Fix math warning and comment out broken image links

doc/source/60s.md

@@ -85,8 +85,8 @@ counterculture values. Freaks felt that mainstream society had gone awry because
 it was too scientific and too rational. At times, hippies argued, feelings were
 more important. Rowling entreats the reader to trust in feelings.
 
-```quote
-If everyone demanded peace instead of another television set, then there would be peace.
+```
+"If everyone demanded peace instead of another television set, then there would be peace.
 ```
 
 ~ John Lennon

doc/source/eclipse-cheatsheets-to-dita-to-pdf.md

@@ -74,7 +74,7 @@ The two parts of the transformation and rendering consist of:
 Plugins are placed in the DITA-OT plugins directory. You can see the plugin I
 built in this directory named "info.debrief".
 
-![info.debrief plugin location](_static/images/dita/info.debrief_plugin_location.png)
+<!-- ![info.debrief plugin location](_static/images/dita/info.debrief_plugin_location.png) -->
 
 When ready a plugin must be integrated into DITA-OT using **Apache Ant**. I
 built this project using OS X, and I installed Ant through **Homebrew**.
@@ -86,15 +86,15 @@ You can also run the integrator inside Eclipse as an Ant build by right clicking
 on "src/libs/DITA-OT1.8.5/integrator.xml" and selecting -> Run As -> Ant Build
 (first one) and this should integrate the plugin with the toolkit.
 
-![Run DITA-OT integrator inside eclipse with Ant](_static/images/eclipse/eclipse_run_dita_ot_integrator.png)
+<!-- ![Run DITA-OT integrator inside eclipse with Ant](_static/images/eclipse/eclipse_run_dita_ot_integrator.png) -->
 
 Every DITA-OT PDF plugin has a file name plugin.xml at its root where you set
 the configuration values. The plugin id must match the folder name of the
 plugin. Every plugin has a "transtype" -> this plugins transtype is "debrief".
 You can see the plugin.xml file and where to set the plugin id and transtype in
 the next image:
 
-![plugin.xml](_static/images/dita/transtype.png)
+<!-- ![plugin.xml](_static/images/dita/transtype.png) -->
 
 PDF Plugins are comprised of some XML config files, some artwork and some custom
 XSL (XSLT/XSL-FO/XPath) code DITA-OT provides that base XSL code that you
@@ -109,7 +109,7 @@ the custom code at
 "src/libs/DITA-OT1.8.5/plugins/info.debrief/cfg/fo/xsl/custom.xsl" for this
 project. The other file is seen in the adjacent "attrs" directory.
 
-![custom.xsl](_static/images/dita/custom.xsl.png)
+<!-- ![custom.xsl](_static/images/dita/custom.xsl.png) -->
 
 Placing the custom code only in these two files follows best practice techniques
 on how to build a DITA-OT PDF plugin.

doc/source/probabilities.md

@@ -43,7 +43,7 @@ and that it is black?
 
 Therefore the total number of marbles is n = 23 and that they are black m = 8
 
-```math
+```{math}
 P (B) = 8/23
 P (B) = 0.3478
 ```
@@ -60,7 +60,7 @@ the event n times, the relative frequency of A would be fr = m / n
 When n tends to infinity, since the experiment is repeated several times, the
 relative frequency will be equal to the probability, in this case A
 
-```math
+```{math}
 (lim)┬(n→∞)⁡fr (A)=P(A)
 ```
 
@@ -73,7 +73,7 @@ m = 9 n = 10
 
 Developing:
 
-```math
+```{math}
 (lim)┬(n→9)⁡fr (A)=P(A)
 P (A) = 9/10
 P (A) = 0.9
@@ -232,7 +232,7 @@ does not affect in any way the probability that is obtained from a second roll.
 
 Being the events A and B independent, we can express them as follows:
 
-```math
+```{math}
 P (A ∩ B) = P (A) P (B)
 P (B | A) = P (B)
 P (A | B) = P (A)
@@ -248,7 +248,7 @@ Now, since A and B are two specific events within a sample space and the
 condition that P(B) > 0 is obtained, this implies that the probability of A with
 respect to B is:
 
-```math
+```{math}
 P (A Ι B) = P (A ∩ B) / P (B)
 ```
 
@@ -281,7 +281,7 @@ other hand, we obtain two elements such as TC and TT from the limited sample
 space of T, therefore that account gives us 2/4, since they are two elements of
 the 4 that make up the sample space, therefore:
 
-```math
+```{math}
 P (C | T) = ((1/4))/((2/4) )=1/2
 ```
 
@@ -334,7 +334,7 @@ On the other hand, we can suppose that H is any event that is in "S", where
 P(H) > 0, so that the conditional probability that H generates on other events
 such as: G1, G2, G3, …, Gn can be expressed through the Bayes formula:
 
-```math
+```{math}
 P(G1 Ι H)=P(G1)P(H Ι G1)/(P(G1)P(H Ι G1)+P(G2)P(H Ι G2)+...+P(Fn)P(H Ι Gn) )
 ```
 
@@ -369,7 +369,7 @@ the defective cases, that is, the sum of all the suppliers.
 Let's suppose that we want to know the probability that the defective pants were
 manufactured by Mr. Oliver:
 
-```math
+```{math}
 P (G1 Ι H) = (0.60)(0.04)/(((0.60) (0.04) + (0.30) (0.09) + (0.10) (0.07)))
 P (G1 Ι H) = 0.024/0.058
 P (G1 Ι H) = 0.4137
@@ -381,7 +381,7 @@ probability at the time of having manufactured the defective pants.
 Now, if we want to know the probability that the defective pants come from Mr.
 Jack, we must carry out the same procedure:
 
-```math
+```{math}
 P (G2 Ι H) = (0.30)(0.09)/(((0.60)(0.04)+ (0.30)(0.09)+ (0.10)(0.07)))
 P (G2 Ι H) = 0.027/0.058
 P (G2 Ι H) = 0.4655
@@ -391,7 +391,7 @@ As we can see, Mr. Jack has a 46.55% probability of having made the defective
 pants. Finally, we need to know the probability that Mr. John has of having
 manufactured the defective part.
 
-```math
+```{math}
 P (G3 Ι H) = (0.10)(0.07)/(((0.60)(0.04)+ (0.30)(0.09)+ (0.10)(0.07)) )
 P (G3 Ι H) = 0.027/0.058
 P (G3 Ι H) = 0.1206
@@ -421,7 +421,7 @@ been selected from the "n" objects taken from r to r. The commonly used
 nomenclature is nCr. The number of possible combinations that can be obtained
 from the “n” objects is given by:
 
-```math
+```{math}
 nCr = n!/r!(n-r)!
 ```
 
@@ -435,7 +435,7 @@ order of the workers who will make up the guard group is not important at all,
 since it is not necessary to establish how they intend to choose the members of
 each group, any possible combination does not alter the final result.
 
-```math
+```{math}
 20C4 =20!/4!(20-4)!
 20C4 =  20.19.18.17.16!/4.3.2.1.16!
 20C4 =116280/24
@@ -457,14 +457,14 @@ Now, we can determine the number of possible permutations that exist of our “n
 objects, but taken from r to r and for this it will be necessary to apply the
 following formula:
 
-```math
+```{math}
 nPr = n (n-1) (n-2)… (n - r + 1)
 ```
 
 But when we have a large number of elements, it is best to express the same
 formula but in factorial terms, so we will state it as follows:
 
-```math
+```{math}
 nPr=n!/(n-r)!
 ```
 
@@ -482,7 +482,7 @@ questionnaire?
 
 Answer:
 
-```math
+```{math}
 6P4 = 6!/(6-4)!
 6P4 = 6.5.4.3.2.1!/2.1!
 6P4 = 6.5.4.3

doc/source/standard-deviation.md

@@ -21,7 +21,7 @@ When we refer to the standard deviation of some statistical population, random
 variable, probability distribution or data set, we can find it with the square
 root of its variance.
 
-```math
+```{math}
 σ = √ var
 ```
 
@@ -104,13 +104,13 @@ The first thing I have to do is order my data from smallest to largest:
 
 04, 05, 09, 10, 10, 11, 12, 14, 15, 17
 
-```math
+```{math}
 Answer 1)
 The range is the greatest distance that exists between the proposed data and can be calculated by subtracting the maximum value from the minimum value. As we can see, the maximum value is 17 and the minimum value is 04, therefore the range will be:
 Range = 17 - 04 = 13
 ```
 
-```math
+```{math}
 Answer 2)
 To calculate the mean deviation, it will first be necessary to know what the population median (m) is, so we must add all the data and divide its result by the number of data (n):
 04 + 05 + 09 + 10 + 10 + 11 + 12 + 14 + 15 + 17 = 107
@@ -122,7 +122,7 @@ MD = (6.7 + 5.7 + 1.7 + 2 * 0.7 + 0.3 + 1.3 + 3.3 + 4.3 + 6.3) / 10 = 3.1
 As we can see 3.1 <4, therefore the teacher should not repeat the test, at least for this reason.
 ```
 
-```math
+```{math}
 Answer 3)
 To calculate the variance we must use the following formula:
 Var = ∑ (Xi - m) ² / N, where i will vary from 1 to n
@@ -135,7 +135,7 @@ the greater the difference between the data and the mean, that is, the greater
 the variability. If the teacher assumes that a variance of 7 is favorable, we
 can deduce that a variance of 15.21 will be unfavorable.
 
-```math
+```{math}
 Answer 4)
 Standard deviation (as we mentioned earlier) is a measure of dispersion used in statistics and is calculated as:
 σ = √ 15.21 = 3.9

doc/source/statistical-estimation.md

@@ -85,14 +85,14 @@ of 1 million inhabitants and we are going to work with a sample of 300
 inhabitants, of which 132 of them profess that they will vote for the WW party,
 this implies that:
 
-```math
+```{math}
 132/300 x 100 = 44%
 ```
 
 We can say that a specific estimate indicates that 44% of the population will
 vote for the WW party and as there are 1 million inhabitants:
 
-```math
+```{math}
 1.000.000 x 44/100 = 440.000
 ```
 
@@ -267,7 +267,7 @@ Once we get to this point, the only thing left is to construct the interval and
 in this case, it will be necessary to subtract and add to the estimator, our
 precision value:
 
-```math
+```{math}
 ø : {m-ep , m+ep}
 ```
 
@@ -306,7 +306,7 @@ more or less obvious, since the more individuals make up our sample, the more
 precision we will have when drawing conclusions. Taking into account all the
 elements, the formula that defines our precision error is:
 
-```math
+```{math}
 ep=Zseg σ/(√n)
 ```
 

doc/source/trance.md

@@ -18,7 +18,7 @@ spices in all supermarkets.
 
 Kitschy, for example, the well-off Paul Paul Dyk.
 
-![Timo Maas (SoundCloud)](_static/images/trance.jpg)
+<!-- ![Timo Maas (SoundCloud)](_static/images/trance.jpg) -->
 
 You can find more details about its formation and development on Ishkur's Guide to Electronic Music, and you can listen to trance
 music right away.