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some fixes

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This commit is contained in:
Jan Benda
2020-12-21 22:07:36 +01:00
parent f6b52d32cb
commit 60a94c9ce6
3 changed files with 21 additions and 9 deletions
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10
bootstrap/lecture/bootstrap.tex
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@@ -5,7 +5,7 @@
\exercisechapter{Resampling methods}
\entermde{Resampling methoden}{Resampling methods} are applied to
\entermde{Resampling-Methoden}{Resampling methods} are applied to
generate distributions of statistical measures via resampling of
existing samples. Resampling offers several advantages:
\begin{itemize}
@@ -80,10 +80,10 @@ distribution of average values around the true mean of the population
Alternatively, we can use \enterm{bootstrapping}
(\determ[Bootstrap!Verfahren]{Bootstrapverfahren}) to generate new
samples from one set of measurements
(\entermde{Resampling}{resampling}). From these bootstrapped samples
we compute the desired statistical measure and estimate their
distribution (\entermde{Bootstrap!Verteilung}{bootstrap distribution},
samples from one set of measurements by means of resampling. From
these bootstrapped samples we compute the desired statistical measure
and estimate their distribution
(\entermde{Bootstrap!Verteilung}{bootstrap distribution},
\subfigref{bootstrapsamplingdistributionfig}{c}). Interestingly, this
distribution is very similar to the sampling distribution regarding
its width. The only difference is that the bootstrapped values are

2
bootstrap/lecture/bootstrapsem.py
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@@ -24,7 +24,7 @@ for i in range(nresamples) :
musrs.append(np.mean(rng.randn(nsamples)))
hmusrs, _ = np.histogram(musrs, bins, density=True)
fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.05*figure_height))
fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.1*figure_height))
fig.subplots_adjust(**adjust_fs(left=4.0, bottom=2.7, right=1.5))
ax.set_xlabel('Mean')
ax.set_xlim(-0.4, 0.4)