diff --git a/04.03-estimation.html b/04.03-estimation.html index 089c0219..8066bd09 100644 --- a/04.03-estimation.html +++ b/04.03-estimation.html @@ -540,7 +540,7 @@

11.1.5. Population parameters and sample
-_images/a12c09dd4e27407a5b2d21a54004f6f7e4096a78c6e79ea1cd62885d6175fd00.png +_images/888742b4266b459b8a8fce3026799b4f7fac879414bc2b7c540959b10bb738d2.png

Now suppose I run an experiment. I select 100 people at random and administer an IQ test, giving me a simple random sample from the population. My sample would consist of a collection of numbers like this:

@@ -571,9 +571,9 @@

11.2. The law of large numbers -
10 samples. Mean:  107.3600096748558  Standard deviation:  13.52992391769343
-100 samples. Mean:  99.7044051060008  Standard deviation:  16.003466334432083
-10000 samples. Mean:  99.77112912667978  Standard deviation:  15.099208297733181
+
10 samples. Mean:  97.4356583710217  Standard deviation:  12.797182424268012
+100 samples. Mean:  101.2154850507139  Standard deviation:  13.4022151683824
+10000 samples. Mean:  100.03319490190297  Standard deviation:  15.054039128997667
@@ -607,8 +607,8 @@

11.3.1. Sampling distribution of the mea

-
Simulated data:  [103 109  91 115  98]
-Mean of simulated data:  103
+
Simulated data:  [ 74 101 100  87  67]
+Mean of simulated data:  85
@@ -623,8 +623,8 @@

11.3.1. Sampling distribution of the mea

-
Simulated data:  [120  88 120 113 121]
-Mean of simulated data:  112
+
Simulated data:  [ 84  95 121  93  59]
+Mean of simulated data:  90
@@ -683,93 +683,93 @@

11.3.1. Sampling distribution of the mea Replication 1 - 108 - 99 - 83 - 102 - 105 - 99.4 + 91 + 113 + 93 + 103 + 90 + 98.0 Replication 2 - 122 - 112 + 71 95 - 121 - 91 - 108.2 + 87 + 89 + 93 + 87.0 Replication 3 - 109 - 83 - 106 - 82 + 86 113 - 98.6 + 93 + 92 + 102 + 97.2 Replication 4 - 125 - 89 - 94 - 78 92 - 95.6 + 110 + 108 + 96 + 110 + 103.2 Replication 5 - 111 - 114 - 102 - 97 91 - 103.0 + 105 + 73 + 81 + 90 + 88.0 Replication 6 - 94 - 98 - 98 - 113 - 122 - 105.0 + 96 + 92 + 108 + 78 + 106 + 96.0 Replication 7 - 80 - 129 - 113 - 91 - 78 - 98.2 + 102 + 95 + 117 + 102 + 106 + 104.4 Replication 8 - 99 - 136 - 84 - 104 - 88 - 102.2 + 111 + 134 + 121 + 101 + 96 + 112.6 Replication 9 - 75 - 87 - 120 - 83 - 115 - 96.0 + 116 + 92 + 98 + 67 + 114 + 97.4 Replication 10 - 98 - 84 - 90 - 103 - 89 - 92.8 + 131 + 95 + 110 + 62 + 99 + 99.4 @@ -783,7 +783,7 @@

11.3.1. Sampling distribution of the mea

-
[99.4, 108.2, 98.6, 95.6, 103.0, 105.0, 98.2, 102.2, 96.0, 92.8]
+
[98.0, 87.0, 97.2, 103.2, 88.0, 96.0, 104.4, 112.6, 97.4, 99.4]
@@ -839,7 +839,7 @@

11.3.1. Sampling distribution of the mea

-_images/5228159f99640bcc1810133100916a923f275a5d91f1bbc0b01249fed6203e18.png +_images/5c0d3b5f19cdd937a2d45968e34b2a5e680fc928cacda30adc406e84d4df07d4.png

Sampling distributions are another important theoretical idea in statistics, and they’re crucial for understanding the behaviour of small samples. For instance, when I ran the very first “five IQ scores” experiment, the sample mean turned out to be 95. What the sampling distribution in fig-IQ_samp_dist tells us, though, is that the “five IQ scores” experiment is not very accurate. If I repeat the experiment, the sampling distribution tells me that I can expect to see a sample mean anywhere between 80 and 120.

@@ -898,7 +898,7 @@

11.3.2. Sampling distributions exist for
-_images/80be27851666309adc7c817c071b6138d2d29105b1d95896855a499a069c7c3c.png +_images/809e22592113fe2323f922fa2db082adbe08a5ff25765455d67dcec9ca88a734.png
@@ -964,7 +964,7 @@

11.3.2. Sampling distributions exist for
-_images/becced44f3a2f9408fc92b4295106806b6dc94a4a0074dd3b4be3e89ae0e369c.png +_images/3fd69a07024098810b7d932e06f49b4d20f0d272631c1283197d5dc63e8d5273.png

Okay, so that’s one part of the story. However, there’s something I’ve been glossing over so far. All my examples up to this point have been based on the “IQ scores” experiments, and because IQ scores are roughly normally distributed, I’ve assumed that the population distribution is normal. What if it isn’t normal? What happens to the sampling distribution of the mean? The remarkable thing is this: no matter what shape your population distribution is, as \(N\) increases the sampling distribution of the mean starts to look more like a normal distribution. To give you a sense of this, I ran some simulations using Python. To do this, I wrote a function called plotSamples that produces the “ramped” beta distribution shown in the first histogram below when \(N=1\). You can use the “click to show” button to take a look at the code, if you want to see how it works. The important thing for our purposes is that, as you can see by comparing the triangular shaped histogram to the bell curve plotted by the black line, the population distribution doesn’t look very much like a normal distribution at all. Then I provided the function with increasingly larger numbers of “participants” for each simulated experiment. As the size of \(N\) increases, the sampling distribution of the mean looks increasingly normal, and by the time we reach a sample size of \(N=8\) it’s almost perfectly normal. In other words, as long as your sample size isn’t tiny, the sampling distribution of the mean will be approximately normal no matter what your population distribution looks like!

@@ -1033,7 +1033,7 @@

11.3.2. Sampling distributions exist for
-_images/b5cbc182512e597d5c04ed82cbb846c78919ef7bb06d821d80dbfb02d532e22b.png +_images/ac93d6bb7864ce6dc0f44651589f4f7eaef31f7350869e9905d1fc2b8e67ff9a.png
@@ -1043,7 +1043,7 @@

11.3.2. Sampling distributions exist for

-_images/500a1de2eaf10514f1dc4bfc2d92104c06d0513089021c052a5fc3775fb2d0c3.png +_images/9f17a2bdc5c147d967cbe471173a24fbf1d5f28d75de3dc77d8496432602d901.png
@@ -1053,7 +1053,7 @@

11.3.2. Sampling distributions exist for

-_images/b3296e9e5e0d03e036d4617af345bfc438ed576848b7afa35ef03fba033e1f57.png +_images/8595c65d76c818576b9a6d780e3498e0ce34108ff14ee0c9eb2d73db557049b4.png
@@ -1063,7 +1063,7 @@

11.3.2. Sampling distributions exist for

-_images/e55d53e3091b99e97f530247c17b39d877b2d8e562b8d30d9371d42d52db6ecb.png +_images/25e1348e60084a33ff89375749afaa4c4429f529268e2143f497033ad904f8e3.png

On the basis of these figures, it seems like we have evidence for all of the following claims about the sampling distribution of the mean:

@@ -1141,7 +1141,7 @@

11.4.2. Estimating the population standa n = 2 sample_sds = [] for i in range(1,10000): - sample_sd = statistics.stdev(np.random.normal(loc=100,scale=15,size=n).astype(int)) + sample_sd = statistics.stdev(np.random.normal(loc=100,scale=15,size=n)) sample_sds.append(sample_sd) @@ -1166,32 +1166,7 @@

11.4.2. Estimating the population standa
-
---------------------------------------------------------------------------
-AttributeError                            Traceback (most recent call last)
-Cell In[15], line 9
-      7 sample_sds = []
-      8 for i in range(1,10000):
-----> 9     sample_sd = statistics.stdev(np.random.normal(loc=100,scale=15,size=n).astype(int))
-     10     sample_sds.append(sample_sd)
-     13 # plot a histogram of the distribution of sample standard deviations, together with dashed line indicating 
-     14 # population standard deviation
-     15 #fig, ax = plt.subplots()
-
-File ~/opt/miniconda3/envs/pythonbook3/lib/python3.11/statistics.py:922, in stdev(data, xbar)
-    920 if issubclass(T, Decimal):
-    921     return _decimal_sqrt_of_frac(mss.numerator, mss.denominator)
---> 922 return _float_sqrt_of_frac(mss.numerator, mss.denominator)
-
-File ~/opt/miniconda3/envs/pythonbook3/lib/python3.11/statistics.py:374, in _float_sqrt_of_frac(n, m)
-    372 """Square root of n/m as a float, correctly rounded."""
-    373 # See principle and proof sketch at: https://bugs.python.org/msg407078
---> 374 q = (n.bit_length() - m.bit_length() - _sqrt_bit_width) // 2
-    375 if q >= 0:
-    376     numerator = _integer_sqrt_of_frac_rto(n, m << 2 * q) << q
-
-AttributeError: 'numpy.int64' object has no attribute 'bit_length'
-
-
+_images/5f7c80d4c7e67431cb2136b4c129494366dab43a62119b701489a271dcde910d.png

This intuition feels right, but it would be nice to demonstrate this somehow. There are in fact mathematical proofs that confirm this intuition, but unless you have the right mathematical background they don’t help very much. Instead, what I’ll do is use R to simulate the results of some experiments. With that in mind, let’s return to our IQ studies. Suppose the true population mean IQ is 100 and the standard deviation is 15. I can use the rnorm() function to generate the the results of an experiment in which I measure \(N=2\) IQ scores, and calculate the sample standard deviation. If I do this over and over again, and plot a histogram of these sample standard deviations, what I have is the sampling distribution of the standard deviation. I’ve plotted this distribution in fig-sampdistsd. Even though the true population standard deviation is 15, the average of the sample standard deviations is only 8.5. Notice that this is a very different result to what we found in fig-IQ-clm when we plotted the sampling distribution of the mean. If you look at that sampling distribution, what you see is that the population mean is 100, and the average of the sample means is also 100.

@@ -1273,7 +1248,30 @@

11.4.2. Estimating the population standa
-_images/7b91b9fd777b7b36f6daad9dd8eed8abb65d35918dad20423238d3d6919f5102.png +
---------------------------------------------------------------------------
+AttributeError                            Traceback (most recent call last)
+Cell In[16], line 20
+     18 sample_means = []
+     19 for i in range(1,10000):
+---> 20     sample_sd = statistics.stdev(np.random.normal(loc=100,scale=15,size=n).astype(int))
+     21     sample_sds.append(sample_sd)
+     22     sample_mean = statistics.mean(np.random.normal(loc=100,scale=15,size=n).astype(int))
+
+File ~/opt/miniconda3/envs/pythonbook3/lib/python3.11/statistics.py:922, in stdev(data, xbar)
+    920 if issubclass(T, Decimal):
+    921     return _decimal_sqrt_of_frac(mss.numerator, mss.denominator)
+--> 922 return _float_sqrt_of_frac(mss.numerator, mss.denominator)
+
+File ~/opt/miniconda3/envs/pythonbook3/lib/python3.11/statistics.py:374, in _float_sqrt_of_frac(n, m)
+    372 """Square root of n/m as a float, correctly rounded."""
+    373 # See principle and proof sketch at: https://bugs.python.org/msg407078
+--> 374 q = (n.bit_length() - m.bit_length() - _sqrt_bit_width) // 2
+    375 if q >= 0:
+    376     numerator = _integer_sqrt_of_frac_rto(n, m << 2 * q) << q
+
+AttributeError: 'numpy.int64' object has no attribute 'bit_length'
+
+

The fix to this systematic bias turns out to be very simple. Here’s how it works. Before tackling the standard deviation, let’s look at the variance. If you recall from the section on measures of variability, the sample variance is defined to be the average of the squared deviations from the sample mean. That is:

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a/_sources/04.03-estimation.ipynb b/_sources/04.03-estimation.ipynb index c818e0bc..1e576a7b 100644 --- a/_sources/04.03-estimation.ipynb +++ b/_sources/04.03-estimation.ipynb @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 1, "metadata": { "tags": [ "hide-input" @@ -157,14 +157,12 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -314,15 +312,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Simulated data: [ 94 88 89 105 82]\n", - "Mean of simulated data: 91\n" + "Simulated data: [140 116 96 119 122]\n", + "Mean of simulated data: 118\n" ] } ], @@ -345,15 +343,15 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Simulated data: [ 74 105 100 92 60]\n", - "Mean of simulated data: 86\n" + "Simulated data: [ 97 95 73 111 123]\n", + "Mean of simulated data: 99\n" ] } ], @@ -373,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": { "tags": [ "hide-input" @@ -412,93 +410,93 @@ " \n", " \n", " Replication 1\n", - " 96\n", - " 66\n", - " 111\n", + " 101\n", " 104\n", - " 118\n", - " 99.0\n", + " 129\n", + " 117\n", + " 100\n", + " 110.2\n", " \n", " \n", " Replication 2\n", - " 96\n", - " 96\n", + " 108\n", + " 105\n", + " 111\n", " 113\n", - " 114\n", - " 94\n", - " 102.6\n", + " 100\n", + " 107.4\n", " \n", " \n", " Replication 3\n", - " 113\n", - " 80\n", - " 110\n", - " 108\n", - " 141\n", - " 110.4\n", + " 81\n", + " 99\n", + " 100\n", + " 83\n", + " 86\n", + " 89.8\n", " \n", " \n", " Replication 4\n", - " 105\n", - " 110\n", - " 90\n", - " 119\n", - " 74\n", - " 99.6\n", + " 108\n", + " 87\n", + " 80\n", + " 93\n", + " 103\n", + " 94.2\n", " \n", " \n", " Replication 5\n", - " 96\n", - " 114\n", - " 135\n", - " 101\n", - " 89\n", - " 107.0\n", + " 102\n", + " 106\n", + " 117\n", + " 88\n", + " 80\n", + " 98.6\n", " \n", " \n", " Replication 6\n", - " 96\n", - " 102\n", - " 89\n", - " 111\n", - " 118\n", - " 103.2\n", + " 107\n", + " 91\n", + " 101\n", + " 114\n", + " 107\n", + " 104.0\n", " \n", " \n", " Replication 7\n", - " 92\n", - " 115\n", - " 94\n", - " 111\n", - " 87\n", - " 99.8\n", + " 106\n", + " 93\n", + " 98\n", + " 106\n", + " 108\n", + " 102.2\n", " \n", " \n", " Replication 8\n", - " 112\n", - " 96\n", + " 98\n", " 107\n", - " 75\n", - " 93\n", - " 96.6\n", + " 102\n", + " 94\n", + " 126\n", + " 105.4\n", " \n", " \n", " Replication 9\n", - " 106\n", - " 76\n", - " 84\n", - " 103\n", - " 102\n", - " 94.2\n", + " 104\n", + " 85\n", + " 74\n", + " 75\n", + " 115\n", + " 90.6\n", " \n", " \n", " Replication 10\n", - " 110\n", - " 87\n", - " 115\n", - " 74\n", - " 100\n", - " 97.2\n", + " 81\n", + " 114\n", + " 79\n", + " 95\n", + " 94\n", + " 92.6\n", " \n", " \n", "\n", @@ -506,19 +504,19 @@ ], "text/plain": [ " Person 1 Person 2 Person 3 Person 4 Person 5 Sample Mean\n", - "Replication 1 96 66 111 104 118 99.0\n", - "Replication 2 96 96 113 114 94 102.6\n", - "Replication 3 113 80 110 108 141 110.4\n", - "Replication 4 105 110 90 119 74 99.6\n", - "Replication 5 96 114 135 101 89 107.0\n", - "Replication 6 96 102 89 111 118 103.2\n", - "Replication 7 92 115 94 111 87 99.8\n", - "Replication 8 112 96 107 75 93 96.6\n", - "Replication 9 106 76 84 103 102 94.2\n", - "Replication 10 110 87 115 74 100 97.2" + "Replication 1 101 104 129 117 100 110.2\n", + "Replication 2 108 105 111 113 100 107.4\n", + "Replication 3 81 99 100 83 86 89.8\n", + "Replication 4 108 87 80 93 103 94.2\n", + "Replication 5 102 106 117 88 80 98.6\n", + "Replication 6 107 91 101 114 107 104.0\n", + "Replication 7 106 93 98 106 108 102.2\n", + "Replication 8 98 107 102 94 126 105.4\n", + "Replication 9 104 85 74 75 115 90.6\n", + "Replication 10 81 114 79 95 94 92.6" ] }, - "execution_count": 18, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -555,16 +553,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[99.0, 102.6, 110.4, 99.6, 107.0, 103.2, 99.8, 96.6, 94.2, 97.2]" + "[110.2, 107.4, 89.8, 94.2, 98.6, 104.0, 102.2, 105.4, 90.6, 92.6]" ] }, - "execution_count": 24, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -583,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": { "tags": [ "hide-input" @@ -592,14 +590,12 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -686,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 7, "metadata": { "tags": [ "hide-input" @@ -695,24 +691,12 @@ "outputs": [ { "data": { + "image/png": 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", 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" ] }, - "execution_count": 65, "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", "text/plain": [ - "Text(0.5, 1.0, 'Population Standard Deviation')" + "
" ] }, - "execution_count": 33, "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, "output_type": "display_data" } ], @@ -1159,7 +1131,7 @@ "n = 2\n", "sample_sds = []\n", "for i in range(1,10000):\n", - " sample_sd = statistics.stdev(np.random.normal(loc=100,scale=15,size=n).astype(int))\n", + " sample_sd = statistics.stdev(np.random.normal(loc=100,scale=15,size=n))\n", " sample_sds.append(sample_sd)\n", "\n", "\n", @@ -1977,7 +1949,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.3" } }, "nbformat": 4, diff --git a/reports/04.03-estimation.err.log b/reports/04.03-estimation.err.log index 2ecd8d7b..d82a352a 100644 --- a/reports/04.03-estimation.err.log +++ b/reports/04.03-estimation.err.log @@ -21,31 +21,68 @@ nbclient.exceptions.CellExecutionError: An error occurred while executing the fo import statistics import numpy as np import seaborn as sns +import pandas as pd +from matplotlib import pyplot as plt -# generate data from 10000 "IQ" studies, where each study consists of two scores -n = 2 -sample_sds = [] + + +ns = range(2,11) + + +averageSampleSds = [] +averageSampleMeans = [] + +# Simulate data for N = 2 to 10 +for n in ns: + sample_sds = [] + sample_means = [] + for i in range(1,10000): + sample_sd = statistics.stdev(np.random.normal(loc=100,scale=15,size=n).astype(int)) + sample_sds.append(sample_sd) + sample_mean = statistics.mean(np.random.normal(loc=100,scale=15,size=n).astype(int)) + sample_means.append(sample_mean) + averageSampleSds.append(statistics.mean(sample_sds)) + averageSampleMeans.append(statistics.mean(sample_means)) + +# Simulate data for N = 1. This is not possible in the loop above, because Python can't calculate a SD +# from only one observation +n = 1 +sample_mean_1 = [] for i in range(1,10000): - sample_sd = statistics.stdev(np.random.normal(loc=100,scale=15,size=n).astype(int)) - sample_sds.append(sample_sd) + sample_mean = statistics.mean(np.random.normal(loc=100,scale=15,size=n).astype(int)) + sample_mean_1.append(sample_mean) + +# Add in sample mean and SD for N=1 at the beginning of the lists +# For N = 1, the sample SD is simply 0 +averageSampleSds.insert(0,0) +averageSampleMeans.insert(0,statistics.mean(sample_mean_1)) + +# Collect simulated data in a dataframe, together with a vector from 1 to 10 representing N +df = pd.DataFrame( + {'N': range(1,11), + 'SampleMeans': averageSampleMeans, + 'SampleSDs': averageSampleSds + }) +# Plot the data +fig, axes = plt.subplots(1, 2, figsize=(15, 5), sharey=False) +fig.suptitle('Simulated IQ Data') -# plot a histogram of the distribution of sample standard deviations, together with dashed line indicating -# population standard deviation -#fig, ax = plt.subplots() -ax=sns.histplot(sample_sds, binwidth=4) -plt.axvline(15, color = 'black', linestyle = "dashed") +# Format the figure +sns.lineplot(data=df, x='N', y='SampleMeans',ax=axes[0], linestyle = "dashdot") +sns.lineplot(data=df, x='N', y='SampleSDs',ax=axes[1], linestyle = "dashdot") +axes[0].set(ylim=(0,120)) +axes[1].set(ylim=(0,17)) +axes[0].axhline(100, color = 'black', linestyle = "dashed") +axes[1].axhline(15, color = 'black', linestyle = "dashed") +axes[0].set_title("Sample Means") +axes[1].set_title("Sample Standard Deviations") +axes[0].spines[['top', 'right']].set_visible(False) +axes[1].spines[['top', 'right']].set_visible(False) -ax.set(yticklabels=[]) -ax.set(ylabel=None) -ax.set(xlabel='Sample Standard Deviation') -ax.set_title("Population Standard Deviation") -ax.tick_params(left=False) -ax.spines[['top', 'right']].set_visible(False) -ax.tick_params(axis='both', - which='both', - left=False, - right=False) +labels = ['A', 'B'] +for s, ax in enumerate(axes): + axes[s].text(-0.1, 1, labels[s], transform=axes[s].transAxes,fontsize=16, fontweight='bold', va='top', ha='right') @@ -54,14 +91,12 @@ ax.tick_params(axis='both', --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) -Cell In[15], line 9 - 7 sample_sds = [] - 8 for i in range(1,10000): -----> 9 sample_sd = statistics.stdev(np.random.normal(loc=100,scale=15,size=n).astype(int)) - 10 sample_sds.append(sample_sd) - 13 # plot a histogram of the distribution of sample standard deviations, together with dashed line indicating  - 14 # population standard deviation - 15 #fig, ax = plt.subplots() +Cell In[16], line 20 + 18 sample_means = [] + 19 for i in range(1,10000): +---> 20 sample_sd = statistics.stdev(np.random.normal(loc=100,scale=15,size=n).astype(int)) + 21 sample_sds.append(sample_sd) + 22 sample_mean = statistics.mean(np.random.normal(loc=100,scale=15,size=n).astype(int)) File ~/opt/miniconda3/envs/pythonbook3/lib/python3.11/statistics.py:922, in stdev(data, xbar)  920 if issubclass(T, Decimal): diff --git a/searchindex.js b/searchindex.js index 881fa238..c3f95258 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["01.01-intro", "01.02-studydesign", "02.01-getting_started_with_python", "02.02-more_python_concepts", "03.01-descriptives", "03.02-drawing_graphs", "03.03-pragmatic_matters", "03.04-basic_programming", "04.01-intro-to-probability", "04.02-probability", "04.03-estimation", "04.04-hypothesis-testing", "05.01-chisquare", "05.02-ttest", "05.03-anova", "05.04-regression", "05.05-anova2", "06.01-bayes", "06.02-epilogue", "bibliography", "landingpage"], "filenames": ["01.01-intro.ipynb", "01.02-studydesign.ipynb", "02.01-getting_started_with_python.ipynb", "02.02-more_python_concepts.ipynb", "03.01-descriptives.ipynb", "03.02-drawing_graphs.ipynb", "03.03-pragmatic_matters.ipynb", "03.04-basic_programming.ipynb", "04.01-intro-to-probability.ipynb", "04.02-probability.ipynb", "04.03-estimation.ipynb", "04.04-hypothesis-testing.ipynb", "05.01-chisquare.ipynb", "05.02-ttest.ipynb", "05.03-anova.ipynb", "05.04-regression.ipynb", "05.05-anova2.ipynb", "06.01-bayes.ipynb", "06.02-epilogue.ipynb", "bibliography.md", "landingpage.md"], "titles": ["1. 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Why do we learn statistics?", "2. A brief introduction to research design", "3. Getting Started with Python", "4. More Python Concepts", "5. Descriptive statistics", "6. Drawing Graphs", "7. Data Wrangling", "8. Basic Programming", "9. Statistical theory", "10. Introduction to Probability", "11. Estimating unknown quantities from a sample", "12. Hypothesis Testing", "13. Categorical data analysis", "14. Comparing Two Means", "15. Comparing several means (one-way ANOVA)", "16. Linear regression", "17. Factorial ANOVA", "18. Bayesian Statistics", "19. Epilogue", "20. 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