From f41345d8ebddb869a986a5b2056727c1a57fbb36 Mon Sep 17 00:00:00 2001
From: Bree Norlander <norlab@uw.edu>
Date: Sun, 23 Jul 2017 16:10:03 -0700
Subject: [PATCH] Clarifications

---
 Estimating-AI-Scores.ipynb | 1556 ++++++++++++------------------------
 1 file changed, 508 insertions(+), 1048 deletions(-)

diff --git a/Estimating-AI-Scores.ipynb b/Estimating-AI-Scores.ipynb
index 04ec4e8..13e9890 100644
--- a/Estimating-AI-Scores.ipynb
+++ b/Estimating-AI-Scores.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 691,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,13 +24,17 @@
     "from sklearn.feature_selection import f_regression\n",
     "from sklearn.preprocessing import MinMaxScaler\n",
     "from sklearn.ensemble import RandomForestRegressor\n",
-    "from pandas.tools.plotting import scatter_matrix"
+    "from pandas.tools.plotting import scatter_matrix\n",
+    "from scipy import stats\n",
+    "import math"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 692,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Import file\n",
@@ -41,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 693,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +104,7 @@
        "2   130214813          762  "
       ]
      },
-     "execution_count": 693,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -111,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 694,
+   "execution_count": 4,
    "metadata": {
     "collapsed": true
    },
@@ -123,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 695,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -202,7 +206,7 @@
        "2            NaN                 NaN            NaN          NaN  NaN  "
       ]
      },
-     "execution_count": 695,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -213,8 +217,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 696,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Find all the 2015 ai scores\n",
@@ -223,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 697,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -287,7 +293,7 @@
        "5717            NaN                 NaN            NaN          NaN  NaN  "
       ]
      },
-     "execution_count": 697,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -298,8 +304,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 698,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Create a new dataframe that merges acounts and ai2015\n",
@@ -308,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 699,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -461,7 +469,7 @@
        "4          NaN  NaN  "
       ]
      },
-     "execution_count": 699,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -472,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 700,
+   "execution_count": 10,
    "metadata": {
     "collapsed": true
    },
@@ -484,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 701,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -597,7 +605,7 @@
        "2  NaN             3.278049  "
       ]
      },
-     "execution_count": 701,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -608,14 +616,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 702,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "data": {
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3B5INSURERMpJnGRkCnCnu6eTDkZERETKT8HJiLuvLEYgIiIiUp46lYyY2XpgrLtvNrMH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\n7O05E+cBXwM2RuYZwZYhJubTmaquAZ4DnhOR/wZexywkP8S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PM6sxsyPNbFjve0sxrFmzmfb2szK+1t4+hdWrNxc5IhERkQ5ZJx5mdpWZHRB9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\n8EgkEolEIlE1kuGRSCQSiUSiavwfIiKe6TduAwIAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11c8a02e8>"
+       "<matplotlib.figure.Figure at 0x11996ba58>"
       ]
      },
      "metadata": {},
@@ -624,16 +632,90 @@
    ],
    "source": [
     "# Quick plot of article influence score and average citations per paper per journal\n",
-    "plt.plot(merged['article_influence'], merged['avg_cites_per_paper'], 'o')\n",
-    "plt.xlabel('2015 AI Score for Given Journal')\n",
-    "plt.ylabel('Avg Citations per Paper for Given Journal')\n",
+    "plt.plot(merged['avg_cites_per_paper'], merged['article_influence'], 'o')\n",
+    "plt.ylabel('2015 AI Score for Given Journal')\n",
+    "plt.xlabel('Total Avg Citations per Paper for Given Journal')\n",
     "\n",
     "plt.savefig('ai_avg_citation_scatterplot.jpg')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 703,
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ISSN                                      1556-276X\n",
+       "journal_name             nanoscale research letters\n",
+       "citation_count                               691173\n",
+       "journal_id_x                               55093224\n",
+       "paper_count                                    3948\n",
+       "id                                             6087\n",
+       "article_influence                             0.672\n",
+       "est_article_influence                           NaN\n",
+       "date_stamp                               2015-12-31\n",
+       "journal_id_y                              1556-276X\n",
+       "ai_percentile                                   NaN\n",
+       "cost_effectiveness                              NaN\n",
+       "ef_percentile                                   NaN\n",
+       "eigenfactor                                     NaN\n",
+       "efn                                             NaN\n",
+       "avg_cites_per_paper                         175.069\n",
+       "Name: 230, dtype: object"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Which journal has the max average citations per paper?\n",
+    "merged.loc[merged['avg_cites_per_paper'].idxmax()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ISSN                                        1433-8351\n",
+       "journal_name             living reviews in relativity\n",
+       "citation_count                                   2007\n",
+       "journal_id_x                                110783047\n",
+       "paper_count                                        97\n",
+       "id                                               5860\n",
+       "article_influence                              12.463\n",
+       "est_article_influence                             NaN\n",
+       "date_stamp                                 2015-12-31\n",
+       "journal_id_y                                1433-8351\n",
+       "ai_percentile                                     NaN\n",
+       "cost_effectiveness                                NaN\n",
+       "ef_percentile                                     NaN\n",
+       "eigenfactor                                       NaN\n",
+       "efn                                               NaN\n",
+       "avg_cites_per_paper                           20.6907\n",
+       "Name: 75, dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Which journal has the max article influence score?\n",
+    "merged.loc[merged['article_influence'].idxmax()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -642,19 +724,19 @@
        "LinregressResult(slope=4.0835852489503166, intercept=7.566289282849195, rvalue=0.27872078724894739, pvalue=1.01518940477298e-07, stderr=0.75103171798427193)"
       ]
      },
-     "execution_count": 703,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Check linear regression results\n",
-    "linregress(merged['article_influence'], merged['avg_cites_per_paper'])"
+    "stats.linregress(merged['article_influence'], merged['avg_cites_per_paper'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 704,
+   "execution_count": 24,
    "metadata": {
     "collapsed": true
    },
@@ -669,7 +751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 705,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -729,7 +811,7 @@
        "19664  2015   179257517              43"
       ]
      },
-     "execution_count": 705,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -740,8 +822,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 706,
-   "metadata": {},
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "#Read in file\n",
@@ -752,7 +836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 707,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -812,7 +896,7 @@
        "4  2014   124795565          717"
       ]
      },
-     "execution_count": 707,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -823,7 +907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 708,
+   "execution_count": 28,
    "metadata": {
     "collapsed": true
    },
@@ -835,7 +919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 709,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -941,7 +1025,7 @@
        "9  2013    72004196           335.0          NaN"
       ]
      },
-     "execution_count": 709,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -952,8 +1036,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 710,
-   "metadata": {},
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Left join with acounts df\n",
@@ -962,8 +1048,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 711,
-   "metadata": {},
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# save to csv\n",
@@ -972,7 +1060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 712,
+   "execution_count": 32,
    "metadata": {
     "collapsed": true
    },
@@ -984,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 713,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1135,7 +1223,7 @@
        "9  bulletin of the american museum of natural his...                  NaN  "
       ]
      },
-     "execution_count": 713,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1146,7 +1234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 714,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1243,7 +1331,7 @@
        "academic psychiatry           1545-7230            6.0         6.0  "
       ]
      },
-     "execution_count": 714,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1260,8 +1348,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 715,
-   "metadata": {},
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# sum and len will now become rows\n",
@@ -1270,7 +1360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 716,
+   "execution_count": 36,
    "metadata": {
     "collapsed": true
    },
@@ -1282,8 +1372,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 717,
-   "metadata": {},
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# unstack and place sum and len back in columns\n",
@@ -1292,8 +1384,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 718,
-   "metadata": {},
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Flatten pivot table for easier indexing\n",
@@ -1302,18 +1396,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 719,
-   "metadata": {},
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Keep only specific columns\n",
-    "counts_pivot_table = counts_pivot_table[['citation_count_sum', 'citation_count_len', 'paper_count_sum', 'avg_cites_per_paper_sum']]"
+    "counts_pivot_table = counts_pivot_table[['citation_count_sum', 'citation_count_len', \n",
+    "                                         'paper_count_sum', 'avg_cites_per_paper_sum']]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 720,
-   "metadata": {},
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Rename certain columns\n",
@@ -1325,7 +1424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 721,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1410,7 +1509,7 @@
        "4                    6.0           1735.0             1.572911  "
       ]
      },
-     "execution_count": 721,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1423,7 +1522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 722,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -2314,7 +2413,7 @@
        "[341 rows x 8 columns]"
       ]
      },
-     "execution_count": 722,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2330,14 +2429,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 723,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
      "data": {
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TGUnP03l3bAH1wGrg0zWKqyoR8QrwWPbyPkm7kWplzs3iG0Xn3plRwH3Z86eAjSSNKOmd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\nYgtpZ0PgLmA28AnwGLBmyhNwKjAdmAuMB3bJXFu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ba0DOojyJx+mEScvOIPTxOBiYZWYvuvsvW3vQhAkT6NevX4ttY8eOZezYsWUIUUREpHup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\nWuPAw8zMzFrjwMPMzMxa8z9odhCksGnBAgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1206d8b00>"
+       "<matplotlib.figure.Figure at 0x1197f3fd0>"
       ]
      },
      "metadata": {},
@@ -2345,23 +2444,23 @@
     }
    ],
    "source": [
-    "plt.plot(pivot_table_merged['article_influence'], pivot_table_merged['avg_cites_per_paper'], 'o')\n",
-    "plt.xlabel('2015 AI Score for Given Journal')\n",
-    "plt.ylabel('Avg Citations per Paper for Given Journal 6 Years \\n 2010-2015')\n",
+    "plt.plot(pivot_table_merged['avg_cites_per_paper'], pivot_table_merged['article_influence'], 'o')\n",
+    "plt.ylabel('2015 AI Score for Given Journal')\n",
+    "plt.xlabel('Avg Citations per Paper for Given Journal 6 Years \\n 2010-2015')\n",
     "\n",
     "plt.savefig('ai_avg_citation_5_years_scatterplot.jpg')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 724,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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FUcqPW32zb9+SpARXIRS6m9Gj17F790YikUjZzlPpbNiwgQ0bNqSsa2tr47777gOYLCJ7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fAB7BOgRVwL9jnYCv0tExmDlzPuKzzOKyHfATr9tGNPoqixc/waBB7wOCnJGd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\nix3Z9QV+l5lmZmuAhwh3FlfHCrlphVKVeYQ+DkNnbsU0+W1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m0to6Gth6/62tu/PrX89l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\n55xzruZ5wuKcc865mvf/Ae3+4z+M8RBAAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1206c24e0>"
+       "<matplotlib.figure.Figure at 0x119c75da0>"
       ]
      },
      "metadata": {},
@@ -2370,42 +2469,16 @@
    ],
    "source": [
     "# Try a log log plot because of the density of most points\n",
-    "plt.plot(np.log(pivot_table_merged['article_influence']), np.log(pivot_table_merged['avg_cites_per_paper']), 'o')\n",
-    "plt.xlabel('log 2015 AI Score for Given Journal')\n",
-    "plt.ylabel('log Avg Citations per Paper for Given Journal 6 Years \\n 2010-2015')\n",
+    "plt.plot(np.log10(pivot_table_merged['avg_cites_per_paper']), np.log10(pivot_table_merged['article_influence']), 'o')\n",
+    "plt.ylabel('log10 2015 AI Score for Given Journal')\n",
+    "plt.xlabel('log10 Avg Citations per Paper for Given Journal 6 Years \\n 2010-2015')\n",
     "\n",
     "plt.savefig('ai_avg_citation_5_years_scatterplot_log_log.jpg')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 725,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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wVDliEhERkfbF6hMCECUdfythLCXh7pvN7O/AQcC9hLbye9J6NGRP4NH27jV58mQGDx7c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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11c7a5b00>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(pivot_table_merged['article_influence'], pivot_table_merged['avg_cites_per_paper'], 'o')\n",
-    "plt.xlabel('2015 AI Score for Given Journal')\n",
-    "plt.ylabel('Avg Citations per Paper for Given Journal 6 Years \\n 2010-2015')\n",
-    "plt.xlim(0,13)\n",
-    "plt.ylim(0,70)\n",
-    "\n",
-    "plt.savefig('ai_avg_citation_5_years_scatterplot_zoomed.jpg')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 726,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -2414,14 +2487,15 @@
        "LinregressResult(slope=2.8381638747589903, intercept=5.8630182583180499, rvalue=0.14123056048556826, pvalue=0.0093246181474941313, stderr=1.0853358124970376)"
       ]
      },
-     "execution_count": 726,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# Linear regression not working because of NAs - run without NAs\n",
     "mask = ~np.isnan(pivot_table_merged['article_influence']) & ~np.isnan(pivot_table_merged['avg_cites_per_paper'])\n",
-    "linregress(pivot_table_merged['article_influence'][mask], pivot_table_merged['avg_cites_per_paper'][mask])"
+    "stats.linregress(pivot_table_merged['article_influence'][mask], pivot_table_merged['avg_cites_per_paper'][mask])"
    ]
   },
   {
@@ -2433,7 +2507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 727,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -2509,20 +2583,23 @@
        "317              0.166  0034-7744  "
       ]
      },
-     "execution_count": 727,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# Create table with the NA rows and output\n",
     "nan_rows = pivot_table_merged[pivot_table_merged.isnull().T.any().T]\n",
     "nan_rows"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 728,
-   "metadata": {},
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Remove the 3 rows\n",
@@ -2531,7 +2608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 729,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -2597,850 +2674,49 @@
        "      <td>0.112</td>\n",
        "      <td>1646-0758</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>acta neurobiologiae experimentalis</td>\n",
-       "      <td>0065-1400</td>\n",
-       "      <td>4243.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>604.0</td>\n",
-       "      <td>7.024834</td>\n",
-       "      <td>0.506</td>\n",
-       "      <td>0065-1400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>acta scientiarum-agronomy</td>\n",
-       "      <td>1807-8621</td>\n",
-       "      <td>832.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1131.0</td>\n",
-       "      <td>0.735632</td>\n",
-       "      <td>0.307</td>\n",
-       "      <td>1807-8621</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>acta zoologica academiae scientiarum hungaricae</td>\n",
-       "      <td>1217-8837</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>7.666667</td>\n",
-       "      <td>0.187</td>\n",
-       "      <td>1217-8837</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>adansonia</td>\n",
-       "      <td>1280-8571</td>\n",
-       "      <td>157.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>139.0</td>\n",
-       "      <td>1.129496</td>\n",
-       "      <td>0.193</td>\n",
-       "      <td>1280-8571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>advances in difference equations</td>\n",
-       "      <td>1687-1847</td>\n",
-       "      <td>2610.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1590.0</td>\n",
-       "      <td>1.641509</td>\n",
-       "      <td>0.113</td>\n",
-       "      <td>1687-1847</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>advances in mechanical engineering</td>\n",
-       "      <td>1687-8140</td>\n",
-       "      <td>1091.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>2160.0</td>\n",
-       "      <td>0.505093</td>\n",
-       "      <td>0.198</td>\n",
-       "      <td>1687-8140</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>aids research and therapy</td>\n",
-       "      <td>1742-6405</td>\n",
-       "      <td>1881.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>247.0</td>\n",
-       "      <td>7.615385</td>\n",
-       "      <td>0.601</td>\n",
-       "      <td>1742-6405</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>aip advances</td>\n",
-       "      <td>2158-3226</td>\n",
-       "      <td>2576.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>2574.0</td>\n",
-       "      <td>1.000777</td>\n",
-       "      <td>0.452</td>\n",
-       "      <td>2158-3226</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>algorithms for molecular biology</td>\n",
-       "      <td>1748-7188</td>\n",
-       "      <td>1382.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>192.0</td>\n",
-       "      <td>7.197917</td>\n",
-       "      <td>1.902</td>\n",
-       "      <td>1748-7188</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>amb express</td>\n",
-       "      <td>2191-0855</td>\n",
-       "      <td>1203.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>401.0</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>0.566</td>\n",
-       "      <td>2191-0855</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>annals of clinical microbiology and antimicrob...</td>\n",
-       "      <td>1476-0711</td>\n",
-       "      <td>2855.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>254.0</td>\n",
-       "      <td>11.240157</td>\n",
-       "      <td>0.740</td>\n",
-       "      <td>1476-0711</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>annals of general psychiatry</td>\n",
-       "      <td>1744-859X</td>\n",
-       "      <td>2329.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>454.0</td>\n",
-       "      <td>5.129956</td>\n",
-       "      <td>0.416</td>\n",
-       "      <td>1744-859X</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>annals of indian academy of neurology</td>\n",
-       "      <td>0972-2327</td>\n",
-       "      <td>1155.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>798.0</td>\n",
-       "      <td>1.447368</td>\n",
-       "      <td>0.224</td>\n",
-       "      <td>0972-2327</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>annals of intensive care</td>\n",
-       "      <td>2110-5820</td>\n",
-       "      <td>1585.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>263.0</td>\n",
-       "      <td>6.026616</td>\n",
-       "      <td>1.366</td>\n",
-       "      <td>2110-5820</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>annals of thoracic medicine</td>\n",
-       "      <td>1817-1737</td>\n",
-       "      <td>1201.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>353.0</td>\n",
-       "      <td>3.402266</td>\n",
-       "      <td>0.369</td>\n",
-       "      <td>1817-1737</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>aob plants</td>\n",
-       "      <td>2041-2851</td>\n",
-       "      <td>946.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>412.0</td>\n",
-       "      <td>2.296117</td>\n",
-       "      <td>0.737</td>\n",
-       "      <td>2041-2851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>aquatic biology</td>\n",
-       "      <td>1864-7790</td>\n",
-       "      <td>3354.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>435.0</td>\n",
-       "      <td>7.710345</td>\n",
-       "      <td>0.521</td>\n",
-       "      <td>1864-7790</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>archives of metallurgy and materials</td>\n",
-       "      <td>1733-3490</td>\n",
-       "      <td>1118.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1210.0</td>\n",
-       "      <td>0.923967</td>\n",
-       "      <td>0.117</td>\n",
-       "      <td>1733-3490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>arthritis research &amp; therapy</td>\n",
-       "      <td>1478-6354</td>\n",
-       "      <td>34503.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>2313.0</td>\n",
-       "      <td>14.916991</td>\n",
-       "      <td>1.559</td>\n",
-       "      <td>1478-6354</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>asn neuro</td>\n",
-       "      <td>1759-0914</td>\n",
-       "      <td>1747.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>174.0</td>\n",
-       "      <td>10.040230</td>\n",
-       "      <td>1.145</td>\n",
-       "      <td>1759-0914</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>atmosphere</td>\n",
-       "      <td>2073-4433</td>\n",
-       "      <td>586.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>516.0</td>\n",
-       "      <td>1.135659</td>\n",
-       "      <td>0.520</td>\n",
-       "      <td>2073-4433</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>atmospheric science letters</td>\n",
-       "      <td>1530-261X</td>\n",
-       "      <td>2929.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>518.0</td>\n",
-       "      <td>5.654440</td>\n",
-       "      <td>0.768</td>\n",
-       "      <td>1530-261X</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>bangladesh journal of plant taxonomy</td>\n",
-       "      <td>1028-2092</td>\n",
-       "      <td>217.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>149.0</td>\n",
-       "      <td>1.456376</td>\n",
-       "      <td>0.080</td>\n",
-       "      <td>1028-2092</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>behavioral and brain functions</td>\n",
-       "      <td>1744-9081</td>\n",
-       "      <td>5072.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>326.0</td>\n",
-       "      <td>15.558282</td>\n",
-       "      <td>0.693</td>\n",
-       "      <td>1744-9081</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>beilstein journal of organic chemistry</td>\n",
-       "      <td>1860-5397</td>\n",
-       "      <td>6167.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1552.0</td>\n",
-       "      <td>3.973582</td>\n",
-       "      <td>0.683</td>\n",
-       "      <td>1860-5397</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>biodata mining</td>\n",
-       "      <td>1756-0381</td>\n",
-       "      <td>719.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>163.0</td>\n",
-       "      <td>4.411043</td>\n",
-       "      <td>0.668</td>\n",
-       "      <td>1756-0381</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>321</th>\n",
-       "      <td>salud colectiva</td>\n",
-       "      <td>1851-8265</td>\n",
-       "      <td>111.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>407.0</td>\n",
-       "      <td>0.272727</td>\n",
-       "      <td>0.077</td>\n",
-       "      <td>1851-8265</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>322</th>\n",
-       "      <td>scandinavian journal of trauma, resuscitation ...</td>\n",
-       "      <td>1757-7241</td>\n",
-       "      <td>2254.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>839.0</td>\n",
-       "      <td>2.686532</td>\n",
-       "      <td>0.782</td>\n",
-       "      <td>1757-7241</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>323</th>\n",
-       "      <td>science of sintering</td>\n",
-       "      <td>0350-820X</td>\n",
-       "      <td>566.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>185.0</td>\n",
-       "      <td>3.059459</td>\n",
-       "      <td>0.134</td>\n",
-       "      <td>0350-820X</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>324</th>\n",
-       "      <td>scientia agricola</td>\n",
-       "      <td>1678-992X</td>\n",
-       "      <td>4593.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>599.0</td>\n",
-       "      <td>7.667780</td>\n",
-       "      <td>0.345</td>\n",
-       "      <td>1678-992X</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>325</th>\n",
-       "      <td>scientific reports</td>\n",
-       "      <td>2045-2322</td>\n",
-       "      <td>29396.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>13260.0</td>\n",
-       "      <td>2.216893</td>\n",
-       "      <td>1.482</td>\n",
-       "      <td>2045-2322</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>326</th>\n",
-       "      <td>sensors</td>\n",
-       "      <td>1424-8220</td>\n",
-       "      <td>39346.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>7717.0</td>\n",
-       "      <td>5.098613</td>\n",
-       "      <td>0.546</td>\n",
-       "      <td>1424-8220</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>328</th>\n",
-       "      <td>sola</td>\n",
-       "      <td>1349-6476</td>\n",
-       "      <td>1765.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>265.0</td>\n",
-       "      <td>6.660377</td>\n",
-       "      <td>0.605</td>\n",
-       "      <td>1349-6476</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>329</th>\n",
-       "      <td>south african journal of economic and manageme...</td>\n",
-       "      <td>2222-3436</td>\n",
-       "      <td>367.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>421.0</td>\n",
-       "      <td>0.871734</td>\n",
-       "      <td>0.077</td>\n",
-       "      <td>2222-3436</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>330</th>\n",
-       "      <td>south african journal of industrial engineering</td>\n",
-       "      <td>2224-7890</td>\n",
-       "      <td>230.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>498.0</td>\n",
-       "      <td>0.461847</td>\n",
-       "      <td>0.076</td>\n",
-       "      <td>2224-7890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>331</th>\n",
-       "      <td>standards in genomic sciences</td>\n",
-       "      <td>1944-3277</td>\n",
-       "      <td>1388.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>566.0</td>\n",
-       "      <td>2.452297</td>\n",
-       "      <td>0.422</td>\n",
-       "      <td>1944-3277</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>332</th>\n",
-       "      <td>stem cell research &amp; therapy</td>\n",
-       "      <td>1757-6512</td>\n",
-       "      <td>2775.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>739.0</td>\n",
-       "      <td>3.755074</td>\n",
-       "      <td>1.123</td>\n",
-       "      <td>1757-6512</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>333</th>\n",
-       "      <td>substance abuse treatment prevention and policy</td>\n",
-       "      <td>1747-597X</td>\n",
-       "      <td>1750.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>252.0</td>\n",
-       "      <td>6.944444</td>\n",
-       "      <td>0.628</td>\n",
-       "      <td>1747-597X</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>334</th>\n",
-       "      <td>sustainability</td>\n",
-       "      <td>2071-1050</td>\n",
-       "      <td>4624.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>2239.0</td>\n",
-       "      <td>2.065208</td>\n",
-       "      <td>0.333</td>\n",
-       "      <td>2071-1050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>335</th>\n",
-       "      <td>symmetry</td>\n",
-       "      <td>2073-8994</td>\n",
-       "      <td>705.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>335.0</td>\n",
-       "      <td>2.104478</td>\n",
-       "      <td>0.430</td>\n",
-       "      <td>2073-8994</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>336</th>\n",
-       "      <td>symmetry integrability and geometry-methods an...</td>\n",
-       "      <td>1815-0659</td>\n",
-       "      <td>1386.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>547.0</td>\n",
-       "      <td>2.533821</td>\n",
-       "      <td>0.611</td>\n",
-       "      <td>1815-0659</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>337</th>\n",
-       "      <td>tellus a</td>\n",
-       "      <td>1600-0870</td>\n",
-       "      <td>13727.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>699.0</td>\n",
-       "      <td>19.638054</td>\n",
-       "      <td>1.033</td>\n",
-       "      <td>1600-0870</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>338</th>\n",
-       "      <td>the international food and agribusiness manage...</td>\n",
-       "      <td>1559-2448</td>\n",
-       "      <td>1403.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>238.0</td>\n",
-       "      <td>5.894958</td>\n",
-       "      <td>0.167</td>\n",
-       "      <td>1559-2448</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>339</th>\n",
-       "      <td>the plant genome</td>\n",
-       "      <td>1940-3372</td>\n",
-       "      <td>1725.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>164.0</td>\n",
-       "      <td>10.518293</td>\n",
-       "      <td>1.287</td>\n",
-       "      <td>1940-3372</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>340</th>\n",
-       "      <td>theoretical biology and medical modelling</td>\n",
-       "      <td>1742-4682</td>\n",
-       "      <td>2747.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>312.0</td>\n",
-       "      <td>8.804487</td>\n",
-       "      <td>0.376</td>\n",
-       "      <td>1742-4682</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>341</th>\n",
-       "      <td>therapeutics and clinical risk management</td>\n",
-       "      <td>1178-203X</td>\n",
-       "      <td>3998.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>564.0</td>\n",
-       "      <td>7.088652</td>\n",
-       "      <td>0.490</td>\n",
-       "      <td>1178-203X</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>thermal science</td>\n",
-       "      <td>0354-9836</td>\n",
-       "      <td>2062.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1303.0</td>\n",
-       "      <td>1.582502</td>\n",
-       "      <td>0.147</td>\n",
-       "      <td>0354-9836</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>tobacco induced diseases</td>\n",
-       "      <td>1617-9625</td>\n",
-       "      <td>807.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>185.0</td>\n",
-       "      <td>4.362162</td>\n",
-       "      <td>0.673</td>\n",
-       "      <td>1617-9625</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>344</th>\n",
-       "      <td>toxins</td>\n",
-       "      <td>2072-6651</td>\n",
-       "      <td>4389.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1047.0</td>\n",
-       "      <td>4.191977</td>\n",
-       "      <td>0.805</td>\n",
-       "      <td>2072-6651</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>345</th>\n",
-       "      <td>translational oncology</td>\n",
-       "      <td>1936-5233</td>\n",
-       "      <td>2655.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>400.0</td>\n",
-       "      <td>6.637500</td>\n",
-       "      <td>0.854</td>\n",
-       "      <td>1936-5233</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>347</th>\n",
-       "      <td>trials</td>\n",
-       "      <td>1745-6215</td>\n",
-       "      <td>11452.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>2852.0</td>\n",
-       "      <td>4.015428</td>\n",
-       "      <td>0.893</td>\n",
-       "      <td>1745-6215</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>348</th>\n",
-       "      <td>veterinarni medicina</td>\n",
-       "      <td>0375-8427</td>\n",
-       "      <td>977.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>436.0</td>\n",
-       "      <td>2.240826</td>\n",
-       "      <td>0.217</td>\n",
-       "      <td>0375-8427</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>349</th>\n",
-       "      <td>virology journal</td>\n",
-       "      <td>1743-422X</td>\n",
-       "      <td>17658.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>2111.0</td>\n",
-       "      <td>8.364756</td>\n",
-       "      <td>0.635</td>\n",
-       "      <td>1743-422X</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>350</th>\n",
-       "      <td>water</td>\n",
-       "      <td>2073-4441</td>\n",
-       "      <td>1837.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>905.0</td>\n",
-       "      <td>2.029834</td>\n",
-       "      <td>0.435</td>\n",
-       "      <td>2073-4441</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>351</th>\n",
-       "      <td>world journal of emergency surgery</td>\n",
-       "      <td>1749-7922</td>\n",
-       "      <td>1568.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>314.0</td>\n",
-       "      <td>4.993631</td>\n",
-       "      <td>0.557</td>\n",
-       "      <td>1749-7922</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>352</th>\n",
-       "      <td>world journal of surgical oncology</td>\n",
-       "      <td>1477-7819</td>\n",
-       "      <td>8119.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1638.0</td>\n",
-       "      <td>4.956654</td>\n",
-       "      <td>0.392</td>\n",
-       "      <td>1477-7819</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>338 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                          journal_name       ISSN  \\\n",
-       "0                                       acta bioethica  1726-569X   \n",
-       "1                               acta botanica croatica  0365-0588   \n",
-       "2                                  acta medica okayama  0386-300X   \n",
-       "3                               acta medica portuguesa  1646-0758   \n",
-       "4                   acta neurobiologiae experimentalis  0065-1400   \n",
-       "5                            acta scientiarum-agronomy  1807-8621   \n",
-       "6      acta zoologica academiae scientiarum hungaricae  1217-8837   \n",
-       "7                                            adansonia  1280-8571   \n",
-       "8                     advances in difference equations  1687-1847   \n",
-       "9                   advances in mechanical engineering  1687-8140   \n",
-       "10                           aids research and therapy  1742-6405   \n",
-       "11                                        aip advances  2158-3226   \n",
-       "12                    algorithms for molecular biology  1748-7188   \n",
-       "13                                         amb express  2191-0855   \n",
-       "14   annals of clinical microbiology and antimicrob...  1476-0711   \n",
-       "15                        annals of general psychiatry  1744-859X   \n",
-       "16               annals of indian academy of neurology  0972-2327   \n",
-       "17                            annals of intensive care  2110-5820   \n",
-       "18                         annals of thoracic medicine  1817-1737   \n",
-       "19                                          aob plants  2041-2851   \n",
-       "21                                     aquatic biology  1864-7790   \n",
-       "22                archives of metallurgy and materials  1733-3490   \n",
-       "23                        arthritis research & therapy  1478-6354   \n",
-       "24                                           asn neuro  1759-0914   \n",
-       "25                                          atmosphere  2073-4433   \n",
-       "26                         atmospheric science letters  1530-261X   \n",
-       "27                bangladesh journal of plant taxonomy  1028-2092   \n",
-       "28                      behavioral and brain functions  1744-9081   \n",
-       "29              beilstein journal of organic chemistry  1860-5397   \n",
-       "30                                      biodata mining  1756-0381   \n",
-       "..                                                 ...        ...   \n",
-       "321                                    salud colectiva  1851-8265   \n",
-       "322  scandinavian journal of trauma, resuscitation ...  1757-7241   \n",
-       "323                               science of sintering  0350-820X   \n",
-       "324                                  scientia agricola  1678-992X   \n",
-       "325                                 scientific reports  2045-2322   \n",
-       "326                                            sensors  1424-8220   \n",
-       "328                                               sola  1349-6476   \n",
-       "329  south african journal of economic and manageme...  2222-3436   \n",
-       "330    south african journal of industrial engineering  2224-7890   \n",
-       "331                      standards in genomic sciences  1944-3277   \n",
-       "332                       stem cell research & therapy  1757-6512   \n",
-       "333    substance abuse treatment prevention and policy  1747-597X   \n",
-       "334                                     sustainability  2071-1050   \n",
-       "335                                           symmetry  2073-8994   \n",
-       "336  symmetry integrability and geometry-methods an...  1815-0659   \n",
-       "337                                           tellus a  1600-0870   \n",
-       "338  the international food and agribusiness manage...  1559-2448   \n",
-       "339                                   the plant genome  1940-3372   \n",
-       "340          theoretical biology and medical modelling  1742-4682   \n",
-       "341          therapeutics and clinical risk management  1178-203X   \n",
-       "342                                    thermal science  0354-9836   \n",
-       "343                           tobacco induced diseases  1617-9625   \n",
-       "344                                             toxins  2072-6651   \n",
-       "345                             translational oncology  1936-5233   \n",
-       "347                                             trials  1745-6215   \n",
-       "348                               veterinarni medicina  0375-8427   \n",
-       "349                                   virology journal  1743-422X   \n",
-       "350                                              water  2073-4441   \n",
-       "351                 world journal of emergency surgery  1749-7922   \n",
-       "352                 world journal of surgical oncology  1477-7819   \n",
-       "\n",
-       "     citation_count_sum  number_years_captured  paper_count_sum  \\\n",
-       "0                 135.0                    6.0            490.0   \n",
-       "1                 645.0                    6.0            209.0   \n",
-       "2                2625.0                    6.0            325.0   \n",
-       "3                 964.0                    6.0            979.0   \n",
-       "4                4243.0                    6.0            604.0   \n",
-       "5                 832.0                    6.0           1131.0   \n",
-       "6                  69.0                    6.0              9.0   \n",
-       "7                 157.0                    6.0            139.0   \n",
-       "8                2610.0                    6.0           1590.0   \n",
-       "9                1091.0                    6.0           2160.0   \n",
-       "10               1881.0                    6.0            247.0   \n",
-       "11               2576.0                    6.0           2574.0   \n",
-       "12               1382.0                    6.0            192.0   \n",
-       "13               1203.0                    6.0            401.0   \n",
-       "14               2855.0                    6.0            254.0   \n",
-       "15               2329.0                    6.0            454.0   \n",
-       "16               1155.0                    6.0            798.0   \n",
-       "17               1585.0                    6.0            263.0   \n",
-       "18               1201.0                    6.0            353.0   \n",
-       "19                946.0                    6.0            412.0   \n",
-       "21               3354.0                    6.0            435.0   \n",
-       "22               1118.0                    6.0           1210.0   \n",
-       "23              34503.0                    6.0           2313.0   \n",
-       "24               1747.0                    6.0            174.0   \n",
-       "25                586.0                    6.0            516.0   \n",
-       "26               2929.0                    6.0            518.0   \n",
-       "27                217.0                    6.0            149.0   \n",
-       "28               5072.0                    6.0            326.0   \n",
-       "29               6167.0                    6.0           1552.0   \n",
-       "30                719.0                    6.0            163.0   \n",
-       "..                  ...                    ...              ...   \n",
-       "321               111.0                    6.0            407.0   \n",
-       "322              2254.0                    6.0            839.0   \n",
-       "323               566.0                    6.0            185.0   \n",
-       "324              4593.0                    6.0            599.0   \n",
-       "325             29396.0                    6.0          13260.0   \n",
-       "326             39346.0                    6.0           7717.0   \n",
-       "328              1765.0                    6.0            265.0   \n",
-       "329               367.0                    6.0            421.0   \n",
-       "330               230.0                    6.0            498.0   \n",
-       "331              1388.0                    6.0            566.0   \n",
-       "332              2775.0                    6.0            739.0   \n",
-       "333              1750.0                    6.0            252.0   \n",
-       "334              4624.0                    6.0           2239.0   \n",
-       "335               705.0                    6.0            335.0   \n",
-       "336              1386.0                    6.0            547.0   \n",
-       "337             13727.0                    6.0            699.0   \n",
-       "338              1403.0                    6.0            238.0   \n",
-       "339              1725.0                    6.0            164.0   \n",
-       "340              2747.0                    6.0            312.0   \n",
-       "341              3998.0                    6.0            564.0   \n",
-       "342              2062.0                    6.0           1303.0   \n",
-       "343               807.0                    6.0            185.0   \n",
-       "344              4389.0                    6.0           1047.0   \n",
-       "345              2655.0                    6.0            400.0   \n",
-       "347             11452.0                    6.0           2852.0   \n",
-       "348               977.0                    6.0            436.0   \n",
-       "349             17658.0                    6.0           2111.0   \n",
-       "350              1837.0                    6.0            905.0   \n",
-       "351              1568.0                    6.0            314.0   \n",
-       "352              8119.0                    6.0           1638.0   \n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             journal_name       ISSN  citation_count_sum  \\\n",
+       "0          acta bioethica  1726-569X               135.0   \n",
+       "1  acta botanica croatica  0365-0588               645.0   \n",
+       "2     acta medica okayama  0386-300X              2625.0   \n",
+       "3  acta medica portuguesa  1646-0758               964.0   \n",
        "\n",
-       "     avg_cites_per_paper  article_influence journal_id  \n",
-       "0               0.275510              0.030  1726-569X  \n",
-       "1               3.086124              0.181  0365-0588  \n",
-       "2               8.076923              0.230  0386-300X  \n",
-       "3               0.984678              0.112  1646-0758  \n",
-       "4               7.024834              0.506  0065-1400  \n",
-       "5               0.735632              0.307  1807-8621  \n",
-       "6               7.666667              0.187  1217-8837  \n",
-       "7               1.129496              0.193  1280-8571  \n",
-       "8               1.641509              0.113  1687-1847  \n",
-       "9               0.505093              0.198  1687-8140  \n",
-       "10              7.615385              0.601  1742-6405  \n",
-       "11              1.000777              0.452  2158-3226  \n",
-       "12              7.197917              1.902  1748-7188  \n",
-       "13              3.000000              0.566  2191-0855  \n",
-       "14             11.240157              0.740  1476-0711  \n",
-       "15              5.129956              0.416  1744-859X  \n",
-       "16              1.447368              0.224  0972-2327  \n",
-       "17              6.026616              1.366  2110-5820  \n",
-       "18              3.402266              0.369  1817-1737  \n",
-       "19              2.296117              0.737  2041-2851  \n",
-       "21              7.710345              0.521  1864-7790  \n",
-       "22              0.923967              0.117  1733-3490  \n",
-       "23             14.916991              1.559  1478-6354  \n",
-       "24             10.040230              1.145  1759-0914  \n",
-       "25              1.135659              0.520  2073-4433  \n",
-       "26              5.654440              0.768  1530-261X  \n",
-       "27              1.456376              0.080  1028-2092  \n",
-       "28             15.558282              0.693  1744-9081  \n",
-       "29              3.973582              0.683  1860-5397  \n",
-       "30              4.411043              0.668  1756-0381  \n",
-       "..                   ...                ...        ...  \n",
-       "321             0.272727              0.077  1851-8265  \n",
-       "322             2.686532              0.782  1757-7241  \n",
-       "323             3.059459              0.134  0350-820X  \n",
-       "324             7.667780              0.345  1678-992X  \n",
-       "325             2.216893              1.482  2045-2322  \n",
-       "326             5.098613              0.546  1424-8220  \n",
-       "328             6.660377              0.605  1349-6476  \n",
-       "329             0.871734              0.077  2222-3436  \n",
-       "330             0.461847              0.076  2224-7890  \n",
-       "331             2.452297              0.422  1944-3277  \n",
-       "332             3.755074              1.123  1757-6512  \n",
-       "333             6.944444              0.628  1747-597X  \n",
-       "334             2.065208              0.333  2071-1050  \n",
-       "335             2.104478              0.430  2073-8994  \n",
-       "336             2.533821              0.611  1815-0659  \n",
-       "337            19.638054              1.033  1600-0870  \n",
-       "338             5.894958              0.167  1559-2448  \n",
-       "339            10.518293              1.287  1940-3372  \n",
-       "340             8.804487              0.376  1742-4682  \n",
-       "341             7.088652              0.490  1178-203X  \n",
-       "342             1.582502              0.147  0354-9836  \n",
-       "343             4.362162              0.673  1617-9625  \n",
-       "344             4.191977              0.805  2072-6651  \n",
-       "345             6.637500              0.854  1936-5233  \n",
-       "347             4.015428              0.893  1745-6215  \n",
-       "348             2.240826              0.217  0375-8427  \n",
-       "349             8.364756              0.635  1743-422X  \n",
-       "350             2.029834              0.435  2073-4441  \n",
-       "351             4.993631              0.557  1749-7922  \n",
-       "352             4.956654              0.392  1477-7819  \n",
+       "   number_years_captured  paper_count_sum  avg_cites_per_paper  \\\n",
+       "0                    6.0            490.0             0.275510   \n",
+       "1                    6.0            209.0             3.086124   \n",
+       "2                    6.0            325.0             8.076923   \n",
+       "3                    6.0            979.0             0.984678   \n",
        "\n",
-       "[338 rows x 8 columns]"
+       "   article_influence journal_id  \n",
+       "0              0.030  1726-569X  \n",
+       "1              0.181  0365-0588  \n",
+       "2              0.230  0386-300X  \n",
+       "3              0.112  1646-0758  "
       ]
      },
-     "execution_count": 729,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pivot_table_merged"
+    "pivot_table_merged.head(4)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 730,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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XSS9IOqfW98TMzMxqr5o6TddIOjx7vwPwDHA/cAnwAPCUpO27pZed+w0wFBiWvT5capB0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\n6Z/tVYUu2ysc6ppT7+R7ZWZ9gUeaNiA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K2LZJwAnuvlOG40cAzc3NzYwYMSLpcERERMpWS0sLdXV1AHXu3lLoeCC7npWN055vA7wd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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11d216e10>"
+       "<matplotlib.figure.Figure at 0x119bc89e8>"
       ]
      },
      "metadata": {},
@@ -3448,10 +2724,9 @@
     }
    ],
    "source": [
-    "plt.plot(pivot_table_merged['article_influence'], \n",
-    "         pivot_table_merged['citation_count_sum'], 'o')\n",
-    "plt.xlabel('2015 AI Score for Given Journal')\n",
-    "plt.ylabel('Citation Count per Journal 6 Years \\n 2010-2015')\n",
+    "plt.plot(pivot_table_merged['citation_count_sum'], pivot_table_merged['article_influence'], 'o')\n",
+    "plt.ylabel('2015 AI Score for Given Journal')\n",
+    "plt.xlabel('Citation Count per Journal 6 Years \\n 2010-2015')\n",
     "\n",
     "\n",
     "plt.savefig('ai_citation_ct_5_years_scatterplot.jpg')"
@@ -3459,14 +2734,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 731,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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ZPoUgU7i7j8hxPv1eO4G3uPtfC+rEMDOUSgCkU8VtEREZSP0pAVDwSFKR6xjp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\nIW3A54HTgFOBF7EvNLNFZFWhwtdFUvvlXaARC5aMswtQib/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KP/3pru4cuoiISMUo5BbDx4F3AdvN7Odm9j4z6/aaB9k0Nzczc+Y8RoyYwLBh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\n0JVcKi0wVAlw6S5532Ig5B/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f6qnjvA78iiOPfBZYxJNP9iX7BbgW+Ar9+9/L/vvvZtu2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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11eb744a8>"
+       "<matplotlib.figure.Figure at 0x117001ba8>"
       ]
      },
      "metadata": {},
@@ -3474,39 +2749,44 @@
     }
    ],
    "source": [
-    "plt.plot(pivot_table_merged['article_influence'], \n",
-    "         pivot_table_merged['citation_count_sum'], 'o')\n",
-    "plt.xlabel('2015 AI Score for Given Journal')\n",
-    "plt.ylabel('Citation Count per Journal 6 Years \\n 2010-2015')\n",
-    "plt.ylim(0,110000)\n",
-    "plt.xlim(0, 8)\n",
+    "plt.plot(pivot_table_merged['citation_count_sum'], pivot_table_merged['article_influence'], 'o')\n",
+    "\n",
+    "plt.xlabel('Citation Count per Journal 6 Years \\n 2010-2015')\n",
+    "plt.ylabel('2015 AI Score for Given Journal')\n",
+    "\n",
+    "plt.xlim(0,110000) # limit the x axis\n",
+    "plt.ylim(0, 8) # limit the y axis\n",
+    "\n",
     "plt.savefig('ai_citation_ct_5_years_scatterplot_zoomed.jpg')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 732,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinregressResult(slope=5479.7435994663083, intercept=4378.3079765581606, rvalue=0.15148402527331556, pvalue=0.0052577020088959581, stderr=1950.6651845140079)"
+       "LinregressResult(slope=0.95968708627030619, intercept=3.6813869181378762, rvalue=0.59053941374286478, pvalue=3.7664268469984426e-33, stderr=0.07154666474935821)"
       ]
      },
-     "execution_count": 732,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "linregress(pivot_table_merged['article_influence'], pivot_table_merged['citation_count_sum'])"
+    "# Test linear regression results using log10 dependent and independent variables \n",
+    "stats.linregress(np.log10(pivot_table_merged['article_influence']), np.log10(pivot_table_merged['citation_count_sum']))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 733,
-   "metadata": {},
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Set seed for reproducibility\n",
@@ -3517,8 +2797,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 734,
-   "metadata": {},
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Run linear regression with three features\n",
@@ -3532,7 +2814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 735,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -3550,10 +2832,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   28.42</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Fri, 21 Jul 2017</td>  <th>  Prob (F-statistic):</th> <td>5.70e-16</td>\n",
+       "  <th>Date:</th>             <td>Sun, 23 Jul 2017</td>  <th>  Prob (F-statistic):</th> <td>5.70e-16</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>09:28:18</td>      <th>  Log-Likelihood:    </th> <td> -327.13</td>\n",
+       "  <th>Time:</th>                 <td>14:52:40</td>      <th>  Log-Likelihood:    </th> <td> -327.13</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   270</td>       <th>  AIC:               </th> <td>   662.3</td>\n",
@@ -3608,8 +2890,8 @@
        "Dep. Variable:      article_influence   R-squared:                       0.243\n",
        "Model:                            OLS   Adj. R-squared:                  0.234\n",
        "Method:                 Least Squares   F-statistic:                     28.42\n",
-       "Date:                Fri, 21 Jul 2017   Prob (F-statistic):           5.70e-16\n",
-       "Time:                        09:28:18   Log-Likelihood:                -327.13\n",
+       "Date:                Sun, 23 Jul 2017   Prob (F-statistic):           5.70e-16\n",
+       "Time:                        14:52:40   Log-Likelihood:                -327.13\n",
        "No. Observations:                 270   AIC:                             662.3\n",
        "Df Residuals:                     266   BIC:                             676.6\n",
        "Df Model:                           3                                         \n",
@@ -3635,7 +2917,7 @@
        "\"\"\""
       ]
      },
-     "execution_count": 735,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3647,7 +2929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 736,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -3656,7 +2938,7 @@
        "1.4733898156917509"
       ]
      },
-     "execution_count": 736,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3668,7 +2950,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 737,
+   "execution_count": 62,
    "metadata": {
     "collapsed": true
    },
@@ -3676,7 +2958,7 @@
    "source": [
     "# Run linear regression with three features\n",
     "pt_log_lr = smf.ols(formula=\n",
-    "                   'np.log(article_influence) ~ np.log(citation_count_sum) + np.log(paper_count_sum) + np.log(avg_cites_per_paper)', \n",
+    "                   'np.log10(article_influence) ~ np.log10(citation_count_sum) + np.log10(paper_count_sum) + np.log10(avg_cites_per_paper)', \n",
     "                data=pt_train).fit()\n",
     "# Create an array of predictions for the training and testing data\n",
     "pt_log_train_pred = pt_log_lr.predict(pt_train)\n",
@@ -3685,7 +2967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 738,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -3694,48 +2976,48 @@
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>    <td>np.log(article_influence)</td> <th>  R-squared:         </th> <td>   0.431</td>\n",
+       "  <th>Dep. Variable:</th>    <td>np.log10(article_influence)</td> <th>  R-squared:         </th> <td>   0.431</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>                       <td>OLS</td>            <th>  Adj. R-squared:    </th> <td>   0.427</td>\n",
+       "  <th>Model:</th>                        <td>OLS</td>             <th>  Adj. R-squared:    </th> <td>   0.427</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>                 <td>Least Squares</td>       <th>  F-statistic:       </th> <td>   101.3</td>\n",
+       "  <th>Method:</th>                  <td>Least Squares</td>        <th>  F-statistic:       </th> <td>   101.3</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>                 <td>Fri, 21 Jul 2017</td>      <th>  Prob (F-statistic):</th> <td>1.81e-33</td>\n",
+       "  <th>Date:</th>                  <td>Sun, 23 Jul 2017</td>       <th>  Prob (F-statistic):</th> <td>1.81e-33</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                     <td>09:28:18</td>          <th>  Log-Likelihood:    </th> <td> -283.64</td>\n",
+       "  <th>Time:</th>                      <td>14:55:20</td>           <th>  Log-Likelihood:    </th> <td> -58.450</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>No. Observations:</th>          <td>   270</td>           <th>  AIC:               </th> <td>   573.3</td>\n",
+       "  <th>No. Observations:</th>           <td>   270</td>            <th>  AIC:               </th> <td>   122.9</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Df Residuals:</th>              <td>   267</td>           <th>  BIC:               </th> <td>   584.1</td>\n",
+       "  <th>Df Residuals:</th>               <td>   267</td>            <th>  BIC:               </th> <td>   133.7</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Df Model:</th>                  <td>     2</td>           <th>                     </th>     <td> </td>   \n",
+       "  <th>Df Model:</th>                   <td>     2</td>            <th>                     </th>     <td> </td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Covariance Type:</th>          <td>nonrobust</td>         <th>                     </th>     <td> </td>   \n",
+       "  <th>Covariance Type:</th>           <td>nonrobust</td>          <th>                     </th>     <td> </td>   \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "               <td></td>                  <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
+       "                <td></td>                   <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Intercept</th>                   <td>   -3.1557</td> <td>    0.241</td> <td>  -13.069</td> <td> 0.000</td> <td>   -3.631    -2.680</td>\n",
+       "  <th>Intercept</th>                     <td>   -1.3705</td> <td>    0.105</td> <td>  -13.069</td> <td> 0.000</td> <td>   -1.577    -1.164</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>np.log(citation_count_sum)</th>  <td>    0.2754</td> <td>    0.020</td> <td>   14.047</td> <td> 0.000</td> <td>    0.237     0.314</td>\n",
+       "  <th>np.log10(citation_count_sum)</th>  <td>    0.2754</td> <td>    0.020</td> <td>   14.047</td> <td> 0.000</td> <td>    0.237     0.314</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>np.log(paper_count_sum)</th>     <td>    0.0010</td> <td>    0.025</td> <td>    0.038</td> <td> 0.970</td> <td>   -0.048     0.050</td>\n",
+       "  <th>np.log10(paper_count_sum)</th>     <td>    0.0010</td> <td>    0.025</td> <td>    0.038</td> <td> 0.970</td> <td>   -0.048     0.050</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>np.log(avg_cites_per_paper)</th> <td>    0.2744</td> <td>    0.029</td> <td>    9.438</td> <td> 0.000</td> <td>    0.217     0.332</td>\n",
+       "  <th>np.log10(avg_cites_per_paper)</th> <td>    0.2744</td> <td>    0.029</td> <td>    9.438</td> <td> 0.000</td> <td>    0.217     0.332</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
@@ -3749,46 +3031,46 @@
        "  <th>Skew:</th>          <td>-0.158</td> <th>  Prob(JB):          </th> <td>6.64e-25</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Kurtosis:</th>      <td> 6.130</td> <th>  Cond. No.          </th> <td>3.43e+16</td>\n",
+       "  <th>Kurtosis:</th>      <td> 6.130</td> <th>  Cond. No.          </th> <td>1.91e+16</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
-       "                                OLS Regression Results                               \n",
-       "=====================================================================================\n",
-       "Dep. Variable:     np.log(article_influence)   R-squared:                       0.431\n",
-       "Model:                                   OLS   Adj. R-squared:                  0.427\n",
-       "Method:                        Least Squares   F-statistic:                     101.3\n",
-       "Date:                       Fri, 21 Jul 2017   Prob (F-statistic):           1.81e-33\n",
-       "Time:                               09:28:18   Log-Likelihood:                -283.64\n",
-       "No. Observations:                        270   AIC:                             573.3\n",
-       "Df Residuals:                            267   BIC:                             584.1\n",
-       "Df Model:                                  2                                         \n",
-       "Covariance Type:                   nonrobust                                         \n",
-       "===============================================================================================\n",
-       "                                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
-       "-----------------------------------------------------------------------------------------------\n",
-       "Intercept                      -3.1557      0.241    -13.069      0.000        -3.631    -2.680\n",
-       "np.log(citation_count_sum)      0.2754      0.020     14.047      0.000         0.237     0.314\n",
-       "np.log(paper_count_sum)         0.0010      0.025      0.038      0.970        -0.048     0.050\n",
-       "np.log(avg_cites_per_paper)     0.2744      0.029      9.438      0.000         0.217     0.332\n",
+       "                                 OLS Regression Results                                \n",
+       "=======================================================================================\n",
+       "Dep. Variable:     np.log10(article_influence)   R-squared:                       0.431\n",
+       "Model:                                     OLS   Adj. R-squared:                  0.427\n",
+       "Method:                          Least Squares   F-statistic:                     101.3\n",
+       "Date:                         Sun, 23 Jul 2017   Prob (F-statistic):           1.81e-33\n",
+       "Time:                                 14:55:20   Log-Likelihood:                -58.450\n",
+       "No. Observations:                          270   AIC:                             122.9\n",
+       "Df Residuals:                              267   BIC:                             133.7\n",
+       "Df Model:                                    2                                         \n",
+       "Covariance Type:                     nonrobust                                         \n",
+       "=================================================================================================\n",
+       "                                    coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
+       "-------------------------------------------------------------------------------------------------\n",
+       "Intercept                        -1.3705      0.105    -13.069      0.000        -1.577    -1.164\n",
+       "np.log10(citation_count_sum)      0.2754      0.020     14.047      0.000         0.237     0.314\n",
+       "np.log10(paper_count_sum)         0.0010      0.025      0.038      0.970        -0.048     0.050\n",
+       "np.log10(avg_cites_per_paper)     0.2744      0.029      9.438      0.000         0.217     0.332\n",
        "==============================================================================\n",
        "Omnibus:                       26.311   Durbin-Watson:                   1.951\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              111.344\n",
        "Skew:                          -0.158   Prob(JB):                     6.64e-25\n",
-       "Kurtosis:                       6.130   Cond. No.                     3.43e+16\n",
+       "Kurtosis:                       6.130   Cond. No.                     1.91e+16\n",
        "==============================================================================\n",
        "\n",
        "Warnings:\n",
        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
-       "[2] The smallest eigenvalue is 2.54e-29. This might indicate that there are\n",
+       "[2] The smallest eigenvalue is 1.6e-29. This might indicate that there are\n",
        "strong multicollinearity problems or that the design matrix is singular.\n",
        "\"\"\""
       ]
      },
-     "execution_count": 738,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3800,34 +3082,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 739,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.61684121391405322"
+       "0.26789073541337655"
       ]
      },
-     "execution_count": 739,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Calculate the RMSE of the log of testing actual values versus predicted (log) values\n",
-    "np.sqrt(mean_squared_error(np.log(pt_test['article_influence']), pt_log_test_pred))"
+    "np.sqrt(mean_squared_error(np.log10(pt_test['article_influence']), pt_log_test_pred))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 740,
+   "execution_count": 80,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.8492686189780783"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert the log10 RMSEs of predictions\n",
+    "math.pow(10,0.267)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Run linear regression with two features\n",
     "pt_log_lr2 = smf.ols(formula=\n",
-    "                   'np.log(article_influence) ~ np.log(citation_count_sum) + np.log(avg_cites_per_paper)', \n",
+    "                   'np.log10(article_influence) ~ np.log10(citation_count_sum) + np.log10(avg_cites_per_paper)', \n",
     "                data=pt_train).fit()\n",
     "# Create an array of predictions for the training and testing data\n",
     "pt_log_train_pred2 = pt_log_lr2.predict(pt_train)\n",
@@ -3836,7 +3141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 741,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -3845,45 +3150,45 @@
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>    <td>np.log(article_influence)</td> <th>  R-squared:         </th> <td>   0.431</td>\n",
+       "  <th>Dep. Variable:</th>    <td>np.log10(article_influence)</td> <th>  R-squared:         </th> <td>   0.431</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>                       <td>OLS</td>            <th>  Adj. R-squared:    </th> <td>   0.427</td>\n",
+       "  <th>Model:</th>                        <td>OLS</td>             <th>  Adj. R-squared:    </th> <td>   0.427</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>                 <td>Least Squares</td>       <th>  F-statistic:       </th> <td>   101.3</td>\n",
+       "  <th>Method:</th>                  <td>Least Squares</td>        <th>  F-statistic:       </th> <td>   101.3</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>                 <td>Fri, 21 Jul 2017</td>      <th>  Prob (F-statistic):</th> <td>1.81e-33</td>\n",
+       "  <th>Date:</th>                  <td>Sun, 23 Jul 2017</td>       <th>  Prob (F-statistic):</th> <td>1.81e-33</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                     <td>09:28:18</td>          <th>  Log-Likelihood:    </th> <td> -283.64</td>\n",
+       "  <th>Time:</th>                      <td>15:00:17</td>           <th>  Log-Likelihood:    </th> <td> -58.450</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>No. Observations:</th>          <td>   270</td>           <th>  AIC:               </th> <td>   573.3</td>\n",
+       "  <th>No. Observations:</th>           <td>   270</td>            <th>  AIC:               </th> <td>   122.9</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Df Residuals:</th>              <td>   267</td>           <th>  BIC:               </th> <td>   584.1</td>\n",
+       "  <th>Df Residuals:</th>               <td>   267</td>            <th>  BIC:               </th> <td>   133.7</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Df Model:</th>                  <td>     2</td>           <th>                     </th>     <td> </td>   \n",
+       "  <th>Df Model:</th>                   <td>     2</td>            <th>                     </th>     <td> </td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Covariance Type:</th>          <td>nonrobust</td>         <th>                     </th>     <td> </td>   \n",
+       "  <th>Covariance Type:</th>           <td>nonrobust</td>          <th>                     </th>     <td> </td>   \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "               <td></td>                  <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
+       "                <td></td>                   <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Intercept</th>                   <td>   -3.1557</td> <td>    0.241</td> <td>  -13.069</td> <td> 0.000</td> <td>   -3.631    -2.680</td>\n",
+       "  <th>Intercept</th>                     <td>   -1.3705</td> <td>    0.105</td> <td>  -13.069</td> <td> 0.000</td> <td>   -1.577    -1.164</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>np.log(citation_count_sum)</th>  <td>    0.2763</td> <td>    0.034</td> <td>    8.086</td> <td> 0.000</td> <td>    0.209     0.344</td>\n",
+       "  <th>np.log10(citation_count_sum)</th>  <td>    0.2763</td> <td>    0.034</td> <td>    8.086</td> <td> 0.000</td> <td>    0.209     0.344</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>np.log(avg_cites_per_paper)</th> <td>    0.2735</td> <td>    0.051</td> <td>    5.414</td> <td> 0.000</td> <td>    0.174     0.373</td>\n",
+       "  <th>np.log10(avg_cites_per_paper)</th> <td>    0.2735</td> <td>    0.051</td> <td>    5.414</td> <td> 0.000</td> <td>    0.174     0.373</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
@@ -3897,35 +3202,35 @@
        "  <th>Skew:</th>          <td>-0.158</td> <th>  Prob(JB):          </th> <td>6.64e-25</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Kurtosis:</th>      <td> 6.130</td> <th>  Cond. No.          </th> <td>    48.0</td>\n",
+       "  <th>Kurtosis:</th>      <td> 6.130</td> <th>  Cond. No.          </th> <td>    22.5</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
-       "                                OLS Regression Results                               \n",
-       "=====================================================================================\n",
-       "Dep. Variable:     np.log(article_influence)   R-squared:                       0.431\n",
-       "Model:                                   OLS   Adj. R-squared:                  0.427\n",
-       "Method:                        Least Squares   F-statistic:                     101.3\n",
-       "Date:                       Fri, 21 Jul 2017   Prob (F-statistic):           1.81e-33\n",
-       "Time:                               09:28:18   Log-Likelihood:                -283.64\n",
-       "No. Observations:                        270   AIC:                             573.3\n",
-       "Df Residuals:                            267   BIC:                             584.1\n",
-       "Df Model:                                  2                                         \n",
-       "Covariance Type:                   nonrobust                                         \n",
-       "===============================================================================================\n",
-       "                                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
-       "-----------------------------------------------------------------------------------------------\n",
-       "Intercept                      -3.1557      0.241    -13.069      0.000        -3.631    -2.680\n",
-       "np.log(citation_count_sum)      0.2763      0.034      8.086      0.000         0.209     0.344\n",
-       "np.log(avg_cites_per_paper)     0.2735      0.051      5.414      0.000         0.174     0.373\n",
+       "                                 OLS Regression Results                                \n",
+       "=======================================================================================\n",
+       "Dep. Variable:     np.log10(article_influence)   R-squared:                       0.431\n",
+       "Model:                                     OLS   Adj. R-squared:                  0.427\n",
+       "Method:                          Least Squares   F-statistic:                     101.3\n",
+       "Date:                         Sun, 23 Jul 2017   Prob (F-statistic):           1.81e-33\n",
+       "Time:                                 15:00:17   Log-Likelihood:                -58.450\n",
+       "No. Observations:                          270   AIC:                             122.9\n",
+       "Df Residuals:                              267   BIC:                             133.7\n",
+       "Df Model:                                    2                                         \n",
+       "Covariance Type:                     nonrobust                                         \n",
+       "=================================================================================================\n",
+       "                                    coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
+       "-------------------------------------------------------------------------------------------------\n",
+       "Intercept                        -1.3705      0.105    -13.069      0.000        -1.577    -1.164\n",
+       "np.log10(citation_count_sum)      0.2763      0.034      8.086      0.000         0.209     0.344\n",
+       "np.log10(avg_cites_per_paper)     0.2735      0.051      5.414      0.000         0.174     0.373\n",
        "==============================================================================\n",
        "Omnibus:                       26.311   Durbin-Watson:                   1.951\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              111.344\n",
        "Skew:                          -0.158   Prob(JB):                     6.64e-25\n",
-       "Kurtosis:                       6.130   Cond. No.                         48.0\n",
+       "Kurtosis:                       6.130   Cond. No.                         22.5\n",
        "==============================================================================\n",
        "\n",
        "Warnings:\n",
@@ -3933,7 +3238,7 @@
        "\"\"\""
       ]
      },
-     "execution_count": 741,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3945,23 +3250,178 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 742,
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.26789073541337666"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate the RMSE of the log of testing actual values versus predicted (log) values\n",
+    "np.sqrt(mean_squared_error(np.log10(pt_test['article_influence']), pt_log_test_pred2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>    <td>np.log10(article_influence)</td> <th>  R-squared:         </th> <td>   0.292</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                        <td>OLS</td>             <th>  Adj. R-squared:    </th> <td>   0.290</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>                  <td>Least Squares</td>        <th>  F-statistic:       </th> <td>   110.7</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>                  <td>Sun, 23 Jul 2017</td>       <th>  Prob (F-statistic):</th> <td>6.84e-22</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                      <td>15:02:27</td>           <th>  Log-Likelihood:    </th> <td> -88.022</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>           <td>   270</td>            <th>  AIC:               </th> <td>   180.0</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>               <td>   268</td>            <th>  BIC:               </th> <td>   187.2</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>                   <td>     1</td>            <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>           <td>nonrobust</td>          <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "                <td></td>                   <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th>                     <td>   -0.5701</td> <td>    0.039</td> <td>  -14.781</td> <td> 0.000</td> <td>   -0.646    -0.494</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>np.log10(avg_cites_per_paper)</th> <td>    0.4961</td> <td>    0.047</td> <td>   10.520</td> <td> 0.000</td> <td>    0.403     0.589</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>24.617</td> <th>  Durbin-Watson:     </th> <td>   1.904</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  67.262</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-0.358</td> <th>  Prob(JB):          </th> <td>2.48e-15</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 5.338</td> <th>  Cond. No.          </th> <td>    3.56</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                                 OLS Regression Results                                \n",
+       "=======================================================================================\n",
+       "Dep. Variable:     np.log10(article_influence)   R-squared:                       0.292\n",
+       "Model:                                     OLS   Adj. R-squared:                  0.290\n",
+       "Method:                          Least Squares   F-statistic:                     110.7\n",
+       "Date:                         Sun, 23 Jul 2017   Prob (F-statistic):           6.84e-22\n",
+       "Time:                                 15:02:27   Log-Likelihood:                -88.022\n",
+       "No. Observations:                          270   AIC:                             180.0\n",
+       "Df Residuals:                              268   BIC:                             187.2\n",
+       "Df Model:                                    1                                         \n",
+       "Covariance Type:                     nonrobust                                         \n",
+       "=================================================================================================\n",
+       "                                    coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
+       "-------------------------------------------------------------------------------------------------\n",
+       "Intercept                        -0.5701      0.039    -14.781      0.000        -0.646    -0.494\n",
+       "np.log10(avg_cites_per_paper)     0.4961      0.047     10.520      0.000         0.403     0.589\n",
+       "==============================================================================\n",
+       "Omnibus:                       24.617   Durbin-Watson:                   1.904\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               67.262\n",
+       "Skew:                          -0.358   Prob(JB):                     2.48e-15\n",
+       "Kurtosis:                       5.338   Cond. No.                         3.56\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Run linear regression with log10 of article_influence and avg_cites_per_paper \n",
+    "pt_log_lr3 = smf.ols(formula=\n",
+    "                   'np.log10(article_influence) ~ np.log10(avg_cites_per_paper)', \n",
+    "                data=pt_train).fit()\n",
+    "# Create an array of predictions for the training and testing data\n",
+    "pt_log_train_pred3 = pt_log_lr3.predict(pt_train)\n",
+    "pt_log_test_pred3 = pt_log_lr3.predict(pt_test)\n",
+    "\n",
+    "# Output results of linear regression\n",
+    "pt_log_lr3.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.61684121391405333"
+       "0.26596710783312943"
       ]
      },
-     "execution_count": 742,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Calculate the RMSE of the log of testing actual values versus predicted (log) values\n",
-    "np.sqrt(mean_squared_error(np.log(pt_test['article_influence']), pt_log_test_pred2))"
+    "np.sqrt(mean_squared_error(np.log10(pt_test['article_influence']), pt_log_test_pred3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.840772001468956"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert the log10 RMSEs of predictions\n",
+    "math.pow(10,0.265)"
    ]
   }
  ],