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a/_sources/notebooks/33_intro_to_NHST.ipynb b/_sources/notebooks/33_intro_to_NHST.ipynb index 187e48ce..8f6f00cc 100644 --- a/_sources/notebooks/33_intro_to_NHST.ipynb +++ b/_sources/notebooks/33_intro_to_NHST.ipynb @@ -49,7 +49,7 @@ "outputs": [], "source": [ "# Plot helper functions\n", - "from minsitats import nicebins" + "from ministats import nicebins" ] }, { diff --git a/blogposts/cut_material.html b/blogposts/cut_material.html index 083ad9d3..c9b525a6 100644 --- a/blogposts/cut_material.html +++ b/blogposts/cut_material.html @@ -544,9 +544,6 @@

Notebook setup -
Matplotlib is building the font cache; this may take a moment.
-
-
<Figure size 640x480 with 0 Axes>
diff --git a/examples/ANOVA.html b/examples/ANOVA.html index b4517983..7f99ce91 100644 --- a/examples/ANOVA.html +++ b/examples/ANOVA.html @@ -707,7 +707,7 @@

Equivalence between ANOVA and OLS
<Axes: xlabel='group', ylabel='score'>
-../_images/7774bcf673a90801d096af56c75b8ade966e5d4951761b7c8fa5e97f6fef5aee.png +../_images/bb885c7fae2ce41687378bb070737bd81f3c21999c641d6600645c0aa0d7ecf5.png
diff --git a/exercises/exercises_21_discrete_RVs.html b/exercises/exercises_21_discrete_RVs.html index 8965e8b0..c12f9d5e 100644 --- a/exercises/exercises_21_discrete_RVs.html +++ b/exercises/exercises_21_discrete_RVs.html @@ -1173,7 +1173,7 @@

E2.16#

-
array([36, 35, 42, 45, 54, 29, 34, 35, 44, 47])
+
array([36, 39, 40, 39, 45, 36, 41, 42, 32, 44])
diff --git a/exercises/exercises_35_two_sample_tests.html b/exercises/exercises_35_two_sample_tests.html index e11766df..9ab3b966 100644 --- a/exercises/exercises_35_two_sample_tests.html +++ b/exercises/exercises_35_two_sample_tests.html @@ -591,7 +591,7 @@

E1. Permutation test for the sleep score sin the doctors -
0.0523
+
0.0509
@@ -605,7 +605,7 @@

E1. Permutation test for the sleep score sin the doctors -
0.0527947205279472
+
0.05219478052194781
diff --git a/exercises/problems_1_data.html b/exercises/problems_1_data.html index c30ffbcb..bee2a609 100644 --- a/exercises/problems_1_data.html +++ b/exercises/problems_1_data.html @@ -578,7 +578,7 @@

Problem {prob:bar-chart_score-vs-curriculum}
<Axes: xlabel='curriculum', ylabel='score'>

-../_images/926593044408511881c40a380b122f40852e750b662642f9ce787433f71e3fee.png +../_images/2db420a3e907704f4a2f4dacd0962ac37972d274cf83f364eb1b8ec0ed2827ac.png

diff --git a/exercises/solutions/exercises_31_estimtors.html b/exercises/solutions/exercises_31_estimtors.html index be2dc90a..233bad7f 100644 --- a/exercises/solutions/exercises_31_estimtors.html +++ b/exercises/solutions/exercises_31_estimtors.html @@ -751,7 +751,7 @@

Exercise 3.4
<Axes: ylabel='Count'>

-../../_images/c35dbfab062b61a772db592a5c9640512818003e1017db9f72eb9b82e484ecc8.png +../../_images/465edf326777f94560653bb218937aa573c7d314073d7ba17100fa7670f56c7d.png @@ -797,7 +797,7 @@

Exercise 3.5
<Axes: xlabel='x', ylabel='$f_{X}$'>
-../../_images/d9a3b5f6671ab61689cce12eff733cbe0ed37f1ac3ad727a488eb7ec01998e7b.png +../../_images/9b74072acd40bef54e4cdf4bda51569130132276552bc06f1555b4f252ac279f.png
@@ -820,7 +820,7 @@

Exercise 3.5
<Axes: xlabel='x', ylabel='$f_{X}$'>

-../../_images/e5ca810b7a619cd61276e9a555c92d21608dd6ccd46eb9dec891ed031d5dbaed.png +../../_images/e7e0dbff01c73ab4a739be4145284bc30f612732ad1bc104e4d75c1edc368c60.png
@@ -843,7 +843,7 @@

Exercise 3.5
<Axes: xlabel='x', ylabel='$f_{X}$'>

-../../_images/4224a65fac69276abb3c4935776681bc0f66bdb11e863e715d5e480fd9475302.png +../../_images/c13e6f46da086ee52022626b055af671a2f02da256b7bc487bd718705132a6f1.png @@ -892,7 +892,7 @@

Exercise 3.6
<Axes: xlabel='x', ylabel='$f_{X}$'>
-../../_images/ab84b393ebb4e1b01a697cf1bc37ca509b8b11f82d3487d55fb4cb87099abc3f.png +../../_images/a10437ed736e8d8c0fa0c2fc190436e945b16c6f6369cfcc3c073b6b8c70fb58.png @@ -930,7 +930,7 @@

Exercise 3.7
<Axes: ylabel='Density'>
-../../_images/192bba20ecdae7da9fd6a15e53a6077b9e01a9e3fdc7f48b22652b3a789eb867.png +../../_images/ad8bb3acd192e5dda83233b36ce11e7073562c239558c0cb38a53dd6860efc63.png
@@ -940,7 +940,7 @@

Exercise 3.7 -
(202.59024, 3.4120250533403507)
+
(202.62868, 3.3701503612747015)
@@ -982,7 +982,7 @@

Exercise 3.8
<Axes: ylabel='Density'>

-../../_images/b666ee31a98e09ac6daa8364b7185f0a9317d9bd7e5d70b45c12df60132d4083.png +../../_images/396b4b9ebbfe75c6ea92733a310e9c65b2195fed2b56531ed5d1ec37915aa607.png

Even when we take into account of the possible variability @@ -1039,7 +1039,7 @@

Exercise 3.9 -../../_images/f5df2a5b34d3ca25ad9b4a183a3d07ad2b72350c579fdd4d58edd4549df0682c.png +../../_images/e133eb21b9d5d1cba350431bb731c578601cdb7b02e0c36e9d25d2a8472a660e.png @@ -1114,7 +1114,7 @@

Exercise 3.11
<Axes: ylabel='Density'>
-../../_images/3681a00cf3324700010f095221675e9f5da4272b07b810bce1f19ba8856f7fc1.png +../../_images/32f16d0c765ee0b6a37577a7c55dc9036bafd895036b22624d97ce2237a339e9.png

The boostrap etimates we obverve are very far from the @@ -1212,7 +1212,7 @@

Exercise 3.12
<Axes: xlabel='d', ylabel='$f_{D}$'>
-../../_images/a1191a35129b911e90ba286cf81891e47b38d1273ed51685304e6edc11f32126.png +../../_images/599957a85ea9d935a1dc7596bd13b106904261ec2f72a080e8d19001f0f62125.png @@ -1237,7 +1237,7 @@

Exercise 3.13
<Axes: ylabel='Density'>
-../../_images/578fac1f096b9a2cc93e2d3e9a04dbe5a2bf52af6823d63ffe5aa79f4c45d486.png +../../_images/5fca0cadb42afea792a63add2ec7ffab206b18b8182f7e8ef9869f540199f392.png
@@ -1248,7 +1248,7 @@

Exercise 3.13 -
2.2284457431181997
+
2.234579454312682
@@ -1278,7 +1278,7 @@

Exercise 3.14
<Axes: ylabel='Density'>

-../../_images/dc3975407047c1d030324b818ec6657508ac98b72cc9712e26b4838e09323ee7.png +../../_images/4020207dd6b75c3d0664115004e0011b416c9773ec3b0ac5da6e40bec179b884.png

@@ -1289,7 +1289,7 @@

Exercise 3.14 -
2.906217394092687
+
2.942715050474587
diff --git a/notebooks/11_intro_to_data.html b/notebooks/11_intro_to_data.html index a09932f8..35baa472 100644 --- a/notebooks/11_intro_to_data.html +++ b/notebooks/11_intro_to_data.html @@ -540,7 +540,7 @@

Random selection -
[6, 8, 4]
+
[1, 7, 5]
diff --git a/notebooks/12_data_in_practice.html b/notebooks/12_data_in_practice.html index 6bfd8707..534068c1 100644 --- a/notebooks/12_data_in_practice.html +++ b/notebooks/12_data_in_practice.html @@ -2019,7 +2019,7 @@

Strip plot of the -../_images/3b2196e33cc62cd5355a0cc40146bc4e09ba61c5e38c2fae348ccfc7dfb13548.png +../_images/f6e4e33e4956453ca356b1ff27808cff7f06a67160e53d9665625b4ac037b46e.png

We can enhance the strip plot by mapping the ezlvl variable to the colour (hue) of the points in the plot.

@@ -2030,7 +2030,7 @@

Strip plot of the -../_images/ebbe265d329064a88633b098c5a55e447392feeed8646eaaee49d53bb22cbc18.png +../_images/64a63e073ab8f51e294af27535b96f6eae88c704140e335953df4f00ee764c63.png

@@ -2078,7 +2078,7 @@

Studying the effect of
-../_images/9955cb3ab979ab8221c4f60919b7f132690d4171ea8b126888a3ca62cf27ea6c.png +../_images/21530409bec2713b49c7186aeee85ed780ae699f5833f79f9b42deb28bd62c1f.png
@@ -2390,7 +2390,7 @@

Electricity prices -../_images/31e2731e5a95bc2c8d59967178a6a385e2b448df6be960a8095a678421c76415.png +../_images/44d819e21afac61867e64c2395e905ba4c2d3ddf95e613950f0e53c01b936bbb.png

@@ -2520,7 +2520,7 @@

Students’ scores -../_images/c64dd0bf274b681c19946a9eb317bc7cfc915eeb7152b7cea5deae87e904f95f.png +../_images/11efacba9922033bd5096bd4b89e44aac319519a35bae2e4c2ca3d7fd8ca926a.png
@@ -2644,7 +2644,7 @@

Kombucha volumes -../_images/66007abe13528ce102c2b15016aa9fbc57e9e665399407160b4a8624bac71301.png +../_images/e8711ef3b78c4aa6528201908758cfee2492a6cd1be1036c41d218f35c1e61e0.png

@@ -2772,7 +2772,7 @@

Doctors’ sleep study
-../_images/667f3c660c0ddb4edc790c61a2e966ad0188daf5d9a1ae25dca78bc55dc5137a.png +../_images/09f43d7d3c0fa1bf32960ac543ef5c84bca738489d17ccc8c814b7c844d25303.png
diff --git a/notebooks/13_descriptive_statistics.html b/notebooks/13_descriptive_statistics.html index ac1701f2..6f050286 100644 --- a/notebooks/13_descriptive_statistics.html +++ b/notebooks/13_descriptive_statistics.html @@ -1612,7 +1612,7 @@

Strip plots
<Axes: xlabel='score', ylabel='curriculum'>
-../_images/d925d1082c879f5cb5872c8d1a7468ca6373c171970feb1e61669388eb0483e6.png +../_images/d061ec1f3177e334f3b8747635740a094b7c4b0baec9b94d7bd1aa5bc9fe5f63.png

@@ -1658,7 +1658,7 @@

Histograms -
<matplotlib.legend.Legend at 0x7fc003e14820>
+
<matplotlib.legend.Legend at 0x7f0a1ea47af0>
 

 

 ../_images/277661225b1e76c86e3729310b44e2c25cd3f272881e7c9ee6233ed631b40ffb.png
diff --git a/notebooks/25_continuous_random_vars.html b/notebooks/25_continuous_random_vars.html
index 8bf16429..0cbde1b2 100644
--- a/notebooks/25_continuous_random_vars.html
+++ b/notebooks/25_continuous_random_vars.html
@@ -1465,7 +1465,7 @@ 
Example 1: Multivariable uniform
-
<matplotlib.contour.QuadContourSet at 0x7fa4c4b6a400>
+
<matplotlib.contour.QuadContourSet at 0x7f92e4f14100>
 

 

 ../_images/03ba3c479c6a555ea789d4d52961ccde1213e2f441dc877ec6fa9ce444e104bf.png
@@ -1525,7 +1525,7 @@ 
Example 2: Kombucha volume increasing with temperature
 

 

-
<matplotlib.contour.QuadContourSet at 0x7fa4c5aeb7f0>
+
<matplotlib.contour.QuadContourSet at 0x7f92f3f471c0>
 

 

 ../_images/5131e76b2e4feb9d17e3d9356e78c050de3211c601dac96566eb2ad9d1701a26.png
@@ -1579,7 +1579,7 @@ 
Example 3: Temperature-dependent variability
-
<matplotlib.contour.QuadContourSet at 0x7fa4c5a2ecd0>
+
<matplotlib.contour.QuadContourSet at 0x7f92e4bebe80>
 

 

 ../_images/c30500c25ad22d1c238e3d9dea1dec4fa04237ca6869ac42152b84a1819f8b27.png
diff --git a/notebooks/33_intro_to_NHST.html b/notebooks/33_intro_to_NHST.html
index e9e6404a..bc52ef67 100644
--- a/notebooks/33_intro_to_NHST.html
+++ b/notebooks/33_intro_to_NHST.html
@@ -588,18 +588,7 @@ 
Notebook setup
 

 
# Plot helper functions
-from minsitats import nicebins
-

-

-

-

-
---------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[2], line 2
-      1 # Plot helper functions
-----> 2 from minsitats import nicebins
-
-ModuleNotFoundError: No module named 'minsitats'
+from ministats import nicebins
 

 

 

@@ -896,10 +885,7 @@ 
Sampling distribution of the mean under the null
-
<Axes: ylabel='Count'>
-

-

-../_images/7dec87d178c4ba269743007fb33ceae262ae996ebf42884ba91fbf759c54e88d.png
+../_images/216844cd2167b065bbd75aadae6978ca5e2549c0fb0193add71307860e142c5f.png
 

 

 
The sampling distribution \(\overline{\mathbf{K}}_0 = \tt{kbars40}\)
@@ -1066,7 +1052,7 @@ 
Sampling distribution of the mean under the null hypothesis
-../_images/3f11849bd36cdda8240cc55cec76b7d293ec9ccff8954d4e34a6837f03e660f6.png
+../_images/5326f1b6bb089905132189c57b8dafc867ec43e5a07a780d2726796668016058.png
 

 

 
Note the \(p\)-value calculation includes simulated observations in the left tail of the distribution:
@@ -1222,7 +1208,7 @@ 
Example 2S: test for the mean of Batch 01
-../_images/72bb5359bd3aedc9269c3c944bf66ce6359e09e85e7eca95051799f7cb5e1298.png
+../_images/121cfbdd3a00e870c97b9e0c6d089ecad0218746e9e495a89a68399a42f969e5.png

@@ -1250,7 +1236,7 @@

Sampling distribution of the variance under the null
-../_images/3960dce051c0babdb4669a6beff584ce0ae78bd3cc945d6dff45666448b7dfac.png +../_images/779ccdf4918630f3c3a6a9ad0b76adadd49cdbd27685b7c087d097dfc4718788.png
@@ -1339,7 +1325,7 @@

Example 3S: test for the variance of Batch 02 -../_images/a6d1aa8bfac5068a5076d52130dac56c3200af510ba6015cb1c18fd42a2171b2.png +../_images/d67ae2913d0d3531c66bd3483d912d88c902aadb988e375c3defed853c5f10e4.png diff --git a/notebooks/34_analytical_approx.html b/notebooks/34_analytical_approx.html index 0b39736a..3fead6c0 100644 --- a/notebooks/34_analytical_approx.html +++ b/notebooks/34_analytical_approx.html @@ -974,9 +974,7 @@

Calculating \(p\)-values f 
Saved figure to figures/stats/intro_to_NHST/panel_p-values_t_left_twotailed_right_tests.pdf
-
-
-
Saved figure to figures/stats/intro_to_NHST/panel_p-values_t_left_twotailed_right_tests.png
+Saved figure to figures/stats/intro_to_NHST/panel_p-values_t_left_twotailed_right_tests.png
../_images/acba1607c655d8c20babe983b11f5bbbb88fef4df8d5b3e52253045c16ac1dfd.png diff --git a/notebooks/35_two_sample_tests.html b/notebooks/35_two_sample_tests.html index 294c13fb..f9860375 100644 --- a/notebooks/35_two_sample_tests.html +++ b/notebooks/35_two_sample_tests.html @@ -1056,9 +1056,7 @@

Running a permutation test 
Saved figure to figures/stats/two_sample_tests/hist_p-value_dmeans_eprices.pdf
-
-
-
Saved figure to figures/stats/two_sample_tests/hist_p-value_dmeans_eprices.png
+Saved figure to figures/stats/two_sample_tests/hist_p-value_dmeans_eprices.png
../_images/978fb77f610373cb61914053c688599908c374eac88dd9c81393f9b4f530f704.png @@ -1293,9 +1291,7 @@

Example 6P: lecture and debate curriculums 
Saved figure to figures/stats/two_sample_tests/hist_p-value_dmeans_students.pdf
-
-
-
Saved figure to figures/stats/two_sample_tests/hist_p-value_dmeans_students.png
+Saved figure to figures/stats/two_sample_tests/hist_p-value_dmeans_students.png
../_images/c66bb4ea088c797e97578027f608ddf6ca2bff5345af680acc73b7d1bfa2c356.png diff --git a/notebooks/40_extra_cut_material.html b/notebooks/40_extra_cut_material.html index ef02dcb1..20c21d1f 100644 --- a/notebooks/40_extra_cut_material.html +++ b/notebooks/40_extra_cut_material.html @@ -725,7 +725,7 @@

Partial regression plots -
/tmp/ipykernel_3021/3264559806.py:31: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.
+
/tmp/ipykernel_2961/3264559806.py:31: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.
   ax.scatter(x, y, c=h[0].get_color(), **scatter_kws)
 

 

@@ -894,7 +894,7 @@ 
Sheffé bands (several different ways)
-
<matplotlib.collections.PolyCollection at 0x7f49774ec940>
+
<matplotlib.collections.PolyCollection at 0x7f88ac27ef10>
 

 

 ../_images/2b84074d2f669c2eaa973234a5ec48cd2fbc96acf5b3df525fb983870572c22e.png
@@ -1027,7 +1027,7 @@ 
Sheffé bands (several different ways)
<matplotlib.legend.Legend at 0x7f49936caa60>
+
<matplotlib.legend.Legend at 0x7f8889688a90>
 

 

 ../_images/13880e32eb34785d52979791fd8aa0a93fcb2d51699410c94df5c68d6e857628.png
diff --git a/notebooks/41_simple_linear_regression.html b/notebooks/41_simple_linear_regression.html
index 03608dee..991a98d2 100644
--- a/notebooks/41_simple_linear_regression.html
+++ b/notebooks/41_simple_linear_regression.html
@@ -1469,7 +1469,7 @@ 
Model summary table
-
Error in callback <function _draw_all_if_interactive at 0x7f1f34bbb310> (for post_execute):
+
Error in callback <function _draw_all_if_interactive at 0x7ff859dbc310> (for post_execute):
 

 

 
---------------------------------------------------------------------------
diff --git a/notebooks/44_regression_with_categorical_predictors.html b/notebooks/44_regression_with_categorical_predictors.html
index 6a84ba29..9da69df3 100644
--- a/notebooks/44_regression_with_categorical_predictors.html
+++ b/notebooks/44_regression_with_categorical_predictors.html
@@ -991,7 +991,7 @@ 
Example 3: improved model for the sleep scores
     

-      
-      2.22% [133/6000 00:00<00:03 Sampling 2 chains, 0 divergences]
+      
+      100.00% [6000/6000 00:02<00:00 Sampling 2 chains, 0 divergences]
     

-    
---------------------------------------------------------------------------
-ValueError                                Traceback (most recent call last)
-Cell In[1], line 22
-     19 y_obs = pm.Normal("y_obs", mu=mu, sigma=sigma, observed=y)
-     21 # Sampling
----> 22 trace = pm.sample(2000, return_inferencedata=True)
-
-File /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/pymc/sampling/mcmc.py:791, in sample(draws, tune, chains, cores, random_seed, progressbar, step, nuts_sampler, initvals, init, jitter_max_retries, n_init, trace, discard_tuned_samples, compute_convergence_checks, keep_warning_stat, return_inferencedata, idata_kwargs, nuts_sampler_kwargs, callback, mp_ctx, model, **kwargs)
-    787 t_sampling = time.time() - t_start
-    789 # Packaging, validating and returning the result was extracted
-    790 # into a function to make it easier to test and refactor.
---> 791 return _sample_return(
-    792     run=run,
-    793     traces=traces,
-    794     tune=tune,
-    795     t_sampling=t_sampling,
-    796     discard_tuned_samples=discard_tuned_samples,
-    797     compute_convergence_checks=compute_convergence_checks,
-    798     return_inferencedata=return_inferencedata,
-    799     keep_warning_stat=keep_warning_stat,
-    800     idata_kwargs=idata_kwargs or {},
-    801     model=model,
-    802 )
-
-File /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/pymc/sampling/mcmc.py:822, in _sample_return(run, traces, tune, t_sampling, discard_tuned_samples, compute_convergence_checks, return_inferencedata, keep_warning_stat, idata_kwargs, model)
-    820 # Pick and slice chains to keep the maximum number of samples
-    821 if discard_tuned_samples:
---> 822     traces, length = _choose_chains(traces, tune)
-    823 else:
-    824     traces, length = _choose_chains(traces, 0)
-
-File /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/pymc/backends/base.py:601, in _choose_chains(traces, tune)
-    599 lengths = [max(0, len(trace) - tune) for trace in traces]
-    600 if not sum(lengths):
---> 601     raise ValueError("Not enough samples to build a trace.")
-    603 idxs = np.argsort(lengths)
-    604 l_sort = np.array(lengths)[idxs]
-
-ValueError: Not enough samples to build a trace.
+    
Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 3 seconds.
+

+

+
We recommend running at least 4 chains for robust computation of convergence diagnostics
 

 

 

@@ -649,8 +614,8 @@ 
Bayesian p-value
     

-      
-      100.00% [8000/8000 00:00<00:00]
+      
+      100.00% [4000/4000 00:00<00:00]
     

     

 

@@ -668,7 +633,7 @@ 
Bayesian p-value
-
Bayesian p-value: 0.4925
+
Bayesian p-value: 0.49
 

 

 

diff --git a/notebooks/51_intro_to_Bayesian_stats.html b/notebooks/51_intro_to_Bayesian_stats.html
index d54c58fc..1aae34f9 100644
--- a/notebooks/51_intro_to_Bayesian_stats.html
+++ b/notebooks/51_intro_to_Bayesian_stats.html
@@ -679,7 +679,7 @@ 
Example 1: estimating the probability of a biased coin
 

 

-
<matplotlib.colorbar.Colorbar at 0x7fb5ef3f55b0>
+
<matplotlib.colorbar.Colorbar at 0x7f800d5b7f10>
 

 

 ../_images/d3d691c99f489baeb4ffd5ab63652dfe9a4c7a6d103841d9e39f77892f3a5ec3.png
diff --git a/notebooks/52_Bayesian_inference_computations.html b/notebooks/52_Bayesian_inference_computations.html
index f941d047..94a2e526 100644
--- a/notebooks/52_Bayesian_inference_computations.html
+++ b/notebooks/52_Bayesian_inference_computations.html
@@ -629,10 +629,10 @@ 
Example 1: estimating the probability of a biased coin
 

     

-      
-      100.00% [4000/4000 00:01<00:00 Sampling 2 chains, 0 divergences]
+      
+      60.30% [2412/4000 00:00<00:00 Sampling 2 chains, 0 divergences]
     

-    
Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 1 seconds.
+    
Sampling 2 chains for 1_000 tune and 208 draw iterations (2_000 + 416 draws total) took 1 seconds.
 

 

 
We recommend running at least 4 chains for robust computation of convergence diagnostics
@@ -680,14 +680,14 @@ 
Example 1: estimating the probability of a biased coin
     
       p
-      0.684
-      0.099
-      0.513
-      0.868
-      0.003
+      0.688
+      0.092
+      0.522
+      0.861
+      0.002
       0.002
-      862.0
-      1304.0
+      1601.0
+      2528.0
       1.0
     
   
@@ -703,9 +703,7 @@ 
Example 1: estimating the probability of a biased coin
 

 
Sampling: [p, y_obs]
-

-

-
Sampling: [y_obs]
+Sampling: [y_obs]
 

 

 

@@ -726,8 +724,8 @@ 
Example 1: estimating the probability of a biased coin
 

     

-      
-      100.00% [2000/2000 00:00<00:00]
+      
+      100.00% [4000/4000 00:00<00:00]
     

     

 

@@ -766,7 +764,7 @@ 
Example 1: estimating the probability of a biased coin
 

 

-../_images/1dfd788426c9ce57e6cda198a3dd9423e8494c49e0716c01c5bb02d8d4873750.png
+../_images/77c6a5e1d886687484dce40172e0e010e7d2bb7599c2dca7f80f178a8587d0ba.png
 

 

 

@@ -787,7 +785,7 @@ 
Example 1: estimating the probability of a biased coin
 

 

-../_images/aa554fd940665088ca108d40500d3437e7d882788ae25b3dc1652c11a54d6f04.png
+../_images/d0a3b86500375bbc20fab8f14b7f6371e6e8ff679e46e042daf942fdefcdacf6.png
 

 

 
@@ -835,7 +833,7 @@ 
Choice of priors
-../_images/1653eb3ef0ddc25899b3197f96e7367594c4fe180aafd76872786fb91d873f5c.png
+../_images/9461f4eec19c4cf0d45cfad6f72a9821926ae30ac8cf651084ded9bc2f076342.png
 

 

 
diff --git a/notebooks/53_difference_between_means.html b/notebooks/53_difference_between_means.html
index 03710926..199ad00c 100644
--- a/notebooks/53_difference_between_means.html
+++ b/notebooks/53_difference_between_means.html
@@ -798,7 +798,7 @@ 
Example movies genres

     

       
-      100.00% [4000/4000 00:02<00:00 Sampling 2 chains, 0 divergences]
+      100.00% [4000/4000 00:01<00:00 Sampling 2 chains, 0 divergences]
     

     
Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 2 seconds.
 

@@ -847,35 +847,35 @@ 
Example movies genres
     
       Intercept
-      5.959
-      0.070
-      5.758
-      6.159
+      5.958
+      0.068
+      5.753
+      6.154
       0.003
-      2891.153
-      1635.0
+      2217.972
+      1310.0
       1.0
     
     
       C(genre, levels=levels)[Action]
-      -0.663
+      -0.667
       0.101
-      -0.961
-      -0.362
+      -0.957
+      -0.372
       0.004
-      2584.164
-      1335.0
+      2690.820
+      1756.0
       1.0
     
     
       rating_sigma
       1.527
-      0.038
-      1.429
-      1.629
+      0.036
+      1.430
+      1.633
       0.001
-      2429.451
-      1399.0
+      2445.794
+      1090.0
       1.0
     
   
@@ -1011,58 +1011,58 @@ 
Regression, assuming unequal variances
-
0.9762857097579093
+
0.9792189645694596
 

 

 

@@ -1233,7 +1233,7 @@ 
BEST#

     

       
-      100.00% [4000/4000 00:03<00:00 Sampling 2 chains, 6 divergences]
+      100.00% [4000/4000 00:03<00:00 Sampling 2 chains, 5 divergences]
     

     
Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 4 seconds.
 

@@ -1281,57 +1281,57 @@ 
BEST#
     
       Intercept
-      5.978
-      0.072
-      5.774
-      6.175
-      0.003
-      1960.321
-      1340.0
+      5.977
+      0.074
+      5.766
+      6.185
+      0.002
+      2097.512
+      1709.0
       1.0
     
     
       C(genre, levels=levels)[Action]
-      -0.680
-      0.099
-      -0.950
-      -0.381
-      0.006
-      1847.065
-      1631.0
+      -0.677
+      0.107
+      -0.965
+      -0.388
+      0.004
+      2264.192
+      1554.0
       1.0
     
     
       sigma_Intercept
-      0.386
-      0.038
-      0.267
-      0.489
-      0.001
-      1890.536
-      1062.0
+      0.388
+      0.039
+      0.269
+      0.494
+      0.002
+      1647.180
+      1347.0
       1.0
     
     
       sigma_C(genre, levels=levels)[Action]
-      -0.002
+      -0.004
       0.051
-      -0.137
-      0.140
+      -0.142
+      0.143
       0.002
-      2222.471
-      1604.0
+      1819.344
+      1288.0
       1.0
     
     
       rating_nu
-      30.456
-      12.903
-      9.856
-      109.208
-      0.489
-      1725.582
-      1105.0
+      31.845
+      13.797
+      9.915
+      116.776
+      0.979
+      1586.884
+      1283.0
       1.0
     
     
@@ -1347,57 +1347,57 @@ 
BEST#
     
       sigma[395]
-      1.471
-      0.055
-      1.307
-      1.630
-      0.002
-      1890.536
-      1062.0
+      1.475
+      0.057
+      1.309
+      1.638
+      0.003
+      1647.180
+      1347.0
       1.0
     
     
       sigma[396]
-      1.471
-      0.055
-      1.307
-      1.630
-      0.002
-      1890.536
-      1062.0
+      1.475
+      0.057
+      1.309
+      1.638
+      0.003
+      1647.180
+      1347.0
       1.0
     
     
       sigma[397]
-      1.471
-      0.055
-      1.307
-      1.630
-      0.002
-      1890.536
-      1062.0
+      1.475
+      0.057
+      1.309
+      1.638
+      0.003
+      1647.180
+      1347.0
       1.0
     
     
       sigma[398]
-      1.471
-      0.055
-      1.307
-      1.630
-      0.002
-      1890.536
-      1062.0
+      1.475
+      0.057
+      1.309
+      1.638
+      0.003
+      1647.180
+      1347.0
       1.0
     
     
       sigma[399]
-      1.471
-      0.055
-      1.307
-      1.630
-      0.002
-      1890.536
-      1062.0
+      1.475
+      0.057
+      1.309
+      1.638
+      0.003
+      1647.180
+      1347.0
       1.0
     
   
@@ -1489,7 +1489,7 @@ 
BEST with priors on variables instead of difference

     

       
-      100.00% [4000/4000 00:03<00:00 Sampling 2 chains, 9 divergences]
+      100.00% [4000/4000 00:03<00:00 Sampling 2 chains, 1 divergences]
     

     
Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 3 seconds.
 

@@ -1537,57 +1537,57 @@ 
BEST with priors on variables instead of difference
     
       sigma_C(genre, levels=levels)[Comedy]
-      0.384
-      0.040
-      0.260
-      0.494
+      0.386
+      0.038
+      0.270
+      0.498
       0.002
-      2159.017
-      770.0
+      1501.399
+      1194.0
       1.0
     
     
       sigma_C(genre, levels=levels)[Action]
-      0.385
-      0.038
-      0.270
-      0.485
-      0.001
-      2035.420
-      757.0
+      0.383
+      0.036
+      0.276
+      0.495
+      0.002
+      1874.356
+      1602.0
       1.0
     
     
       rating_nu
-      30.312
-      13.026
-      9.449
-      107.329
-      0.524
-      1823.599
-      623.0
+      30.677
+      12.750
+      10.125
+      115.099
+      0.769
+      1271.913
+      1263.0
       1.0
     
     
       C(genre, levels=levels)[Comedy]
-      5.988
-      0.073
-      5.796
-      6.181
+      5.987
+      0.071
+      5.786
+      6.189
       0.003
-      1611.842
-      1177.0
+      2130.681
+      1558.0
       1.0
     
     
       C(genre, levels=levels)[Action]
-      5.297
-      0.068
-      5.100
-      5.483
-      0.003
-      1663.670
-      1336.0
+      5.293
+      0.073
+      5.094
+      5.498
+      0.002
+      2144.733
+      1660.0
       1.0
     
     
@@ -1603,57 +1603,57 @@ 
BEST with priors on variables instead of difference
     
       sigma[395]
-      1.468
-      0.059
-      1.297
-      1.639
+      1.471
+      0.055
+      1.310
+      1.646
       0.002
-      2159.017
-      770.0
+      1501.399
+      1194.0
       1.0
     
     
       sigma[396]
-      1.468
-      0.059
-      1.297
-      1.639
+      1.471
+      0.055
+      1.310
+      1.646
       0.002
-      2159.017
-      770.0
+      1501.399
+      1194.0
       1.0
     
     
       sigma[397]
-      1.468
-      0.059
-      1.297
-      1.639
+      1.471
+      0.055
+      1.310
+      1.646
       0.002
-      2159.017
-      770.0
+      1501.399
+      1194.0
       1.0
     
     
       sigma[398]
-      1.468
-      0.059
-      1.297
-      1.639
+      1.471
+      0.055
+      1.310
+      1.646
       0.002
-      2159.017
-      770.0
+      1501.399
+      1194.0
       1.0
     
     
       sigma[399]
-      1.468
-      0.059
-      1.297
-      1.639
+      1.471
+      0.055
+      1.310
+      1.646
       0.002
-      2159.017
-      770.0
+      1501.399
+      1194.0
       1.0
     
   
@@ -1668,7 +1668,7 @@ 
BEST with priors on variables instead of difference
 

 

-
1.4696143214411443
+
1.4666780300683986
 

 

 

diff --git a/notebooks/54_Bayesian_linear_models.html b/notebooks/54_Bayesian_linear_models.html
index c9463985..efc5d44e 100644
--- a/notebooks/54_Bayesian_linear_models.html
+++ b/notebooks/54_Bayesian_linear_models.html
@@ -682,24 +682,24 @@ 
Summary using mean
-../_images/80c5529eb521d25833f837d0e232c29b93f5154f6b867f07b377170b6e64debf.png
+../_images/fde0d87b338b3868d8a37364a2f9445cdc99749675e2ffc0d33a6e88d9d17403.png
 

 

 
diff --git a/reports/notebooks/33_intro_to_NHST.err.log b/reports/notebooks/33_intro_to_NHST.err.log
deleted file mode 100644
index 67d9c013..00000000
--- a/reports/notebooks/33_intro_to_NHST.err.log
+++ /dev/null
@@ -1,30 +0,0 @@
-Traceback (most recent call last):
-  File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution
-    executenb(
-  File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/nbclient/client.py", line 1305, in execute
-    return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute()
-  File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped
-    return loop.run_until_complete(inner)
-  File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
-    return future.result()
-  File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/nbclient/client.py", line 705, in async_execute
-    await self.async_execute_cell(
-  File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/nbclient/client.py", line 1058, in async_execute_cell
-    await self._check_raise_for_error(cell, cell_index, exec_reply)
-  File "/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/nbclient/client.py", line 914, in _check_raise_for_error
-    raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
-nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
-------------------
-# Plot helper functions
-from minsitats import nicebins
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[2], line 2
-      1 # Plot helper functions
-----> 2 from minsitats import nicebins
-
-ModuleNotFoundError: No module named 'minsitats'
-
diff --git a/reports/notebooks/50_extra_bayesian_stuff.err.log b/reports/notebooks/52_Bayesian_inference_computations.err.log
similarity index 87%
rename from reports/notebooks/50_extra_bayesian_stuff.err.log
rename to reports/notebooks/52_Bayesian_inference_computations.err.log
index b512a518..25cf0fe7 100644
--- a/reports/notebooks/50_extra_bayesian_stuff.err.log
+++ b/reports/notebooks/52_Bayesian_inference_computations.err.log
@@ -30,8 +30,8 @@ nbclient.exceptions.CellTimeoutError: A cell timed out while it was being execut
 The message was: Cell execution timed out.
 Here is a preview of the cell contents:
 -------------------
-['import pymc as pm', 'import numpy as np', 'import arviz as az', '', '# Simulated data']
+['# Declare a model in PyMC', '', 'import pymc as pm', '', 'with pm.Model() as model:']
 ...
-['    mu = beta0 + beta1 * x', '    y_obs = pm.Normal("y_obs", mu=mu, sigma=sigma, observed=y)', '    ', '    # Sampling', '    trace = pm.sample(2000, return_inferencedata=True)']
+['    # Specify the likelihood distribution and condition on the observed data', '    y_obs = pm.Binomial("y_obs", n=1, p=p, observed=tosses)', '', '    # Sample from the posterior distribution', '    idata = pm.sample(1000)']
 -------------------
 
diff --git a/searchindex.js b/searchindex.js
index 6c299c2b..af873a7e 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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diff --git a/stats_overview/01_DATA.html b/stats_overview/01_DATA.html
index 7e7eed62..108d3de3 100644
--- a/stats_overview/01_DATA.html
+++ b/stats_overview/01_DATA.html
@@ -1415,7 +1415,7 @@ 
Visualizations
<Axes: xlabel='ELV', ylabel='group'>
 

 

-../_images/a8664b40fd1f1c5d76f5a759a3956d463793dc66e0976025c62451932b9b49bb.png
+../_images/5cfae4213c73934fd21cd1b5b48190b6afd67403497e03c29de44227157392d9.png
 

 

 

@@ -1439,7 +1439,7 @@ 
Visualizations
-
<matplotlib.lines.Line2D at 0x7f5a506260d0>
+
<matplotlib.lines.Line2D at 0x7f7dc280afd0>
 

 

 ../_images/6403f4edbc5ee6cf4d8e2d75a250d6174fea64bffc1a86b12ccfb44885e7b275.png
diff --git a/stats_overview/02_PROB.html b/stats_overview/02_PROB.html
index 37a0edfd..4a50f911 100644
--- a/stats_overview/02_PROB.html
+++ b/stats_overview/02_PROB.html
@@ -628,7 +628,7 @@ 
Random process examples using Python
-
0.8944929475332453
+
0.5362406298505092
 

 

 

@@ -665,7 +665,7 @@ 
Random process examples using Python
-
0.4939
+
0.4972
 

 

 

@@ -700,19 +700,19 @@ 
Random process examples using Python
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<Axes: >
 

 

-../_images/8762a18fe84e2b0ba0e20403f6454d845785be82407216c4891f275a6f1602c0.png
+../_images/f7e324c549188e1bed30ce4fcddf712b685d5654d8b59758ddc3cca49b55946b.png
 

 

 

@@ -778,7 +778,7 @@ 
Uniform distribution
 

 

-
<scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f277a2c80a0>
+
<scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f2e6ecb25e0>
 

 

 

diff --git a/stats_overview/04_LINEAR_MODELS.html b/stats_overview/04_LINEAR_MODELS.html
index e7fcd5a0..69b5e400 100644
--- a/stats_overview/04_LINEAR_MODELS.html
+++ b/stats_overview/04_LINEAR_MODELS.html
@@ -803,7 +803,7 @@ 
Using statsmodels formula API
-
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7ff5cd9f3c10>
+
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f30619b0be0>
 

 

 

diff --git a/tutorials/src/pycut_material.html b/tutorials/src/pycut_material.html
index c23b25d6..4baa458d 100644
--- a/tutorials/src/pycut_material.html
+++ b/tutorials/src/pycut_material.html
@@ -1346,7 +1346,7 @@ 
Defining new types of objects
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+
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