761 2.528 2.371 2.257 2.169 2.100 2.043 1.995

60 4.001 3.150 2.758 2.525 2.368 2.254 2.167 2.097 2.040 1.993

61 3.998 3.148 2.755 2.523 2.366 2.251 2.164 2.094 2.037 1.990

62 3.996 3.145 2.753 2.520 2.363 2.249 2.161 2.092 2.035 1.987

63 3.993 3.143 2.751 2.518 2.361 2.246 2.159 2.089 2.032 1.985

64 3.991 3.140 2.748 2.515 2.358 2.244 2.156 2.087 2.030 1.982

65 3.989 3.138 2.746 2.513 2.356 2.242 2.154 2.084 2.027 1.980

66 3.986 3.136 2.744 2.511 2.354 2.239 2.152 2.082 2.025 1.977

67 3.984 3.134 2.742 2.509 2.352 2.237 2.150 2.080 2.023 1.975

68 3.982 3.132 2.740 2.507 2.350 2.235 2.148 2.078 2.021 1.973

69 3.980 3.130 2.737 2.505 2.348 2.233 2.145 2.076 2.019 1.971

70 3.978 3.128 2.736 2.503 2.346 2.231 2.143 2.074 2.017 1.969

71 3.976 3.126 2.734 2.501 2.344 2.229 2.142 2.072 2.015 1.967

72 3.974 3.124 2.732 2.499 2.342 2.227 2.140 2.070 2.013 1.965

73 3.972 3.122 2.730 2.497 2.340 2.226 2.138 2.068 2.011 1.963

74 3.970 3.120 2.728 2.495 2.338 2.224 2.136 2.066 2.009 1.961

75 3.968 3.119 2.727 2.494 2.337 2.222 2.134 2.064 2.007 1.959

76 3.967 3.117 2.725 2.492 2.335 2.220 2.133 2.063 2.006 1.958

77 3.965 3.115 2.723 2.490 2.333 2.219 2.131 2.061 2.004 1.956

78 3.963 3.114 2.722 2.489 2.332 2.217 2.129 2.059 2.002 1.954

79 3.962 3.112 2.720 2.487 2.330 2.216 2.128 2.058 2.001 1.953

80 3.960 3.111 2.719 2.486 2.329 2.214 2.126 2.056 1.999 1.951

81 3.959 3.109 2.717 2.484 2.327 2.213 2.125 2.055 1.998 1.950

82 3.957 3.108 2.716 2.483 2.326 2.211 2.123 2.053 1.996 1.948

83 3.956 3.107 2.715 2.482 2.324 2.210 2.122 2.052 1.995 1.947

84 3.955 3.105 2.713 2.480 2.323 2.209 2.121 2.051 1.993 1.945

85 3.953 3.104 2.712 2.479 2.322 2.207 2.119 2.049 1.992 1.944

86 3.952 3.103 2.711 2.478 2.321 2.206 2.118 2.048 1.991 1.943

87 3.951 3.101 2.709 2.476 2.319 2.205 2.117 2.047 1.989 1.941

88 3.949 3.100 2.708 2.475 2.318 2.203 2.115 2.045 1.988 1.940

89 3.948 3.099 2.707 2.474 2.317 2.202 2.114 2.044 1.987 1.939

90 3.947 3.098 2.706 2.473 2.316 2.201 2.113 2.043 1.986 1.938

91 3.946 3.097 2.705 2.472 2.315 2.200 2.112 2.042 1.984 1.936

92 3.945 3.095 2.704 2.471 2.313 2.199 2.111 2.041 1.983 1.935

93 3.943 3.094 2.703 2.470 2.312 2.198 2.110 2.040 1.982 1.934

94 3.942 3.093 2.701 2.469 2.311 2.197 2.109 2.038 1.981 1.933

95 3.941 3.092 2.700 2.467 2.310 2.196 2.108 2.037 1.980 1.932

96 3.940 3.091 2.699 2.466 2.309 2.195 2.106 2.036 1.979 1.931

97 3.939 3.090 2.698 2.465 2.308 2.194 2.105 2.035 1.978 1.930

98 3.938 3.089 2.697 2.465 2.307 2.193 2.104 2.034 1.977 1.929

99 3.937 3.088 2.696 2.464 2.306 2.192 2.103 2.033 1.976 1.928 100 3.936 3.087 2.696 2.463 2.305 2.191 2.103 2.032 1.975 1.927 101 3.935 3.086 2.695 2.462 2.304 2.190 2.102 2.031 1.974 1.926 102 3.934 3.085 2.694 2.461 2.303 2.189 2.101 2.030 1.973 1.925 103 3.933 3.085 2.693 2.460 2.303 2.188 2.100 2.030 1.972 1.924 104 3.932 3.084 2.692 2.459 2.302 2.187 2.099 2.029 1.971 1.923 105 3.932 3.083 2.691 2.458 2.301 2.186 2.098 2.028 1.970 1.922 106 3.931 3.082 2.690 2.457 2.300 2.185 2.097 2.027 1.969 1.921 107 3.930 3.081 2.689 2.457 2.299 2.184 2.096 2.026 1.969 1.920 108 3.929 3.080 2.689 2.456 2.298 2.184 2.096 2.025 1.968 1.919 109 3.928 3.080 2.688 2.455 2.298 2.183 2.095 2.024 1.967 1.919 110 3.927 3.079 2.687 2.454 2.297 2.182 2.094 2.024 1.966 1.918 111 3.927 3.078 2.686 2.453 2.296 2.181 2.093 2.023 1.965 1.917 112 3.926 3.077 2.686 2.453 2.295 2.181 2.092 2.022 1.964 1.916 113 3.925 3.077 2.685 2.452 2.295 2.180 2.092 2.021 1.964 1.915 114 3.924 3.076 2.684 2.451 2.294 2.179 2.091 2.021 1.963 1.915 115 3.924 3.075 2.683 2.451 2.293 2.178 2.090 2.020 1.962 1.914 116 3.923 3.074 2.683 2.450 2.293 2.178 2.089 2.019 1.962 1.913 117 3.922 3.074 2.682 2.449 2.292 2.177 2.089 2.018 1.961 1.913 118 3.921 3.073 2.681 2.449 2.291 2.176 2.088 2.018 1.960 1.912 119 3.921 3.072 2.681 2.448 2.290 2.176 2.087 2.017 1.959 1.911 120 3.920 3.072 2.680 2.447 2.290 2.175 2.087 2.016 1.959 1.910 121 3.919 3.071 2.680 2.447 2.289 2.174 2.086 2.016 1.958 1.910 122 3.919 3.071 2.679 2.446 2.289 2.174 2.085 2.015 1.957 1.909 123 3.918 3.070 2.678 2.445 2.288 2.173 2.085 2.014 1.957 1.908 124 3.918 3.069 2.678 2.445 2.287 2.173 2.084 2.014 1.956 1.908 125 3.917 3.069 2.677 2.444 2.287 2.172 2.084 2.013 1.956 1.907 126 3.916 3.068 2.677 2.444 2.286 2.171 2.083 2.013 1.955 1.907 127 3.916 3.068 2.676 2.443 2.286 2.171 2.082 2.012 1.954 1.906 128 3.915 3.067 2.675 2.442 2.285 2.170 2.082 2.011 1.954 1.905 129 3.915 3.066 2.675 2.442 2.284 2.170 2.081 2.011 1.953 1.905 130 3.914 3.066 2.674 2.441 2.284 2.169 2.081 2.010 1.953 1.904 131 3.913 3.065 2.674 2.441 2.283 2.168 2.080 2.010 1.952 1.904 132 3.913 3.065 2.673 2.440 2.283 2.168 2.080 2.009 1.951 1.903 133 3.912 3.064 2.673 2.440 2.282 2.167 2.079 2.009 1.951 1.903 134 3.912 3.064 2.672 2.439 2.282 2.167 2.079 2.008 1.950 1.902 135 3.911 3.063 2.672 2.439 2.281 2.166 2.078 2.008 1.950 1.901

136 3.911 3.063 2.671 2.438 2.281 2.166 2.078 2.007 1.949 1.901 137 3.910 3.062 2.671 2.438 2.280 2.165 2.077 2.007 1.949 1.900 138 3.910 3.062 2.670 2.437 2.280 2.165 2.077 2.006 1.948 1.900 139 3.909 3.061 2.670 2.437 2.279 2.164 2.076 2.006 1.948 1.899 140 3.909 3.061 2.669 2.436 2.279 2.164 2.076 2.005 1.947 1.899 141 3.908 3.060 2.669 2.436 2.278 2.163 2.075 2.005 1.947 1.898 142 3.908 3.060 2.668 2.435 2.278 2.163 2.075 2.004 1.946 1.898 143 3.907 3.059 2.668 2.435 2.277 2.163 2.074 2.004 1.946 1.897 144 3.907 3.059 2.667 2.435 2.277 2.162 2.074 2.003 1.945 1.897 145 3.906 3.058 2.667 2.434 2.277 2.162 2.073 2.003 1.945 1.897 146 3.906 3.058 2.667 2.434 2.276 2.161 2.073 2.002 1.945 1.896 147 3.905 3.058 2.666 2.433 2.276 2.161 2.072 2.002 1.944 1.896 148 3.905 3.057 2.666 2.433 2.275 2.160 2.072 2.001 1.944 1.895 149 3.905 3.057 2.665 2.432 2.275 2.160 2.072 2.001 1.943 1.895 150 3.904 3.056 2.665 2.432 2.274 2.160 2.071 2.001 1.943 1.894 151 3.904 3.056 2.665 2.432 2.274 2.159 2.071 2.000 1.942 1.894 152 3.903 3.056 2.664 2.431 2.274 2.159 2.070 2.000 1.942 1.893 153 3.903 3.055 2.664 2.431 2.273 2.158 2.070 1.999 1.942 1.893 154 3.903 3.055 2.663 2.430 2.273 2.158 2.070 1.999 1.941 1.893 155 3.902 3.054 2.663 2.430 2.273 2.158 2.069 1.999 1.941 1.892 156 3.902 3.054 2.663 2.430 2.272 2.157 2.069 1.998 1.940 1.892 157 3.901 3.054 2.662 2.429 2.272 2.157 2.068 1.998 1.940 1.891 158 3.901 3.053 2.662 2.429 2.271 2.156 2.068 1.997 1.940 1.891 159 3.901 3.053 2.661 2.429 2.271 2.156 2.068 1.997 1.939 1.891 160 3.900 3.053 2.661 2.428 2.271 2.156 2.067 1.997 1.939 1.890 161 3.900 3.052 2.661 2.428 2.270 2.155 2.067 1.996 1.938 1.890 162 3.900 3.052 2.660 2.427 2.270 2.155 2.067 1.996 1.938 1.890 163 3.899 3.051 2.660 2.427 2.270 2.155 2.066 1.996 1.938 1.889 164 3.899 3.051 2.660 2.427 2.269 2.154 2.066 1.995 1.937 1.889 165 3.898 3.051 2.659 2.426 2.269 2.154 2.065 1.995 1.937 1.888 166 3.898 3.050 2.659 2.426 2.269 2.154 2.065 1.995 1.937 1.888 167 3.898 3.050 2.659 2.426 2.268 2.153 2.065 1.994 1.936 1.888 168 3.897 3.050 2.658 2.425 2.268 2.153 2.064 1.994 1.936 1.887 169 3.897 3.049 2.658 2.425 2.268 2.153 2.064 1.994 1.936 1.887 170 3.897 3.049 2.658 2.425 2.267 2.152 2.064 1.993 1.935 1.887 171 3.896 3.049 2.657 2.425 2.267 2.152 2.063 1.993 1.935 1.886 172 3.896 3.049 2.657 2.424 2.267 2.152 2.063 1.993 1.935 1.886 173 3.896 3.048 2.657 2.424 2.266 2.151 2.063 1.992 1.934 1.886 174 3.895 3.048 2.657 2.424 2.266 2.151 2.063 1.992 1.934 1.885 175 3.895 3.048 2.656 2.423 2.266 2.151 2.062 1.992 1.934 1.885 176 3.895 3.047 2.656 2.423 2.265 2.150 2.062 1.991 1.933 1.885 177 3.895 3.047 2.656 2.423 2.265 2.150 2.062 1.991 1.933 1.885 178 3.894 3.047 2.655 2.422 2.265 2.150 2.061 1.991 1.933 1.884 179 3.894 3.046 2.655 2.422 2.265 2.150 2.061 1.990 1.932 1.884 180 3.894 3.046 2.655 2.422 2.264 2.149 2.061 1.990 1.932 1.884 181 3.893 3.046 2.655 2.422 2.264 2.149 2.060 1.990 1.932 1.883 182 3.893 3.046 2.654 2.421 2.264 2.149 2.060 1.990 1.932 1.883

183 3.893 3.045 2.654 2.421 2.263 2.148 2.060 1.989 1.931 1.883 184 3.892 3.045 2.654 2.421 2.263 2.148 2.060 1.989 1.931 1.882 185 3.892 3.045 2.653 2.420 2.263 2.148 2.059 1.989 1.931 1.882 186 3.892 3.045 2.653 2.420 2.263 2.148 2.059 1.988 1.931 1.882 187 3.892 3.044 2.653 2.420 2.262 2.147 2.059 1.988 1.930 1.882 188 3.891 3.044 2.653 2.420 2.262 2.147 2.059 1.988 1.930 1.881 189 3.891 3.044 2.652 2.419 2.262 2.147 2.058 1.988 1.930 1.881 190 3.891 3.043 2.652 2.419 2.262 2.147 2.058 1.987 1.929 1.881 191 3.891 3.043 2.652 2.419 2.261 2.146 2.058 1.987 1.929 1.881 192 3.890 3.043 2.652 2.419 2.261 2.146 2.058 1.987 1.929 1.880 193 3.890 3.043 2.651 2.418 2.261 2.146 2.057 1.987 1.929 1.880 194 3.890 3.042 2.651 2.418 2.261 2.146 2.057 1.986 1.928 1.880 195 3.890 3.042 2.651 2.418 2.260 2.145 2.057 1.986 1.928 1.880 196 3.889 3.042 2.651 2.418 2.260 2.145 2.057 1.986 1.928 1.879 197 3.889 3.042 2.650 2.417 2.260 2.145 2.056 1.986 1.928 1.879 198 3.889 3.042 2.650 2.417 2.260 2.145 2.056 1.985 1.927 1.879 199 3.889 3.041 2.650 2.417 2.259 2.144 2.056 1.985 1.927 1.879 200 3.888 3.041 2.650 2.417 2.259 2.144 2.056 1.985 1.927 1.878 201 3.888 3.041 2.650 2.417 2.259 2.144 2.055 1.985 1.927 1.878 202 3.888 3.041 2.649 2.416 2.259 2.144 2.055 1.984 1.926 1.878 203 3.888 3.040 2.649 2.416 2.259 2.143 2.055 1.984 1.926 1.878 204 3.887 3.040 2.649 2.416 2.258 2.143 2.055 1.984 1.926 1.877 205 3.887 3.040 2.649 2.416 2.258 2.143 2.054 1.984 1.926 1.877 206 3.887 3.040 2.648 2.415 2.258 2.143 2.054 1.984 1.926 1.877 207 3.887 3.040 2.648 2.415 2.258 2.143 2.054 1.983 1.925 1.877 208 3.887 3.039 2.648 2.415 2.257 2.142 2.054 1.983 1.925 1.876 209 3.886 3.039 2.648 2.415 2.257 2.142 2.054 1.983 1.925 1.876 210 3.886 3.039 2.648 2.415 2.257 2.142 2.053 1.983 1.925 1.876 211 3.886 3.039 2.647 2.414 2.257 2.142 2.053 1.982 1.924 1.876 212 3.886 3.038 2.647 2.414 2.257 2.142 2.053 1.982 1.924 1.876 213 3.885 3.038 2.647 2.414 2.256 2.141 2.053 1.982 1.924 1.875 214 3.885 3.038 2.647 2.414 2.256 2.141 2.053 1.982 1.924 1.875 215 3.885 3.038 2.647 2.414 2.256 2.141 2.052 1.982 1.924 1.875 216 3.885 3.038 2.646 2.413 2.256 2.141 2.052 1.981 1.923 1.875 217 3.885 3.037 2.646 2.413 2.256 2.141 2.052 1.981 1.923 1.875 218 3.884 3.037 2.646 2.413 2.255 2.140 2.052 1.981 1.923 1.874 219 3.884 3.037 2.646 2.413 2.255 2.140 2.052 1.981 1.923 1.874 220 3.884 3.037 2.646 2.413 2.255 2.140 2.051 1.981 1.923 1.874 221 3.884 3.037 2.645 2.412 2.255 2.140 2.051 1.980 1.922 1.874 222 3.884 3.037 2.645 2.412 2.255 2.140 2.051 1.980 1.922 1.874 223 3.883 3.036 2.645 2.412 2.255 2.139 2.051 1.980 1.922 1.873 224 3.883 3.036 2.645 2.412 2.254 2.139 2.051 1.980 1.922 1.873 225 3.883 3.036 2.645 2.412 2.254 2.139 2.050 1.980 1.922 1.873 226 3.883 3.036 2.645 2.412 2.254 2.139 2.050 1.980 1.921 1.873 227 3.883 3.036 2.644 2.411 2.254 2.139 2.050 1.979 1.921 1.873 228 3.883 3.035 2.644 2.411 2.254 2.138 2.050 1.979 1.921 1.872 229 3.882 3.035 2.644 2.411 2.253 2.138 2.050 1.979 1.921 1.872

230 3.882 3.035 2.644 2.411 2.253 2.138 2.050 1.979 1.921 1.872 231 3.882 3.035 2.644 2.411 2.253 2.138 2.049 1.979 1.921 1.872 232 3.882 3.035 2.644 2.411 2.253 2.138 2.049 1.978 1.920 1.872 233 3.882 3.035 2.643 2.410 2.253 2.138 2.049 1.978 1.920 1.871 234 3.882 3.034 2.643 2.410 2.253 2.137 2.049 1.978 1.920 1.871 235 3.881 3.034 2.643 2.410 2.252 2.137 2.049 1.978 1.920 1.871 236 3.881 3.034 2.643 2.410 2.252 2.137 2.049 1.978 1.920 1.871 237 3.881 3.034 2.643 2.410 2.252 2.137 2.048 1.978 1.920 1.871 238 3.881 3.034 2.643 2.410 2.252 2.137 2.048 1.977 1.919 1.871 239 3.881 3.034 2.642 2.409 2.252 2.137 2.048 1.977 1.919 1.870 240 3.880 3.033 2.642 2.409 2.252 2.136 2.048 1.977 1.919 1.870 241 3.880 3.033 2.642 2.409 2.251 2.136 2.048 1.977 1.919 1.870 242 3.880 3.033 2.642 2.409 2.251 2.136 2.048 1.977 1.919 1.870 243 3.880 3.033 2.642 2.409 2.251 2.136 2.047 1.977 1.919 1.870 244 3.880 3.033 2.642 2.409 2.251 2.136 2.047 1.976 1.918 1.870 245 3.880 3.033 2.641 2.408 2.251 2.136 2.047 1.976 1.918 1.869 246 3.880 3.033 2.641 2.408 2.251 2.136 2.047 1.976 1.918 1.869 247 3.879 3.032 2.641 2.408 2.251 2.135 2.047 1.976 1.918 1.869 248 3.879 3.032 2.641 2.408 2.250 2.135 2.047 1.976 1.918 1.869 249 3.879 3.032 2.641 2.408 2.250 2.135 2.046 1.976 1.918 1.869 250 3.879 3.032 2.641 2.408 2.250 2.135 2.046 1.976 1.917 1.869 251 3.879 3.032 2.641 2.408 2.250 2.135 2.046 1.975 1.917 1.869 252 3.879 3.032 2.640 2.407 2.250 2.135 2.046 1.975 1.917 1.868 253 3.878 3.031 2.640 2.407 2.250 2.135 2.046 1.975 1.917 1.868 254 3.878 3.031 2.640 2.407 2.250 2.134 2.046 1.975 1.917 1.868 255 3.878 3.031 2.640 2.407 2.249 2.134 2.046 1.975 1.917 1.868 256 3.878 3.031 2.640 2.407 2.249 2.134 2.045 1.975 1.917 1.868 257 3.878 3.031 2.640 2.407 2.249 2.134 2.045 1.975 1.916 1.868 258 3.878 3.031 2.640 2.407 2.249 2.134 2.045 1.974 1.916 1.868 259 3.878 3.031 2.639 2.406 2.249 2.134 2.045 1.974 1.916 1.867 260 3.877 3.031 2.639 2.406 2.249 2.134 2.045 1.974 1.916 1.867 261 3.877 3.030 2.639 2.406 2.249 2.133 2.045 1.974 1.916 1.867 262 3.877 3.030 2.639 2.406 2.248 2.133 2.045 1.974 1.916 1.867 263 3.877 3.030 2.639 2.406 2.248 2.133 2.044 1.974 1.916 1.867 264 3.877 3.030 2.639 2.406 2.248 2.133 2.044 1.974 1.915 1.867 265 3.877 3.030 2.639 2.406 2.248 2.133 2.044 1.973 1.915 1.867 266 3.877 3.030 2.639 2.406 2.248 2.133 2.044 1.973 1.915 1.866 267 3.877 3.030 2.638 2.405 2.248 2.133 2.044 1.973 1.915 1.866 268 3.876 3.029 2.638 2.405 2.248 2.132 2.044 1.973 1.915 1.866 269 3.876 3.029 2.638 2.405 2.248 2.132 2.044 1.973 1.915 1.866 270 3.876 3.029 2.638 2.405 2.247 2.132 2.044 1.973 1.915 1.866 271 3.876 3.029 2.638 2.405 2.247 2.132 2.043 1.973 1.915 1.866 272 3.876 3.029 2.638 2.405 2.247 2.132 2.043 1.973 1.914 1.866 273 3.876 3.029 2.638 2.405 2.247 2.132 2.043 1.972 1.914 1.865 274 3.876 3.029 2.638 2.405 2.247 2.132 2.043 1.972 1.914 1.865 275 3.875 3.029 2.637 2.404 2.247 2.132 2.043 1.972 1.914 1.865 276 3.875 3.028 2.637 2.404 2.247 2.132 2.043 1.972 1.914 1.865

277 3.875 3.028 2.637 2.404 2.247 2.131 2.043 1.972 1.914 1.865 278 3.875 3.028 2.637 2.404 2.246 2.131 2.043 1.972 1.914 1.865 279 3.875 3.028 2.637 2.404 2.246 2.131 2.042 1.972 1.914 1.865 280 3.875 3.028 2.637 2.404 2.246 2.131 2.042 1.972 1.913 1.865 281 3.875 3.028 2.637 2.404 2.246 2.131 2.042 1.971 1.913 1.864 282 3.875 3.028 2.637 2.404 2.246 2.131 2.042 1.971 1.913 1.864 283 3.875 3.028 2.637 2.404 2.246 2.131 2.042 1.971 1.913 1.864 284 3.874 3.028 2.636 2.403 2.246 2.131 2.042 1.971 1.913 1.864 285 3.874 3.027 2.636 2.403 2.246 2.130 2.042 1.971 1.913 1.864 286 3.874 3.027 2.636 2.403 2.246 2.130 2.042 1.971 1.913 1.864 287 3.874 3.027 2.636 2.403 2.245 2.130 2.042 1.971 1.913 1.864 288 3.874 3.027 2.636 2.403 2.245 2.130 2.041 1.971 1.912 1.864 289 3.874 3.027 2.636 2.403 2.245 2.130 2.041 1.971 1.912 1.864 290 3.874 3.027 2.636 2.403 2.245 2.130 2.041 1.970 1.912 1.863 291 3.874 3.027 2.636 2.403 2.245 2.130 2.041 1.970 1.912 1.863 292 3.874 3.027 2.636 2.403 2.245 2.130 2.041 1.970 1.912 1.863 293 3.873 3.027 2.635 2.402 2.245 2.130 2.041 1.970 1.912 1.863 294 3.873 3.026 2.635 2.402 2.245 2.129 2.041 1.970 1.912 1.863 295 3.873 3.026 2.635 2.402 2.245 2.129 2.041 1.970 1.912 1.863 296 3.873 3.026 2.635 2.402 2.244 2.129 2.041 1.970 1.912 1.863 297 3.873 3.026 2.635 2.402 2.244 2.129 2.040 1.970 1.911 1.863 298 3.873 3.026 2.635 2.402 2.244 2.129 2.040 1.970 1.911 1.863 299 3.873 3.026 2.635 2.402 2.244 2.129 2.040 1.969 1.911 1.862 300 3.873 3.026 2.635 2.402 2.244 2.129 2.040 1.969 1.911 1.862 301 3.873 3.026 2.635 2.402 2.244 2.129 2.040 1.969 1.911 1.862 302 3.872 3.026 2.635 2.402 2.244 2.129 2.040 1.969 1.911 1.862 303 3.872 3.026 2.634 2.401 2.244 2.129 2.040 1.969 1.911 1.862 304 3.872 3.025 2.634 2.401 2.244 2.128 2.040 1.969 1.911 1.862 305 3.872 3.025 2.634 2.401 2.244 2.128 2.040 1.969 1.911 1.862 306 3.872 3.025 2.634 2.401 2.243 2.128 2.040 1.969 1.911 1.862 307 3.872 3.025 2.634 2.401 2.243 2.128 2.039 1.969 1.910 1.862 308 3.872 3.025 2.634 2.401 2.243 2.128 2.039 1.969 1.910 1.862 309 3.872 3.025 2.634 2.401 2.243 2.128 2.039 1.968 1.910 1.861 310 3.872 3.025 2.634 2.401 2.243 2.128 2.039 1.968 1.910 1.861 311 3.872 3.025 2.634 2.401 2.243 2.128 2.039 1.968 1.910 1.861 312 3.871 3.025 2.634 2.401 2.243 2.128 2.039 1.968 1.910 1.861 313 3.871 3.025 2.633 2.400 2.243 2.128 2.039 1.968 1.910 1.861 314 3.871 3.024 2.633 2.400 2.243 2.127 2.039 1.968 1.910 1.861 315 3.871 3.024 2.633 2.400 2.243 2.127 2.039 1.968 1.910 1.861 316 3.871 3.024 2.633 2.400 2.243 2.127 2.039 1.968 1.910 1.861 317 3.871 3.024 2.633 2.400 2.242 2.127 2.039 1.968 1.909 1.861 318 3.871 3.024 2.633 2.400 2.242 2.127 2.038 1.968 1.909 1.861 319 3.871 3.024 2.633 2.400 2.242 2.127 2.038 1.967 1.909 1.860 320 3.871 3.024 2.633 2.400 2.242 2.127 2.038 1.967 1.909 1.860 321 3.871 3.024 2.633 2.400 2.242 2.127 2.038 1.967 1.909 1.860 322 3.870 3.024 2.633 2.400 2.242 2.127 2.038 1.967 1.909 1.860 323 3.870 3.024 2.633 2.400 2.242 2.127 2.038 1.967 1.909 1.860

324 3.870 3.024 2.632 2.400 2.242 2.127 2.038 1.967 1.909 1.860 325 3.870 3.024 2.632 2.399 2.242 2.127 2.038 1.967 1.909 1.860 326 3.870 3.023 2.632 2.399 2.242 2.126 2.038 1.967 1.909 1.860 327 3.870 3.023 2.632 2.399 2.242 2.126 2.038 1.967 1.909 1.860 328 3.870 3.023 2.632 2.399 2.242 2.126 2.038 1.967 1.908 1.860 329 3.870 3.023 2.632 2.399 2.241 2.126 2.037 1.967 1.908 1.860 330 3.870 3.023 2.632 2.399 2.241 2.126 2.037 1.966 1.908 1.859 331 3.870 3.023 2.632 2.399 2.241 2.126 2.037 1.966 1.908 1.859 332 3.870 3.023 2.632 2.399 2.241 2.126 2.037 1.966 1.908 1.859 333 3.870 3.023 2.632 2.399 2.241 2.126 2.037 1.966 1.908 1.859 334 3.869 3.023 2.632 2.399 2.241 2.126 2.037 1.966 1.908 1.859 335 3.869 3.023 2.632 2.399 2.241 2.126 2.037 1.966 1.908 1.859 336 3.869 3.023 2.631 2.399 2.241 2.126 2.037 1.966 1.908 1.859 337 3.869 3.023 2.631 2.398 2.241 2.126 2.037 1.966 1.908 1.859 338 3.869 3.022 2.631 2.398 2.241 2.125 2.037 1.966 1.908 1.859 339 3.869 3.022 2.631 2.398 2.241 2.125 2.037 1.966 1.908 1.859 340 3.869 3.022 2.631 2.398 2.241 2.125 2.037 1.966 1.907 1.859 341 3.869 3.022 2.631 2.398 2.240 2.125 2.036 1.966 1.907 1.859 342 3.869 3.022 2.631 2.398 2.240 2.125 2.036 1.966 1.907 1.858 343 3.869 3.022 2.631 2.398 2.240 2.125 2.036 1.965 1.907 1.858 344 3.869 3.022 2.631 2.398 2.240 2.125 2.036 1.965 1.907 1.858 345 3.869 3.022 2.631 2.398 2.240 2.125 2.036 1.965 1.907 1.858 346 3.868 3.022 2.631 2.398 2.240 2.125 2.036 1.965 1.907 1.858 347 3.868 3.022 2.631 2.398 2.240 2.125 2.036 1.965 1.907 1.858 348 3.868 3.022 2.631 2.398 2.240 2.125 2.036 1.965 1.907 1.858 349 3.868 3.022 2.630 2.398 2.240 2.125 2.036 1.965 1.907 1.858 350 3.868 3.022 2.630 2.397 2.240 2.125 2.036 1.965 1.907 1.858 351 3.868 3.021 2.630 2.397 2.240 2.124 2.036 1.965 1.907 1.858 352 3.868 3.021 2.630 2.397 2.240 2.124 2.036 1.965 1.907 1.858 353 3.868 3.021 2.630 2.397 2.240 2.124 2.036 1.965 1.906 1.858 354 3.868 3.021 2.630 2.397 2.239 2.124 2.035 1.965 1.906 1.857 355 3.868 3.021 2.630 2.397 2.239 2.124 2.035 1.965 1.906 1.857 356 3.868 3.021 2.630 2.397 2.239 2.124 2.035 1.964 1.906 1.857 357 3.868 3.021 2.630 2.397 2.239 2.124 2.035 1.964 1.906 1.857 358 3.868 3.021 2.630 2.397 2.239 2.124 2.035 1.964 1.906 1.857 359 3.867 3.021 2.630 2.397 2.239 2.124 2.035 1.964 1.906 1.857 360 3.867 3.021 2.630 2.397 2.239 2.124 2.035 1.964 1.906 1.857 361 3.867 3.021 2.630 2.397 2.239 2.124 2.035 1.964 1.906 1.857 362 3.867 3.021 2.630 2.397 2.239 2.124 2.035 1.964 1.906 1.857 363 3.867 3.021 2.630 2.397 2.239 2.124 2.035 1.964 1.906 1.857 364 3.867 3.021 2.629 2.396 2.239 2.124 2.035 1.964 1.906 1.857 365 3.867 3.020 2.629 2.396 2.239 2.123 2.035 1.964 1.906 1.857 366 3.867 3.020 2.629 2.396 2.239 2.123 2.035 1.964 1.905 1.857 367 3.867 3.020 2.629 2.396 2.239 2.123 2.035 1.964 1.905 1.857 368 3.867 3.020 2.629 2.396 2.239 2.123 2.034 1.964 1.905 1.856 369 3.867 3.020 2.629 2.396 2.238 2.123 2.034 1.964 1.905 1.856 370 3.867 3.020 2.629 2.396 2.238 2.123 2.034 1.963 1.905 1.856

371 3.867 3.020 2.629 2.396 2.238 2.123 2.034 1.963 1.905 1.856 372 3.867 3.020 2.629 2.396 2.238 2.123 2.034 1.963 1.905 1.856 373 3.867 3.020 2.629 2.396 2.238 2.123 2.034 1.963 1.905 1.856 374 3.866 3.020 2.629 2.396 2.238 2.123 2.034 1.963 1.905 1.856 375 3.866 3.020 2.629 2.396 2.238 2.123 2.034 1.963 1.905 1.856 376 3.866 3.020 2.629 2.396 2.238 2.123 2.034 1.963 1.905 1.856 377 3.866 3.020 2.629 2.396 2.238 2.123 2.034 1.963 1.905 1.856 378 3.866 3.020 2.629 2.396 2.238 2.123 2.034 1.963 1.905 1.856 379 3.866 3.020 2.628 2.395 2.238 2.123 2.034 1.963 1.905 1.856 380 3.866 3.019 2.628 2.395 2.238 2.122 2.034 1.963 1.905 1.856 381 3.866 3.019 2.628 2.395 2.238 2.122 2.034 1.963 1.904 1.856 382 3.866 3.019 2.628 2.395 2.238 2.122 2.034 1.963 1.904 1.856 383 3.866 3.019 2.628 2.395 2.238 2.122 2.034 1.963 1.904 1.855 384 3.866 3.019 2.628 2.395 2.237 2.122 2.033 1.963 1.904 1.855 385 3.866 3.019 2.628 2.395 2.237 2.122 2.033 1.962 1.904 1.855 386 3.866 3.019 2.628 2.395 2.237 2.122 2.033 1.962 1.904 1.855 387 3.866 3.019 2.628 2.395 2.237 2.122 2.033 1.962 1.904 1.855 388 3.866 3.019 2.628 2.395 2.237 2.122 2.033 1.962 1.904 1.855 389 3.865 3.019 2.628 2.395 2.237 2.122 2.033 1.962 1.904 1.855 390 3.865 3.019 2.628 2.395 2.237 2.122 2.033 1.962 1.904 1.855 391 3.865 3.019 2.628 2.395 2.237 2.122 2.033 1.962 1.904 1.855 392 3.865 3.019 2.628 2.395 2.237 2.122 2.033 1.962 1.904 1.855 393 3.865 3.019 2.628 2.395 2.237 2.122 2.033 1.962 1.904 1.855 394 3.865 3.019 2.628 2.395 2.237 2.122 2.033 1.962 1.904 1.855 395 3.865 3.019 2.627 2.395 2.237 2.122 2.033 1.962 1.904 1.855 396 3.865 3.019 2.627 2.394 2.237 2.121 2.033 1.962 1.904 1.855 397 3.865 3.018 2.627 2.394 2.237 2.121 2.033 1.962 1.903 1.855 398 3.865 3.018 2.627 2.394 2.237 2.121 2.033 1.962 1.903 1.855 399 3.865 3.018 2.627 2.394 2.237 2.121 2.033 1.962 1.903 1.854 400 3.865 3.018 2.627 2.394 2.237 2.121 2.032 1.962 1.903 1.854 401 3.865 3.018 2.627 2.394 2.236 2.121 2.032 1.962 1.903 1.854 402 3.865 3.018 2.627 2.394 2.236 2.121 2.032 1.961 1.903 1.854 403 3.865 3.018 2.627 2.394 2.236 2.121 2.032 1.961 1.903 1.854 404 3.865 3.018 2.627 2.394 2.236 2.121 2.032 1.961 1.903 1.854 405 3.865 3.018 2.627 2.394 2.236 2.121 2.032 1.961 1.903 1.854 406 3.864 3.018 2.627 2.394 2.236 2.121 2.032 1.961 1.903 1.854 407 3.864 3.018 2.627 2.394 2.236 2.121 2.032 1.961 1.903 1.854 408 3.864 3.018 2.627 2.394 2.236 2.121 2.032 1.961 1.903 1.854 409 3.864 3.018 2.627 2.394 2.236 2.121 2.032 1.961 1.903 1.854 410 3.864 3.018 2.627 2.394 2.236 2.121 2.032 1.961 1.903 1.854 411 3.864 3.018 2.627 2.394 2.236 2.121 2.032 1.961 1.903 1.854 412 3.864 3.018 2.627 2.394 2.236 2.121 2.032 1.961 1.903 1.854 413 3.864 3.018 2.627 2.394 2.236 2.121 2.032 1.961 1.903 1.854 414 3.864 3.018 2.626 2.393 2.236 2.120 2.032 1.961 1.903 1.854 415 3.864 3.017 2.626 2.393 2.236 2.120 2.032 1.961 1.902 1.854 416 3.864 3.017 2.626 2.393 2.236 2.120 2.032 1.961 1.902 1.853 417 3.864 3.017 2.626 2.393 2.236 2.120 2.032 1.961 1.902 1.853

418 3.864 3.017 2.626 2.393 2.236 2.120 2.031 1.961 1.902 1.853 419 3.864 3.017 2.626 2.393 2.236 2.120 2.031 1.961 1.902 1.853 420 3.864 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 421 3.864 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 422 3.864 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 423 3.864 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 424 3.863 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 425 3.863 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 426 3.863 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 427 3.863 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 428 3.863 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 429 3.863 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 430 3.863 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 431 3.863 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 432 3.863 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 433 3.863 3.017 2.626 2.393 2.235 2.120 2.031 1.960 1.902 1.853 434 3.863 3.017 2.625 2.392 2.235 2.119 2.031 1.960 1.901 1.853 435 3.863 3.016 2.625 2.392 2.235 2.119 2.031 1.960 1.901 1.852 436 3.863 3.016 2.625 2.392 2.235 2.119 2.031 1.960 1.901 1.852 437 3.863 3.016 2.625 2.392 2.235 2.119 2.031 1.960 1.901 1.852 438 3.863 3.016 2.625 2.392 2.235 2.119 2.030 1.960 1.901 1.852 439 3.863 3.016 2.625 2.392 2.235 2.119 2.030 1.959 1.901 1.852 440 3.863 3.016 2.625 2.392 2.235 2.119 2.030 1.959 1.901 1.852 441 3.863 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 442 3.863 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 443 3.863 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 444 3.862 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 445 3.862 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 446 3.862 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 447 3.862 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 448 3.862 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 449 3.862 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 450 3.862 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 451 3.862 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 452 3.862 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 453 3.862 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 454 3.862 3.016 2.625 2.392 2.234 2.119 2.030 1.959 1.901 1.852 455 3.862 3.016 2.625 2.392 2.234 2.118 2.030 1.959 1.900 1.852 456 3.862 3.015 2.624 2.391 2.234 2.118 2.030 1.959 1.900 1.851 457 3.862 3.015 2.624 2.391 2.234 2.118 2.030 1.959 1.900 1.851 458 3.862 3.015 2.624 2.391 2.234 2.118 2.030 1.959 1.900 1.851 459 3.862 3.015 2.624 2.391 2.234 2.118 2.030 1.959 1.900 1.851 460 3.862 3.015 2.624 2.391 2.234 2.118 2.029 1.959 1.900 1.851 461 3.862 3.015 2.624 2.391 2.234 2.118 2.029 1.958 1.900 1.851 462 3.862 3.015 2.624 2.391 2.234 2.118 2.029 1.958 1.900 1.851 463 3.862 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 464 3.862 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851

465 3.862 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 466 3.861 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 467 3.861 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 468 3.861 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 469 3.861 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 470 3.861 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 471 3.861 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 472 3.861 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 473 3.861 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 474 3.861 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 475 3.861 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 476 3.861 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 477 3.861 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.900 1.851 478 3.861 3.015 2.624 2.391 2.233 2.118 2.029 1.958 1.899 1.851 479 3.861 3.015 2.624 2.391 2.233 2.117 2.029 1.958 1.899 1.850 480 3.861 3.015 2.623 2.391 2.233 2.117 2.029 1.958 1.899 1.850 481 3.861 3.014 2.623 2.390 2.233 2.117 2.029 1.958 1.899 1.850 482 3.861 3.014 2.623 2.390 2.233 2.117 2.029 1.958 1.899 1.850 483 3.861 3.014 2.623 2.390 2.233 2.117 2.029 1.958 1.899 1.850 484 3.861 3.014 2.623 2.390 2.233 2.117 2.028 1.958 1.899 1.850 485 3.861 3.014 2.623 2.390 2.233 2.117 2.028 1.957 1.899 1.850 486 3.861 3.014 2.623 2.390 2.233 2.117 2.028 1.957 1.899 1.850 487 3.861 3.014 2.623 2.390 2.233 2.117 2.028 1.957 1.899 1.850 488 3.861 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 489 3.861 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 490 3.861 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 491 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 492 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 493 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 494 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 495 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 496 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 497 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 498 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 499 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 500 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 501 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 502 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.899 1.850 503 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.898 1.850 504 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.898 1.849 505 3.860 3.014 2.623 2.390 2.232 2.117 2.028 1.957 1.898 1.849 506 3.860 3.014 2.623 2.390 2.232 2.116 2.028 1.957 1.898 1.849 507 3.860 3.014 2.622 2.390 2.232 2.116 2.028 1.957 1.898 1.849 508 3.860 3.013 2.622 2.389 2.232 2.116 2.028 1.957 1.898 1.849 509 3.860 3.013 2.622 2.389 2.232 2.116 2.028 1.957 1.898 1.849 510 3.860 3.013 2.622 2.389 2.232 2.116 2.028 1.957 1.898 1.849 511 3.860 3.013 2.622 2.389 2.232 2.116 2.027 1.957 1.898 1.849

512 3.860 3.013 2.622 2.389 2.232 2.116 2.027 1.956 1.898 1.849 513 3.860 3.013 2.622 2.389 2.232 2.116 2.027 1.956 1.898 1.849 514 3.860 3.013 2.622 2.389 2.232 2.116 2.027 1.956 1.898 1.849 515 3.860 3.013 2.622 2.389 2.232 2.116 2.027 1.956 1.898 1.849 516 3.860 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 517 3.860 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 518 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 519 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 520 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 521 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 522 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 523 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 524 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 525 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 526 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 527 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 528 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 529 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 530 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 531 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.898 1.849 532 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.897 1.848 533 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.897 1.848 534 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.897 1.848 535 3.859 3.013 2.622 2.389 2.231 2.116 2.027 1.956 1.897 1.848 536 3.859 3.013 2.622 2.389 2.231 2.115 2.027 1.956 1.897 1.848 537 3.859 3.013 2.622 2.389 2.231 2.115 2.027 1.956 1.897 1.848 538 3.859 3.012 2.621 2.389 2.231 2.115 2.027 1.956 1.897 1.848 539 3.859 3.012 2.621 2.388 2.231 2.115 2.027 1.956 1.897 1.848 540 3.859 3.012 2.621 2.388 2.231 2.115 2.027 1.956 1.897 1.848 541 3.859 3.012 2.621 2.388 2.231 2.115 2.026 1.956 1.897 1.848 542 3.859 3.012 2.621 2.388 2.231 2.115 2.026 1.955 1.897 1.848 543 3.859 3.012 2.621 2.388 2.231 2.115 2.026 1.955 1.897 1.848 544 3.859 3.012 2.621 2.388 2.231 2.115 2.026 1.955 1.897 1.848 545 3.859 3.012 2.621 2.388 2.231 2.115 2.026 1.955 1.897 1.848 546 3.859 3.012 2.621 2.388 2.231 2.115 2.026 1.955 1.897 1.848 547 3.859 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 548 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 549 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 550 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 551 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 552 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 553 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 554 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 555 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 556 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 557 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 558 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848

559 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 560 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 561 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 562 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.897 1.848 563 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.896 1.848 564 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.896 1.847 565 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.896 1.847 566 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.896 1.847 567 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.896 1.847 568 3.858 3.012 2.621 2.388 2.230 2.115 2.026 1.955 1.896 1.847 569 3.858 3.012 2.621 2.388 2.230 2.114 2.026 1.955 1.896 1.847 570 3.858 3.012 2.621 2.388 2.230 2.114 2.026 1.955 1.896 1.847 571 3.858 3.012 2.621 2.388 2.230 2.114 2.026 1.955 1.896 1.847 572 3.858 3.011 2.620 2.388 2.230 2.114 2.026 1.955 1.896 1.847 573 3.858 3.011 2.620 2.387 2.230 2.114 2.026 1.955 1.896 1.847 574 3.858 3.011 2.620 2.387 2.230 2.114 2.026 1.955 1.896 1.847 575 3.858 3.011 2.620 2.387 2.230 2.114 2.025 1.954 1.896 1.847 576 3.858 3.011 2.620 2.387 2.230 2.114 2.025 1.954 1.896 1.847 577 3.858 3.011 2.620 2.387 2.230 2.114 2.025 1.954 1.896 1.847 578 3.858 3.011 2.620 2.387 2.230 2.114 2.025 1.954 1.896 1.847 579 3.858 3.011 2.620 2.387 2.230 2.114 2.025 1.954 1.896 1.847 580 3.858 3.011 2.620 2.387 2.230 2.114 2.025 1.954 1.896 1.847 581 3.858 3.011 2.620 2.387 2.230 2.114 2.025 1.954 1.896 1.847 582 3.857 3.011 2.620 2.387 2.230 2.114 2.025 1.954 1.896 1.847 583 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 584 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 585 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 586 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 587 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 588 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 589 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 590 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 591 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 592 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 593 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 594 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 595 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 596 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 597 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 598 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.896 1.847 599 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.895 1.846 600 3.857 3.011 2.620 2.387 2.229 2.114 2.025 1.954 1.895 1.846

统计分布临界值表.pdf

统计分布临界值表 附录 附表一:随机数表___________________________________________________ 2附表二:标准正态分布表_____________________________________________ 3附表三:t分布临界值表_____________________________________________ 4附表四:2χ分布临界值表____________________________________________ 5附表五:F分布临界值表(α=0.05)__________________________________ 7附表六:单样本K-S检验统计量表_____________________________________ 9附表七:符号检验界域表____________________________________________ 10附表八:游程检验临界值表__________________________________________ 11附表九:相关系数临界值表__________________________________________ 12附表十:Spearman等级相关系数临界值表_____________________________ 13附表十一:Kendall等级相关系数临界值表____________________________ 14附表十二:控制图系数表____________________________________________ 15

t分布与t检验

t分布 从数理统计的理论上讲,并且上节的实例也已说明,在总体均数为μ,总体标准差为σ的正态总体中随机抽取n相等的许多样本,分别算出样本均数,这些样本均数呈正态分布。而当样本含量n不太小时,即使总体不呈正态分布,样本均数的分布也接近正态。在下式中, 由于μ与(样本均数的标准差)都是常量,又 X呈正态分布,所以u 也呈正态分布。但实际上总体标准差往往是不知道的,上式分母中的σ要由S替代,成为 ,那么由于样本标 准差有抽样波动,SX也有抽样波动,于是,在用S代替σ 后上式等号右边的变量便不呈正态分布而呈t分布,其定义公式是 (6.5)

t分布也是左右对称,但在总体均数附近的面积较正态分布的少些,两端尾部的面积则比正态分布的多些。t分布曲线随自由度而不同(如图6.1)。随着自由度的增大,t分布逐渐接近正态分布,当自由度为无限大时,t分布成为正态分布。 图6.1t分布(实线)与正态分布(虚线) 与正态分布相似,我们把t分布左右两端尾部面积之和α=0.05(即每侧尾部面积为0.025)相应的t值称为5%界,符号为t0.05,,,这里ν是自由度。把左右两端尾部面积之和α为0.01相应的t值称为1%界,符号为t0.01,,。t的5%界与1%界可查附表3,t值表。例如当自由度为10-1=9时,t0.05,9=2.262,t0.01,9=3.250。 可信区间的估计 一、参数估计的意义 一组调查或实验数据,如果是计量资料可求得平均数,标准差等统计指标,如果是计数资料则求百分率藉以概括说明这群观察数据的特征,故称特征值。由于样本特征值是通过统计求得的,所以又称为统计量以区别于总体特征值。总体特征值一般称为参数(总体量)。我们进行科研所要探索的是总体特征值即总体参数,而我们得到的却是样本统计量,用样本统计量估计或推论总体参数的过程叫参数估计。

统计临界值表

目录 附表一:随机数表 _________________________________________________________________________ 2附表二:标准正态分布表 ___________________________________________________________________ 3附表三:t分布临界值表____________________________________________________________________ 4 附表四: 2 分布临界值表 __________________________________________________________________ 5 附表五:F分布临界值表(α=0.05)________________________________________________________ 7附表六:单样本K-S检验统计量表___________________________________________________________ 9附表七:符号检验界域表 __________________________________________________________________ 10附表八:游程检验临界值表 _________________________________________________________________ 11附表九:相关系数临界值表 ________________________________________________________________ 12附表十:Spearman等级相关系数临界值表 ___________________________________________________ 13附表十一:Kendall等级相关系数临界值表 ___________________________________________________ 14附表十二:控制图系数表 __________________________________________________________________ 15

t检验

(二)t 检验 当总体呈正态分布,如果总体标准差未知,而且样本容量n <30,那么这时一切可能的样本平均数与总体平均数的离差统计量呈t 分布。 t 检验是用t 分布理论来推论差异发生的概率,从而比较两个平均数的差异是否显著。t 检验分为单总体t 检验和双总体t 检验。 1.单总体t 检验 单总体t 检验是检验一个样本平均数与一已知的总体平均数的差异是否显 著。当总体分布是正态分布,如总体标准差σ未知且样本容量n <30,那么样本平均数与总体平均数的离差统计量呈t 分布。检验统计量为: X t μ σ-= 。 如果样本是属于大样本(n >30)也可写成: X t μ σ-= 。 在这里,t 为样本平均数与总体平均数的离差统计量; X 为样本平均数; μ为总体平均数; X σ为样本标准差; n 为样本容量。 例:某校二年级学生期中英语考试成绩,其平均分数为73分,标准差为17分,期末考试后,随机抽取20人的英语成绩,其平均分数为79.2分。问二年级学生的英语成绩是否有显著性进步? 检验步骤如下: 第一步 建立原假设0H ∶μ=73 第二步 计算t 值 79.273 1.63X t μ σ--= = = 第三步 判断 因为,以0.05为显著性水平,119df n =-=,查t 值表,临界值 0.05(19) 2.093t =,而样本离差的t =1.63小与临界值2.093。所以,接受原假设,即进步不显著。 2.双总体t 检验

双总体t 检验是检验两个样本平均数与其各自所代表的总体的差异是否显著。双总体t 检验又分为两种情况,一是相关样本平均数差异的显著性检验,用于检验匹配而成的两组被试获得的数据或同组被试在不同条件下所获得的数据的差异性,这两种情况组成的样本即为相关样本。二是独立样本平均数的显著性检验。各实验处理组之间毫无相关存在,即为独立样本。该检验用于检验两组非相关样本被试所获得的数据的差异性。 现以相关检验为例,说明检验方法。因为独立样本平均数差异的显著性检验完全类似,只不过0r =。 相关样本的t 检验公式为: t = 在这里,1X ,2X 分别为两样本平均数; 1 2X σ,22 X σ分别为两样本方差; γ为相关样本的相关系数。 例:在小学三年级学生中随机抽取10名学生,在学期初和学期末分别进行了两次推理能力测验,成绩分别为79.5和72分,标准差分别为9.124,9.940。问两次测验成绩是否有显著地差异? 检验步骤为: 第一步 建立原假设0H ∶1μ=2μ 第二步 计算t 值 t = =3.459。 第三步 判断 根据自由度19df n =-=,查t 值表0.05(9) 2.262t =,0.01(9) 3.250t =。由于实际计算出来的t =3.495>3.250=0.01(9)t ,则0.01P <,故拒绝原假设。 结论为:两次测验成绩有及其显著地差异。 由以上可以看出,对平均数差异显著性检验比较复杂,究竟使用Z 检验还是使用t 检验必须根据具体情况而定,为了便于掌握各种情况下的Z 检验或t 检验,我们用以下一览表图示加以说明。

随机变量及其分布考点总结

第二章 随机变量及其分布 复习 一、随机变量. 1. 随机试验的结构应该是不确定的.试验如果满足下述条件: ①试验可以在相同的情形下重复进行;②试验的所有可能结果是明确可知的,并且不止一个;③每次试验总是恰好出现这些结果中的一个,但在一次试验之前却不能肯定这次试验会出现哪一个结果. 它就被称为一个随机试验. 2. 离散型随机变量:如果对于随机变量可能取的值,可以按一定次序一一列出,这样的随机变量叫做离散型随机变量.若ξ是一个随机变量,a ,b 是常数.则b a +=ξη也是一个随机变量.一般地,若ξ是随机变量,)(x f 是连续函数或单调函数,则)(ξf 也是随机变量.也就是说,随机变量的某些函数也是随机变量. 3、分布列:设离散型随机变量ξ可能取的值为:ΛΛ,,,,21i x x x ξ取每一个值),2,1(Λ=i x 的概率p x P ==)(,则表称为随机变量ξ的概率分布,简称ξ的分布列. 121i 注意:若随机变量可以取某一区间内的一切值,这样的变量叫做连续型随机变量.例如:]5,0[∈ξ即ξ可以取0~5之间的一切数,包括整数、小数、无理数. 典型例题: 1、随机变量ξ的分布列为(),1,2,3(1) c P k k k k ξ== =+……,则P(13)____ξ≤≤= 2、袋中装有黑球和白球共7个,从中任取两个球都是白球的概率为1 7 ,现在甲乙两人从袋中轮流摸去一 球,甲先取,乙后取,然后甲再取……,取后不放回,直到两人中有一人取到白球时终止,用ξ表示取球的次数。(1)求ξ的分布列(2)求甲取到白球的的概率 3、5封不同的信,放入三个不同的信箱,且每封信投入每个信箱的机会均等,X 表示三哥信箱中放有信件树木的最大值,求X 的分布列。 4 已知在全部50人中随机抽取1人抽到喜爱打篮球的学生的概率为5 . (1)请将上面的列联表补充完整; (2)是否有99.5%的把握认为喜爱打篮球与性别有关?说明你的理由; (3)已知喜爱打篮球的10位女生中,12345,,A A A A A ,,还喜欢打羽毛球,123B B B ,,还喜欢打乒乓球,12C C ,还喜欢踢足球,现再从喜欢打羽毛球、喜欢打乒乓球、喜欢踢足球的女生中各选出1名进行其他方面的调查,求1B 和1C 不全被选中的概率. (参考公式:2 ()()()()() n ad bc K a b c d a c b d -=++++,其中n a b c d =+++)

附表3统计分布临界值表

附 录 附表一:随机数表_________________________________________________________________________2 附表二:标准正态分布表___________________________________________________________________3 附表三:t 分布临界值表____________________________________________________________________4 附表四:分布临界值表__________________________________________________________________5 2χ附表五:F 分布临界值表(α=0.05)_________________________________________________________7 附表六:单样本K-S 检验统计量表____________________________________________________________9 附表七:符号检验界域表__________________________________________________________________10 附表八:游程检验临界值表_________________________________________________________________11 附表九:相关系数临界值表________________________________________________________________12 附表十:Spearman 等级相关系数临界值表___________________________________________________13 附表十一:Kendall τ等级相关系数临界值表__________________________________________________14 附表十二:控制图系数表__________________________________________________________________15 附表十三 威尔克逊秩和检验临界表(01.0=α)____________________________________________16 附表十四 威尔克逊秩和检验临界表(025.0=α)___________________________________________17 附表十五 威尔克逊秩和检验临界表(05.0=α)____________________________________________18 附表十六 威尔克逊符号秩和检验临界表____________________________________________________19 附表十七 Durbin Watson 序列相关检验表(05.0=α)_________________________________________20

T检验临界值表

自由度自由度(df )0.100.05 0.01 (df )0.100.05 0.01 n -m -1n -m -11 6.31412.70663.657301 1.650 1.968 2.5922 2.920 4.3039.925302 1.650 1.968 2.5923 2.353 3.182 5.841303 1.650 1.968 2.5924 2.132 2.776 4.604304 1.650 1.968 2.5925 2.015 2.571 4.032305 1.650 1.968 2.5926 1.943 2.447 3.707306 1.650 1.968 2.5927 1.895 2.365 3.499307 1.650 1.968 2.5928 1.860 2.306 3.355308 1.650 1.968 2.5929 1.833 2.262 3.250309 1.650 1.968 2.59210 1.812 2.228 3.169310 1.650 1.968 2.59211 1.796 2.201 3.106311 1.650 1.968 2.59212 1.782 2.179 3.055312 1.650 1.968 2.59213 1.771 2.160 3.012313 1.650 1.968 2.59214 1.761 2.145 2.977314 1.650 1.968 2.59215 1.753 2.131 2.947315 1.650 1.968 2.59216 1.746 2.120 2.921316 1.650 1.967 2.59117 1.740 2.110 2.898317 1.650 1.967 2.59118 1.734 2.101 2.878318 1.650 1.967 2.59119 1.729 2.093 2.861319 1.650 1.967 2.59120 1.725 2.086 2.845320 1.650 1.967 2.59121 1.721 2.080 2.831321 1.650 1.967 2.59122 1.717 2.074 2.819322 1.650 1.967 2.59123 1.714 2.069 2.807323 1.650 1.967 2.59124 1.711 2.064 2.797324 1.650 1.967 2.59125 1.708 2.060 2.787325 1.650 1.967 2.59126 1.706 2.056 2.779326 1.650 1.967 2.59127 1.703 2.052 2.771327 1.650 1.967 2.59128 1.701 2.048 2.763328 1.650 1.967 2.59129 1.699 2.045 2.756329 1.649 1.967 2.59130 1.697 2.042 2.750330 1.649 1.967 2.59131 1.696 2.040 2.744331 1.649 1.967 2.59132 1.694 2.037 2.738332 1.649 1.967 2.59133 1.692 2.035 2.733333 1.649 1.967 2.59134 1.691 2.032 2.728334 1.649 1.967 2.59135 1.690 2.030 2.724335 1.649 1.967 2.59136 1.688 2.028 2.719336 1.649 1.967 2.59137 1.687 2.026 2.715337 1.649 1.967 2.59038 1.686 2.024 2.712338 1.649 1.967 2.59039 1.685 2.023 2.708339 1.649 1.967 2.59040 1.684 2.021 2.704340 1.649 1.967 2.59041 1.683 2.020 2.701341 1.649 1.967 2.59042 1.682 2.018 2.698342 1.649 1.967 2.59043 1.681 2.017 2.695343 1.649 1.967 2.59044 1.680 2.015 2.692 344 1.649 1.967 2.590 显著性水平(a )显著性水平(a )T 检验临界值表

实验十三 二项分布的计算与中心极限定.

实验十三二项分布的计算与中心极限定 [实验目的] 1.研究用Poisson逼近与正态逼近进行二项分布近似计算的条件 2.检验中心极限定理 §1 引言 二项分布在概率论中占有很重要的地位。N次Bernoulli实验中正好出现K次成功的概 率有下式给出b k;n,p C n k p k1p n k ,k=0,1,2,……..n.二项分布的 值有现成的表可查,这种表对不同的n及p给出了b(k;n.p)的数值。在实际应用中。通常可用二项的Poisson逼近与正态逼近来进行二项分布的近似计算。在本实验中,,我们来具体地研究在什么条件下,可用Poisson逼近与正态逼近来进行二项分布的近似计算。 在概率论中,中心极限定理是一个很重要的内容,在本实验中,我们用随即模拟的方法来检验一个重要的中心极限定理——Liderberg-Levi中心极限定理。 §2 实验内容与练习 1.1二项分布的Poisson逼近 用Mathematica软件可以比较方便地求出二项分布的数值。例如n=20;p=0,1;Table[Binomial[n,k]*p^k*(1-p)(n-k),{k,0,20}]给出了b(k;20,0.1)(k=0,1,2,…..,20)的值。 联系 1 用Mathematica软件给出了b(k;20,0.1),b(k;20,0.3)与 b (k;20,0.5)(k=0,1,2,…..,20)的值。 我们可用Mathematica软件画出上述数据的散点图,下面的语句给出了b(k;20.0.1)的(连线)散点图(图13。1): LISTpOLT[table[Binomi al[20,k]*0.1^k*0.9^(20-k), {k,0,20}],PlotJoined->True] 图13.1 b(k;20,0.1) b k;n,p C n k p k1p n k (k=1,1,2,……,20)的散点图 练习2绘出b(l;20,0.3)与b(k;20,0.5)(k=0,1,2,…,20)的散点图 根据下面的定理,二项分布可用Poisson分布来进行近似计算。 定理13。1 在Bernoulli实验中,以P n 代表事件A在试验中出现的概率,它与试验总数有关. 如果np n→→λ,则当n→∞时,b k;n,p k k e 。 由定理13,1在n很大,p很小,而λ=np大小适中时,有 b k;n.p c k n p k1p n k k k e

统计学附录F分布,t分布临界值表全.docx

统计学附录F—分布临界值表 ——α( 0.005 ―0.10 ) α=0.005 Fα k112345681224∞k2 116211200002161522500230562343723925244262494025465 2198.5199.0199.2199.2199.3199.3199.4199.4199.5199.5 355.5549.8047.4746.1945.3944.8444.1343.3942.6241.83 431.3326.2824.2623.1522.4621.9721.3520.7020.0319.32 522.7818.3116.5315.5614.9414.5113.9613.3812.7812.14 618.6314.4512.9212.0311.4611.0710.5710.039.478.88 716.2412.4010.8810.059.529.168.688.187.657.08 814.6911.049.608.818.307.957.507.01 6.50 5.95 913.6110.118.727.967.477.13 6.69 6.23 5.73 5.19 1012.839.438.087.34 6.87 6.54 6.12 5.66 5.17 4.64 1112.238.917.60 6.88 6.42 6.10 5.68 5.24 4.76 4.23 1211.758.517.23 6.52 6.07 5.76 5.35 4.91 4.43 3.90 1311.378.19 6.93 6.23 5.79 5.48 5.08 4.64 4.17 3.65 1411.067.92 6.68 6.00 5.56 5.26 4.86 4.43 3.96 3.44 1510.807.70 6.48 5.80 5.37 5.07 4.67 4.25 3.79 3.26 1610.587.51 6.30 5.64 5.21 4.91 4.52 4.10 3.64 3.11 1710.387.35 6.16 5.50 5.07 4.78 4.39 3.97 3.51 2.98 1810.227.21 6.03 5.37 4.96 4.66 4.28 3.86 3.40 2.87 1910.077.09 5.92 5.27 4.85 4.56 4.18 3.76 3.31 2.78 209.94 6.99 5.82 5.17 4.76 4.47 4.09 3.68 3.22 2.69

统计分布临界值表

附录 附表一:随机数表 _________________________________________________________________________ 2附表二:标准正态分布表 ___________________________________________________________________ 3附表三:t分布临界值表____________________________________________________________________ 4 附表四: 2 分布临界值表 __________________________________________________________________ 5 附表五:F分布临界值表(α=0.05)________________________________________________________ 7附表六:单样本K-S检验统计量表___________________________________________________________ 9附表七:符号检验界域表 __________________________________________________________________ 10附表八:游程检验临界值表 _________________________________________________________________ 11附表九:相关系数临界值表 ________________________________________________________________ 12附表十:Spearman等级相关系数临界值表 ___________________________________________________ 13附表十一:Kendall等级相关系数临界值表 ___________________________________________________ 14附表十二:控制图系数表 __________________________________________________________________ 15

统计分布临界值表

附表一:随机数表_____________________________________________________________________________ 2附表二:标准正态分布表______________________________________________________________________ 3附表三:t分布临界值表________________________________________________________________________ 4 2 附表四:分布临界值表_____________________________________________________________________ 5附表五:F分布临界值表(a =0.05)7附表六:单样本K-S检验统计量表_______________________________________________________________ 9附表七:符号检验界域表______________________________________________________________________ 10附表八:游程检验临界值表___________________________________________________________________ 11附表九:相关系数临界值表____________________________________________________________________ 12附表十:Spearman等级相关系数临界值表 _____________________________________________________ 13附表十一:Kendall等级相关系数临界值表_______________________________________________________ 14附表十二:控制图系数表_____________________________________________________________________ 15

高中数学人教版 选修2-3(理科) 第二章 随机变量及其分布 2.2.3独立重复试验与二项分布D卷

高中数学人教版选修2-3(理科)第二章随机变量及其分布 2.2.3独立重复试验与 二项分布D卷 姓名:________ 班级:________ 成绩:________ 一、选择题 (共10题;共19分) 1. (2分) (2016高一下·兰州期中) 从一批羽毛球产品中任取一个,质量小于4.8g的概率是0.3,质量不小于4.85g的概率是0.32,那么质量在[4.8,4.85)g范围内的概率是() A . 0.62 B . 0.38 C . 0.7 D . 0.68 2. (2分)已知随机变量ξ服从二项分布ξ~B(n,p),且E(ξ)=7,D(ξ)=6,则p等于() A . B . C . D . 3. (2分) (2016高二下·邯郸期中) 设随机变量X~B(2,p),Y~B(4,p),若P(X≥1)= ,则P(Y≥1)为() A . B . C .

D . 1 4. (2分) (2017高二下·洛阳期末) 设随机变量X~B(2,p),随机变量Y~B(3,p),若P(X≥1)= ,则D( Y+1)=() A . 2 B . 3 C . 6 D . 7 5. (2分)设随机变量X~B(2,P),随机变量Y~B(3,P),若P(X≥1)=,则D(3Y+1)=() A . 2 B . 3 C . 6 D . 7 6. (2分)随机变量ξ服从二项分布ξ~B(n,p),且Eξ=300,Dξ=200,则p等于() A . B . 0 C . 1 D . 7. (2分)某人射击一次击中目标的概率为0.6,此人射击3次恰有两次击中目标的概率为() A . B .

C . D . 8. (2分) (2017高二下·南阳期末) 设随机变量ξ~B(2,p),随机变量η~B(3,p),若,则Eη=() A . B . C . 1 D . 9. (2分) (2018高二下·黄陵期末) 若随机变量X服从二项分布,且 ,则 =________ , =________. 10. (1分) (2018高二下·枣庄期末) 已知随机变量,且,则 ________. 二、填空题 (共2题;共6分) 11. (1分)已知随机变量X服从二项分布B(n,p),若E(X)=40,D(X)=30,则p=________ 12. (5分)(2019·天津) 设甲、乙两位同学上学期间,每天7:30之前到校的概率均为 .假定甲、乙两位同学到校情况互不影响,且任一同学每天到校情况相互独立. (Ⅰ)用表示甲同学上学期间的三天中7:30之前到校的天数,求随机变量的分布列和数学期望; (Ⅱ)设为事件“上学期间的三天中,甲同学在7:30之前到校的天数比乙同学在7:30之前到校的天数恰好多2”,求事件发生的概率. 三、解答题 (共2题;共20分) 13. (10分)(2019·大连模拟) 随着电子阅读的普及,传统纸质媒体遭受到了强烈的冲击.某杂志社近9

二项分布临界值表

附表1 二项分布临界值表 在p=q=下,x或n–x(不论何者为大)的临界值 n 单侧检验()双侧检验()0.050.010.050.01 55———66—6—7777—8788—98989 10910910 119101011 1210111011 1310121112 1411121213 1512131213 1612141314 1713141315 1813151415 1914151516 2015161517 2115171617 2216171718 2316181719 2417191819

2518191820 2618201920 2719202021 2819212022 2920222122 3020222123

附表2 正态分布概率表 Z F(Z)Z F(Z)Z F(Z)Z F(Z) 0.000.00000.350.27370.700.5161 1.050.7063 0.010.00800.360.28120.710.5223 1.060.7109 0.020.01600.370.28860.720.5285 1.070.7154 0.030.02390.380.29610.730.5346 1.080.7199 0.040.03190.390.30350.740.5407 1.090.7243 0.050.03990.400.31080.750.5467 1.100.7287 0.060.04780.410.31820.760.5527 1.110.7330 0.070.05580.420.32550.770.5587 1.120.7373 0.080.06380.430.33280.780.5646 1.130.7415 0.090.07170.440.34010.790.5705 1.140.7457 0.100.07970.450.34730.800.5763 1.150.7499 0.110.08760.460.35450.810.5821 1.160.7540 0.120.09550.470.36160.820.5878 1.170.7580 0.130.10340.480.36880.830.5935 1.180.7620 0.140.11130.490.37590.840.5991 1.190.7660 0.150.11920.500.38290.850.6047 1.200.7699 0.160.12710.510.38990.860.6102 1.210.7737 0.170.13500.520.39690.870.6157 1.220.7775 0.180.14280.530.40390.880.6211 1.230.7813 0.190.15070.540.41080.890.6265 1.240.7850

T分布临界值表 (2)

T分布表 Df 自由度 P 概率0.1 0.05 0.025 0.01 0.005 0.001 0.0005 单尾0.2 0.1 0.05 0.02 0.01 0.002 0.001 双尾 1 3.078 6.314 12.706 31.821 63.657 318.309 636.619 2 1.886 2.920 4.30 3 6.965 9.925 22.327 31.599 3 1.638 2.353 3.182 4.541 5.841 10.215 12.924 4 1.533 2.132 2.776 3.747 4.604 7.173 8.610 5 1.47 6 2.015 2.571 3.365 4.032 5.893 6.869 6 1.440 1.943 2.44 7 3.143 3.707 5.20 8 5.959 7 1.415 1.895 2.365 2.998 3.499 4.785 5.408 8 1.397 1.860 2.306 2.896 3.355 4.501 5.041 9 1.383 1.833 2.262 2.821 3.250 4.297 4.781 10 1.372 1.812 2.228 2.764 3.169 4.144 4.587 11 1.363 1.796 2.201 2.718 3.106 4.025 4.437 12 1.356 1.782 2.179 2.681 3.055 3.930 4.318 13 1.350 1.771 2.160 2.650 3.012 3.852 4.221 14 1.345 1.761 2.145 2.624 2.977 3.787 4.140 15 1.341 1.753 2.131 2.602 2.947 3.733 4.073 16 1.337 1.746 2.120 2.583 2.921 3.686 4.015 17 1.333 1.740 2.110 2.567 2.898 3.646 3.965 18 1.330 1.734 2.101 2.552 2.878 3.610 3.922 19 1.328 1.729 2.093 2.539 2.861 3.579 3.883 20 1.325 1.725 2.086 2.528 2.845 3.552 3.850 21 1.323 1.721 2.080 2.518 2.831 3.527 3.819 22 1.321 1.717 2.074 2.508 2.819 3.505 3.792 23 1.319 1.714 2.069 2.500 2.807 3.485 3.768 24 1.318 1.711 2.064 2.492 2.797 3.467 3.745 25 1.316 1.708 2.060 2.485 2.787 3.450 3.725 26 1.315 1.706 2.056 2.479 2.779 3.435 3.707 27 1.314 1.703 2.052 2.473 2.771 3.421 3.690 28 1.313 1.701 2.048 2.467 2.763 3.408 3.674 29 1.311 1.699 2.045 2.462 2.756 3.396 3.659 30 1.310 1.697 2.042 2.457 2.750 3.385 3.646 31 1.309 1.696 2.040 2.453 2.744 3.375 3.633 32 1.309 1.694 2.037 2.449 2.738 3.365 3.622 33 1.308 1.692 2.035 2.445 2.733 3.356 3.611

Poisson分布的检验

P o i s s o n分布的检验文件排版存档编号:[UYTR-OUPT28-KBNTL98-UYNN208]

目录 承诺保证书……………………………………………………………………I 1 引言 (1) 研究背 景 (1) 研究方法及目 的 (1) 2 Poisson分布检验的步骤和基本理论 (2) 检验步骤 (2) 检验的基本原理 (3) 3 关于Poisson分布检验的三个案例及实际研究 (7) 案例分析 (7)

对单位时间到来顾客数的实际研究 (13) 参考文献 (18) 英文摘要 (19)

关于Poisson分布的检验 肖秋光 摘要:Poisson分布是概率论中的一种重要离散分布,在许多实际问题中都有着广泛应用.本文概括了检验样本数据是否服从泊松分布的一般方法,主要是对随机数据进行图像模拟估计和利用假设检验原理对给定的临界值进行估计.其中2χ检验是众所周知的拟合优度检验,它能适用于任意的备择假设.另外,通过三个例子进行说明,最后用该方法对实测数据进行了分析和检验,并得出了结论. 关键词:Poisson分布假设检验独立变量2χ统计量 1 引言 研究背景 改革开放三十年来随着社会的发展、经济的增长,科学技术日新月异、人民拥有的物质日益丰富、感受到的文化也更加多元、社会的各种法规制度日臻成熟,无论是住房、保险、交通、旅游、高质量产品还是教育、饮食等.其结果是构成了大量的随机数据,而这些数据有没有什么规律可循呢就需要我们对它进行研究.在现实生活中的许多数据经过人们大量的研究是服从泊松分布的.若通过观察记录得到了一组数据,它是否服从泊松分布,则需要我们对其进行检验.

t-分布临界值表

t -分布临界值表 ()(){}1P t n t n αα?=> n α=0.25 0.10 0.05 0.025 0.01 0.005 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1.0000 0.8165 0.7649 0.7407 0.7267 0.7176 0.7111 0.7064 0.7027 0.6998 0.6974 0.6955 0.6938 0.6924 0.6912 0.6901 0.6892 0.6884 0.6876 0.6870 0.6864 0.6858 0.6853 0.6848 0.6844 0.6840 0.6837 0.6834 0.6830 0.6828 3.0777 1.8856 1.6377 1.5332 1.4759 1.4398 1.4149 1.3968 1.3830 1.3722 1.3634 1.3562 1.3502 1.3450 1.3406 1.3368 1.3334 1.3304 1.3277 1.3253 1.3232 1.3212 1.3195 1.3178 1.3163 1.3150 1.3137 1.3125 1.3114 1.3104 6.3138 2.9200 2.3534 2.1318 2.0150 1.9432 1.8946 1.8595 1.8331 1.8125 1.7959 1.7823 1.7709 1.7613 1.7531 1.7459 1.7396 1.7341 1.7291 1.7247 1.7207 1.7171 1.7139 1.7109 1.7081 1.7056 1.7033 1.7011 1.6991 1.6973 12.7062 4.3207 3.1824 2.7764 2.5706 2.4469 2.3646 2.3060 2.2622 2.2281 2.2010 2.1788 2.1604 2.1448 2.1315 2.1199 2.1098 2.1009 2.0930 2.0860 2.0796 2.0739 2.0687 2.0639 2.0595 2.0555 2.0518 2.0484 2.0452 2.0423 31.8207 6.9646 4.5407 3.7469 3.3649 3.1427 2.9980 2.8965 2.8214 2.7638 2.7181 2.6810 2.6503 2.6245 2.6025 2.5835 2.5669 2.5524 2.5395 2.5280 2.5177 2.5083 2.4999 2.4922 2.4851 2.4786 2.4727 2.4671 2.4620 2.4573 63.6574 9.9248 5.8409 4.6041 4.0322 3.7074 3.4995 3.3554 3.2498 3.1693 3.1058 3.0545 3.0123 2.9768 2.9467 2.9028 2.8982 2.8784 2.8609 2.8453 2.8314 2.8188 2.8073 2.7969 2.7874 2.7787 2.7707 2.7633 2.7564 2.7500

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