Outils pour utilisateurs
python:first_course_statistics
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Ci-dessous, les différences entre deux révisions de la page.
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python:first_course_statistics [2016/10/26 19:34] Beretta, Anna Letizia |
python:first_course_statistics [2016/10/31 11:00] Beretta, Anna Letizia [Scatter Plot of PRODJAPN vs QUALJAPN (p. 27)] |
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| </code> | </code> | ||
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| + | ====== The Performance of stock mutual funds (p. 21) ====== | ||
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| + | \\ | ||
| + | |||
| + | ====== Predicting the sales and airplay of popular music (p. 23)====== | ||
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| + | \\ | ||
| + | |||
| + | ====== Another look at the "Old faithful" geyser and adoption visas (p.24) ====== | ||
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| + | Modified the bins of the both histograms: | ||
| + | The Histogram is reliable for the "Old faithful" geyser but not for the Adoption rates. The appearance of the histogram changes quite a lot by changing the bins. | ||
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| + | \\ | ||
| + | |||
| + | ====== Productivity versus quality in the assembly plant (p. 25)====== | ||
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| + | |||
| + | ===== Scatterplot of Productivity vs Quality (p. 26) ===== | ||
| + | <code Python> | ||
| + | import pandas as pd | ||
| + | import matplotlib.pyplot as plt | ||
| + | scatter_plot = pd.read_csv('D:\Python\Libri\A_Casebook_for_a_First_Course_in_Statistics_and_Data_Analysis_Datasets\Data\Tab\\prdq.TAB', '\t') | ||
| + | productivity_Y = scatter_plot['Producti'] | ||
| + | quality_X = scatter_plot['Quality'] | ||
| + | plt.scatter(productivity_Y, quality_X, bins=20, colors='r') | ||
| + | ax = plt.gca() | ||
| + | ax.set_Xlabel('Assembly defects per 100 cars') | ||
| + | ax.set_Ylabel('Hours per vehicle') | ||
| + | ax.set_title('Scatter Plot of Productivity VS Quality') | ||
| + | plt.show() | ||
| + | </code> | ||
| + | |||
| + | \\ | ||
| + | |||
| + | =====Scatter Plot of PRODJAPN vs QUALJAPN (p. 27) ===== | ||
| + | |||
| + | <code Python> | ||
| + | import pandas as pd | ||
| + | import matplotlib.pyplot as plt | ||
| + | scatter_plot = pd.read_csv('D:\Python\Libri\A_Casebook_for_a_First_Course_in_Statistics_and_Data_Analysis_Datasets\Data\Tab\\prdq.TAB', '\t') | ||
| + | productivity_Y = scatter_plot['ProdJapn'] | ||
| + | quality_X = scatter_plot['QualJapn'] | ||
| + | plt.scatter(productivity_Y, quality_X, bins=20, colors='r') | ||
| + | ax = plt.gca() | ||
| + | ax.set_Xlabel('Assembly defects per 100 cars (Japanese origin)') | ||
| + | ax.set_Ylabel('Hours per vehicle (Japanese origin') | ||
| + | ax.set_title('Scatter Plot of PRODJAPN VS QUALJAPN') | ||
| + | plt.show() | ||
| + | </code> | ||
python/first_course_statistics.txt · Dernière modification: 2017/09/26 08:54 par Francesco Beretta
