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@@ -275,7 +275,34 @@ const thebe_selector_output = ".output, .cell_output"
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week36.html">
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Week 36: Linear Rgeression and Statistical interpretations
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Week 36: Linear Regression and Statistical interpretations
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<li class="toctree-l1">
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<a class="reference internal" href="exercisesweek37.html">
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Exercises week 37
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<li class="toctree-l1">
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<a class="reference internal" href="week37.html">
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Week 37: Statistical interpretations and Resampling Methods
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<li class="toctree-l1">
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<a class="reference internal" href="exercisesweek38.html">
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Exercises week 38
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Projects
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</span>
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</p>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1">
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<a class="reference internal" href="project1.html">
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Project 1 on Machine Learning, deadline October 7 (midnight), 2024
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</a>
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</ul>
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@@ -1243,10 +1270,10 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.15792278643800434
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3.469596964296967
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[[0.82924408 2.51097872]
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[2.51097872 8.91660026]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.05005634426334421
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4.366375489616074
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[[ 0.93787605 2.95563211]
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[ 2.95563211 10.33025801]]
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</pre></div>
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@@ -1283,10 +1310,10 @@ a more brute force way. Here we scale the mean values for each column of the des
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.08557805981945521
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1.6515332649941654
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[[1. 0.67509467]
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[0.67509467 1. ]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07808426989543932
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1.4121966338442804
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[[1. 0.70362677]
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[0.70362677 1. ]]
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</pre></div>
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</div>
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</div>
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@@ -1316,30 +1343,30 @@ this matrix we easily see that it is a positive definite matrix.</p>
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-1.2222736 -2.47780495]
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[-0.84963695 -3.33047051]
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[ 1.52869849 3.46922796]
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[-0.31385129 -1.2500845 ]
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[-0.02761005 0.80816668]
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[-0.79036971 -1.8310483 ]
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[ 0.22681817 -0.28207517]
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[-0.69475072 -1.99810021]
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[-0.17979265 -0.38267862]
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[ 2.3227683 7.27486761]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-0.34661376 -1.6809195 ]
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[ 0.05792927 0.30915293]
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[ 0.65183066 3.00564344]
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[ 1.75018686 4.35667342]
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[-0.75682834 -1.67875366]
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[ 1.16654048 3.9065894 ]
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[-1.86267497 -5.53585173]
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[ 0.29803738 2.45731144]
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[-0.63031478 -2.76157429]
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[-0.3280928 -2.37827145]]
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0 1
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0 -1.222274 -2.477805
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1 -0.849637 -3.330471
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2 1.528698 3.469228
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3 -0.313851 -1.250084
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4 -0.027610 0.808167
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5 -0.790370 -1.831048
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6 0.226818 -0.282075
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7 -0.694751 -1.998100
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8 -0.179793 -0.382679
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9 2.322768 7.274868
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0 -0.346614 -1.680920
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1 0.057929 0.309153
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2 0.651831 3.005643
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3 1.750187 4.356673
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4 -0.756828 -1.678754
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5 1.166540 3.906589
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6 -1.862675 -5.535852
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7 0.298037 2.457311
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8 -0.630315 -2.761574
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9 -0.328093 -2.378271
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0 1
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0 1.000000 0.972591
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1 0.972591 1.000000
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0 1.000000 0.959076
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1 0.959076 1.000000
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</pre></div>
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</div>
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</div>
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@@ -1396,37 +1423,37 @@ this matrix we easily see that it is a positive definite matrix.</p>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0 1 2 3 4 5 6 7 \
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0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
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1 0.0 0.078704 0.076964 0.076716 0.075698 0.074706 0.067097 0.066458
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2 0.0 0.076964 0.076620 0.076716 0.076383 0.075972 0.068301 0.068022
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3 0.0 0.076716 0.076716 0.079642 0.079578 0.079412 0.072860 0.072759
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4 0.0 0.075698 0.076383 0.079578 0.079873 0.080016 0.073481 0.073581
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5 0.0 0.074706 0.075972 0.079412 0.080016 0.080425 0.073934 0.074208
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6 0.0 0.067097 0.068301 0.072860 0.073481 0.073934 0.068963 0.069275
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7 0.0 0.066458 0.068022 0.072759 0.073581 0.074208 0.069275 0.069704
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8 0.0 0.065860 0.067729 0.072633 0.073627 0.074407 0.069521 0.070054
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9 0.0 0.065299 0.067429 0.072489 0.073634 0.074548 0.069717 0.070342
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10 0.0 0.058190 0.060003 0.065359 0.066349 0.067149 0.063511 0.064061
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11 0.0 0.057786 0.059797 0.065273 0.066380 0.067284 0.063684 0.064307
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12 0.0 0.057415 0.059597 0.065186 0.066395 0.067392 0.063832 0.064521
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13 0.0 0.057076 0.059406 0.065101 0.066402 0.067482 0.063965 0.064713
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14 0.0 0.056767 0.059226 0.065024 0.066406 0.067560 0.064087 0.064889
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1 0.0 0.084996 0.084434 0.084712 0.085455 0.086212 0.075771 0.076638
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2 0.0 0.084434 0.084514 0.084042 0.085120 0.086230 0.075176 0.076259
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3 0.0 0.084712 0.084042 0.089718 0.090439 0.091159 0.083479 0.084358
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4 0.0 0.085455 0.085120 0.090439 0.091388 0.092347 0.084091 0.085139
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5 0.0 0.086212 0.086230 0.091159 0.092347 0.093554 0.084695 0.085918
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6 0.0 0.075771 0.075176 0.083479 0.084091 0.084695 0.079903 0.080657
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7 0.0 0.076638 0.076259 0.084358 0.085139 0.085918 0.080657 0.081544
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8 0.0 0.077567 0.077411 0.085294 0.086250 0.087210 0.081457 0.082482
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9 0.0 0.078557 0.078636 0.086286 0.087424 0.088574 0.082303 0.083471
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10 0.0 0.066997 0.066458 0.075909 0.076389 0.076857 0.074221 0.074824
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11 0.0 0.067764 0.067380 0.076693 0.077304 0.077906 0.074892 0.075602
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12 0.0 0.068592 0.068369 0.077539 0.078284 0.079027 0.075615 0.076436
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13 0.0 0.069484 0.069427 0.078447 0.079334 0.080222 0.076393 0.077329
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14 0.0 0.070441 0.070558 0.079420 0.080453 0.081494 0.077226 0.078282
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8 9 10 11 12 13 14
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0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
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1 0.065860 0.065299 0.058190 0.057786 0.057415 0.057076 0.056767
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2 0.067729 0.067429 0.060003 0.059797 0.059597 0.059406 0.059226
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3 0.072633 0.072489 0.065359 0.065273 0.065186 0.065101 0.065024
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4 0.073627 0.073634 0.066349 0.066380 0.066395 0.066402 0.066406
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5 0.074407 0.074548 0.067149 0.067284 0.067392 0.067482 0.067560
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6 0.069521 0.069717 0.063511 0.063684 0.063832 0.063965 0.064087
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7 0.070054 0.070342 0.064061 0.064307 0.064521 0.064713 0.064889
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8 0.070496 0.070868 0.064530 0.064841 0.065115 0.065361 0.065588
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9 0.070868 0.071315 0.064933 0.065304 0.065633 0.065930 0.066204
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10 0.064530 0.064933 0.059711 0.060043 0.060340 0.060612 0.060864
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11 0.064841 0.065304 0.060043 0.060422 0.060764 0.061076 0.061367
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12 0.065115 0.065633 0.060340 0.060764 0.061146 0.061496 0.061822
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13 0.065361 0.065930 0.060612 0.061076 0.061496 0.061882 0.062242
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14 0.065588 0.066204 0.060864 0.061367 0.061822 0.062242 0.062634
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1 0.077567 0.078557 0.066997 0.067764 0.068592 0.069484 0.070441
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2 0.077411 0.078636 0.066458 0.067380 0.068369 0.069427 0.070558
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3 0.085294 0.086286 0.075909 0.076693 0.077539 0.078447 0.079420
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4 0.086250 0.087424 0.076389 0.077304 0.078284 0.079334 0.080453
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5 0.087210 0.088574 0.076857 0.077906 0.079027 0.080222 0.081494
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6 0.081457 0.082303 0.074221 0.074892 0.075615 0.076393 0.077226
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7 0.082482 0.083471 0.074824 0.075602 0.076436 0.077329 0.078282
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8 0.083564 0.084702 0.075463 0.076351 0.077301 0.078313 0.079391
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9 0.084702 0.085996 0.076138 0.077141 0.078210 0.079347 0.080555
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10 0.075463 0.076138 0.070095 0.070630 0.071208 0.071831 0.072500
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11 0.076351 0.077141 0.070630 0.071252 0.071921 0.072638 0.073405
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12 0.077301 0.078210 0.071208 0.071921 0.072684 0.073499 0.074368
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13 0.078313 0.079347 0.071831 0.072638 0.073499 0.074417 0.075392
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14 0.079391 0.080555 0.072500 0.073405 0.074368 0.075392 0.076479
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</pre></div>
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</div>
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</div>
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