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Merge pull request #22 from rickecon/mle
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data/maxlikeli/Econ381totpts.txt

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108.25
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380.75
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346.25
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360.75
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196
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414.75
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370.5
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371.75
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143.75
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333.5
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397.5
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105.5
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410
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17
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21.25
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369.625
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318.875
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336.125
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429.875
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407.5
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415.75
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332.375
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397
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375.875
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384.125
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335
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408.5
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294.625
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291.125
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378.125
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442.0
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428.1
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347.3
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431.8
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426.0
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433.5
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331.1
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405.7
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418.6
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404.8
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401.0
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415.3
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390.8
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399.0
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380.0
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397.5
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368.7
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394.7
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304.3
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391.1
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388.4
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370.3
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384.6
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383.5
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305.6
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286.5
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367.9
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329.8
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288.2
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338.5
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333.6
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268.6
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335.2
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296.3
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269.1
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243.2
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159.4
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448.4
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449.8
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435.9
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429.4
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428.3
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427.5
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422.5
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409.8
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415.8
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413.4
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416.8
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406.7
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383.9
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389.0
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387.2
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368.6
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399.5
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382.6
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355.9
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389.9
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342.5
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365.2
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320.3
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341.5
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248.1
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305.0
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279.2
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275.7
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204.5
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102.2
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60.2

docs/book/struct_est/MaxLikelihood.md

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@@ -117,8 +117,8 @@ import requests
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# Download and save the data file Econ381totpts.txt
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url = ('https://raw.githubusercontent.com/OpenSourceEcon/CompMethods/' +
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'main/data/maxlikeli/Econ381totpts.txt')
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data_file = requests.get(url, allow_redirects=True)
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open('../../../data/maxlikeli/Econ381totpts.txt', 'wb').write(data_file.content)
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# data_file = requests.get(url, allow_redirects=True)
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# open('../../../data/maxlikeli/Econ381totpts.txt', 'wb').write(data_file.content)
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# Load the data as a NumPy array
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data = np.loadtxt('../../../data/maxlikeli/Econ381totpts.txt')
@@ -132,9 +132,9 @@ Let's create a histogram of the data.
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import matplotlib.pyplot as plt
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num_bins = 30
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count, bins, ignored = plt.hist(pts, num_bins, density=True,
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count, bins, ignored = plt.hist(data, num_bins, density=True,
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edgecolor='k')
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plt.title('Econ 381 scores: 2011-2012', fontsize=20)
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plt.title('Intermediate macro scores: 2011-2012', fontsize=15)
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plt.xlabel(r'Total points')
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plt.ylabel(r'Percent of scores')
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plt.xlim([0, 550]) # This gives the xmin and xmax to be plotted"

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