@@ -124,6 +124,8 @@ S = np.arange(1, n+1)
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ax.plot(S, u.pmf(S), linestyle='', marker='o', alpha=0.8, ms=4)
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ax.vlines(S, 0, u.pmf(S), lw=0.2)
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ax.set_xticks(S)
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+ ax.set_xlabel('S')
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+ ax.set_ylabel('PMF')
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plt.show()
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```
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@@ -136,6 +138,8 @@ S = np.arange(1, n+1)
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ax.step(S, u.cdf(S))
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ax.vlines(S, 0, u.cdf(S), lw=0.2)
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ax.set_xticks(S)
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+ ax.set_xlabel('S')
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+ ax.set_ylabel('CDF')
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plt.show()
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```
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@@ -232,6 +236,8 @@ S = np.arange(1, n+1)
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ax.plot(S, u.pmf(S), linestyle='', marker='o', alpha=0.8, ms=4)
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ax.vlines(S, 0, u.pmf(S), lw=0.2)
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ax.set_xticks(S)
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+ ax.set_xlabel('S')
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+ ax.set_ylabel('PMF')
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plt.show()
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```
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@@ -244,6 +250,8 @@ S = np.arange(1, n+1)
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ax.step(S, u.cdf(S))
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ax.vlines(S, 0, u.cdf(S), lw=0.2)
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ax.set_xticks(S)
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+ ax.set_xlabel('S')
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+ ax.set_ylabel('CDF')
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plt.show()
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```
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@@ -267,6 +275,8 @@ u_sum = np.cumsum(u.pmf(S))
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ax.step(S, u_sum)
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ax.vlines(S, 0, u_sum, lw=0.2)
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ax.set_xticks(S)
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+ ax.set_xlabel('S')
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+ ax.set_ylabel('CDF')
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plt.show()
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```
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@@ -306,6 +316,8 @@ S = np.arange(1, n+1)
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ax.plot(S, u.pmf(S), linestyle='', marker='o', alpha=0.8, ms=4)
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ax.vlines(S, 0, u.pmf(S), lw=0.2)
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ax.set_xticks(S)
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+ ax.set_xlabel('S')
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+ ax.set_ylabel('PMF')
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plt.show()
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```
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@@ -378,7 +390,8 @@ for μ, σ in zip(μ_vals, σ_vals):
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ax.plot(x_grid, u.pdf(x_grid),
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alpha=0.5, lw=2,
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label=f'$\mu={μ}, \sigma={σ}$')
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-
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+ ax.set_xlabel('x')
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+ ax.set_ylabel('PDF')
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plt.legend()
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plt.show()
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```
@@ -394,6 +407,8 @@ for μ, σ in zip(μ_vals, σ_vals):
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alpha=0.5, lw=2,
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label=f'$\mu={μ}, \sigma={σ}$')
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ax.set_ylim(0, 1)
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+ ax.set_xlabel('x')
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+ ax.set_ylabel('CDF')
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plt.legend()
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plt.show()
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```
@@ -438,7 +453,8 @@ for μ, σ in zip(μ_vals, σ_vals):
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ax.plot(x_grid, u.pdf(x_grid),
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alpha=0.5, lw=2,
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label=f'$\mu={μ}, \sigma={σ}$')
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-
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+ ax.set_xlabel('x')
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+ ax.set_ylabel('PDF')
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plt.legend()
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plt.show()
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```
@@ -453,6 +469,8 @@ for σ in σ_vals:
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label=f'$\mu={μ}, \sigma={σ}$')
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ax.set_ylim(0, 1)
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ax.set_xlim(0, 3)
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+ ax.set_xlabel('x')
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+ ax.set_ylabel('CDF')
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plt.legend()
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plt.show()
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```
@@ -492,6 +510,8 @@ for λ in λ_vals:
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ax.plot(x_grid, u.pdf(x_grid),
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alpha=0.5, lw=2,
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label=f'$\lambda={λ}$')
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+ ax.set_xlabel('x')
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+ ax.set_ylabel('PDF')
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plt.legend()
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plt.show()
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```
@@ -504,6 +524,8 @@ for λ in λ_vals:
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alpha=0.5, lw=2,
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label=f'$\lambda={λ}$')
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ax.set_ylim(0, 1)
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+ ax.set_xlabel('x')
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+ ax.set_ylabel('CDF')
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plt.legend()
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plt.show()
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```
@@ -549,6 +571,8 @@ for α, β in zip(α_vals, β_vals):
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ax.plot(x_grid, u.pdf(x_grid),
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alpha=0.5, lw=2,
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label=fr'$\alpha={α}, \beta={β}$')
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+ ax.set_xlabel('x')
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+ ax.set_ylabel('PDF')
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plt.legend()
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plt.show()
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```
@@ -561,6 +585,8 @@ for α, β in zip(α_vals, β_vals):
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alpha=0.5, lw=2,
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label=fr'$\alpha={α}, \beta={β}$')
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ax.set_ylim(0, 1)
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+ ax.set_xlabel('x')
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+ ax.set_ylabel('CDF')
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plt.legend()
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plt.show()
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```
@@ -606,6 +632,8 @@ for α, β in zip(α_vals, β_vals):
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ax.plot(x_grid, u.pdf(x_grid),
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alpha=0.5, lw=2,
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label=fr'$\alpha={α}, \beta={β}$')
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+ ax.set_xlabel('x')
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+ ax.set_ylabel('PDF')
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plt.legend()
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plt.show()
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```
@@ -618,6 +646,8 @@ for α, β in zip(α_vals, β_vals):
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alpha=0.5, lw=2,
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label=fr'$\alpha={α}, \beta={β}$')
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ax.set_ylim(0, 1)
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+ ax.set_xlabel('x')
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+ ax.set_ylabel('CDF')
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plt.legend()
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plt.show()
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```
@@ -712,6 +742,8 @@ We can histogram the income distribution we just constructed as follows
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x = df['income']
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fig, ax = plt.subplots()
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ax.hist(x, bins=5, density=True, histtype='bar')
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+ ax.set_xlabel('Income')
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+ ax.set_ylabel('Density')
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plt.show()
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```
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@@ -752,6 +784,8 @@ x_amazon = np.asarray(data)
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``` {code-cell} ipython3
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fig, ax = plt.subplots()
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ax.hist(x_amazon, bins=20)
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+ ax.set_xlabel('Monthly Return (Percent Change)')
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+ ax.set_ylabel('Density')
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plt.show()
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```
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@@ -766,6 +800,8 @@ KDE will generate a smooth curve that approximates the PDF.
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``` {code-cell} ipython3
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fig, ax = plt.subplots()
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sns.kdeplot(x_amazon, ax=ax)
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+ ax.set_xlabel('Monthly Return (Percent Change)')
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+ ax.set_ylabel('KDE')
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plt.show()
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```
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@@ -776,6 +812,8 @@ fig, ax = plt.subplots()
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sns.kdeplot(x_amazon, ax=ax, bw_adjust=0.1, alpha=0.5, label="bw=0.1")
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sns.kdeplot(x_amazon, ax=ax, bw_adjust=0.5, alpha=0.5, label="bw=0.5")
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sns.kdeplot(x_amazon, ax=ax, bw_adjust=1, alpha=0.5, label="bw=1")
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+ ax.set_xlabel('Monthly Return (Percent Change)')
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+ ax.set_ylabel('KDE')
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plt.legend()
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plt.show()
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```
@@ -794,6 +832,8 @@ Yet another way to display an observed distribution is via a violin plot.
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``` {code-cell} ipython3
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fig, ax = plt.subplots()
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ax.violinplot(x_amazon)
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+ ax.set_ylabel('Monthly Return (Percent Change)')
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+ ax.set_xlabel('KDE')
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plt.show()
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```
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@@ -814,6 +854,8 @@ x_apple = np.asarray(data)
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``` {code-cell} ipython3
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fig, ax = plt.subplots()
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ax.violinplot([x_amazon, x_apple])
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+ ax.set_ylabel('Monthly Return (Percent Change)')
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+ ax.set_xlabel('KDE')
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plt.show()
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```
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@@ -847,6 +889,8 @@ x_grid = np.linspace(-50, 65, 200)
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fig, ax = plt.subplots()
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ax.plot(x_grid, u.pdf(x_grid))
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ax.hist(x_amazon, density=True, bins=40)
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+ ax.set_xlabel('Monthly Return (Percent Change)')
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+ ax.set_ylabel('Density')
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plt.show()
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```
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@@ -874,6 +918,8 @@ x_grid = np.linspace(-4, 4, 200)
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fig, ax = plt.subplots()
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ax.plot(x_grid, u.pdf(x_grid))
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ax.hist(x_draws, density=True, bins=40)
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+ ax.set_xlabel('x')
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+ ax.set_ylabel('Density')
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plt.show()
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```
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