diff --git a/01_Coursera_3.ipynb b/01_Coursera_3.ipynb
new file mode 100644
index 0000000..f002c4b
--- /dev/null
+++ b/01_Coursera_3.ipynb
@@ -0,0 +1,55 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = 4;\n",
+    "f(x) = b*x\n",
+    "f(6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "function updateSIR(popnvector)\n",
+    "    susceptibles = popnvector[1];\n",
+    "    infecteds = popnvector[2];\n",
+    "    removeds = popnvector[3];\n",
+    "    newS = \n",
+    "end"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.2.0",
+   "language": "julia",
+   "name": "julia-1.2"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.2.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/03_Coursera_2.ipynb b/03_Coursera_2.ipynb
index 5095a06..813ff95 100644
--- a/03_Coursera_2.ipynb
+++ b/03_Coursera_2.ipynb
@@ -1,5 +1,1065 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Oct 11 2016\""
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_date = \"Oct 11 2016\" # needs updating"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using Dates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016-10-11T00:00:00"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Dates.DateTime(my_date,\"u d y\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "54×9 Array{Any,2}:\n",
+       " 613  28637  11314  3804  2536  10675     4808     14122     3955   \n",
+       " 606  28634  11314  3804  2536  10672     4808     14122     3955   \n",
+       " 599  28635  11314  3805  2536  10672     4808     14122     3955   \n",
+       " 592  28607  11314  3810  2536  10672     4808     14089     3955   \n",
+       " 582  28539  11298  3806  2535  10672     4808     14061     3955   \n",
+       " 575  28476  11298  3803  2535  10672     4808     14001     3955   \n",
+       " 568  28454  11297  3800  2534  10672     4808     13982     3955   \n",
+       " 554  28388  11296  3805  2533  10672     4808     13911     3955   \n",
+       " 547  28295  11295  3800  2532  10672     4808     13823     3955   \n",
+       " 540  28220  11291  3792  2530  10672     4808     13756     3953   \n",
+       " 533  28147  11291  3792  2530  10672     4808     13683     3953   \n",
+       " 526  28073  11290  3792  2529  10672     4808     13609     3953   \n",
+       " 512  27952  11284  3786  2524  10672     4808     13494     3952   \n",
+       "   ⋮                                ⋮                               \n",
+       " 140   1835   1011   506   373    599      323       730      315   \n",
+       " 130   1437    825   472   346    391      227       574      252   \n",
+       " 123   1201    672   427   319    249      129       525      224   \n",
+       " 114    982    613   411   310    174      106       397      197   \n",
+       " 102    779    481   412   305    115       75       252      101   \n",
+       "  87    528    337   398   264     33       24        97       49   \n",
+       "  66    309    202   281   186     12       11        16        5   \n",
+       "  51    260    182   248   171     12       11          \"–\"      \"–\"\n",
+       "  40    239    160   226   149     13       11          \"-\"      \"-\"\n",
+       "  23    176    110   168   108      8        2          \"–\"      \"–\"\n",
+       "   9    130     82   122    80      8        2          \"–\"      \"–\"\n",
+       "   0     49     29    49    29       \"–\"      \"–\"       \"–\"      \"–\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "using DelimitedFiles\n",
+    "\n",
+    "indata = DelimitedFiles.readdlm(\"wikipediaEVDdatesconverted.csv\", ',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test = ones(4,5)\n",
+    "\n",
+    "row,col = size(test)\n",
+    "\n",
+    "val = 0\n",
+    "for j = 1:col\n",
+    "    for i = 1:row\n",
+    "        test[i,j] = test[i,j] + val\n",
+    "        val = val + 1\n",
+    "    end\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4×5 Array{Float64,2}:\n",
+       " 1.0  5.0   9.0  13.0  17.0\n",
+       " 2.0  6.0  10.0  14.0  18.0\n",
+       " 3.0  7.0  11.0  15.0  19.0\n",
+       " 4.0  8.0  12.0  16.0  20.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diagsum = 0\n",
+    "for k = 1:2\n",
+    "  diagsum = diagsum + test[k,k]\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diagsum = test[1,1] + test[2,2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diagsum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diagsum = sum(test[1:2,1:2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2×6 Array{Any,2}:\n",
+       " \"The name of the rose\"  \"3.2,\"  \"7,\"   8  9  100\n",
+       " \"Under Milk Wood\"       \"5.7,\"  \"11,\"  3  6   42"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "litsamp = readdlm(\"litsampling.dat\", ' ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"3.2,\""
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "litsamp[1,2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3-element Array{Int64,1}:\n",
+       " 7\n",
+       " 8\n",
+       " 9"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [1,2,3]; b = [4,5,6]; c = [7,8,9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3-element Array{Array{Int64,1},1}:\n",
+       " [1, 2, 3]\n",
+       " [4, 5, 6]\n",
+       " [7, 8, 9]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[a,b,c]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Plots.GRBackend()"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "using Plots\n",
+    "gr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"600\" height=\"400\" viewBox=\"0 0 2400 1600\">\n",
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+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot(a,[b c])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#using Plots\n",
+    "pyplot() # Use PyPlot as a GUI (may already be the default)\n",
+    "\n",
+    "x = collect(1:7)\n",
+    "y(x) = 2 - 2x + x^2/4\n",
+    "\n",
+    "plot(x,y.(x))\n",
+    "plot!(x, y.(x), marker = :diamond, linewidth=2)\n",
+    "plot!(title = \"Sample plot\", leg=false)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot(x,y.(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot!(x, y.(x), marker = :diamond, linewidth=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot!(title = \"Sample plot\", leg=false)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Plots.GRBackend()"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "using Plots\n",
+    "gr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"600\" height=\"400\" viewBox=\"0 0 2400 1600\">\n",
+       "<defs>\n",
+       "  <clipPath id=\"clip100\">\n",
+       "    <rect x=\"0\" y=\"0\" width=\"2400\" height=\"1600\"/>\n",
+       "  </clipPath>\n",
+       "</defs>\n",
+       "<path clip-path=\"url(#clip100)\" d=\"\n",
+       "M0 1600 L2400 1600 L2400 0 L0 0  Z\n",
+       "  \" fill=\"#ffffff\" fill-rule=\"evenodd\" fill-opacity=\"1\"/>\n",
+       "<defs>\n",
+       "  <clipPath id=\"clip101\">\n",
+       "    <rect x=\"480\" y=\"0\" width=\"1681\" height=\"1600\"/>\n",
+       "  </clipPath>\n",
+       "</defs>\n",
+       "<path clip-path=\"url(#clip100)\" d=\"\n",
+       "M147.848 1486.45 L2352.76 1486.45 L2352.76 123.472 L147.848 123.472  Z\n",
+       "  \" fill=\"#ffffff\" fill-rule=\"evenodd\" fill-opacity=\"1\"/>\n",
+       "<defs>\n",
+       "  <clipPath id=\"clip102\">\n",
+       "    <rect x=\"147\" y=\"123\" width=\"2206\" height=\"1364\"/>\n",
+       "  </clipPath>\n",
+       "</defs>\n",
+       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
+       "  210.251,1486.45 210.251,123.472 \n",
+       "  \"/>\n",
+       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
+       "  626.272,1486.45 626.272,123.472 \n",
+       "  \"/>\n",
+       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
+       "  1042.29,1486.45 1042.29,123.472 \n",
+       "  \"/>\n",
+       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
+       "  1458.31,1486.45 1458.31,123.472 \n",
+       "  \"/>\n",
+       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
+       "  1874.33,1486.45 1874.33,123.472 \n",
+       "  \"/>\n",
+       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
+       "  2290.35,1486.45 2290.35,123.472 \n",
+       "  \"/>\n",
+       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
+       "  147.848,1447.87 2352.76,1447.87 \n",
+       "  \"/>\n",
+       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
+       "  147.848,1162.13 2352.76,1162.13 \n",
+       "  \"/>\n",
+       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
+       "  147.848,876.395 2352.76,876.395 \n",
+       "  \"/>\n",
+       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
+       "  147.848,590.656 2352.76,590.656 \n",
+       "  \"/>\n",
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+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = [1 2 3 4 5 6]\n",
+    "y = (x.-3).^2/4\n",
+    "plot(x,y, marker = :hex, leg=false, linewidth = 2, linecolor=:black)\n",
+    "plot!(title=\"Plot for graded quiz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
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+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = [1, 2, 3, 4, 5, 6]\n",
+    "y = (x.-3).^2/4\n",
+    "plot(x,y, marker = :hex, leg=false, linewidth = 2, linecolor=:black)\n",
+    "plot!(title=\"Plot for graded quiz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
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+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = [1 2 3 4 5 6]'\n",
+    "y = (x.-3).^2/4\n",
+    "plot(x,y, marker = :hex, leg=false, linewidth = 2, linecolor=:black)\n",
+    "plot!(title=\"Plot for graded quiz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
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fill=\"#000000\" fill-rule=\"evenodd\" fill-opacity=\"1\" /><path clip-path=\"url(#clip220)\" d=\"M 0 0 M1577.98 27.2059 L1613.38 27.2059 L1613.38 34.0114 L1585.35 66.6212 L1613.38 66.6212 L1613.38 72.576 L1576.97 72.576 L1576.97 65.7705 L1605 33.1607 L1577.98 33.1607 L1577.98 27.2059 Z\" fill=\"#000000\" fill-rule=\"evenodd\" fill-opacity=\"1\" /><polyline clip-path=\"url(#clip222)\" style=\"stroke:#009af9; stroke-width:4; stroke-opacity:1; fill:none\" points=\"\n",
+       "  210.251,876.395 626.272,1305 1042.29,1447.87 1458.31,1305 1874.33,876.395 2290.35,162.047 \n",
+       "  \"/>\n",
+       "<path clip-path=\"url(#clip222)\" d=\"M210.251 892.395 L196.395 884.395 L196.395 868.395 L210.251 860.395 L224.107 868.395 L224.107 884.395 Z\" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"#000000\" stroke-opacity=\"1\" stroke-width=\"3.2\"/>\n",
+       "<path clip-path=\"url(#clip222)\" d=\"M626.272 1321 L612.416 1313 L612.416 1297 L626.272 1289 L640.128 1297 L640.128 1313 Z\" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"#000000\" stroke-opacity=\"1\" stroke-width=\"3.2\"/>\n",
+       "<path clip-path=\"url(#clip222)\" d=\"M1042.29 1463.87 L1028.44 1455.87 L1028.44 1439.87 L1042.29 1431.87 L1056.15 1439.87 L1056.15 1455.87 Z\" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"#000000\" stroke-opacity=\"1\" stroke-width=\"3.2\"/>\n",
+       "<path clip-path=\"url(#clip222)\" d=\"M1458.31 1321 L1444.46 1313 L1444.46 1297 L1458.31 1289 L1472.17 1297 L1472.17 1313 Z\" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"#000000\" stroke-opacity=\"1\" stroke-width=\"3.2\"/>\n",
+       "<path clip-path=\"url(#clip222)\" d=\"M1874.33 892.395 L1860.48 884.395 L1860.48 868.395 L1874.33 860.395 L1888.19 868.395 L1888.19 884.395 Z\" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"#000000\" stroke-opacity=\"1\" stroke-width=\"3.2\"/>\n",
+       "<path clip-path=\"url(#clip222)\" d=\"M2290.35 178.047 L2276.5 170.047 L2276.5 154.047 L2290.35 146.047 L2304.21 154.047 L2304.21 170.047 Z\" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"#000000\" stroke-opacity=\"1\" stroke-width=\"3.2\"/>\n",
+       "</svg>\n"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = [1 2 3 4 5 6]'\n",
+    "y = (x.-3).^2/4\n",
+    "plot(x,y, marker = :hex, leg=false)\n",
+    "plot!(title=\"Plot for graded quiz\", linewidth = 2, linecolor=:black)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/04_Coursera_2.ipynb b/04_Coursera_2.ipynb
deleted file mode 100644
index 813ff95..0000000
--- a/04_Coursera_2.ipynb
+++ /dev/null
@@ -1,1086 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Oct 11 2016\""
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_date = \"Oct 11 2016\" # needs updating"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "using Dates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2016-10-11T00:00:00"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Dates.DateTime(my_date,\"u d y\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "54×9 Array{Any,2}:\n",
-       " 613  28637  11314  3804  2536  10675     4808     14122     3955   \n",
-       " 606  28634  11314  3804  2536  10672     4808     14122     3955   \n",
-       " 599  28635  11314  3805  2536  10672     4808     14122     3955   \n",
-       " 592  28607  11314  3810  2536  10672     4808     14089     3955   \n",
-       " 582  28539  11298  3806  2535  10672     4808     14061     3955   \n",
-       " 575  28476  11298  3803  2535  10672     4808     14001     3955   \n",
-       " 568  28454  11297  3800  2534  10672     4808     13982     3955   \n",
-       " 554  28388  11296  3805  2533  10672     4808     13911     3955   \n",
-       " 547  28295  11295  3800  2532  10672     4808     13823     3955   \n",
-       " 540  28220  11291  3792  2530  10672     4808     13756     3953   \n",
-       " 533  28147  11291  3792  2530  10672     4808     13683     3953   \n",
-       " 526  28073  11290  3792  2529  10672     4808     13609     3953   \n",
-       " 512  27952  11284  3786  2524  10672     4808     13494     3952   \n",
-       "   ⋮                                ⋮                               \n",
-       " 140   1835   1011   506   373    599      323       730      315   \n",
-       " 130   1437    825   472   346    391      227       574      252   \n",
-       " 123   1201    672   427   319    249      129       525      224   \n",
-       " 114    982    613   411   310    174      106       397      197   \n",
-       " 102    779    481   412   305    115       75       252      101   \n",
-       "  87    528    337   398   264     33       24        97       49   \n",
-       "  66    309    202   281   186     12       11        16        5   \n",
-       "  51    260    182   248   171     12       11          \"–\"      \"–\"\n",
-       "  40    239    160   226   149     13       11          \"-\"      \"-\"\n",
-       "  23    176    110   168   108      8        2          \"–\"      \"–\"\n",
-       "   9    130     82   122    80      8        2          \"–\"      \"–\"\n",
-       "   0     49     29    49    29       \"–\"      \"–\"       \"–\"      \"–\""
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "using DelimitedFiles\n",
-    "\n",
-    "indata = DelimitedFiles.readdlm(\"wikipediaEVDdatesconverted.csv\", ',')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = ones(4,5)\n",
-    "\n",
-    "row,col = size(test)\n",
-    "\n",
-    "val = 0\n",
-    "for j = 1:col\n",
-    "    for i = 1:row\n",
-    "        test[i,j] = test[i,j] + val\n",
-    "        val = val + 1\n",
-    "    end\n",
-    "end"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4×5 Array{Float64,2}:\n",
-       " 1.0  5.0   9.0  13.0  17.0\n",
-       " 2.0  6.0  10.0  14.0  18.0\n",
-       " 3.0  7.0  11.0  15.0  19.0\n",
-       " 4.0  8.0  12.0  16.0  20.0"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diagsum = 0\n",
-    "for k = 1:2\n",
-    "  diagsum = diagsum + test[k,k]\n",
-    "end"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7.0"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagsum = test[1,1] + test[2,2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7.0"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagsum"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14.0"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diagsum = sum(test[1:2,1:2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2×6 Array{Any,2}:\n",
-       " \"The name of the rose\"  \"3.2,\"  \"7,\"   8  9  100\n",
-       " \"Under Milk Wood\"       \"5.7,\"  \"11,\"  3  6   42"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "litsamp = readdlm(\"litsampling.dat\", ' ')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"3.2,\""
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "litsamp[1,2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3-element Array{Int64,1}:\n",
-       " 7\n",
-       " 8\n",
-       " 9"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = [1,2,3]; b = [4,5,6]; c = [7,8,9]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3-element Array{Array{Int64,1},1}:\n",
-       " [1, 2, 3]\n",
-       " [4, 5, 6]\n",
-       " [7, 8, 9]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[a,b,c]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Plots.GRBackend()"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "using Plots\n",
-    "gr()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"600\" height=\"400\" viewBox=\"0 0 2400 1600\">\n",
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-       "    <rect x=\"0\" y=\"0\" width=\"2400\" height=\"1600\"/>\n",
-       "  </clipPath>\n",
-       "</defs>\n",
-       "<path clip-path=\"url(#clip700)\" d=\"\n",
-       "M0 1600 L2400 1600 L2400 0 L0 0  Z\n",
-       "  \" fill=\"#ffffff\" fill-rule=\"evenodd\" fill-opacity=\"1\"/>\n",
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-    }
-   ],
-   "source": [
-    "plot(a,[b c])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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"
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#using Plots\n",
-    "pyplot() # Use PyPlot as a GUI (may already be the default)\n",
-    "\n",
-    "x = collect(1:7)\n",
-    "y(x) = 2 - 2x + x^2/4\n",
-    "\n",
-    "plot(x,y.(x))\n",
-    "plot!(x, y.(x), marker = :diamond, linewidth=2)\n",
-    "plot!(title = \"Sample plot\", leg=false)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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"
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot(x,y.(x))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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"
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot!(x, y.(x), marker = :diamond, linewidth=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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"
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot!(title = \"Sample plot\", leg=false)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Plots.GRBackend()"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "using Plots\n",
-    "gr()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"600\" height=\"400\" viewBox=\"0 0 2400 1600\">\n",
-       "<defs>\n",
-       "  <clipPath id=\"clip100\">\n",
-       "    <rect x=\"0\" y=\"0\" width=\"2400\" height=\"1600\"/>\n",
-       "  </clipPath>\n",
-       "</defs>\n",
-       "<path clip-path=\"url(#clip100)\" d=\"\n",
-       "M0 1600 L2400 1600 L2400 0 L0 0  Z\n",
-       "  \" fill=\"#ffffff\" fill-rule=\"evenodd\" fill-opacity=\"1\"/>\n",
-       "<defs>\n",
-       "  <clipPath id=\"clip101\">\n",
-       "    <rect x=\"480\" y=\"0\" width=\"1681\" height=\"1600\"/>\n",
-       "  </clipPath>\n",
-       "</defs>\n",
-       "<path clip-path=\"url(#clip100)\" d=\"\n",
-       "M147.848 1486.45 L2352.76 1486.45 L2352.76 123.472 L147.848 123.472  Z\n",
-       "  \" fill=\"#ffffff\" fill-rule=\"evenodd\" fill-opacity=\"1\"/>\n",
-       "<defs>\n",
-       "  <clipPath id=\"clip102\">\n",
-       "    <rect x=\"147\" y=\"123\" width=\"2206\" height=\"1364\"/>\n",
-       "  </clipPath>\n",
-       "</defs>\n",
-       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
-       "  210.251,1486.45 210.251,123.472 \n",
-       "  \"/>\n",
-       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
-       "  626.272,1486.45 626.272,123.472 \n",
-       "  \"/>\n",
-       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
-       "  1042.29,1486.45 1042.29,123.472 \n",
-       "  \"/>\n",
-       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
-       "  1458.31,1486.45 1458.31,123.472 \n",
-       "  \"/>\n",
-       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
-       "  1874.33,1486.45 1874.33,123.472 \n",
-       "  \"/>\n",
-       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
-       "  2290.35,1486.45 2290.35,123.472 \n",
-       "  \"/>\n",
-       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
-       "  147.848,1447.87 2352.76,1447.87 \n",
-       "  \"/>\n",
-       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
-       "  147.848,1162.13 2352.76,1162.13 \n",
-       "  \"/>\n",
-       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
-       "  147.848,876.395 2352.76,876.395 \n",
-       "  \"/>\n",
-       "<polyline clip-path=\"url(#clip102)\" style=\"stroke:#000000; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
-       "  147.848,590.656 2352.76,590.656 \n",
-       "  \"/>\n",
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-     "execution_count": 2,
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-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "x = [1 2 3 4 5 6]\n",
-    "y = (x.-3).^2/4\n",
-    "plot(x,y, marker = :hex, leg=false, linewidth = 2, linecolor=:black)\n",
-    "plot!(title=\"Plot for graded quiz\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
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-   "source": [
-    "x = [1, 2, 3, 4, 5, 6]\n",
-    "y = (x.-3).^2/4\n",
-    "plot(x,y, marker = :hex, leg=false, linewidth = 2, linecolor=:black)\n",
-    "plot!(title=\"Plot for graded quiz\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
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-     "execution_count": 4,
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-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "x = [1 2 3 4 5 6]'\n",
-    "y = (x.-3).^2/4\n",
-    "plot(x,y, marker = :hex, leg=false, linewidth = 2, linecolor=:black)\n",
-    "plot!(title=\"Plot for graded quiz\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
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-       "  210.251,876.395 626.272,1305 1042.29,1447.87 1458.31,1305 1874.33,876.395 2290.35,162.047 \n",
-       "  \"/>\n",
-       "<path clip-path=\"url(#clip222)\" d=\"M210.251 892.395 L196.395 884.395 L196.395 868.395 L210.251 860.395 L224.107 868.395 L224.107 884.395 Z\" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"#000000\" stroke-opacity=\"1\" stroke-width=\"3.2\"/>\n",
-       "<path clip-path=\"url(#clip222)\" d=\"M626.272 1321 L612.416 1313 L612.416 1297 L626.272 1289 L640.128 1297 L640.128 1313 Z\" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"#000000\" stroke-opacity=\"1\" stroke-width=\"3.2\"/>\n",
-       "<path clip-path=\"url(#clip222)\" d=\"M1042.29 1463.87 L1028.44 1455.87 L1028.44 1439.87 L1042.29 1431.87 L1056.15 1439.87 L1056.15 1455.87 Z\" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"#000000\" stroke-opacity=\"1\" stroke-width=\"3.2\"/>\n",
-       "<path clip-path=\"url(#clip222)\" d=\"M1458.31 1321 L1444.46 1313 L1444.46 1297 L1458.31 1289 L1472.17 1297 L1472.17 1313 Z\" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"#000000\" stroke-opacity=\"1\" stroke-width=\"3.2\"/>\n",
-       "<path clip-path=\"url(#clip222)\" d=\"M1874.33 892.395 L1860.48 884.395 L1860.48 868.395 L1874.33 860.395 L1888.19 868.395 L1888.19 884.395 Z\" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"#000000\" stroke-opacity=\"1\" stroke-width=\"3.2\"/>\n",
-       "<path clip-path=\"url(#clip222)\" d=\"M2290.35 178.047 L2276.5 170.047 L2276.5 154.047 L2290.35 146.047 L2304.21 154.047 L2304.21 170.047 Z\" fill=\"#009af9\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"#000000\" stroke-opacity=\"1\" stroke-width=\"3.2\"/>\n",
-       "</svg>\n"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = [1 2 3 4 5 6]'\n",
-    "y = (x.-3).^2/4\n",
-    "plot(x,y, marker = :hex, leg=false)\n",
-    "plot!(title=\"Plot for graded quiz\", linewidth = 2, linecolor=:black)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Julia 1.2.0",
-   "language": "julia",
-   "name": "julia-1.2"
-  },
-  "language_info": {
-   "file_extension": ".jl",
-   "mimetype": "application/julia",
-   "name": "julia",
-   "version": "1.2.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/COVID_data_proc.ipynb b/COVID_data_proc.ipynb
index 249cdf3..b5a89ba 100644
--- a/COVID_data_proc.ipynb
+++ b/COVID_data_proc.ipynb
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -130,7 +130,7 @@
        "getindexbycountryname (generic function with 1 method)"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -147,27 +147,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "119"
+       "114"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "getindexbycountryname(\"Mexico\")"
+    "getindexbycountryname(\"United States of America\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -211,7 +211,7 @@
        " \"5/13/2020\" "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -242,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -277,7 +277,7 @@
        " 2020-05-13T00:00:00"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -288,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -297,7 +297,7 @@
        "dayssince (generic function with 1 method)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -308,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,7 +317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -337,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -372,7 +372,7 @@
        " 113"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -383,53 +383,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "114×1 Array{Int64,2}:\n",
-       "   0\n",
-       "   1\n",
-       "   2\n",
-       "   3\n",
-       "   4\n",
-       "   5\n",
-       "   6\n",
-       "   7\n",
-       "   8\n",
-       "   9\n",
-       "  10\n",
-       "  11\n",
-       "  12\n",
-       "   ⋮\n",
-       " 102\n",
-       " 103\n",
-       " 104\n",
-       " 105\n",
-       " 106\n",
-       " 107\n",
-       " 108\n",
-       " 109\n",
-       " 110\n",
-       " 111\n",
-       " 112\n",
-       " 113"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "covidbycountry[:,1] = epidays;"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -443,69 +406,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       " 113  84458  24671  16024  10962  11350     45  37  24  27  23  16  11  8  11"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "covidbycountry = convert(Array{Int64,2},covidbycountry);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m\u001b[1m  Updating\u001b[22m\u001b[39m registry at `~/.julia/registries/General`\n",
-      "\u001b[32m\u001b[1m  Updating\u001b[22m\u001b[39m git-repo `https://github.com/JuliaRegistries/General.git`\n",
-      "\u001b[?25l\u001b[2K\u001b[?25h\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n",
-      "\u001b[32m\u001b[1m  Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.2/Project.toml`\n",
-      "\u001b[90m [no changes]\u001b[39m\n",
-      "\u001b[32m\u001b[1m  Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.2/Manifest.toml`\n",
-      "\u001b[90m [no changes]\u001b[39m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#using Pkg\n",
     "#Pkg.add(\"Plots\")"
@@ -513,25 +425,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "┌ Info: Recompiling stale cache file /Users/eddie/.julia/compiled/v1.2/Plots/ld3vC.ji for Plots [91a5bcdd-55d7-5caf-9e0b-520d859cae80]\n",
-      "└ @ Base loading.jl:1240\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "using Plots"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -540,7 +443,7 @@
        "Plots.GRBackend()"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -551,16 +454,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
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+       "114-element Array{Int64,1}:\n",
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+       "     0\n",
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+       "     0\n",
+       "     0\n",
+       "     0\n",
+       "     0\n",
+       "     0\n",
+       "     ⋮\n",
+       " 19224\n",
+       " 20739\n",
+       " 22088\n",
+       " 23471\n",
+       " 24905\n",
+       " 26025\n",
+       " 27634\n",
+       " 29616\n",
+       " 31522\n",
+       " 33460\n",
+       " 35022\n",
+       " 36327"
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-     "execution_count": 27,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -571,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -580,376 +509,1650 @@
        "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n",
        "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"600\" height=\"400\" viewBox=\"0 0 2400 1600\">\n",
        "<defs>\n",
-       "  <clipPath id=\"clip6500\">\n",
+       "  <clipPath id=\"clip030\">\n",
        "    <rect x=\"0\" y=\"0\" width=\"2400\" height=\"1600\"/>\n",
        "  </clipPath>\n",
        "</defs>\n",
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        "  \" fill=\"#ffffff\" fill-rule=\"evenodd\" fill-opacity=\"1\"/>\n",
        "<defs>\n",
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      },
-     "execution_count": 42,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n",
-      "GKS: character ignored due to unicode error\n"
-     ]
     }
    ],
    "source": [
     "plot(covidbycountry[:,1],[covidbycountry[:,119] covidbycountry[:,2]],linetype= :scatter, \n",
+    "    legend = :topleft,\n",
     "    marker= (:diamond,5),\n",
-    "    title = \"COVID-19 Compaarisson China/Mexico\",\n",
+    "    title = \"COVID-19 Comparisson China/Mexico\",\n",
     "    xlabel = \"Days since 21 January 2020\",\n",
     "    ylabel = \"Total cases to date\",\n",
     "    label = [\"Mexico\" \"China\"]\n",
     ")"
    ]
   },
+  {
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+   "execution_count": 29,
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+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot(covidbycountry[:,1],covidbycountry[:,119],linetype= :scatter, \n",
+    "    legend = :topleft,\n",
+    "    marker = (:diamond,5),\n",
+    "    title = \"COVID-19 Mexico cases\",\n",
+    "    xlabel = \"Days since 21 January 2020\",\n",
+    "    ylabel = \"Total cases to date\",\n",
+    "    label = [\"Mexico\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
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+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot(covidbycountry[:,1],[covidbycountry[:,119] covidbycountry[:,25]],linetype= :scatter, \n",
+    "    legend = :topleft,\n",
+    "    marker= (:diamond,5),\n",
+    "    title = \"COVID-19 Comparisson Germany/Mexico\",\n",
+    "    xlabel = \"Days since 21 January 2020\",\n",
+    "    ylabel = \"Total cases to date\",\n",
+    "    label = [\"Mexico\" \"Germany\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
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+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot(covidbycountry[:,1],[covidbycountry[:,114] covidbycountry[:,25]],linetype= :scatter, \n",
+    "    marker= (:diamond,5),\n",
+    "    title = \"COVID-19 Comparisson Germany/USA\",\n",
+    "    xlabel = \"Days since 21 January 2020\",\n",
+    "    ylabel = \"Total cases to date\",\n",
+    "    label = [\"USA\" \"Germany\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
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+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot(covidbycountry[:,1],[covidbycountry[:,119] covidbycountry[:,114]],linetype= :scatter, \n",
+    "    marker= (:diamond,5),\n",
+    "    title = \"COVID-19 Comparisson Mexico/USA\",\n",
+    "    xlabel = \"Days since 21 January 2020\",\n",
+    "    ylabel = \"Total cases to date\",\n",
+    "    label = [\"Mexico\" \"USA\"]\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/coursera_week2_function.ipynb b/coursera_week2_function.ipynb
new file mode 100644
index 0000000..0cfc33a
--- /dev/null
+++ b/coursera_week2_function.ipynb
@@ -0,0 +1,230 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ed's polynomial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "y (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y(x) = x^5 + x^2 + x^3 + x^2 + x + 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A polynomial is a mathematical expression involving a sum of powers in one or more variables multiplied by coefficients. A polynomial in one variable (i.e., a univariate polynomial) with constant coefficients is given by\n",
+    "$a_n x^n + \\dots + a_2 x^2 + a_1 x + a_0$.\n",
+    "The individual summands with the coefficients (usually) included are called monomials, whereas the products of the form $x_1^{a_1} \\dots x_n^{a_n}$ in the multivariate case, i.e., with the coefficients omitted, are called terms. However, the term \"monomial\" is sometimes also used to mean polynomial summands without their coefficients, and in some older works, the definitions of monomial and term are reversed. Care is therefore needed in attempting to distinguish these conflicting usages.\n",
+    "The highest power in a univariate polynomial is called its order, or sometimes its degree.\n",
+    "Any polynomial $P(x)$ with $P(0)!=0$ can be expressed as\n",
+    "$P(x) = P(0) product_ρ(1 - x/ρ)$, where the product runs over the roots $ρ$ of $P(ρ) = 0$ and it is understood that multiple roots are counted with multiplicity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the value of y at -5 is -3200\n",
+      "the value of y at -4 is -1055\n",
+      "the value of y at -3 is -250\n",
+      "the value of y at -2 is -29\n",
+      "the value of y at -1 is 4\n",
+      "the value of y at 0 is 5\n",
+      "the value of y at 1 is 10\n",
+      "the value of y at 2 is 55\n",
+      "the value of y at 3 is 296\n",
+      "the value of y at 4 is 1129\n",
+      "the value of y at 5 is 3310\n"
+     ]
+    }
+   ],
+   "source": [
+    "for x = -5:5\n",
+    "    println(\"the value of y at $x is $(y(x))\")\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
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+       "  267.608,1445.72 470.05,998.35 672.491,830.457 874.933,784.365 1077.37,777.483 1279.82,777.274 1482.26,776.231 1684.7,766.846 1887.14,716.583 2089.58,542.85 \n",
+       "  2292.02,87.9763 \n",
+       "  \"/>\n",
+       "</svg>\n"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "using Plots\n",
+    "gr()\n",
+    "\n",
+    "functionvalues = Array{Int64,2}(undef,11,2)\n",
+    "index = 1\n",
+    "for x = -5:5\n",
+    "    functionvalues[index,1] = x\n",
+    "    functionvalues[index,2] = y(x)\n",
+    "    index = index + 1\n",
+    "end\n",
+    "\n",
+    "plot(functionvalues[:,1],functionvalues[:,2],label=false)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.2.0",
+   "language": "julia",
+   "name": "julia-1.2"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.2.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}