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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 10 Dec 2013 04:50:13 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/10/t1386669120alz0f8unihi57j4.htm/, Retrieved Fri, 29 Mar 2024 07:49:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231841, Retrieved Fri, 29 Mar 2024 07:49:13 +0000
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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact123
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [MR] [2013-12-10 09:50:13] [ca0374cc2e3cbab06789b6eb5e649ce0] [Current]
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Dataseries X:
1 119.992 157.302 74.997 0.00784 0.0037 0.00554 0.04374
1 122.4 148.65 113.819 0.00968 0.00465 0.00696 0.06134
1 116.682 131.111 111.555 0.0105 0.00544 0.00781 0.05233
1 116.676 137.871 111.366 0.00997 0.00502 0.00698 0.05492
1 116.014 141.781 110.655 0.01284 0.00655 0.00908 0.06425
1 120.552 131.162 113.787 0.00968 0.00463 0.0075 0.04701
1 120.267 137.244 114.82 0.00333 0.00155 0.00202 0.01608
1 107.332 113.84 104.315 0.0029 0.00144 0.00182 0.01567
1 95.73 132.068 91.754 0.00551 0.00293 0.00332 0.02093
1 95.056 120.103 91.226 0.00532 0.00268 0.00332 0.02838
1 88.333 112.24 84.072 0.00505 0.00254 0.0033 0.02143
1 91.904 115.871 86.292 0.0054 0.00281 0.00336 0.02752
1 136.926 159.866 131.276 0.00293 0.00118 0.00153 0.01259
1 139.173 179.139 76.556 0.0039 0.00165 0.00208 0.01642
1 152.845 163.305 75.836 0.00294 0.00121 0.00149 0.01828
1 142.167 217.455 83.159 0.00369 0.00157 0.00203 0.01503
1 144.188 349.259 82.764 0.00544 0.00211 0.00292 0.02047
1 168.778 232.181 75.603 0.00718 0.00284 0.00387 0.03327
1 153.046 175.829 68.623 0.00742 0.00364 0.00432 0.05517
1 156.405 189.398 142.822 0.00768 0.00372 0.00399 0.03995
1 153.848 165.738 65.782 0.0084 0.00428 0.0045 0.0381
1 153.88 172.86 78.128 0.0048 0.00232 0.00267 0.04137
1 167.93 193.221 79.068 0.00442 0.0022 0.00247 0.04351
1 173.917 192.735 86.18 0.00476 0.00221 0.00258 0.04192
1 163.656 200.841 76.779 0.00742 0.0038 0.0039 0.01659
1 104.4 206.002 77.968 0.00633 0.00316 0.00375 0.03767
1 171.041 208.313 75.501 0.00455 0.0025 0.00234 0.01966
1 146.845 208.701 81.737 0.00496 0.0025 0.00275 0.01919
1 155.358 227.383 80.055 0.0031 0.00159 0.00176 0.01718
1 162.568 198.346 77.63 0.00502 0.0028 0.00253 0.01791
0 197.076 206.896 192.055 0.00289 0.00166 0.00168 0.01098
0 199.228 209.512 192.091 0.00241 0.00134 0.00138 0.01015
0 198.383 215.203 193.104 0.00212 0.00113 0.00135 0.01263
0 202.266 211.604 197.079 0.0018 0.00093 0.00107 0.00954
0 203.184 211.526 196.16 0.00178 0.00094 0.00106 0.00958
0 201.464 210.565 195.708 0.00198 0.00105 0.00115 0.01194
1 177.876 192.921 168.013 0.00411 0.00233 0.00241 0.02126
1 176.17 185.604 163.564 0.00369 0.00205 0.00218 0.01851
1 180.198 201.249 175.456 0.00284 0.00153 0.00166 0.01444
1 187.733 202.324 173.015 0.00316 0.00168 0.00182 0.01663
1 186.163 197.724 177.584 0.00298 0.00165 0.00175 0.01495
1 184.055 196.537 166.977 0.00258 0.00134 0.00147 0.01463
0 237.226 247.326 225.227 0.00298 0.00169 0.00182 0.01752
0 241.404 248.834 232.483 0.00281 0.00157 0.00173 0.0176
0 243.439 250.912 232.435 0.0021 0.00109 0.00137 0.01419
0 242.852 255.034 227.911 0.00225 0.00117 0.00139 0.01494
0 245.51 262.09 231.848 0.00235 0.00127 0.00148 0.01608
0 252.455 261.487 182.786 0.00185 0.00092 0.00113 0.01152
0 122.188 128.611 115.765 0.00524 0.00169 0.00203 0.01613
0 122.964 130.049 114.676 0.00428 0.00124 0.00155 0.01681
0 124.445 135.069 117.495 0.00431 0.00141 0.00167 0.02184
0 126.344 134.231 112.773 0.00448 0.00131 0.00169 0.02033
0 128.001 138.052 122.08 0.00436 0.00137 0.00166 0.02297
0 129.336 139.867 118.604 0.0049 0.00165 0.00183 0.02498
1 108.807 134.656 102.874 0.00761 0.00349 0.00486 0.02719
1 109.86 126.358 104.437 0.00874 0.00398 0.00539 0.03209
1 110.417 131.067 103.37 0.00784 0.00352 0.00514 0.03715
1 117.274 129.916 110.402 0.00752 0.00299 0.00469 0.02293
1 116.879 131.897 108.153 0.00788 0.00334 0.00493 0.02645
1 114.847 271.314 104.68 0.00867 0.00373 0.0052 0.03225
0 209.144 237.494 109.379 0.00282 0.00147 0.00152 0.01861
0 223.365 238.987 98.664 0.00264 0.00154 0.00151 0.01906
0 222.236 231.345 205.495 0.00266 0.00152 0.00144 0.01643
0 228.832 234.619 223.634 0.00296 0.00175 0.00155 0.01644
0 229.401 252.221 221.156 0.00205 0.00114 0.00113 0.01457
0 228.969 239.541 113.201 0.00238 0.00136 0.0014 0.01745
1 140.341 159.774 67.021 0.00817 0.0043 0.0044 0.03198
1 136.969 166.607 66.004 0.00923 0.00507 0.00463 0.03111
1 143.533 162.215 65.809 0.01101 0.00647 0.00467 0.05384
1 148.09 162.824 67.343 0.00762 0.00467 0.00354 0.05428
1 142.729 162.408 65.476 0.00831 0.00469 0.00419 0.03485
1 136.358 176.595 65.75 0.00971 0.00534 0.00478 0.04978
1 120.08 139.71 111.208 0.00405 0.0018 0.0022 0.01706
1 112.014 588.518 107.024 0.00533 0.00268 0.00329 0.02448
1 110.793 128.101 107.316 0.00494 0.0026 0.00283 0.02442
1 110.707 122.611 105.007 0.00516 0.00277 0.00289 0.02215
1 112.876 148.826 106.981 0.005 0.0027 0.00289 0.03999
1 110.568 125.394 106.821 0.00462 0.00226 0.0028 0.02199
1 95.385 102.145 90.264 0.00608 0.00331 0.00332 0.03202
1 100.77 115.697 85.545 0.01038 0.00622 0.00576 0.03121
1 96.106 108.664 84.51 0.00694 0.00389 0.00415 0.04024
1 95.605 107.715 87.549 0.00702 0.00428 0.00371 0.03156
1 100.96 110.019 95.628 0.00606 0.00351 0.00348 0.02427
1 98.804 102.305 87.804 0.00432 0.00247 0.00258 0.02223
1 176.858 205.56 75.344 0.00747 0.00418 0.0042 0.04795
1 180.978 200.125 155.495 0.00406 0.0022 0.00244 0.03852
1 178.222 202.45 141.047 0.00321 0.00163 0.00194 0.03759
1 176.281 227.381 125.61 0.0052 0.00287 0.00312 0.06511
1 173.898 211.35 74.677 0.00448 0.00237 0.00254 0.06727
1 179.711 225.93 144.878 0.00709 0.00391 0.00419 0.04313
1 166.605 206.008 78.032 0.00742 0.00387 0.00453 0.0664
1 151.955 163.335 147.226 0.00419 0.00224 0.00227 0.07959
1 148.272 164.989 142.299 0.00459 0.0025 0.00256 0.0419
1 152.125 161.469 76.596 0.00382 0.00191 0.00226 0.05925
1 157.821 172.975 68.401 0.00358 0.00196 0.00196 0.03716
1 157.447 163.267 149.605 0.00369 0.00201 0.00197 0.03272
1 159.116 168.913 144.811 0.00342 0.00178 0.00184 0.03381
1 125.036 143.946 116.187 0.0128 0.00743 0.00623 0.03886
1 125.791 140.557 96.206 0.01378 0.00826 0.00655 0.04689
1 126.512 141.756 99.77 0.01936 0.01159 0.0099 0.06734
1 125.641 141.068 116.346 0.03316 0.02144 0.01522 0.09178
1 128.451 150.449 75.632 0.01551 0.00905 0.00909 0.0617
1 139.224 586.567 66.157 0.03011 0.01854 0.01628 0.09419
1 150.258 154.609 75.349 0.00248 0.00105 0.00136 0.01131
1 154.003 160.267 128.621 0.00183 0.00076 0.001 0.0103
1 149.689 160.368 133.608 0.00257 0.00116 0.00134 0.01346
1 155.078 163.736 144.148 0.00168 0.00068 0.00092 0.01064
1 151.884 157.765 133.751 0.00258 0.00115 0.00122 0.0145
1 151.989 157.339 132.857 0.00174 0.00075 0.00096 0.01024
1 193.03 208.9 80.297 0.00766 0.0045 0.00389 0.03044
1 200.714 223.982 89.686 0.00621 0.00371 0.00337 0.02286
1 208.519 220.315 199.02 0.00609 0.00368 0.00339 0.01761
1 204.664 221.3 189.621 0.00841 0.00502 0.00485 0.02378
1 210.141 232.706 185.258 0.00534 0.00321 0.0028 0.0168
1 206.327 226.355 92.02 0.00495 0.00302 0.00246 0.02105
1 151.872 492.892 69.085 0.00856 0.00404 0.00385 0.01843
1 158.219 442.557 71.948 0.00476 0.00214 0.00207 0.01458
1 170.756 450.247 79.032 0.00555 0.00244 0.00261 0.01725
1 178.285 442.824 82.063 0.00462 0.00157 0.00194 0.01279
1 217.116 233.481 93.978 0.00404 0.00127 0.00128 0.01299
1 128.94 479.697 88.251 0.00581 0.00241 0.00314 0.02008
1 176.824 215.293 83.961 0.0046 0.00209 0.00221 0.01169
1 138.19 203.522 83.34 0.00704 0.00406 0.00398 0.04479
1 182.018 197.173 79.187 0.00842 0.00506 0.00449 0.02503
1 156.239 195.107 79.82 0.00694 0.00403 0.00395 0.02343
1 145.174 198.109 80.637 0.00733 0.00414 0.00422 0.02362
1 138.145 197.238 81.114 0.00544 0.00294 0.00327 0.02791
1 166.888 198.966 79.512 0.00638 0.00368 0.00351 0.02857
1 119.031 127.533 109.216 0.0044 0.00214 0.00192 0.01033
1 120.078 126.632 105.667 0.0027 0.00116 0.00135 0.01022
1 120.289 128.143 100.209 0.00492 0.00269 0.00238 0.01412
1 120.256 125.306 104.773 0.00407 0.00224 0.00205 0.01516
1 119.056 125.213 86.795 0.00346 0.00169 0.0017 0.01201
1 118.747 123.723 109.836 0.00331 0.00168 0.00171 0.01043
1 106.516 112.777 93.105 0.00589 0.00291 0.00319 0.04932
1 110.453 127.611 105.554 0.00494 0.00244 0.00315 0.04128
1 113.4 133.344 107.816 0.00451 0.00219 0.00283 0.04879
1 113.166 130.27 100.673 0.00502 0.00257 0.00312 0.05279
1 112.239 126.609 104.095 0.00472 0.00238 0.0029 0.05643
1 116.15 131.731 109.815 0.00381 0.00181 0.00232 0.03026
1 170.368 268.796 79.543 0.00571 0.00232 0.00269 0.03273
1 208.083 253.792 91.802 0.00757 0.00428 0.00428 0.06725
1 198.458 219.29 148.691 0.00376 0.00182 0.00215 0.03527
1 202.805 231.508 86.232 0.0037 0.00189 0.00211 0.01997
1 202.544 241.35 164.168 0.00254 0.001 0.00133 0.02662
1 223.361 263.872 87.638 0.00352 0.00169 0.00188 0.02536
1 169.774 191.759 151.451 0.01568 0.00863 0.00946 0.08143
1 183.52 216.814 161.34 0.01466 0.00849 0.00819 0.0605
1 188.62 216.302 165.982 0.01719 0.00996 0.01027 0.07118
1 202.632 565.74 177.258 0.01627 0.00919 0.00963 0.0717
1 186.695 211.961 149.442 0.01872 0.01075 0.01154 0.0583
1 192.818 224.429 168.793 0.03107 0.018 0.01958 0.11908
1 198.116 233.099 174.478 0.02714 0.01568 0.01699 0.08684
1 121.345 139.644 98.25 0.00684 0.00388 0.00332 0.02534
1 119.1 128.442 88.833 0.00692 0.00393 0.003 0.02682
1 117.87 127.349 95.654 0.00647 0.00356 0.003 0.03087
1 122.336 142.369 94.794 0.00727 0.00415 0.00339 0.02293
1 117.963 134.209 100.757 0.01813 0.01117 0.00718 0.04912
1 126.144 154.284 97.543 0.00975 0.00593 0.00454 0.02852
1 127.93 138.752 112.173 0.00605 0.00321 0.00318 0.03235
1 114.238 124.393 77.022 0.00581 0.00299 0.00316 0.04009
1 115.322 135.738 107.802 0.00619 0.00352 0.00329 0.03273
1 114.554 126.778 91.121 0.00651 0.00366 0.0034 0.03658
1 112.15 131.669 97.527 0.00519 0.00291 0.00284 0.01756
1 102.273 142.83 85.902 0.00907 0.00493 0.00461 0.02814
0 236.2 244.663 102.137 0.00277 0.00154 0.00153 0.02448
0 237.323 243.709 229.256 0.00303 0.00173 0.00159 0.01242
0 260.105 264.919 237.303 0.00339 0.00205 0.00186 0.0203
0 197.569 217.627 90.794 0.00803 0.0049 0.00448 0.02177
0 240.301 245.135 219.783 0.00517 0.00316 0.00283 0.02018
0 244.99 272.21 239.17 0.00451 0.00279 0.00237 0.01897
0 112.547 133.374 105.715 0.00355 0.00166 0.0019 0.01358
0 110.739 113.597 100.139 0.00356 0.0017 0.002 0.01484
0 113.715 116.443 96.913 0.00349 0.00171 0.00203 0.01472
0 117.004 144.466 99.923 0.00353 0.00176 0.00218 0.01657
0 115.38 123.109 108.634 0.00332 0.0016 0.00199 0.01503
0 116.388 129.038 108.97 0.00346 0.00169 0.00213 0.01725
1 151.737 190.204 129.859 0.00314 0.00135 0.00162 0.01469
1 148.79 158.359 138.99 0.00309 0.00152 0.00186 0.01574
1 148.143 155.982 135.041 0.00392 0.00204 0.00231 0.0145
1 150.44 163.441 144.736 0.00396 0.00206 0.00233 0.02551
1 148.462 161.078 141.998 0.00397 0.00202 0.00235 0.01831
1 149.818 163.417 144.786 0.00336 0.00174 0.00198 0.02145
0 117.226 123.925 106.656 0.00417 0.00186 0.0027 0.01909
0 116.848 217.552 99.503 0.00531 0.0026 0.00346 0.01795
0 116.286 177.291 96.983 0.00314 0.00134 0.00192 0.01564
0 116.556 592.03 86.228 0.00496 0.00254 0.00263 0.0166
0 116.342 581.289 94.246 0.00267 0.00115 0.00148 0.013
0 114.563 119.167 86.647 0.00327 0.00146 0.00184 0.01185
0 201.774 262.707 78.228 0.00694 0.00412 0.00396 0.02574
0 174.188 230.978 94.261 0.00459 0.00263 0.00259 0.04087
0 209.516 253.017 89.488 0.00564 0.00331 0.00292 0.02751
0 174.688 240.005 74.287 0.0136 0.00624 0.00564 0.02308
0 198.764 396.961 74.904 0.0074 0.0037 0.0039 0.02296
0 214.289 260.277 77.973 0.00567 0.00295 0.00317 0.01884




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time18 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 18 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231841&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]18 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231841&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231841&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time18 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
status[t] = + 1.27129 -0.00223873`MDVP:Fo(Hz)`[t] -0.000255182`MDVP:Fhi(Hz)`[t] -0.00241088`MDVP:Flo(Hz)`[t] -85.9932`MDVP:Jitter(%)`[t] + 91.0227`MDVP:RAP`[t] + 54.4052`MDVP:PPQ`[t] + 6.90138`MDVP:Shimmer`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
status[t] =  +  1.27129 -0.00223873`MDVP:Fo(Hz)`[t] -0.000255182`MDVP:Fhi(Hz)`[t] -0.00241088`MDVP:Flo(Hz)`[t] -85.9932`MDVP:Jitter(%)`[t] +  91.0227`MDVP:RAP`[t] +  54.4052`MDVP:PPQ`[t] +  6.90138`MDVP:Shimmer`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231841&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]status[t] =  +  1.27129 -0.00223873`MDVP:Fo(Hz)`[t] -0.000255182`MDVP:Fhi(Hz)`[t] -0.00241088`MDVP:Flo(Hz)`[t] -85.9932`MDVP:Jitter(%)`[t] +  91.0227`MDVP:RAP`[t] +  54.4052`MDVP:PPQ`[t] +  6.90138`MDVP:Shimmer`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231841&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231841&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
status[t] = + 1.27129 -0.00223873`MDVP:Fo(Hz)`[t] -0.000255182`MDVP:Fhi(Hz)`[t] -0.00241088`MDVP:Flo(Hz)`[t] -85.9932`MDVP:Jitter(%)`[t] + 91.0227`MDVP:RAP`[t] + 54.4052`MDVP:PPQ`[t] + 6.90138`MDVP:Shimmer`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.271290.1401639.071.6334e-168.16702e-17
`MDVP:Fo(Hz)`-0.002238730.000923601-2.4240.01630640.0081532
`MDVP:Fhi(Hz)`-0.0002551820.000335213-0.76130.4474650.223732
`MDVP:Flo(Hz)`-0.002410880.000822499-2.9310.003798240.00189912
`MDVP:Jitter(%)`-85.993257.6386-1.4920.13740.0687002
`MDVP:RAP`91.022772.1491.2620.2086670.104333
`MDVP:PPQ`54.405249.25581.1050.2707760.135388
`MDVP:Shimmer`6.901382.402262.8730.004537950.00226898

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 1.27129 & 0.140163 & 9.07 & 1.6334e-16 & 8.16702e-17 \tabularnewline
`MDVP:Fo(Hz)` & -0.00223873 & 0.000923601 & -2.424 & 0.0163064 & 0.0081532 \tabularnewline
`MDVP:Fhi(Hz)` & -0.000255182 & 0.000335213 & -0.7613 & 0.447465 & 0.223732 \tabularnewline
`MDVP:Flo(Hz)` & -0.00241088 & 0.000822499 & -2.931 & 0.00379824 & 0.00189912 \tabularnewline
`MDVP:Jitter(%)` & -85.9932 & 57.6386 & -1.492 & 0.1374 & 0.0687002 \tabularnewline
`MDVP:RAP` & 91.0227 & 72.149 & 1.262 & 0.208667 & 0.104333 \tabularnewline
`MDVP:PPQ` & 54.4052 & 49.2558 & 1.105 & 0.270776 & 0.135388 \tabularnewline
`MDVP:Shimmer` & 6.90138 & 2.40226 & 2.873 & 0.00453795 & 0.00226898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231841&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]1.27129[/C][C]0.140163[/C][C]9.07[/C][C]1.6334e-16[/C][C]8.16702e-17[/C][/ROW]
[ROW][C]`MDVP:Fo(Hz)`[/C][C]-0.00223873[/C][C]0.000923601[/C][C]-2.424[/C][C]0.0163064[/C][C]0.0081532[/C][/ROW]
[ROW][C]`MDVP:Fhi(Hz)`[/C][C]-0.000255182[/C][C]0.000335213[/C][C]-0.7613[/C][C]0.447465[/C][C]0.223732[/C][/ROW]
[ROW][C]`MDVP:Flo(Hz)`[/C][C]-0.00241088[/C][C]0.000822499[/C][C]-2.931[/C][C]0.00379824[/C][C]0.00189912[/C][/ROW]
[ROW][C]`MDVP:Jitter(%)`[/C][C]-85.9932[/C][C]57.6386[/C][C]-1.492[/C][C]0.1374[/C][C]0.0687002[/C][/ROW]
[ROW][C]`MDVP:RAP`[/C][C]91.0227[/C][C]72.149[/C][C]1.262[/C][C]0.208667[/C][C]0.104333[/C][/ROW]
[ROW][C]`MDVP:PPQ`[/C][C]54.4052[/C][C]49.2558[/C][C]1.105[/C][C]0.270776[/C][C]0.135388[/C][/ROW]
[ROW][C]`MDVP:Shimmer`[/C][C]6.90138[/C][C]2.40226[/C][C]2.873[/C][C]0.00453795[/C][C]0.00226898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231841&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231841&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.271290.1401639.071.6334e-168.16702e-17
`MDVP:Fo(Hz)`-0.002238730.000923601-2.4240.01630640.0081532
`MDVP:Fhi(Hz)`-0.0002551820.000335213-0.76130.4474650.223732
`MDVP:Flo(Hz)`-0.002410880.000822499-2.9310.003798240.00189912
`MDVP:Jitter(%)`-85.993257.6386-1.4920.13740.0687002
`MDVP:RAP`91.022772.1491.2620.2086670.104333
`MDVP:PPQ`54.405249.25581.1050.2707760.135388
`MDVP:Shimmer`6.901382.402262.8730.004537950.00226898







Multiple Linear Regression - Regression Statistics
Multiple R0.541221
R-squared0.292921
Adjusted R-squared0.266452
F-TEST (value)11.0669
F-TEST (DF numerator)7
F-TEST (DF denominator)187
p-value1.11068e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.369892
Sum Squared Residuals25.5854

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.541221 \tabularnewline
R-squared & 0.292921 \tabularnewline
Adjusted R-squared & 0.266452 \tabularnewline
F-TEST (value) & 11.0669 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 187 \tabularnewline
p-value & 1.11068e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.369892 \tabularnewline
Sum Squared Residuals & 25.5854 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231841&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.541221[/C][/ROW]
[ROW][C]R-squared[/C][C]0.292921[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.266452[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.0669[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]187[/C][/ROW]
[ROW][C]p-value[/C][C]1.11068e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.369892[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]25.5854[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231841&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231841&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.541221
R-squared0.292921
Adjusted R-squared0.266452
F-TEST (value)11.0669
F-TEST (DF numerator)7
F-TEST (DF denominator)187
p-value1.11068e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.369892
Sum Squared Residuals25.5854







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.04758-0.0475792
211.07776-0.077765
311.08596-0.0859564
411.06477-0.0647655
511.13807-0.138069
611.0151-0.0151034
710.7658050.234195
810.8393160.160684
910.9200120.0799881
1010.9708450.0291547
1110.9665720.0334275
1210.9920720.00792846
1310.6330390.366961
1410.7707360.229264
1510.7691450.230855
1610.73680.2632
1710.6842220.315778
1810.7331540.266846
1911.02738-0.0273838
2010.6994470.300553
2110.900980.0990198
2210.9235030.0764966
2310.910230.0897701
2410.8464880.153512
2510.7030430.296957
2611.00432-0.00431612
2710.7524710.247529
2810.7753110.224689
2910.7649240.235076
3010.7540.246
3100.384028-0.384028
3200.368555-0.368555
3300.387859-0.387859
3400.343256-0.343256
3500.345798-0.345798
3600.364981-0.364981
3710.5552750.444725
3810.5508260.449174
3910.4785290.521471
4010.4772260.522774
4110.4682440.531756
4210.4875770.512423
4300.251595-0.251595
4400.223715-0.223715
4500.19299-0.19299
4600.204806-0.204806
4700.20083-0.20083
4800.264345-0.264345
4900.610815-0.610815
5000.631508-0.631508
5100.674252-0.674252
5200.648544-0.648544
5300.65379-0.65379
5400.660889-0.660889
5510.860640.13936
5610.8667130.133287
5710.923680.0763204
5810.7483250.251675
5910.7923760.207624
6010.7920030.207997
6100.581202-0.581202
6200.599229-0.599229
6300.320651-0.320651
6400.262508-0.262508
6500.249691-0.249691
6600.540373-0.540373
6710.9036750.0963246
6810.8973760.102624
6911.01768-0.0176808
7011.07286-0.07286
7110.9332240.0667763
7211.01712-0.0171168
7310.75170.2483
7410.7458550.254145
7510.866190.13381
7610.8575040.142496
7710.9717070.0282926
7810.8467460.153254
7910.9941210.00587883
8011.01225-0.0122484
8111.08544-0.0854404
8211.02425-0.0242547
8310.9418430.0581575
8410.9594210.0405786
8510.9387810.0612191
8610.6898890.310111
8710.7178880.282112
8810.9489540.051046
8911.08093-0.0809286
9010.733850.26615
9111.07651-0.0765066
9211.05084-0.0508395
9310.8154740.184526
9411.08208-0.0820772
9510.9425630.0574372
9610.7150980.284902
9710.7242120.275788
9810.8572410.142759
9910.968690.0313097
10011.10483-0.104832
10111.23497-0.234969
10211.17335-0.173351
10311.28449-0.284491
10410.7481480.251852
10510.6128310.387169
10610.623520.37648
10710.5757160.424284
10810.6178040.382196
10910.6121130.387887
11010.7648630.235137
11110.6933550.306645
11210.3856710.614329
11310.4611890.538811
11410.3960810.603919
11510.6581020.341898
11610.6072390.392761
11710.6293910.370609
11810.5894610.410539
11910.5007450.499255
12010.426550.57345
12110.6766120.323388
12210.6136510.386349
12310.9988670.00113251
12410.7761090.223891
12510.8259170.174083
12610.8404290.159571
12710.8864590.113541
12810.8296680.170332
12910.7011280.298872
13010.7327870.267213
13110.77640.2236
13210.7875520.212448
13310.7952170.204783
13410.7423690.257631
13511.05189-0.0518904
13610.9905280.00947239
13711.02565-0.0256548
13811.0783-0.0782992
13911.09471-0.0947142
14010.8850680.114932
14110.7219050.278095
14210.9549430.0450575
14310.6152710.384729
14410.6567660.343234
14510.4891450.510855
14610.6210580.378942
14710.9909540.00904612
14810.7913750.208625
14910.8720070.127993
15010.7020780.297922
15110.8378430.162157
15211.22908-0.229084
15310.9646750.0353246
15410.8476080.152392
15510.8686720.131328
15610.8882290.111771
15710.8278020.172198
15810.9173340.0826657
15910.8595110.140489
16010.8472390.152761
16111.01924-0.0192422
16210.9115570.0884433
16310.9735590.0264414
16410.8457610.154239
16510.9125780.0874225
16600.587988-0.587988
16700.194219-0.194219
16800.185645-0.185645
16900.704023-0.704023
17000.27718-0.27718
17100.202734-0.202734
17200.773339-0.773339
17300.812794-0.812794
17400.820916-0.820916
17500.821185-0.821185
17600.791799-0.791799
17700.80631-0.80631
17810.612360.38764
17910.6451480.354852
18010.6486050.351395
18110.6936390.306361
18210.6521690.347831
18310.6703240.329676
18400.809446-0.809446
18500.80645-0.80645
18600.796248-0.796248
18700.713712-0.713712
18800.680593-0.680593
18900.809091-0.809091
19000.735243-0.735243
19100.862786-0.862786
19200.686934-0.686934
19300.504472-0.504472
19400.6155-0.6155
19500.620575-0.620575

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 1.04758 & -0.0475792 \tabularnewline
2 & 1 & 1.07776 & -0.077765 \tabularnewline
3 & 1 & 1.08596 & -0.0859564 \tabularnewline
4 & 1 & 1.06477 & -0.0647655 \tabularnewline
5 & 1 & 1.13807 & -0.138069 \tabularnewline
6 & 1 & 1.0151 & -0.0151034 \tabularnewline
7 & 1 & 0.765805 & 0.234195 \tabularnewline
8 & 1 & 0.839316 & 0.160684 \tabularnewline
9 & 1 & 0.920012 & 0.0799881 \tabularnewline
10 & 1 & 0.970845 & 0.0291547 \tabularnewline
11 & 1 & 0.966572 & 0.0334275 \tabularnewline
12 & 1 & 0.992072 & 0.00792846 \tabularnewline
13 & 1 & 0.633039 & 0.366961 \tabularnewline
14 & 1 & 0.770736 & 0.229264 \tabularnewline
15 & 1 & 0.769145 & 0.230855 \tabularnewline
16 & 1 & 0.7368 & 0.2632 \tabularnewline
17 & 1 & 0.684222 & 0.315778 \tabularnewline
18 & 1 & 0.733154 & 0.266846 \tabularnewline
19 & 1 & 1.02738 & -0.0273838 \tabularnewline
20 & 1 & 0.699447 & 0.300553 \tabularnewline
21 & 1 & 0.90098 & 0.0990198 \tabularnewline
22 & 1 & 0.923503 & 0.0764966 \tabularnewline
23 & 1 & 0.91023 & 0.0897701 \tabularnewline
24 & 1 & 0.846488 & 0.153512 \tabularnewline
25 & 1 & 0.703043 & 0.296957 \tabularnewline
26 & 1 & 1.00432 & -0.00431612 \tabularnewline
27 & 1 & 0.752471 & 0.247529 \tabularnewline
28 & 1 & 0.775311 & 0.224689 \tabularnewline
29 & 1 & 0.764924 & 0.235076 \tabularnewline
30 & 1 & 0.754 & 0.246 \tabularnewline
31 & 0 & 0.384028 & -0.384028 \tabularnewline
32 & 0 & 0.368555 & -0.368555 \tabularnewline
33 & 0 & 0.387859 & -0.387859 \tabularnewline
34 & 0 & 0.343256 & -0.343256 \tabularnewline
35 & 0 & 0.345798 & -0.345798 \tabularnewline
36 & 0 & 0.364981 & -0.364981 \tabularnewline
37 & 1 & 0.555275 & 0.444725 \tabularnewline
38 & 1 & 0.550826 & 0.449174 \tabularnewline
39 & 1 & 0.478529 & 0.521471 \tabularnewline
40 & 1 & 0.477226 & 0.522774 \tabularnewline
41 & 1 & 0.468244 & 0.531756 \tabularnewline
42 & 1 & 0.487577 & 0.512423 \tabularnewline
43 & 0 & 0.251595 & -0.251595 \tabularnewline
44 & 0 & 0.223715 & -0.223715 \tabularnewline
45 & 0 & 0.19299 & -0.19299 \tabularnewline
46 & 0 & 0.204806 & -0.204806 \tabularnewline
47 & 0 & 0.20083 & -0.20083 \tabularnewline
48 & 0 & 0.264345 & -0.264345 \tabularnewline
49 & 0 & 0.610815 & -0.610815 \tabularnewline
50 & 0 & 0.631508 & -0.631508 \tabularnewline
51 & 0 & 0.674252 & -0.674252 \tabularnewline
52 & 0 & 0.648544 & -0.648544 \tabularnewline
53 & 0 & 0.65379 & -0.65379 \tabularnewline
54 & 0 & 0.660889 & -0.660889 \tabularnewline
55 & 1 & 0.86064 & 0.13936 \tabularnewline
56 & 1 & 0.866713 & 0.133287 \tabularnewline
57 & 1 & 0.92368 & 0.0763204 \tabularnewline
58 & 1 & 0.748325 & 0.251675 \tabularnewline
59 & 1 & 0.792376 & 0.207624 \tabularnewline
60 & 1 & 0.792003 & 0.207997 \tabularnewline
61 & 0 & 0.581202 & -0.581202 \tabularnewline
62 & 0 & 0.599229 & -0.599229 \tabularnewline
63 & 0 & 0.320651 & -0.320651 \tabularnewline
64 & 0 & 0.262508 & -0.262508 \tabularnewline
65 & 0 & 0.249691 & -0.249691 \tabularnewline
66 & 0 & 0.540373 & -0.540373 \tabularnewline
67 & 1 & 0.903675 & 0.0963246 \tabularnewline
68 & 1 & 0.897376 & 0.102624 \tabularnewline
69 & 1 & 1.01768 & -0.0176808 \tabularnewline
70 & 1 & 1.07286 & -0.07286 \tabularnewline
71 & 1 & 0.933224 & 0.0667763 \tabularnewline
72 & 1 & 1.01712 & -0.0171168 \tabularnewline
73 & 1 & 0.7517 & 0.2483 \tabularnewline
74 & 1 & 0.745855 & 0.254145 \tabularnewline
75 & 1 & 0.86619 & 0.13381 \tabularnewline
76 & 1 & 0.857504 & 0.142496 \tabularnewline
77 & 1 & 0.971707 & 0.0282926 \tabularnewline
78 & 1 & 0.846746 & 0.153254 \tabularnewline
79 & 1 & 0.994121 & 0.00587883 \tabularnewline
80 & 1 & 1.01225 & -0.0122484 \tabularnewline
81 & 1 & 1.08544 & -0.0854404 \tabularnewline
82 & 1 & 1.02425 & -0.0242547 \tabularnewline
83 & 1 & 0.941843 & 0.0581575 \tabularnewline
84 & 1 & 0.959421 & 0.0405786 \tabularnewline
85 & 1 & 0.938781 & 0.0612191 \tabularnewline
86 & 1 & 0.689889 & 0.310111 \tabularnewline
87 & 1 & 0.717888 & 0.282112 \tabularnewline
88 & 1 & 0.948954 & 0.051046 \tabularnewline
89 & 1 & 1.08093 & -0.0809286 \tabularnewline
90 & 1 & 0.73385 & 0.26615 \tabularnewline
91 & 1 & 1.07651 & -0.0765066 \tabularnewline
92 & 1 & 1.05084 & -0.0508395 \tabularnewline
93 & 1 & 0.815474 & 0.184526 \tabularnewline
94 & 1 & 1.08208 & -0.0820772 \tabularnewline
95 & 1 & 0.942563 & 0.0574372 \tabularnewline
96 & 1 & 0.715098 & 0.284902 \tabularnewline
97 & 1 & 0.724212 & 0.275788 \tabularnewline
98 & 1 & 0.857241 & 0.142759 \tabularnewline
99 & 1 & 0.96869 & 0.0313097 \tabularnewline
100 & 1 & 1.10483 & -0.104832 \tabularnewline
101 & 1 & 1.23497 & -0.234969 \tabularnewline
102 & 1 & 1.17335 & -0.173351 \tabularnewline
103 & 1 & 1.28449 & -0.284491 \tabularnewline
104 & 1 & 0.748148 & 0.251852 \tabularnewline
105 & 1 & 0.612831 & 0.387169 \tabularnewline
106 & 1 & 0.62352 & 0.37648 \tabularnewline
107 & 1 & 0.575716 & 0.424284 \tabularnewline
108 & 1 & 0.617804 & 0.382196 \tabularnewline
109 & 1 & 0.612113 & 0.387887 \tabularnewline
110 & 1 & 0.764863 & 0.235137 \tabularnewline
111 & 1 & 0.693355 & 0.306645 \tabularnewline
112 & 1 & 0.385671 & 0.614329 \tabularnewline
113 & 1 & 0.461189 & 0.538811 \tabularnewline
114 & 1 & 0.396081 & 0.603919 \tabularnewline
115 & 1 & 0.658102 & 0.341898 \tabularnewline
116 & 1 & 0.607239 & 0.392761 \tabularnewline
117 & 1 & 0.629391 & 0.370609 \tabularnewline
118 & 1 & 0.589461 & 0.410539 \tabularnewline
119 & 1 & 0.500745 & 0.499255 \tabularnewline
120 & 1 & 0.42655 & 0.57345 \tabularnewline
121 & 1 & 0.676612 & 0.323388 \tabularnewline
122 & 1 & 0.613651 & 0.386349 \tabularnewline
123 & 1 & 0.998867 & 0.00113251 \tabularnewline
124 & 1 & 0.776109 & 0.223891 \tabularnewline
125 & 1 & 0.825917 & 0.174083 \tabularnewline
126 & 1 & 0.840429 & 0.159571 \tabularnewline
127 & 1 & 0.886459 & 0.113541 \tabularnewline
128 & 1 & 0.829668 & 0.170332 \tabularnewline
129 & 1 & 0.701128 & 0.298872 \tabularnewline
130 & 1 & 0.732787 & 0.267213 \tabularnewline
131 & 1 & 0.7764 & 0.2236 \tabularnewline
132 & 1 & 0.787552 & 0.212448 \tabularnewline
133 & 1 & 0.795217 & 0.204783 \tabularnewline
134 & 1 & 0.742369 & 0.257631 \tabularnewline
135 & 1 & 1.05189 & -0.0518904 \tabularnewline
136 & 1 & 0.990528 & 0.00947239 \tabularnewline
137 & 1 & 1.02565 & -0.0256548 \tabularnewline
138 & 1 & 1.0783 & -0.0782992 \tabularnewline
139 & 1 & 1.09471 & -0.0947142 \tabularnewline
140 & 1 & 0.885068 & 0.114932 \tabularnewline
141 & 1 & 0.721905 & 0.278095 \tabularnewline
142 & 1 & 0.954943 & 0.0450575 \tabularnewline
143 & 1 & 0.615271 & 0.384729 \tabularnewline
144 & 1 & 0.656766 & 0.343234 \tabularnewline
145 & 1 & 0.489145 & 0.510855 \tabularnewline
146 & 1 & 0.621058 & 0.378942 \tabularnewline
147 & 1 & 0.990954 & 0.00904612 \tabularnewline
148 & 1 & 0.791375 & 0.208625 \tabularnewline
149 & 1 & 0.872007 & 0.127993 \tabularnewline
150 & 1 & 0.702078 & 0.297922 \tabularnewline
151 & 1 & 0.837843 & 0.162157 \tabularnewline
152 & 1 & 1.22908 & -0.229084 \tabularnewline
153 & 1 & 0.964675 & 0.0353246 \tabularnewline
154 & 1 & 0.847608 & 0.152392 \tabularnewline
155 & 1 & 0.868672 & 0.131328 \tabularnewline
156 & 1 & 0.888229 & 0.111771 \tabularnewline
157 & 1 & 0.827802 & 0.172198 \tabularnewline
158 & 1 & 0.917334 & 0.0826657 \tabularnewline
159 & 1 & 0.859511 & 0.140489 \tabularnewline
160 & 1 & 0.847239 & 0.152761 \tabularnewline
161 & 1 & 1.01924 & -0.0192422 \tabularnewline
162 & 1 & 0.911557 & 0.0884433 \tabularnewline
163 & 1 & 0.973559 & 0.0264414 \tabularnewline
164 & 1 & 0.845761 & 0.154239 \tabularnewline
165 & 1 & 0.912578 & 0.0874225 \tabularnewline
166 & 0 & 0.587988 & -0.587988 \tabularnewline
167 & 0 & 0.194219 & -0.194219 \tabularnewline
168 & 0 & 0.185645 & -0.185645 \tabularnewline
169 & 0 & 0.704023 & -0.704023 \tabularnewline
170 & 0 & 0.27718 & -0.27718 \tabularnewline
171 & 0 & 0.202734 & -0.202734 \tabularnewline
172 & 0 & 0.773339 & -0.773339 \tabularnewline
173 & 0 & 0.812794 & -0.812794 \tabularnewline
174 & 0 & 0.820916 & -0.820916 \tabularnewline
175 & 0 & 0.821185 & -0.821185 \tabularnewline
176 & 0 & 0.791799 & -0.791799 \tabularnewline
177 & 0 & 0.80631 & -0.80631 \tabularnewline
178 & 1 & 0.61236 & 0.38764 \tabularnewline
179 & 1 & 0.645148 & 0.354852 \tabularnewline
180 & 1 & 0.648605 & 0.351395 \tabularnewline
181 & 1 & 0.693639 & 0.306361 \tabularnewline
182 & 1 & 0.652169 & 0.347831 \tabularnewline
183 & 1 & 0.670324 & 0.329676 \tabularnewline
184 & 0 & 0.809446 & -0.809446 \tabularnewline
185 & 0 & 0.80645 & -0.80645 \tabularnewline
186 & 0 & 0.796248 & -0.796248 \tabularnewline
187 & 0 & 0.713712 & -0.713712 \tabularnewline
188 & 0 & 0.680593 & -0.680593 \tabularnewline
189 & 0 & 0.809091 & -0.809091 \tabularnewline
190 & 0 & 0.735243 & -0.735243 \tabularnewline
191 & 0 & 0.862786 & -0.862786 \tabularnewline
192 & 0 & 0.686934 & -0.686934 \tabularnewline
193 & 0 & 0.504472 & -0.504472 \tabularnewline
194 & 0 & 0.6155 & -0.6155 \tabularnewline
195 & 0 & 0.620575 & -0.620575 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231841&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1[/C][C]1.04758[/C][C]-0.0475792[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.07776[/C][C]-0.077765[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.08596[/C][C]-0.0859564[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]1.06477[/C][C]-0.0647655[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]1.13807[/C][C]-0.138069[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]1.0151[/C][C]-0.0151034[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]0.765805[/C][C]0.234195[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]0.839316[/C][C]0.160684[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.920012[/C][C]0.0799881[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]0.970845[/C][C]0.0291547[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.966572[/C][C]0.0334275[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]0.992072[/C][C]0.00792846[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]0.633039[/C][C]0.366961[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]0.770736[/C][C]0.229264[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.769145[/C][C]0.230855[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.7368[/C][C]0.2632[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.684222[/C][C]0.315778[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.733154[/C][C]0.266846[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.02738[/C][C]-0.0273838[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.699447[/C][C]0.300553[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]0.90098[/C][C]0.0990198[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.923503[/C][C]0.0764966[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]0.91023[/C][C]0.0897701[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]0.846488[/C][C]0.153512[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.703043[/C][C]0.296957[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]1.00432[/C][C]-0.00431612[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.752471[/C][C]0.247529[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]0.775311[/C][C]0.224689[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]0.764924[/C][C]0.235076[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]0.754[/C][C]0.246[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.384028[/C][C]-0.384028[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.368555[/C][C]-0.368555[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.387859[/C][C]-0.387859[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.343256[/C][C]-0.343256[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.345798[/C][C]-0.345798[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.364981[/C][C]-0.364981[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.555275[/C][C]0.444725[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]0.550826[/C][C]0.449174[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]0.478529[/C][C]0.521471[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]0.477226[/C][C]0.522774[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.468244[/C][C]0.531756[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]0.487577[/C][C]0.512423[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.251595[/C][C]-0.251595[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.223715[/C][C]-0.223715[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.19299[/C][C]-0.19299[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.204806[/C][C]-0.204806[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.20083[/C][C]-0.20083[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.264345[/C][C]-0.264345[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.610815[/C][C]-0.610815[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.631508[/C][C]-0.631508[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.674252[/C][C]-0.674252[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.648544[/C][C]-0.648544[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.65379[/C][C]-0.65379[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.660889[/C][C]-0.660889[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]0.86064[/C][C]0.13936[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.866713[/C][C]0.133287[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0.92368[/C][C]0.0763204[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0.748325[/C][C]0.251675[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.792376[/C][C]0.207624[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.792003[/C][C]0.207997[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.581202[/C][C]-0.581202[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.599229[/C][C]-0.599229[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.320651[/C][C]-0.320651[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.262508[/C][C]-0.262508[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.249691[/C][C]-0.249691[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.540373[/C][C]-0.540373[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.903675[/C][C]0.0963246[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]0.897376[/C][C]0.102624[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]1.01768[/C][C]-0.0176808[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.07286[/C][C]-0.07286[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]0.933224[/C][C]0.0667763[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]1.01712[/C][C]-0.0171168[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]0.7517[/C][C]0.2483[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0.745855[/C][C]0.254145[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.86619[/C][C]0.13381[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.857504[/C][C]0.142496[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.971707[/C][C]0.0282926[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0.846746[/C][C]0.153254[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.994121[/C][C]0.00587883[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.01225[/C][C]-0.0122484[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.08544[/C][C]-0.0854404[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]1.02425[/C][C]-0.0242547[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0.941843[/C][C]0.0581575[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.959421[/C][C]0.0405786[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0.938781[/C][C]0.0612191[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0.689889[/C][C]0.310111[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0.717888[/C][C]0.282112[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0.948954[/C][C]0.051046[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]1.08093[/C][C]-0.0809286[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0.73385[/C][C]0.26615[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]1.07651[/C][C]-0.0765066[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]1.05084[/C][C]-0.0508395[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0.815474[/C][C]0.184526[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]1.08208[/C][C]-0.0820772[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0.942563[/C][C]0.0574372[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0.715098[/C][C]0.284902[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0.724212[/C][C]0.275788[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0.857241[/C][C]0.142759[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0.96869[/C][C]0.0313097[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]1.10483[/C][C]-0.104832[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]1.23497[/C][C]-0.234969[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.17335[/C][C]-0.173351[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]1.28449[/C][C]-0.284491[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0.748148[/C][C]0.251852[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0.612831[/C][C]0.387169[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0.62352[/C][C]0.37648[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0.575716[/C][C]0.424284[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0.617804[/C][C]0.382196[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0.612113[/C][C]0.387887[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0.764863[/C][C]0.235137[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0.693355[/C][C]0.306645[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0.385671[/C][C]0.614329[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0.461189[/C][C]0.538811[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0.396081[/C][C]0.603919[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0.658102[/C][C]0.341898[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0.607239[/C][C]0.392761[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0.629391[/C][C]0.370609[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0.589461[/C][C]0.410539[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0.500745[/C][C]0.499255[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0.42655[/C][C]0.57345[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0.676612[/C][C]0.323388[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0.613651[/C][C]0.386349[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0.998867[/C][C]0.00113251[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0.776109[/C][C]0.223891[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0.825917[/C][C]0.174083[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]0.840429[/C][C]0.159571[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]0.886459[/C][C]0.113541[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0.829668[/C][C]0.170332[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]0.701128[/C][C]0.298872[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0.732787[/C][C]0.267213[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]0.7764[/C][C]0.2236[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0.787552[/C][C]0.212448[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]0.795217[/C][C]0.204783[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]0.742369[/C][C]0.257631[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]1.05189[/C][C]-0.0518904[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]0.990528[/C][C]0.00947239[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.02565[/C][C]-0.0256548[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]1.0783[/C][C]-0.0782992[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]1.09471[/C][C]-0.0947142[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]0.885068[/C][C]0.114932[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.721905[/C][C]0.278095[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]0.954943[/C][C]0.0450575[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]0.615271[/C][C]0.384729[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]0.656766[/C][C]0.343234[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]0.489145[/C][C]0.510855[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]0.621058[/C][C]0.378942[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]0.990954[/C][C]0.00904612[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]0.791375[/C][C]0.208625[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]0.872007[/C][C]0.127993[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]0.702078[/C][C]0.297922[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]0.837843[/C][C]0.162157[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]1.22908[/C][C]-0.229084[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.964675[/C][C]0.0353246[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]0.847608[/C][C]0.152392[/C][/ROW]
[ROW][C]155[/C][C]1[/C][C]0.868672[/C][C]0.131328[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]0.888229[/C][C]0.111771[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]0.827802[/C][C]0.172198[/C][/ROW]
[ROW][C]158[/C][C]1[/C][C]0.917334[/C][C]0.0826657[/C][/ROW]
[ROW][C]159[/C][C]1[/C][C]0.859511[/C][C]0.140489[/C][/ROW]
[ROW][C]160[/C][C]1[/C][C]0.847239[/C][C]0.152761[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]1.01924[/C][C]-0.0192422[/C][/ROW]
[ROW][C]162[/C][C]1[/C][C]0.911557[/C][C]0.0884433[/C][/ROW]
[ROW][C]163[/C][C]1[/C][C]0.973559[/C][C]0.0264414[/C][/ROW]
[ROW][C]164[/C][C]1[/C][C]0.845761[/C][C]0.154239[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]0.912578[/C][C]0.0874225[/C][/ROW]
[ROW][C]166[/C][C]0[/C][C]0.587988[/C][C]-0.587988[/C][/ROW]
[ROW][C]167[/C][C]0[/C][C]0.194219[/C][C]-0.194219[/C][/ROW]
[ROW][C]168[/C][C]0[/C][C]0.185645[/C][C]-0.185645[/C][/ROW]
[ROW][C]169[/C][C]0[/C][C]0.704023[/C][C]-0.704023[/C][/ROW]
[ROW][C]170[/C][C]0[/C][C]0.27718[/C][C]-0.27718[/C][/ROW]
[ROW][C]171[/C][C]0[/C][C]0.202734[/C][C]-0.202734[/C][/ROW]
[ROW][C]172[/C][C]0[/C][C]0.773339[/C][C]-0.773339[/C][/ROW]
[ROW][C]173[/C][C]0[/C][C]0.812794[/C][C]-0.812794[/C][/ROW]
[ROW][C]174[/C][C]0[/C][C]0.820916[/C][C]-0.820916[/C][/ROW]
[ROW][C]175[/C][C]0[/C][C]0.821185[/C][C]-0.821185[/C][/ROW]
[ROW][C]176[/C][C]0[/C][C]0.791799[/C][C]-0.791799[/C][/ROW]
[ROW][C]177[/C][C]0[/C][C]0.80631[/C][C]-0.80631[/C][/ROW]
[ROW][C]178[/C][C]1[/C][C]0.61236[/C][C]0.38764[/C][/ROW]
[ROW][C]179[/C][C]1[/C][C]0.645148[/C][C]0.354852[/C][/ROW]
[ROW][C]180[/C][C]1[/C][C]0.648605[/C][C]0.351395[/C][/ROW]
[ROW][C]181[/C][C]1[/C][C]0.693639[/C][C]0.306361[/C][/ROW]
[ROW][C]182[/C][C]1[/C][C]0.652169[/C][C]0.347831[/C][/ROW]
[ROW][C]183[/C][C]1[/C][C]0.670324[/C][C]0.329676[/C][/ROW]
[ROW][C]184[/C][C]0[/C][C]0.809446[/C][C]-0.809446[/C][/ROW]
[ROW][C]185[/C][C]0[/C][C]0.80645[/C][C]-0.80645[/C][/ROW]
[ROW][C]186[/C][C]0[/C][C]0.796248[/C][C]-0.796248[/C][/ROW]
[ROW][C]187[/C][C]0[/C][C]0.713712[/C][C]-0.713712[/C][/ROW]
[ROW][C]188[/C][C]0[/C][C]0.680593[/C][C]-0.680593[/C][/ROW]
[ROW][C]189[/C][C]0[/C][C]0.809091[/C][C]-0.809091[/C][/ROW]
[ROW][C]190[/C][C]0[/C][C]0.735243[/C][C]-0.735243[/C][/ROW]
[ROW][C]191[/C][C]0[/C][C]0.862786[/C][C]-0.862786[/C][/ROW]
[ROW][C]192[/C][C]0[/C][C]0.686934[/C][C]-0.686934[/C][/ROW]
[ROW][C]193[/C][C]0[/C][C]0.504472[/C][C]-0.504472[/C][/ROW]
[ROW][C]194[/C][C]0[/C][C]0.6155[/C][C]-0.6155[/C][/ROW]
[ROW][C]195[/C][C]0[/C][C]0.620575[/C][C]-0.620575[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231841&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231841&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.04758-0.0475792
211.07776-0.077765
311.08596-0.0859564
411.06477-0.0647655
511.13807-0.138069
611.0151-0.0151034
710.7658050.234195
810.8393160.160684
910.9200120.0799881
1010.9708450.0291547
1110.9665720.0334275
1210.9920720.00792846
1310.6330390.366961
1410.7707360.229264
1510.7691450.230855
1610.73680.2632
1710.6842220.315778
1810.7331540.266846
1911.02738-0.0273838
2010.6994470.300553
2110.900980.0990198
2210.9235030.0764966
2310.910230.0897701
2410.8464880.153512
2510.7030430.296957
2611.00432-0.00431612
2710.7524710.247529
2810.7753110.224689
2910.7649240.235076
3010.7540.246
3100.384028-0.384028
3200.368555-0.368555
3300.387859-0.387859
3400.343256-0.343256
3500.345798-0.345798
3600.364981-0.364981
3710.5552750.444725
3810.5508260.449174
3910.4785290.521471
4010.4772260.522774
4110.4682440.531756
4210.4875770.512423
4300.251595-0.251595
4400.223715-0.223715
4500.19299-0.19299
4600.204806-0.204806
4700.20083-0.20083
4800.264345-0.264345
4900.610815-0.610815
5000.631508-0.631508
5100.674252-0.674252
5200.648544-0.648544
5300.65379-0.65379
5400.660889-0.660889
5510.860640.13936
5610.8667130.133287
5710.923680.0763204
5810.7483250.251675
5910.7923760.207624
6010.7920030.207997
6100.581202-0.581202
6200.599229-0.599229
6300.320651-0.320651
6400.262508-0.262508
6500.249691-0.249691
6600.540373-0.540373
6710.9036750.0963246
6810.8973760.102624
6911.01768-0.0176808
7011.07286-0.07286
7110.9332240.0667763
7211.01712-0.0171168
7310.75170.2483
7410.7458550.254145
7510.866190.13381
7610.8575040.142496
7710.9717070.0282926
7810.8467460.153254
7910.9941210.00587883
8011.01225-0.0122484
8111.08544-0.0854404
8211.02425-0.0242547
8310.9418430.0581575
8410.9594210.0405786
8510.9387810.0612191
8610.6898890.310111
8710.7178880.282112
8810.9489540.051046
8911.08093-0.0809286
9010.733850.26615
9111.07651-0.0765066
9211.05084-0.0508395
9310.8154740.184526
9411.08208-0.0820772
9510.9425630.0574372
9610.7150980.284902
9710.7242120.275788
9810.8572410.142759
9910.968690.0313097
10011.10483-0.104832
10111.23497-0.234969
10211.17335-0.173351
10311.28449-0.284491
10410.7481480.251852
10510.6128310.387169
10610.623520.37648
10710.5757160.424284
10810.6178040.382196
10910.6121130.387887
11010.7648630.235137
11110.6933550.306645
11210.3856710.614329
11310.4611890.538811
11410.3960810.603919
11510.6581020.341898
11610.6072390.392761
11710.6293910.370609
11810.5894610.410539
11910.5007450.499255
12010.426550.57345
12110.6766120.323388
12210.6136510.386349
12310.9988670.00113251
12410.7761090.223891
12510.8259170.174083
12610.8404290.159571
12710.8864590.113541
12810.8296680.170332
12910.7011280.298872
13010.7327870.267213
13110.77640.2236
13210.7875520.212448
13310.7952170.204783
13410.7423690.257631
13511.05189-0.0518904
13610.9905280.00947239
13711.02565-0.0256548
13811.0783-0.0782992
13911.09471-0.0947142
14010.8850680.114932
14110.7219050.278095
14210.9549430.0450575
14310.6152710.384729
14410.6567660.343234
14510.4891450.510855
14610.6210580.378942
14710.9909540.00904612
14810.7913750.208625
14910.8720070.127993
15010.7020780.297922
15110.8378430.162157
15211.22908-0.229084
15310.9646750.0353246
15410.8476080.152392
15510.8686720.131328
15610.8882290.111771
15710.8278020.172198
15810.9173340.0826657
15910.8595110.140489
16010.8472390.152761
16111.01924-0.0192422
16210.9115570.0884433
16310.9735590.0264414
16410.8457610.154239
16510.9125780.0874225
16600.587988-0.587988
16700.194219-0.194219
16800.185645-0.185645
16900.704023-0.704023
17000.27718-0.27718
17100.202734-0.202734
17200.773339-0.773339
17300.812794-0.812794
17400.820916-0.820916
17500.821185-0.821185
17600.791799-0.791799
17700.80631-0.80631
17810.612360.38764
17910.6451480.354852
18010.6486050.351395
18110.6936390.306361
18210.6521690.347831
18310.6703240.329676
18400.809446-0.809446
18500.80645-0.80645
18600.796248-0.796248
18700.713712-0.713712
18800.680593-0.680593
18900.809091-0.809091
19000.735243-0.735243
19100.862786-0.862786
19200.686934-0.686934
19300.504472-0.504472
19400.6155-0.6155
19500.620575-0.620575







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
111.31756e-542.63511e-541
121.50074e-663.00148e-661
131.95601e-933.91202e-931
141.52177e-923.04354e-921
155.01206e-1081.00241e-1071
16001
171.33047e-1482.66093e-1481
186.78118e-1551.35624e-1541
193.16834e-1696.33668e-1691
206.66198e-1931.3324e-1921
211.40041e-2252.80082e-2251
221.04064e-2172.08129e-2171
231.71687e-2293.43373e-2291
247.85121e-2481.57024e-2471
251.59875e-2663.19751e-2661
269.72252e-3081.9445e-3071
271.92285e-2963.8457e-2961
284.25369e-3078.50737e-3071
29001
30001
315.92628e-091.18526e-081
321.14697e-082.29395e-081
337.28136e-091.45627e-081
343.00812e-096.01625e-091
351.1308e-092.2616e-091
364.32827e-108.65654e-101
378.22123e-071.64425e-060.999999
381.57777e-053.15554e-050.999984
390.0001825880.0003651760.999817
400.0007811030.001562210.999219
410.002213280.004426560.997787
420.003950.00790.99605
430.002967540.005935070.997032
440.002035650.00407130.997964
450.001326960.002653920.998673
460.0008702960.001740590.99913
470.0005583370.001116670.999442
480.0003942080.0007884150.999606
490.002486610.004973220.997513
500.003545130.007090250.996455
510.005041860.01008370.994958
520.005385920.01077180.994614
530.00626190.01252380.993738
540.008096540.01619310.991903
550.006851690.01370340.993148
560.006388440.01277690.993612
570.005241460.01048290.994759
580.00771210.01542420.992288
590.007294430.01458890.992706
600.005257430.01051490.994743
610.01798180.03596360.982018
620.04168120.08336230.958319
630.03890480.07780970.961095
640.03505110.07010210.964949
650.03144960.06289920.96855
660.04326180.08652350.956738
670.03483470.06966940.965165
680.02954010.05908030.97046
690.02398840.04797680.976012
700.01873350.03746690.981267
710.01444780.02889560.985552
720.01091020.02182030.98909
730.009251410.01850280.990749
740.01382410.02764830.986176
750.01052250.02104490.989478
760.007970790.01594160.992029
770.005854540.01170910.994145
780.004393570.008787150.995606
790.003266510.006533020.996733
800.003438960.006877920.996561
810.002713250.00542650.997287
820.002145240.004290480.997855
830.001618270.003236540.998382
840.001212990.002425990.998787
850.0008577570.001715510.999142
860.001037150.002074310.998963
870.00107760.002155190.998922
880.0007812780.001562560.999219
890.0005374590.001074920.999463
900.0004623960.0009247920.999538
910.0003157280.0006314550.999684
920.0002430790.0004861580.999757
930.0001861850.0003723710.999814
940.0001249710.0002499420.999875
958.32443e-050.0001664890.999917
967.61697e-050.0001523390.999924
976.78186e-050.0001356370.999932
984.5336e-059.06721e-050.999955
992.91296e-055.82593e-050.999971
1002.04084e-054.08167e-050.99998
1011.78016e-053.56033e-050.999982
1021.40312e-052.80624e-050.999986
1031.84462e-053.68925e-050.999982
1041.46978e-052.93956e-050.999985
1051.50295e-053.00591e-050.999985
1061.5011e-053.00221e-050.999985
1071.68059e-053.36119e-050.999983
1081.70425e-053.40849e-050.999983
1091.71751e-053.43503e-050.999983
1101.35897e-052.71795e-050.999986
1111.25195e-052.50391e-050.999987
1123.46463e-056.92926e-050.999965
1136.71224e-050.0001342450.999933
1140.0001498930.0002997860.99985
1150.0001497750.000299550.99985
1160.0001493560.0002987130.999851
1170.0001453230.0002906460.999855
1180.0001617330.0003234660.999838
1190.000239730.000479460.99976
1200.0005114630.001022930.999489
1210.000721690.001443380.999278
1220.0009776480.00195530.999022
1230.0006949290.001389860.999305
1240.0006535980.00130720.999346
1250.0006422330.001284470.999358
1260.0006585040.001317010.999341
1270.0006008150.001201630.999399
1280.0005969360.001193870.999403
1290.0005849710.001169940.999415
1300.0006041660.001208330.999396
1310.0005900360.001180070.99941
1320.0005715310.001143060.999428
1330.0006549340.001309870.999345
1340.0008148980.00162980.999185
1350.0005794450.001158890.999421
1360.0003920520.0007841040.999608
1370.0002655210.0005310420.999734
1380.0001879030.0003758050.999812
1390.0001604950.000320990.99984
1400.000111570.0002231390.999888
1410.0001155140.0002310290.999884
1427.97951e-050.000159590.99992
1438.34482e-050.0001668960.999917
1440.0002690970.0005381940.999731
1450.0004987780.0009975550.999501
1460.002841840.005683680.997158
1470.002360320.004720640.99764
1480.001851690.003703390.998148
1490.001337670.002675340.998662
1500.001215390.002430780.998785
1510.001322390.002644780.998678
1520.001279090.002558180.998721
1530.0008556020.00171120.999144
1540.0007526550.001505310.999247
1550.0006240440.001248090.999376
1560.0004587770.0009175550.999541
1570.0005423010.00108460.999458
1580.0008563410.001712680.999144
1590.0005423030.001084610.999458
1600.0003829080.0007658160.999617
1610.0002523640.0005047280.999748
1620.0001605910.0003211830.999839
1630.0001039080.0002078160.999896
1640.0001889070.0003778140.999811
1650.0003516430.0007032860.999648
1660.0004170520.0008341040.999583
1670.0003804910.0007609810.99962
1680.0006749510.00134990.999325
1690.0017250.003450010.998275
1700.002657390.005314790.997343
1710.9996310.0007375860.000368793
1720.9996980.0006043530.000302177
1730.9994960.001008030.000504015
1740.999170.001660510.000830254
1750.9984980.003004820.00150241
1760.998930.002139530.00106976
1770.9995030.0009945750.000497288
1780.9993540.001292130.000646066
1790.9981570.003685180.00184259
1800.9959860.008027410.0040137
1810.9896810.02063780.0103189
1820.9755590.04888160.0244408
183100
184100

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 1.31756e-54 & 2.63511e-54 & 1 \tabularnewline
12 & 1.50074e-66 & 3.00148e-66 & 1 \tabularnewline
13 & 1.95601e-93 & 3.91202e-93 & 1 \tabularnewline
14 & 1.52177e-92 & 3.04354e-92 & 1 \tabularnewline
15 & 5.01206e-108 & 1.00241e-107 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 1.33047e-148 & 2.66093e-148 & 1 \tabularnewline
18 & 6.78118e-155 & 1.35624e-154 & 1 \tabularnewline
19 & 3.16834e-169 & 6.33668e-169 & 1 \tabularnewline
20 & 6.66198e-193 & 1.3324e-192 & 1 \tabularnewline
21 & 1.40041e-225 & 2.80082e-225 & 1 \tabularnewline
22 & 1.04064e-217 & 2.08129e-217 & 1 \tabularnewline
23 & 1.71687e-229 & 3.43373e-229 & 1 \tabularnewline
24 & 7.85121e-248 & 1.57024e-247 & 1 \tabularnewline
25 & 1.59875e-266 & 3.19751e-266 & 1 \tabularnewline
26 & 9.72252e-308 & 1.9445e-307 & 1 \tabularnewline
27 & 1.92285e-296 & 3.8457e-296 & 1 \tabularnewline
28 & 4.25369e-307 & 8.50737e-307 & 1 \tabularnewline
29 & 0 & 0 & 1 \tabularnewline
30 & 0 & 0 & 1 \tabularnewline
31 & 5.92628e-09 & 1.18526e-08 & 1 \tabularnewline
32 & 1.14697e-08 & 2.29395e-08 & 1 \tabularnewline
33 & 7.28136e-09 & 1.45627e-08 & 1 \tabularnewline
34 & 3.00812e-09 & 6.01625e-09 & 1 \tabularnewline
35 & 1.1308e-09 & 2.2616e-09 & 1 \tabularnewline
36 & 4.32827e-10 & 8.65654e-10 & 1 \tabularnewline
37 & 8.22123e-07 & 1.64425e-06 & 0.999999 \tabularnewline
38 & 1.57777e-05 & 3.15554e-05 & 0.999984 \tabularnewline
39 & 0.000182588 & 0.000365176 & 0.999817 \tabularnewline
40 & 0.000781103 & 0.00156221 & 0.999219 \tabularnewline
41 & 0.00221328 & 0.00442656 & 0.997787 \tabularnewline
42 & 0.00395 & 0.0079 & 0.99605 \tabularnewline
43 & 0.00296754 & 0.00593507 & 0.997032 \tabularnewline
44 & 0.00203565 & 0.0040713 & 0.997964 \tabularnewline
45 & 0.00132696 & 0.00265392 & 0.998673 \tabularnewline
46 & 0.000870296 & 0.00174059 & 0.99913 \tabularnewline
47 & 0.000558337 & 0.00111667 & 0.999442 \tabularnewline
48 & 0.000394208 & 0.000788415 & 0.999606 \tabularnewline
49 & 0.00248661 & 0.00497322 & 0.997513 \tabularnewline
50 & 0.00354513 & 0.00709025 & 0.996455 \tabularnewline
51 & 0.00504186 & 0.0100837 & 0.994958 \tabularnewline
52 & 0.00538592 & 0.0107718 & 0.994614 \tabularnewline
53 & 0.0062619 & 0.0125238 & 0.993738 \tabularnewline
54 & 0.00809654 & 0.0161931 & 0.991903 \tabularnewline
55 & 0.00685169 & 0.0137034 & 0.993148 \tabularnewline
56 & 0.00638844 & 0.0127769 & 0.993612 \tabularnewline
57 & 0.00524146 & 0.0104829 & 0.994759 \tabularnewline
58 & 0.0077121 & 0.0154242 & 0.992288 \tabularnewline
59 & 0.00729443 & 0.0145889 & 0.992706 \tabularnewline
60 & 0.00525743 & 0.0105149 & 0.994743 \tabularnewline
61 & 0.0179818 & 0.0359636 & 0.982018 \tabularnewline
62 & 0.0416812 & 0.0833623 & 0.958319 \tabularnewline
63 & 0.0389048 & 0.0778097 & 0.961095 \tabularnewline
64 & 0.0350511 & 0.0701021 & 0.964949 \tabularnewline
65 & 0.0314496 & 0.0628992 & 0.96855 \tabularnewline
66 & 0.0432618 & 0.0865235 & 0.956738 \tabularnewline
67 & 0.0348347 & 0.0696694 & 0.965165 \tabularnewline
68 & 0.0295401 & 0.0590803 & 0.97046 \tabularnewline
69 & 0.0239884 & 0.0479768 & 0.976012 \tabularnewline
70 & 0.0187335 & 0.0374669 & 0.981267 \tabularnewline
71 & 0.0144478 & 0.0288956 & 0.985552 \tabularnewline
72 & 0.0109102 & 0.0218203 & 0.98909 \tabularnewline
73 & 0.00925141 & 0.0185028 & 0.990749 \tabularnewline
74 & 0.0138241 & 0.0276483 & 0.986176 \tabularnewline
75 & 0.0105225 & 0.0210449 & 0.989478 \tabularnewline
76 & 0.00797079 & 0.0159416 & 0.992029 \tabularnewline
77 & 0.00585454 & 0.0117091 & 0.994145 \tabularnewline
78 & 0.00439357 & 0.00878715 & 0.995606 \tabularnewline
79 & 0.00326651 & 0.00653302 & 0.996733 \tabularnewline
80 & 0.00343896 & 0.00687792 & 0.996561 \tabularnewline
81 & 0.00271325 & 0.0054265 & 0.997287 \tabularnewline
82 & 0.00214524 & 0.00429048 & 0.997855 \tabularnewline
83 & 0.00161827 & 0.00323654 & 0.998382 \tabularnewline
84 & 0.00121299 & 0.00242599 & 0.998787 \tabularnewline
85 & 0.000857757 & 0.00171551 & 0.999142 \tabularnewline
86 & 0.00103715 & 0.00207431 & 0.998963 \tabularnewline
87 & 0.0010776 & 0.00215519 & 0.998922 \tabularnewline
88 & 0.000781278 & 0.00156256 & 0.999219 \tabularnewline
89 & 0.000537459 & 0.00107492 & 0.999463 \tabularnewline
90 & 0.000462396 & 0.000924792 & 0.999538 \tabularnewline
91 & 0.000315728 & 0.000631455 & 0.999684 \tabularnewline
92 & 0.000243079 & 0.000486158 & 0.999757 \tabularnewline
93 & 0.000186185 & 0.000372371 & 0.999814 \tabularnewline
94 & 0.000124971 & 0.000249942 & 0.999875 \tabularnewline
95 & 8.32443e-05 & 0.000166489 & 0.999917 \tabularnewline
96 & 7.61697e-05 & 0.000152339 & 0.999924 \tabularnewline
97 & 6.78186e-05 & 0.000135637 & 0.999932 \tabularnewline
98 & 4.5336e-05 & 9.06721e-05 & 0.999955 \tabularnewline
99 & 2.91296e-05 & 5.82593e-05 & 0.999971 \tabularnewline
100 & 2.04084e-05 & 4.08167e-05 & 0.99998 \tabularnewline
101 & 1.78016e-05 & 3.56033e-05 & 0.999982 \tabularnewline
102 & 1.40312e-05 & 2.80624e-05 & 0.999986 \tabularnewline
103 & 1.84462e-05 & 3.68925e-05 & 0.999982 \tabularnewline
104 & 1.46978e-05 & 2.93956e-05 & 0.999985 \tabularnewline
105 & 1.50295e-05 & 3.00591e-05 & 0.999985 \tabularnewline
106 & 1.5011e-05 & 3.00221e-05 & 0.999985 \tabularnewline
107 & 1.68059e-05 & 3.36119e-05 & 0.999983 \tabularnewline
108 & 1.70425e-05 & 3.40849e-05 & 0.999983 \tabularnewline
109 & 1.71751e-05 & 3.43503e-05 & 0.999983 \tabularnewline
110 & 1.35897e-05 & 2.71795e-05 & 0.999986 \tabularnewline
111 & 1.25195e-05 & 2.50391e-05 & 0.999987 \tabularnewline
112 & 3.46463e-05 & 6.92926e-05 & 0.999965 \tabularnewline
113 & 6.71224e-05 & 0.000134245 & 0.999933 \tabularnewline
114 & 0.000149893 & 0.000299786 & 0.99985 \tabularnewline
115 & 0.000149775 & 0.00029955 & 0.99985 \tabularnewline
116 & 0.000149356 & 0.000298713 & 0.999851 \tabularnewline
117 & 0.000145323 & 0.000290646 & 0.999855 \tabularnewline
118 & 0.000161733 & 0.000323466 & 0.999838 \tabularnewline
119 & 0.00023973 & 0.00047946 & 0.99976 \tabularnewline
120 & 0.000511463 & 0.00102293 & 0.999489 \tabularnewline
121 & 0.00072169 & 0.00144338 & 0.999278 \tabularnewline
122 & 0.000977648 & 0.0019553 & 0.999022 \tabularnewline
123 & 0.000694929 & 0.00138986 & 0.999305 \tabularnewline
124 & 0.000653598 & 0.0013072 & 0.999346 \tabularnewline
125 & 0.000642233 & 0.00128447 & 0.999358 \tabularnewline
126 & 0.000658504 & 0.00131701 & 0.999341 \tabularnewline
127 & 0.000600815 & 0.00120163 & 0.999399 \tabularnewline
128 & 0.000596936 & 0.00119387 & 0.999403 \tabularnewline
129 & 0.000584971 & 0.00116994 & 0.999415 \tabularnewline
130 & 0.000604166 & 0.00120833 & 0.999396 \tabularnewline
131 & 0.000590036 & 0.00118007 & 0.99941 \tabularnewline
132 & 0.000571531 & 0.00114306 & 0.999428 \tabularnewline
133 & 0.000654934 & 0.00130987 & 0.999345 \tabularnewline
134 & 0.000814898 & 0.0016298 & 0.999185 \tabularnewline
135 & 0.000579445 & 0.00115889 & 0.999421 \tabularnewline
136 & 0.000392052 & 0.000784104 & 0.999608 \tabularnewline
137 & 0.000265521 & 0.000531042 & 0.999734 \tabularnewline
138 & 0.000187903 & 0.000375805 & 0.999812 \tabularnewline
139 & 0.000160495 & 0.00032099 & 0.99984 \tabularnewline
140 & 0.00011157 & 0.000223139 & 0.999888 \tabularnewline
141 & 0.000115514 & 0.000231029 & 0.999884 \tabularnewline
142 & 7.97951e-05 & 0.00015959 & 0.99992 \tabularnewline
143 & 8.34482e-05 & 0.000166896 & 0.999917 \tabularnewline
144 & 0.000269097 & 0.000538194 & 0.999731 \tabularnewline
145 & 0.000498778 & 0.000997555 & 0.999501 \tabularnewline
146 & 0.00284184 & 0.00568368 & 0.997158 \tabularnewline
147 & 0.00236032 & 0.00472064 & 0.99764 \tabularnewline
148 & 0.00185169 & 0.00370339 & 0.998148 \tabularnewline
149 & 0.00133767 & 0.00267534 & 0.998662 \tabularnewline
150 & 0.00121539 & 0.00243078 & 0.998785 \tabularnewline
151 & 0.00132239 & 0.00264478 & 0.998678 \tabularnewline
152 & 0.00127909 & 0.00255818 & 0.998721 \tabularnewline
153 & 0.000855602 & 0.0017112 & 0.999144 \tabularnewline
154 & 0.000752655 & 0.00150531 & 0.999247 \tabularnewline
155 & 0.000624044 & 0.00124809 & 0.999376 \tabularnewline
156 & 0.000458777 & 0.000917555 & 0.999541 \tabularnewline
157 & 0.000542301 & 0.0010846 & 0.999458 \tabularnewline
158 & 0.000856341 & 0.00171268 & 0.999144 \tabularnewline
159 & 0.000542303 & 0.00108461 & 0.999458 \tabularnewline
160 & 0.000382908 & 0.000765816 & 0.999617 \tabularnewline
161 & 0.000252364 & 0.000504728 & 0.999748 \tabularnewline
162 & 0.000160591 & 0.000321183 & 0.999839 \tabularnewline
163 & 0.000103908 & 0.000207816 & 0.999896 \tabularnewline
164 & 0.000188907 & 0.000377814 & 0.999811 \tabularnewline
165 & 0.000351643 & 0.000703286 & 0.999648 \tabularnewline
166 & 0.000417052 & 0.000834104 & 0.999583 \tabularnewline
167 & 0.000380491 & 0.000760981 & 0.99962 \tabularnewline
168 & 0.000674951 & 0.0013499 & 0.999325 \tabularnewline
169 & 0.001725 & 0.00345001 & 0.998275 \tabularnewline
170 & 0.00265739 & 0.00531479 & 0.997343 \tabularnewline
171 & 0.999631 & 0.000737586 & 0.000368793 \tabularnewline
172 & 0.999698 & 0.000604353 & 0.000302177 \tabularnewline
173 & 0.999496 & 0.00100803 & 0.000504015 \tabularnewline
174 & 0.99917 & 0.00166051 & 0.000830254 \tabularnewline
175 & 0.998498 & 0.00300482 & 0.00150241 \tabularnewline
176 & 0.99893 & 0.00213953 & 0.00106976 \tabularnewline
177 & 0.999503 & 0.000994575 & 0.000497288 \tabularnewline
178 & 0.999354 & 0.00129213 & 0.000646066 \tabularnewline
179 & 0.998157 & 0.00368518 & 0.00184259 \tabularnewline
180 & 0.995986 & 0.00802741 & 0.0040137 \tabularnewline
181 & 0.989681 & 0.0206378 & 0.0103189 \tabularnewline
182 & 0.975559 & 0.0488816 & 0.0244408 \tabularnewline
183 & 1 & 0 & 0 \tabularnewline
184 & 1 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231841&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]1.31756e-54[/C][C]2.63511e-54[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]1.50074e-66[/C][C]3.00148e-66[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]1.95601e-93[/C][C]3.91202e-93[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]1.52177e-92[/C][C]3.04354e-92[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]5.01206e-108[/C][C]1.00241e-107[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]1.33047e-148[/C][C]2.66093e-148[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]6.78118e-155[/C][C]1.35624e-154[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]3.16834e-169[/C][C]6.33668e-169[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]6.66198e-193[/C][C]1.3324e-192[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]1.40041e-225[/C][C]2.80082e-225[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]1.04064e-217[/C][C]2.08129e-217[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]1.71687e-229[/C][C]3.43373e-229[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]7.85121e-248[/C][C]1.57024e-247[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]1.59875e-266[/C][C]3.19751e-266[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]9.72252e-308[/C][C]1.9445e-307[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]1.92285e-296[/C][C]3.8457e-296[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]4.25369e-307[/C][C]8.50737e-307[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]5.92628e-09[/C][C]1.18526e-08[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]1.14697e-08[/C][C]2.29395e-08[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]7.28136e-09[/C][C]1.45627e-08[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]3.00812e-09[/C][C]6.01625e-09[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]1.1308e-09[/C][C]2.2616e-09[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]4.32827e-10[/C][C]8.65654e-10[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]8.22123e-07[/C][C]1.64425e-06[/C][C]0.999999[/C][/ROW]
[ROW][C]38[/C][C]1.57777e-05[/C][C]3.15554e-05[/C][C]0.999984[/C][/ROW]
[ROW][C]39[/C][C]0.000182588[/C][C]0.000365176[/C][C]0.999817[/C][/ROW]
[ROW][C]40[/C][C]0.000781103[/C][C]0.00156221[/C][C]0.999219[/C][/ROW]
[ROW][C]41[/C][C]0.00221328[/C][C]0.00442656[/C][C]0.997787[/C][/ROW]
[ROW][C]42[/C][C]0.00395[/C][C]0.0079[/C][C]0.99605[/C][/ROW]
[ROW][C]43[/C][C]0.00296754[/C][C]0.00593507[/C][C]0.997032[/C][/ROW]
[ROW][C]44[/C][C]0.00203565[/C][C]0.0040713[/C][C]0.997964[/C][/ROW]
[ROW][C]45[/C][C]0.00132696[/C][C]0.00265392[/C][C]0.998673[/C][/ROW]
[ROW][C]46[/C][C]0.000870296[/C][C]0.00174059[/C][C]0.99913[/C][/ROW]
[ROW][C]47[/C][C]0.000558337[/C][C]0.00111667[/C][C]0.999442[/C][/ROW]
[ROW][C]48[/C][C]0.000394208[/C][C]0.000788415[/C][C]0.999606[/C][/ROW]
[ROW][C]49[/C][C]0.00248661[/C][C]0.00497322[/C][C]0.997513[/C][/ROW]
[ROW][C]50[/C][C]0.00354513[/C][C]0.00709025[/C][C]0.996455[/C][/ROW]
[ROW][C]51[/C][C]0.00504186[/C][C]0.0100837[/C][C]0.994958[/C][/ROW]
[ROW][C]52[/C][C]0.00538592[/C][C]0.0107718[/C][C]0.994614[/C][/ROW]
[ROW][C]53[/C][C]0.0062619[/C][C]0.0125238[/C][C]0.993738[/C][/ROW]
[ROW][C]54[/C][C]0.00809654[/C][C]0.0161931[/C][C]0.991903[/C][/ROW]
[ROW][C]55[/C][C]0.00685169[/C][C]0.0137034[/C][C]0.993148[/C][/ROW]
[ROW][C]56[/C][C]0.00638844[/C][C]0.0127769[/C][C]0.993612[/C][/ROW]
[ROW][C]57[/C][C]0.00524146[/C][C]0.0104829[/C][C]0.994759[/C][/ROW]
[ROW][C]58[/C][C]0.0077121[/C][C]0.0154242[/C][C]0.992288[/C][/ROW]
[ROW][C]59[/C][C]0.00729443[/C][C]0.0145889[/C][C]0.992706[/C][/ROW]
[ROW][C]60[/C][C]0.00525743[/C][C]0.0105149[/C][C]0.994743[/C][/ROW]
[ROW][C]61[/C][C]0.0179818[/C][C]0.0359636[/C][C]0.982018[/C][/ROW]
[ROW][C]62[/C][C]0.0416812[/C][C]0.0833623[/C][C]0.958319[/C][/ROW]
[ROW][C]63[/C][C]0.0389048[/C][C]0.0778097[/C][C]0.961095[/C][/ROW]
[ROW][C]64[/C][C]0.0350511[/C][C]0.0701021[/C][C]0.964949[/C][/ROW]
[ROW][C]65[/C][C]0.0314496[/C][C]0.0628992[/C][C]0.96855[/C][/ROW]
[ROW][C]66[/C][C]0.0432618[/C][C]0.0865235[/C][C]0.956738[/C][/ROW]
[ROW][C]67[/C][C]0.0348347[/C][C]0.0696694[/C][C]0.965165[/C][/ROW]
[ROW][C]68[/C][C]0.0295401[/C][C]0.0590803[/C][C]0.97046[/C][/ROW]
[ROW][C]69[/C][C]0.0239884[/C][C]0.0479768[/C][C]0.976012[/C][/ROW]
[ROW][C]70[/C][C]0.0187335[/C][C]0.0374669[/C][C]0.981267[/C][/ROW]
[ROW][C]71[/C][C]0.0144478[/C][C]0.0288956[/C][C]0.985552[/C][/ROW]
[ROW][C]72[/C][C]0.0109102[/C][C]0.0218203[/C][C]0.98909[/C][/ROW]
[ROW][C]73[/C][C]0.00925141[/C][C]0.0185028[/C][C]0.990749[/C][/ROW]
[ROW][C]74[/C][C]0.0138241[/C][C]0.0276483[/C][C]0.986176[/C][/ROW]
[ROW][C]75[/C][C]0.0105225[/C][C]0.0210449[/C][C]0.989478[/C][/ROW]
[ROW][C]76[/C][C]0.00797079[/C][C]0.0159416[/C][C]0.992029[/C][/ROW]
[ROW][C]77[/C][C]0.00585454[/C][C]0.0117091[/C][C]0.994145[/C][/ROW]
[ROW][C]78[/C][C]0.00439357[/C][C]0.00878715[/C][C]0.995606[/C][/ROW]
[ROW][C]79[/C][C]0.00326651[/C][C]0.00653302[/C][C]0.996733[/C][/ROW]
[ROW][C]80[/C][C]0.00343896[/C][C]0.00687792[/C][C]0.996561[/C][/ROW]
[ROW][C]81[/C][C]0.00271325[/C][C]0.0054265[/C][C]0.997287[/C][/ROW]
[ROW][C]82[/C][C]0.00214524[/C][C]0.00429048[/C][C]0.997855[/C][/ROW]
[ROW][C]83[/C][C]0.00161827[/C][C]0.00323654[/C][C]0.998382[/C][/ROW]
[ROW][C]84[/C][C]0.00121299[/C][C]0.00242599[/C][C]0.998787[/C][/ROW]
[ROW][C]85[/C][C]0.000857757[/C][C]0.00171551[/C][C]0.999142[/C][/ROW]
[ROW][C]86[/C][C]0.00103715[/C][C]0.00207431[/C][C]0.998963[/C][/ROW]
[ROW][C]87[/C][C]0.0010776[/C][C]0.00215519[/C][C]0.998922[/C][/ROW]
[ROW][C]88[/C][C]0.000781278[/C][C]0.00156256[/C][C]0.999219[/C][/ROW]
[ROW][C]89[/C][C]0.000537459[/C][C]0.00107492[/C][C]0.999463[/C][/ROW]
[ROW][C]90[/C][C]0.000462396[/C][C]0.000924792[/C][C]0.999538[/C][/ROW]
[ROW][C]91[/C][C]0.000315728[/C][C]0.000631455[/C][C]0.999684[/C][/ROW]
[ROW][C]92[/C][C]0.000243079[/C][C]0.000486158[/C][C]0.999757[/C][/ROW]
[ROW][C]93[/C][C]0.000186185[/C][C]0.000372371[/C][C]0.999814[/C][/ROW]
[ROW][C]94[/C][C]0.000124971[/C][C]0.000249942[/C][C]0.999875[/C][/ROW]
[ROW][C]95[/C][C]8.32443e-05[/C][C]0.000166489[/C][C]0.999917[/C][/ROW]
[ROW][C]96[/C][C]7.61697e-05[/C][C]0.000152339[/C][C]0.999924[/C][/ROW]
[ROW][C]97[/C][C]6.78186e-05[/C][C]0.000135637[/C][C]0.999932[/C][/ROW]
[ROW][C]98[/C][C]4.5336e-05[/C][C]9.06721e-05[/C][C]0.999955[/C][/ROW]
[ROW][C]99[/C][C]2.91296e-05[/C][C]5.82593e-05[/C][C]0.999971[/C][/ROW]
[ROW][C]100[/C][C]2.04084e-05[/C][C]4.08167e-05[/C][C]0.99998[/C][/ROW]
[ROW][C]101[/C][C]1.78016e-05[/C][C]3.56033e-05[/C][C]0.999982[/C][/ROW]
[ROW][C]102[/C][C]1.40312e-05[/C][C]2.80624e-05[/C][C]0.999986[/C][/ROW]
[ROW][C]103[/C][C]1.84462e-05[/C][C]3.68925e-05[/C][C]0.999982[/C][/ROW]
[ROW][C]104[/C][C]1.46978e-05[/C][C]2.93956e-05[/C][C]0.999985[/C][/ROW]
[ROW][C]105[/C][C]1.50295e-05[/C][C]3.00591e-05[/C][C]0.999985[/C][/ROW]
[ROW][C]106[/C][C]1.5011e-05[/C][C]3.00221e-05[/C][C]0.999985[/C][/ROW]
[ROW][C]107[/C][C]1.68059e-05[/C][C]3.36119e-05[/C][C]0.999983[/C][/ROW]
[ROW][C]108[/C][C]1.70425e-05[/C][C]3.40849e-05[/C][C]0.999983[/C][/ROW]
[ROW][C]109[/C][C]1.71751e-05[/C][C]3.43503e-05[/C][C]0.999983[/C][/ROW]
[ROW][C]110[/C][C]1.35897e-05[/C][C]2.71795e-05[/C][C]0.999986[/C][/ROW]
[ROW][C]111[/C][C]1.25195e-05[/C][C]2.50391e-05[/C][C]0.999987[/C][/ROW]
[ROW][C]112[/C][C]3.46463e-05[/C][C]6.92926e-05[/C][C]0.999965[/C][/ROW]
[ROW][C]113[/C][C]6.71224e-05[/C][C]0.000134245[/C][C]0.999933[/C][/ROW]
[ROW][C]114[/C][C]0.000149893[/C][C]0.000299786[/C][C]0.99985[/C][/ROW]
[ROW][C]115[/C][C]0.000149775[/C][C]0.00029955[/C][C]0.99985[/C][/ROW]
[ROW][C]116[/C][C]0.000149356[/C][C]0.000298713[/C][C]0.999851[/C][/ROW]
[ROW][C]117[/C][C]0.000145323[/C][C]0.000290646[/C][C]0.999855[/C][/ROW]
[ROW][C]118[/C][C]0.000161733[/C][C]0.000323466[/C][C]0.999838[/C][/ROW]
[ROW][C]119[/C][C]0.00023973[/C][C]0.00047946[/C][C]0.99976[/C][/ROW]
[ROW][C]120[/C][C]0.000511463[/C][C]0.00102293[/C][C]0.999489[/C][/ROW]
[ROW][C]121[/C][C]0.00072169[/C][C]0.00144338[/C][C]0.999278[/C][/ROW]
[ROW][C]122[/C][C]0.000977648[/C][C]0.0019553[/C][C]0.999022[/C][/ROW]
[ROW][C]123[/C][C]0.000694929[/C][C]0.00138986[/C][C]0.999305[/C][/ROW]
[ROW][C]124[/C][C]0.000653598[/C][C]0.0013072[/C][C]0.999346[/C][/ROW]
[ROW][C]125[/C][C]0.000642233[/C][C]0.00128447[/C][C]0.999358[/C][/ROW]
[ROW][C]126[/C][C]0.000658504[/C][C]0.00131701[/C][C]0.999341[/C][/ROW]
[ROW][C]127[/C][C]0.000600815[/C][C]0.00120163[/C][C]0.999399[/C][/ROW]
[ROW][C]128[/C][C]0.000596936[/C][C]0.00119387[/C][C]0.999403[/C][/ROW]
[ROW][C]129[/C][C]0.000584971[/C][C]0.00116994[/C][C]0.999415[/C][/ROW]
[ROW][C]130[/C][C]0.000604166[/C][C]0.00120833[/C][C]0.999396[/C][/ROW]
[ROW][C]131[/C][C]0.000590036[/C][C]0.00118007[/C][C]0.99941[/C][/ROW]
[ROW][C]132[/C][C]0.000571531[/C][C]0.00114306[/C][C]0.999428[/C][/ROW]
[ROW][C]133[/C][C]0.000654934[/C][C]0.00130987[/C][C]0.999345[/C][/ROW]
[ROW][C]134[/C][C]0.000814898[/C][C]0.0016298[/C][C]0.999185[/C][/ROW]
[ROW][C]135[/C][C]0.000579445[/C][C]0.00115889[/C][C]0.999421[/C][/ROW]
[ROW][C]136[/C][C]0.000392052[/C][C]0.000784104[/C][C]0.999608[/C][/ROW]
[ROW][C]137[/C][C]0.000265521[/C][C]0.000531042[/C][C]0.999734[/C][/ROW]
[ROW][C]138[/C][C]0.000187903[/C][C]0.000375805[/C][C]0.999812[/C][/ROW]
[ROW][C]139[/C][C]0.000160495[/C][C]0.00032099[/C][C]0.99984[/C][/ROW]
[ROW][C]140[/C][C]0.00011157[/C][C]0.000223139[/C][C]0.999888[/C][/ROW]
[ROW][C]141[/C][C]0.000115514[/C][C]0.000231029[/C][C]0.999884[/C][/ROW]
[ROW][C]142[/C][C]7.97951e-05[/C][C]0.00015959[/C][C]0.99992[/C][/ROW]
[ROW][C]143[/C][C]8.34482e-05[/C][C]0.000166896[/C][C]0.999917[/C][/ROW]
[ROW][C]144[/C][C]0.000269097[/C][C]0.000538194[/C][C]0.999731[/C][/ROW]
[ROW][C]145[/C][C]0.000498778[/C][C]0.000997555[/C][C]0.999501[/C][/ROW]
[ROW][C]146[/C][C]0.00284184[/C][C]0.00568368[/C][C]0.997158[/C][/ROW]
[ROW][C]147[/C][C]0.00236032[/C][C]0.00472064[/C][C]0.99764[/C][/ROW]
[ROW][C]148[/C][C]0.00185169[/C][C]0.00370339[/C][C]0.998148[/C][/ROW]
[ROW][C]149[/C][C]0.00133767[/C][C]0.00267534[/C][C]0.998662[/C][/ROW]
[ROW][C]150[/C][C]0.00121539[/C][C]0.00243078[/C][C]0.998785[/C][/ROW]
[ROW][C]151[/C][C]0.00132239[/C][C]0.00264478[/C][C]0.998678[/C][/ROW]
[ROW][C]152[/C][C]0.00127909[/C][C]0.00255818[/C][C]0.998721[/C][/ROW]
[ROW][C]153[/C][C]0.000855602[/C][C]0.0017112[/C][C]0.999144[/C][/ROW]
[ROW][C]154[/C][C]0.000752655[/C][C]0.00150531[/C][C]0.999247[/C][/ROW]
[ROW][C]155[/C][C]0.000624044[/C][C]0.00124809[/C][C]0.999376[/C][/ROW]
[ROW][C]156[/C][C]0.000458777[/C][C]0.000917555[/C][C]0.999541[/C][/ROW]
[ROW][C]157[/C][C]0.000542301[/C][C]0.0010846[/C][C]0.999458[/C][/ROW]
[ROW][C]158[/C][C]0.000856341[/C][C]0.00171268[/C][C]0.999144[/C][/ROW]
[ROW][C]159[/C][C]0.000542303[/C][C]0.00108461[/C][C]0.999458[/C][/ROW]
[ROW][C]160[/C][C]0.000382908[/C][C]0.000765816[/C][C]0.999617[/C][/ROW]
[ROW][C]161[/C][C]0.000252364[/C][C]0.000504728[/C][C]0.999748[/C][/ROW]
[ROW][C]162[/C][C]0.000160591[/C][C]0.000321183[/C][C]0.999839[/C][/ROW]
[ROW][C]163[/C][C]0.000103908[/C][C]0.000207816[/C][C]0.999896[/C][/ROW]
[ROW][C]164[/C][C]0.000188907[/C][C]0.000377814[/C][C]0.999811[/C][/ROW]
[ROW][C]165[/C][C]0.000351643[/C][C]0.000703286[/C][C]0.999648[/C][/ROW]
[ROW][C]166[/C][C]0.000417052[/C][C]0.000834104[/C][C]0.999583[/C][/ROW]
[ROW][C]167[/C][C]0.000380491[/C][C]0.000760981[/C][C]0.99962[/C][/ROW]
[ROW][C]168[/C][C]0.000674951[/C][C]0.0013499[/C][C]0.999325[/C][/ROW]
[ROW][C]169[/C][C]0.001725[/C][C]0.00345001[/C][C]0.998275[/C][/ROW]
[ROW][C]170[/C][C]0.00265739[/C][C]0.00531479[/C][C]0.997343[/C][/ROW]
[ROW][C]171[/C][C]0.999631[/C][C]0.000737586[/C][C]0.000368793[/C][/ROW]
[ROW][C]172[/C][C]0.999698[/C][C]0.000604353[/C][C]0.000302177[/C][/ROW]
[ROW][C]173[/C][C]0.999496[/C][C]0.00100803[/C][C]0.000504015[/C][/ROW]
[ROW][C]174[/C][C]0.99917[/C][C]0.00166051[/C][C]0.000830254[/C][/ROW]
[ROW][C]175[/C][C]0.998498[/C][C]0.00300482[/C][C]0.00150241[/C][/ROW]
[ROW][C]176[/C][C]0.99893[/C][C]0.00213953[/C][C]0.00106976[/C][/ROW]
[ROW][C]177[/C][C]0.999503[/C][C]0.000994575[/C][C]0.000497288[/C][/ROW]
[ROW][C]178[/C][C]0.999354[/C][C]0.00129213[/C][C]0.000646066[/C][/ROW]
[ROW][C]179[/C][C]0.998157[/C][C]0.00368518[/C][C]0.00184259[/C][/ROW]
[ROW][C]180[/C][C]0.995986[/C][C]0.00802741[/C][C]0.0040137[/C][/ROW]
[ROW][C]181[/C][C]0.989681[/C][C]0.0206378[/C][C]0.0103189[/C][/ROW]
[ROW][C]182[/C][C]0.975559[/C][C]0.0488816[/C][C]0.0244408[/C][/ROW]
[ROW][C]183[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]184[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231841&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231841&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
111.31756e-542.63511e-541
121.50074e-663.00148e-661
131.95601e-933.91202e-931
141.52177e-923.04354e-921
155.01206e-1081.00241e-1071
16001
171.33047e-1482.66093e-1481
186.78118e-1551.35624e-1541
193.16834e-1696.33668e-1691
206.66198e-1931.3324e-1921
211.40041e-2252.80082e-2251
221.04064e-2172.08129e-2171
231.71687e-2293.43373e-2291
247.85121e-2481.57024e-2471
251.59875e-2663.19751e-2661
269.72252e-3081.9445e-3071
271.92285e-2963.8457e-2961
284.25369e-3078.50737e-3071
29001
30001
315.92628e-091.18526e-081
321.14697e-082.29395e-081
337.28136e-091.45627e-081
343.00812e-096.01625e-091
351.1308e-092.2616e-091
364.32827e-108.65654e-101
378.22123e-071.64425e-060.999999
381.57777e-053.15554e-050.999984
390.0001825880.0003651760.999817
400.0007811030.001562210.999219
410.002213280.004426560.997787
420.003950.00790.99605
430.002967540.005935070.997032
440.002035650.00407130.997964
450.001326960.002653920.998673
460.0008702960.001740590.99913
470.0005583370.001116670.999442
480.0003942080.0007884150.999606
490.002486610.004973220.997513
500.003545130.007090250.996455
510.005041860.01008370.994958
520.005385920.01077180.994614
530.00626190.01252380.993738
540.008096540.01619310.991903
550.006851690.01370340.993148
560.006388440.01277690.993612
570.005241460.01048290.994759
580.00771210.01542420.992288
590.007294430.01458890.992706
600.005257430.01051490.994743
610.01798180.03596360.982018
620.04168120.08336230.958319
630.03890480.07780970.961095
640.03505110.07010210.964949
650.03144960.06289920.96855
660.04326180.08652350.956738
670.03483470.06966940.965165
680.02954010.05908030.97046
690.02398840.04797680.976012
700.01873350.03746690.981267
710.01444780.02889560.985552
720.01091020.02182030.98909
730.009251410.01850280.990749
740.01382410.02764830.986176
750.01052250.02104490.989478
760.007970790.01594160.992029
770.005854540.01170910.994145
780.004393570.008787150.995606
790.003266510.006533020.996733
800.003438960.006877920.996561
810.002713250.00542650.997287
820.002145240.004290480.997855
830.001618270.003236540.998382
840.001212990.002425990.998787
850.0008577570.001715510.999142
860.001037150.002074310.998963
870.00107760.002155190.998922
880.0007812780.001562560.999219
890.0005374590.001074920.999463
900.0004623960.0009247920.999538
910.0003157280.0006314550.999684
920.0002430790.0004861580.999757
930.0001861850.0003723710.999814
940.0001249710.0002499420.999875
958.32443e-050.0001664890.999917
967.61697e-050.0001523390.999924
976.78186e-050.0001356370.999932
984.5336e-059.06721e-050.999955
992.91296e-055.82593e-050.999971
1002.04084e-054.08167e-050.99998
1011.78016e-053.56033e-050.999982
1021.40312e-052.80624e-050.999986
1031.84462e-053.68925e-050.999982
1041.46978e-052.93956e-050.999985
1051.50295e-053.00591e-050.999985
1061.5011e-053.00221e-050.999985
1071.68059e-053.36119e-050.999983
1081.70425e-053.40849e-050.999983
1091.71751e-053.43503e-050.999983
1101.35897e-052.71795e-050.999986
1111.25195e-052.50391e-050.999987
1123.46463e-056.92926e-050.999965
1136.71224e-050.0001342450.999933
1140.0001498930.0002997860.99985
1150.0001497750.000299550.99985
1160.0001493560.0002987130.999851
1170.0001453230.0002906460.999855
1180.0001617330.0003234660.999838
1190.000239730.000479460.99976
1200.0005114630.001022930.999489
1210.000721690.001443380.999278
1220.0009776480.00195530.999022
1230.0006949290.001389860.999305
1240.0006535980.00130720.999346
1250.0006422330.001284470.999358
1260.0006585040.001317010.999341
1270.0006008150.001201630.999399
1280.0005969360.001193870.999403
1290.0005849710.001169940.999415
1300.0006041660.001208330.999396
1310.0005900360.001180070.99941
1320.0005715310.001143060.999428
1330.0006549340.001309870.999345
1340.0008148980.00162980.999185
1350.0005794450.001158890.999421
1360.0003920520.0007841040.999608
1370.0002655210.0005310420.999734
1380.0001879030.0003758050.999812
1390.0001604950.000320990.99984
1400.000111570.0002231390.999888
1410.0001155140.0002310290.999884
1427.97951e-050.000159590.99992
1438.34482e-050.0001668960.999917
1440.0002690970.0005381940.999731
1450.0004987780.0009975550.999501
1460.002841840.005683680.997158
1470.002360320.004720640.99764
1480.001851690.003703390.998148
1490.001337670.002675340.998662
1500.001215390.002430780.998785
1510.001322390.002644780.998678
1520.001279090.002558180.998721
1530.0008556020.00171120.999144
1540.0007526550.001505310.999247
1550.0006240440.001248090.999376
1560.0004587770.0009175550.999541
1570.0005423010.00108460.999458
1580.0008563410.001712680.999144
1590.0005423030.001084610.999458
1600.0003829080.0007658160.999617
1610.0002523640.0005047280.999748
1620.0001605910.0003211830.999839
1630.0001039080.0002078160.999896
1640.0001889070.0003778140.999811
1650.0003516430.0007032860.999648
1660.0004170520.0008341040.999583
1670.0003804910.0007609810.99962
1680.0006749510.00134990.999325
1690.0017250.003450010.998275
1700.002657390.005314790.997343
1710.9996310.0007375860.000368793
1720.9996980.0006043530.000302177
1730.9994960.001008030.000504015
1740.999170.001660510.000830254
1750.9984980.003004820.00150241
1760.998930.002139530.00106976
1770.9995030.0009945750.000497288
1780.9993540.001292130.000646066
1790.9981570.003685180.00184259
1800.9959860.008027410.0040137
1810.9896810.02063780.0103189
1820.9755590.04888160.0244408
183100
184100







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1450.833333NOK
5% type I error level1670.95977NOK
10% type I error level1741NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 145 & 0.833333 & NOK \tabularnewline
5% type I error level & 167 & 0.95977 & NOK \tabularnewline
10% type I error level & 174 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231841&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]145[/C][C]0.833333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]167[/C][C]0.95977[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]174[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231841&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231841&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1450.833333NOK
5% type I error level1670.95977NOK
10% type I error level1741NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}