|
815 | 815 | " <tr>\n",
|
816 | 816 | " <th>0</th>\n",
|
817 | 817 | " <td>1</td>\n",
|
818 |
| - " <td>9.842380</td>\n", |
819 |
| - " <td>9.842380</td>\n", |
| 818 | + " <td>10.038295</td>\n", |
| 819 | + " <td>10.038295</td>\n", |
820 | 820 | " <td>NaN</td>\n",
|
821 | 821 | " <td>NaN</td>\n",
|
822 | 822 | " <td>NaN</td>\n",
|
|
826 | 826 | " <tr>\n",
|
827 | 827 | " <th>1</th>\n",
|
828 | 828 | " <td>2</td>\n",
|
829 |
| - " <td>10.060424</td>\n", |
830 |
| - " <td>9.951402</td>\n", |
| 829 | + " <td>10.224439</td>\n", |
| 830 | + " <td>10.131367</td>\n", |
831 | 831 | " <td>NaN</td>\n",
|
832 | 832 | " <td>NaN</td>\n",
|
833 | 833 | " <td>NaN</td>\n",
|
|
837 | 837 | " <tr>\n",
|
838 | 838 | " <th>2</th>\n",
|
839 | 839 | " <td>3</td>\n",
|
840 |
| - " <td>9.925090</td>\n", |
841 |
| - " <td>9.942631</td>\n", |
842 |
| - " <td>0.110075</td>\n", |
843 |
| - " <td>9.669189</td>\n", |
844 |
| - " <td>10.216073</td>\n", |
845 |
| - " <td>0.027502</td>\n", |
| 840 | + " <td>9.901037</td>\n", |
| 841 | + " <td>10.054590</td>\n", |
| 842 | + " <td>0.162316</td>\n", |
| 843 | + " <td>9.651375</td>\n", |
| 844 | + " <td>10.457806</td>\n", |
| 845 | + " <td>0.040103</td>\n", |
846 | 846 | " <td>mean_time_with_nurse</td>\n",
|
847 | 847 | " </tr>\n",
|
848 | 848 | " <tr>\n",
|
849 | 849 | " <th>3</th>\n",
|
850 | 850 | " <td>4</td>\n",
|
851 |
| - " <td>9.938504</td>\n", |
852 |
| - " <td>9.941599</td>\n", |
853 |
| - " <td>0.089900</td>\n", |
854 |
| - " <td>9.798549</td>\n", |
855 |
| - " <td>10.084650</td>\n", |
856 |
| - " <td>0.014389</td>\n", |
| 851 | + " <td>10.002341</td>\n", |
| 852 | + " <td>10.041528</td>\n", |
| 853 | + " <td>0.135081</td>\n", |
| 854 | + " <td>9.826584</td>\n", |
| 855 | + " <td>10.256472</td>\n", |
| 856 | + " <td>0.021405</td>\n", |
857 | 857 | " <td>mean_time_with_nurse</td>\n",
|
858 | 858 | " </tr>\n",
|
859 | 859 | " <tr>\n",
|
860 | 860 | " <th>4</th>\n",
|
861 | 861 | " <td>5</td>\n",
|
862 |
| - " <td>10.016611</td>\n", |
863 |
| - " <td>9.956602</td>\n", |
864 |
| - " <td>0.084775</td>\n", |
865 |
| - " <td>9.851339</td>\n", |
866 |
| - " <td>10.061864</td>\n", |
867 |
| - " <td>0.010572</td>\n", |
| 862 | + " <td>10.011373</td>\n", |
| 863 | + " <td>10.035497</td>\n", |
| 864 | + " <td>0.117758</td>\n", |
| 865 | + " <td>9.889281</td>\n", |
| 866 | + " <td>10.181713</td>\n", |
| 867 | + " <td>0.014570</td>\n", |
868 | 868 | " <td>mean_time_with_nurse</td>\n",
|
869 | 869 | " </tr>\n",
|
870 | 870 | " <tr>\n",
|
|
881 | 881 | " <tr>\n",
|
882 | 882 | " <th>35</th>\n",
|
883 | 883 | " <td>36</td>\n",
|
884 |
| - " <td>0.492981</td>\n", |
885 |
| - " <td>0.497883</td>\n", |
886 |
| - " <td>0.007143</td>\n", |
887 |
| - " <td>0.495466</td>\n", |
888 |
| - " <td>0.500300</td>\n", |
889 |
| - " <td>0.004854</td>\n", |
| 884 | + " <td>0.490657</td>\n", |
| 885 | + " <td>0.496990</td>\n", |
| 886 | + " <td>0.007191</td>\n", |
| 887 | + " <td>0.494557</td>\n", |
| 888 | + " <td>0.499423</td>\n", |
| 889 | + " <td>0.004896</td>\n", |
890 | 890 | " <td>mean_nurse_utilisation</td>\n",
|
891 | 891 | " </tr>\n",
|
892 | 892 | " <tr>\n",
|
893 | 893 | " <th>36</th>\n",
|
894 | 894 | " <td>37</td>\n",
|
895 |
| - " <td>0.508118</td>\n", |
896 |
| - " <td>0.498159</td>\n", |
897 |
| - " <td>0.007242</td>\n", |
898 |
| - " <td>0.495745</td>\n", |
899 |
| - " <td>0.500574</td>\n", |
900 |
| - " <td>0.004847</td>\n", |
| 895 | + " <td>0.506302</td>\n", |
| 896 | + " <td>0.497242</td>\n", |
| 897 | + " <td>0.007254</td>\n", |
| 898 | + " <td>0.494823</td>\n", |
| 899 | + " <td>0.499660</td>\n", |
| 900 | + " <td>0.004864</td>\n", |
901 | 901 | " <td>mean_nurse_utilisation</td>\n",
|
902 | 902 | " </tr>\n",
|
903 | 903 | " <tr>\n",
|
904 | 904 | " <th>37</th>\n",
|
905 | 905 | " <td>38</td>\n",
|
906 |
| - " <td>0.502153</td>\n", |
907 |
| - " <td>0.498264</td>\n", |
908 |
| - " <td>0.007172</td>\n", |
909 |
| - " <td>0.495907</td>\n", |
910 |
| - " <td>0.500622</td>\n", |
911 |
| - " <td>0.004731</td>\n", |
| 906 | + " <td>0.508797</td>\n", |
| 907 | + " <td>0.497546</td>\n", |
| 908 | + " <td>0.007396</td>\n", |
| 909 | + " <td>0.495115</td>\n", |
| 910 | + " <td>0.499977</td>\n", |
| 911 | + " <td>0.004886</td>\n", |
912 | 912 | " <td>mean_nurse_utilisation</td>\n",
|
913 | 913 | " </tr>\n",
|
914 | 914 | " <tr>\n",
|
915 | 915 | " <th>38</th>\n",
|
916 | 916 | " <td>39</td>\n",
|
917 |
| - " <td>0.499857</td>\n", |
918 |
| - " <td>0.498305</td>\n", |
919 |
| - " <td>0.007082</td>\n", |
920 |
| - " <td>0.496010</td>\n", |
921 |
| - " <td>0.500601</td>\n", |
922 |
| - " <td>0.004607</td>\n", |
| 917 | + " <td>0.501514</td>\n", |
| 918 | + " <td>0.497647</td>\n", |
| 919 | + " <td>0.007326</td>\n", |
| 920 | + " <td>0.495273</td>\n", |
| 921 | + " <td>0.500022</td>\n", |
| 922 | + " <td>0.004772</td>\n", |
923 | 923 | " <td>mean_nurse_utilisation</td>\n",
|
924 | 924 | " </tr>\n",
|
925 | 925 | " <tr>\n",
|
926 | 926 | " <th>39</th>\n",
|
927 | 927 | " <td>40</td>\n",
|
928 |
| - " <td>0.512021</td>\n", |
929 |
| - " <td>0.498648</td>\n", |
930 |
| - " <td>0.007319</td>\n", |
931 |
| - " <td>0.496307</td>\n", |
932 |
| - " <td>0.500989</td>\n", |
933 |
| - " <td>0.004694</td>\n", |
| 928 | + " <td>0.504045</td>\n", |
| 929 | + " <td>0.497807</td>\n", |
| 930 | + " <td>0.007302</td>\n", |
| 931 | + " <td>0.495472</td>\n", |
| 932 | + " <td>0.500143</td>\n", |
| 933 | + " <td>0.004691</td>\n", |
934 | 934 | " <td>mean_nurse_utilisation</td>\n",
|
935 | 935 | " </tr>\n",
|
936 | 936 | " </tbody>\n",
|
|
940 | 940 | ],
|
941 | 941 | "text/plain": [
|
942 | 942 | " replications data cumulative_mean stdev lower_ci upper_ci \\\n",
|
943 |
| - "0 1 9.842380 9.842380 NaN NaN NaN \n", |
944 |
| - "1 2 10.060424 9.951402 NaN NaN NaN \n", |
945 |
| - "2 3 9.925090 9.942631 0.110075 9.669189 10.216073 \n", |
946 |
| - "3 4 9.938504 9.941599 0.089900 9.798549 10.084650 \n", |
947 |
| - "4 5 10.016611 9.956602 0.084775 9.851339 10.061864 \n", |
| 943 | + "0 1 10.038295 10.038295 NaN NaN NaN \n", |
| 944 | + "1 2 10.224439 10.131367 NaN NaN NaN \n", |
| 945 | + "2 3 9.901037 10.054590 0.162316 9.651375 10.457806 \n", |
| 946 | + "3 4 10.002341 10.041528 0.135081 9.826584 10.256472 \n", |
| 947 | + "4 5 10.011373 10.035497 0.117758 9.889281 10.181713 \n", |
948 | 948 | ".. ... ... ... ... ... ... \n",
|
949 |
| - "35 36 0.492981 0.497883 0.007143 0.495466 0.500300 \n", |
950 |
| - "36 37 0.508118 0.498159 0.007242 0.495745 0.500574 \n", |
951 |
| - "37 38 0.502153 0.498264 0.007172 0.495907 0.500622 \n", |
952 |
| - "38 39 0.499857 0.498305 0.007082 0.496010 0.500601 \n", |
953 |
| - "39 40 0.512021 0.498648 0.007319 0.496307 0.500989 \n", |
| 949 | + "35 36 0.490657 0.496990 0.007191 0.494557 0.499423 \n", |
| 950 | + "36 37 0.506302 0.497242 0.007254 0.494823 0.499660 \n", |
| 951 | + "37 38 0.508797 0.497546 0.007396 0.495115 0.499977 \n", |
| 952 | + "38 39 0.501514 0.497647 0.007326 0.495273 0.500022 \n", |
| 953 | + "39 40 0.504045 0.497807 0.007302 0.495472 0.500143 \n", |
954 | 954 | "\n",
|
955 | 955 | " deviation metric \n",
|
956 | 956 | "0 NaN mean_time_with_nurse \n",
|
957 | 957 | "1 NaN mean_time_with_nurse \n",
|
958 |
| - "2 0.027502 mean_time_with_nurse \n", |
959 |
| - "3 0.014389 mean_time_with_nurse \n", |
960 |
| - "4 0.010572 mean_time_with_nurse \n", |
| 958 | + "2 0.040103 mean_time_with_nurse \n", |
| 959 | + "3 0.021405 mean_time_with_nurse \n", |
| 960 | + "4 0.014570 mean_time_with_nurse \n", |
961 | 961 | ".. ... ... \n",
|
962 |
| - "35 0.004854 mean_nurse_utilisation \n", |
963 |
| - "36 0.004847 mean_nurse_utilisation \n", |
964 |
| - "37 0.004731 mean_nurse_utilisation \n", |
965 |
| - "38 0.004607 mean_nurse_utilisation \n", |
966 |
| - "39 0.004694 mean_nurse_utilisation \n", |
| 962 | + "35 0.004896 mean_nurse_utilisation \n", |
| 963 | + "36 0.004864 mean_nurse_utilisation \n", |
| 964 | + "37 0.004886 mean_nurse_utilisation \n", |
| 965 | + "38 0.004772 mean_nurse_utilisation \n", |
| 966 | + "39 0.004691 mean_nurse_utilisation \n", |
967 | 967 | "\n",
|
968 | 968 | "[120 rows x 8 columns]"
|
969 | 969 | ]
|
|
1015 | 1015 | }
|
1016 | 1016 | ],
|
1017 | 1017 | "metadata": {
|
1018 |
| - "kernelspec": { |
1019 |
| - "display_name": "template-des", |
1020 |
| - "language": "python", |
1021 |
| - "name": "python3" |
1022 |
| - }, |
1023 | 1018 | "language_info": {
|
1024 |
| - "codemirror_mode": { |
1025 |
| - "name": "ipython", |
1026 |
| - "version": 3 |
1027 |
| - }, |
1028 |
| - "file_extension": ".py", |
1029 |
| - "mimetype": "text/x-python", |
1030 |
| - "name": "python", |
1031 |
| - "nbconvert_exporter": "python", |
1032 |
| - "pygments_lexer": "ipython3", |
1033 |
| - "version": "3.13.1" |
| 1019 | + "name": "python" |
1034 | 1020 | }
|
1035 | 1021 | },
|
1036 | 1022 | "nbformat": 4,
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|
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