Minor changes

source/conf.py

@@ -6,7 +6,7 @@
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
-project = 'ScienceNotes'
+project = 'Science Notes'
 copyright = '2023, Loïc Guégan'
 author = 'Loïc Guégan'
 release = '0.1'

source/index.rst

@@ -8,9 +8,13 @@ Welcome to ScienceNotes's documentation!
 
 .. toctree::
    :maxdepth: 2
-   :caption: Contents:
+   :numbered:
+   :caption: Statistics
 
-   statistics/index
+   statistics/notations
+   statistics/metrics
+   statistics/bessel_correction
+   statistics/bayes_theorem
 
 
 Indices and tables

source/statistics/index.rst

@@ -1,13 +0,0 @@
-Statistics
-=========================
-
-.. toctree::
-   :numbered:
-   :maxdepth: 2
-
-   notations
-   metrics
-   bessel_correction
-   bayes_theorem
-
-Statistics notes.

source/statistics/metrics.rst

@@ -12,7 +12,7 @@ occurring we have:
 .. math::
    \mathbb{E}[X]=x_1p_1+x_2p_2+\cdots+x_np_n
 
-When working with a sample, the following is an unbiased estimator of the expected value (`source <https://stats.stackexchange.com/questions/518084/whats-the-difference-between-the-mean-and-expected-value-of-a-normal-distributi>`_):
+When working with a sample, the following is an unbiased estimator of the expected value (`source <https://stats.stackexchange.com/questions/518084/whats-the-difference-between-the-mean-and-expected-value-of-a-normal-distributi>`__):
 
 .. math::
    \overline{x}=\frac{\sum_{i=1}^n x_i}{n}
@@ -36,7 +36,7 @@ Covariance
 ------------------
 
 Covariance is a way to quantify the relationship between two random variables :math:`X` and
-:math:`Y` (`source <https://www.youtube.com/watch?v=qtaqvPAeEJY>`_). Covariance **DOES NOT**
+:math:`Y` (`source <https://www.youtube.com/watch?v=qtaqvPAeEJY>`__). Covariance **DOES NOT**
 quantify how strong this correlation is! If covariance is:
 
 Positive
@@ -72,6 +72,10 @@ Standard Error of the Mean (SEM) quantifies the error that is potentially made w
 .. math::
    \mathrm{SEM}=\sigma_X^{-}=\sqrt{\frac{\mathbb{V}[X]}{n}}=\frac{\sigma}{\sqrt{n}}
 
+When working with a sample of :math:`n` individuals, an estimator of the SEM is:
+
+.. math::
+   s_{\overline{x}}=\frac{s}{\sqrt{n}}
 
 Here is how to interpret it.
 If :math:`n=1`, the error is at most :math:`\sqrt{\mathbb{V}[X]}=\sigma_X` which is the standard deviation or :math:`X`.
@@ -96,11 +100,6 @@ Output example:
    ----- Experiment 3 -----
    Means SD: 1.27
    SEM       1.26
-
-When working with a sample of :math:`n` individuals, an estimator of the SEM is:
-
-.. math::
-   s_{\overline{x}}=\frac{s}{\sqrt{n}}
    
 Degree of Freedom
 --------------------