Add files via upload
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Peter Norvig authored and 
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@@ -30,7 +30,7 @@
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"We don't know who else will be on the ballot and what their approval levels will be, we don't know if there is systematic bias in the polling data, we don't know how many people will vote for a candidate they disapprove of or against a candidate they approve of, and we don't know who will decline to vote.\n",
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"I have five ways of understanding the fluidity of the situation:\n",
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"\n",
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"- **Undecided**: If many voters are undecided, the net approval could change a lot. So I track the number of states for which at least 5% of voters are undecided. At the inauguration in 2017, all 51 states (including DC) had at least 5% undecided; now there is only one such state (Alaska). Overall 4% of voters are undecided. Most people have made up their mind.\n",
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"- **Undecided**: If many voters are undecided, the net approval could change a lot. So I track the number of states for which at least 5% of voters are undecided. At the inauguration in 2017, all 51 states (including DC) had at least 5% undecided; now there is only one such state (Alaska). Overall 4% of voters are undecided. Most people have made up their mind. In [one poll](https://www.pbs.org/newshour/politics/57-percent-of-voters-say-they-wont-support-trump-in-2020) 57% said they would definitely not vote for Trump in 2020; other polls have this in the 50% to 55% range.\n",
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"\n",
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"- **Variance**: How much are voters changing their minds from month to month in each state? I track the standard deviation, 𝝈, of the net approval for each state over the last 12 months.\n",
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"\n",
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@@ -57,7 +57,7 @@
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"text": [
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" % Total % Received % Xferd Average Speed Time Time Time Current\n",
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" Dload Upload Total Spent Left Speed\n",
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"100 116k 0 116k 0 0 242k 0 --:--:-- --:--:-- --:--:-- 242k\n"
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"100 116k 0 116k 0 0 216k 0 --:--:-- --:--:-- --:--:-- 216k\n"
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]
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}
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],
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@@ -286,7 +286,7 @@
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"image/png": "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 truncated
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"text/plain": [
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"<Figure size 864x720 with 1 Axes>"
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]
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@@ -305,10 +305,10 @@
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" N = len(dates)\n",
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" err = [[EV(states, date) - EV(states, date, -swing) for date in dates],\n",
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" [EV(states, date, swing) - EV(states, date) for date in dates]]\n",
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" plt.plot(range(N), [270] * N, color='darkorange', label=\"270\")\n",
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" plt.plot(range(N), [270] * N, color='darkorange', label=\"270 EVs\")\n",
|
||||
" plt.errorbar(range(N), [EV(states, date) for date in dates], fmt='D-',\n",
|
||||
" yerr=err, ecolor='grey', capsize=7, label='EVs ±3% swing')\n",
|
||||
" labels('Months into term', 'Electoral Votes with Net Positive Approval')\n",
|
||||
" yerr=err, ecolor='grey', capsize=7, label='Trump EVs ±3% swing')\n",
|
||||
" labels('Months into term', 'Electoral Votes')\n",
|
||||
" \n",
|
||||
"plot1(states, dates)"
|
||||
]
|
||||
@@ -329,7 +329,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 864x720 with 1 Axes>"
|
||||
]
|
||||
@@ -342,7 +342,7 @@
|
||||
"def plot2(states, dates):\n",
|
||||
" N = len(dates)\n",
|
||||
" plt.plot(range(N), [0] * N, label='Net zero', color='darkorange')\n",
|
||||
" plt.plot(range(N), [-margin(states, date) for date in dates], 'D-', label='Margin')\n",
|
||||
" plt.plot(range(N), [-margin(states, date) for date in dates], 'D-', label='Margin to 270')\n",
|
||||
" plt.plot(range(N), [net_usa[date] for date in dates], 'go-', label='Country-wide Net')\n",
|
||||
" labels('Months into term', 'Net popularity')\n",
|
||||
" \n",
|
||||
@@ -633,7 +633,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
"version": "3.7.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Reference in new issue
Block a user