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diff --git a/biostats-ISCIII/diapos/datos/challenger.txt b/biostats-ISCIII/diapos/datos/challenger.txt
@@ -0,0 +1,24 @@
+"flight"	"date"	"nfails.field"	"nfails.nozzle"	"fail.field"	"fail.nozzle"	"temp"	"pres.field"	"pres.nozzle"
+"1"	"12/04/81"	0	0	0	0	18.9	50	50
+"2"	"12/11/81"	1	0	1	0	21.1	50	50
+"3"	"22/03/82"	0	0	0	0	20.6	50	50
+"5"	"11/11/82"	0	0	0	0	20	50	50
+"6"	"04/04/83"	0	2	0	1	19.4	50	50
+"7"	"18/06/83"	0	0	0	0	22.2	50	50
+"8"	"30/08/83"	0	0	0	0	22.8	100	50
+"9"	"28/11/83"	0	0	0	0	21.1	100	100
+"41-B"	"03/02/84"	1	1	1	1	13.9	100	100
+"41-C"	"06/04/84"	1	1	1	1	17.2	200	200
+"41-D"	"30/08/84"	1	1	1	1	21.1	200	200
+"41-G"	"05/10/84"	0	0	0	0	25.6	200	200
+"51-A"	"08/11/84"	0	0	0	0	19.4	200	200
+"51-C"	"24/01/85"	2	2	1	1	11.7	200	200
+"51-D"	"12/04/85"	0	2	0	1	19.4	200	200
+"51-B"	"29/04/85"	0	2	0	1	23.9	200	100
+"51-G"	"17/06/85"	0	2	0	1	21.1	200	200
+"51-F"	"29/07/85"	0	0	0	0	27.2	200	200
+"51-I"	"27/08/85"	0	0	0	0	24.4	200	200
+"51-J"	"03/10/85"	0	0	0	0	26.1	200	200
+"61-A"	"30/10/85"	2	0	1	0	23.9	200	200
+"61-B"	"26/11/85"	0	2	0	1	24.4	200	200
+"61-C"	"12/01/86"	1	2	1	1	14.4	200	200
diff --git a/biostats-ISCIII/diapos/datos/manif.rda b/biostats-ISCIII/diapos/datos/manif.rda
diff --git a/biostats-ISCIII/diapos/datos/titanic.RData b/biostats-ISCIII/diapos/datos/titanic.RData
diff --git a/biostats-ISCIII/material/.DS_Store b/biostats-ISCIII/material/.DS_Store
diff --git a/biostats-ISCIII/material/datos/challenger.txt b/biostats-ISCIII/material/datos/challenger.txt
@@ -0,0 +1,24 @@
+"flight"	"date"	"nfails.field"	"nfails.nozzle"	"fail.field"	"fail.nozzle"	"temp"	"pres.field"	"pres.nozzle"
+"1"	"12/04/81"	0	0	0	0	18.9	50	50
+"2"	"12/11/81"	1	0	1	0	21.1	50	50
+"3"	"22/03/82"	0	0	0	0	20.6	50	50
+"5"	"11/11/82"	0	0	0	0	20	50	50
+"6"	"04/04/83"	0	2	0	1	19.4	50	50
+"7"	"18/06/83"	0	0	0	0	22.2	50	50
+"8"	"30/08/83"	0	0	0	0	22.8	100	50
+"9"	"28/11/83"	0	0	0	0	21.1	100	100
+"41-B"	"03/02/84"	1	1	1	1	13.9	100	100
+"41-C"	"06/04/84"	1	1	1	1	17.2	200	200
+"41-D"	"30/08/84"	1	1	1	1	21.1	200	200
+"41-G"	"05/10/84"	0	0	0	0	25.6	200	200
+"51-A"	"08/11/84"	0	0	0	0	19.4	200	200
+"51-C"	"24/01/85"	2	2	1	1	11.7	200	200
+"51-D"	"12/04/85"	0	2	0	1	19.4	200	200
+"51-B"	"29/04/85"	0	2	0	1	23.9	200	100
+"51-G"	"17/06/85"	0	2	0	1	21.1	200	200
+"51-F"	"29/07/85"	0	0	0	0	27.2	200	200
+"51-I"	"27/08/85"	0	0	0	0	24.4	200	200
+"51-J"	"03/10/85"	0	0	0	0	26.1	200	200
+"61-A"	"30/10/85"	2	0	1	0	23.9	200	200
+"61-B"	"26/11/85"	0	2	0	1	24.4	200	200
+"61-C"	"12/01/86"	1	2	1	1	14.4	200	200
diff --git a/biostats-ISCIII/material/datos/manif.rda b/biostats-ISCIII/material/datos/manif.rda
diff --git a/biostats-ISCIII/material/datos/titanic.RData b/biostats-ISCIII/material/datos/titanic.RData
diff --git a/biostats-ISCIII/material/practicas_guiada/reg-lineal/ejercicio_clase11.qmd b/biostats-ISCIII/material/practicas_guiada/reg-lineal/ejercicio_clase11.qmd
@@ -13,9 +13,8 @@ format:
 > Descárgate el fichero `colesterol.csv`, muévelo a la carpeta de tu proyecto y carga el archivo. Carga antes las librerías que vayas a necesitar
 
 ```{r}
-#| eval: false
-library(...)
-datos <- ...
+library(readr)
+datos <- read_csv(file = "./colesterol.csv")
 datos
 ```
 
@@ -24,10 +23,10 @@ datos
 > Usa tidyverse para limpair de ausentes tu variable objetivo (colesterol)
 
 ```{r}
-#| eval: false
+library(tidyverse)
 datos <-
   datos |> 
-  ...(colesterol)
+  drop_na(colesterol)
 datos
 ```
 
@@ -72,8 +71,7 @@ modelo |> summary()
 
 
 ```{r}
-#| eval: false
-modelo <- ...
+modelo <- lm(data = datos, formula = colesterol ~ edad + actividad_fisica)
 modelo |> summary()
 ```
 
@@ -246,8 +244,8 @@ modelo |> summary()
 El valor esperado de $Y$ es $\beta_0+\beta_1x_1 + ... = ... + ...*edad + ...$. Entonces si el paciente tuviera 51 años y actividad física de 2, su colesterol esperado sería de ... 
 
 ```{r}
-#| eval: false
-predict(...)
+predict(modelo, tibble("edad" = c(51, 15),
+                       "actividad_fisica" = c(2, 4)))
 ```
 
 > Haz otra predicción para un paciente de 15 años y actividad fisica de 4
@@ -275,7 +273,7 @@ modelo |> summary()
 
 ```{r}
 #| eval: false
-modelo_saturado <- ...
+modelo_saturado <- lm(data = datos, formula = colesterol ~ .)
 performance::compare_performance(modelo, modelo_saturado)
 ```
 
@@ -287,16 +285,29 @@ performance::compare_performance(modelo, modelo_saturado)
 #| eval: false
 datos_ruido_3 <-
   datos |> 
-  mutate("colesterol" = colesterol + rnorm(..., ..., sd = 3))
-datos_ruido_7 <- ...
-datos_ruido_15 <- ...
+  mutate("colesterol" = colesterol + rnorm(n = nrow(datos), mean = 0, sd = 3))
+datos_ruido_7 <-
+  datos |> 
+  mutate("colesterol" = colesterol + rnorm(n = nrow(datos), mean = 0, sd = 7))
+datos_ruido_15 <-
+  datos |> 
+  mutate("colesterol" = colesterol + rnorm(n = nrow(datos), mean = 0, sd = 15))
+datos_ruido_25 <-
+  datos |> 
+  mutate("colesterol" = colesterol + rnorm(n = nrow(datos), mean = 0, sd = 25))
 
-modelo_ruido_3 <- ...
-modelo_ruido_7 <- ...
-modelo_ruido_15 <- ...
+modelo_ruido_3 <- lm(data = datos_ruido_3,
+                    formula = colesterol ~ edad + actividad_fisica)
+modelo_ruido_7 <- lm(data = datos_ruido_7,
+                    formula = colesterol ~ edad + actividad_fisica)
+modelo_ruido_15 <- lm(data = datos_ruido_15,
+                    formula = colesterol ~ edad + actividad_fisica)
+modelo_ruido_25 <- lm(data = datos_ruido_25,
+                    formula = colesterol ~ edad + actividad_fisica)
 
 performance::compare_performance(modelo,
-                                 ..., ..., ...)
+                                 modelo_ruido_3, modelo_ruido_7,
+                                 modelo_ruido_15, modelo_ruido_25)
 
 ```
 

diff --git a/time-series/diapos/index.html b/time-series/diapos/index.html
@@ -1191,7 +1191,7 @@ <h2>Variables de fecha</h2>
 <div class="cell">
 <div class="sourceCode cell-code" id="cb42"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1"></a><span class="fu">today</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-11-15"</code></pre>
+<pre><code>[1] "2024-11-17"</code></pre>
 </div>
 </div>
 <div class="fragment">
@@ -1201,7 +1201,7 @@ <h2>Variables de fecha</h2>
 <div class="cell">
 <div class="sourceCode cell-code" id="cb44"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1"></a><span class="fu">now</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>[1] "2024-11-15 15:16:14 CET"</code></pre>
+<pre><code>[1] "2024-11-17 22:08:01 CET"</code></pre>
 </div>
 </div>
 </div>
@@ -2673,12 +2673,12 @@ <h2>Evitando bucles</h2>
 <span id="cb147-5"><a href="#cb147-5"></a>               <span class="at">times =</span> <span class="dv">500</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>Unit: microseconds
-                                    expr      min        lq        mean
-                                y &lt;- x^2    1.722    2.2755    3.079182
- for (i in 1:100) {     y[i] &lt;- x[i]^2 } 1368.908 1621.2425 2117.548976
-   median       uq       max neval
-    2.583    3.198    15.457   500
- 1757.198 1929.870 30093.672   500</code></pre>
+                                    expr      min       lq        mean   median
+                                y &lt;- x^2    1.681    1.927    2.174722    2.050
+ for (i in 1:100) {     y[i] &lt;- x[i]^2 } 1361.569 1382.622 1531.750488 1407.715
+       uq      max neval
+    2.214   12.218   500
+ 1480.818 4990.356   500</code></pre>
 </div>
 </div>
 </section>
@@ -4749,18 +4749,18 @@ <h2>Sin tendencia</h2>
 <span id="cb308-5"><a href="#cb308-5"></a>datos_tidy</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code># A tibble: 2,000 × 3
-       t     X_t sd    
-   &lt;int&gt;   &lt;dbl&gt; &lt;chr&gt; 
- 1     1  0.537  sd_0.5
- 2     2 -1.05   sd_0.5
- 3     3 -0.644  sd_0.5
- 4     4  0.117  sd_0.5
- 5     5  0.585  sd_0.5
- 6     6 -0.788  sd_0.5
- 7     7 -1.04   sd_0.5
- 8     8 -0.441  sd_0.5
- 9     9  0.109  sd_0.5
-10    10 -0.0355 sd_0.5
+       t    X_t sd    
+   &lt;int&gt;  &lt;dbl&gt; &lt;chr&gt; 
+ 1     1 -0.108 sd_0.5
+ 2     2 -1.13  sd_0.5
+ 3     3 -0.102 sd_0.5
+ 4     4 -0.438 sd_0.5
+ 5     5 -0.808 sd_0.5
+ 6     6 -0.667 sd_0.5
+ 7     7 -0.334 sd_0.5
+ 8     8  0.658 sd_0.5
+ 9     9  0.600 sd_0.5
+10    10  0.218 sd_0.5
 # ℹ 1,990 more rows</code></pre>
 </div>
 </div>
@@ -5142,18 +5142,18 @@ <h2>Recapitulando</h2>
 <span id="cb323-12"><a href="#cb323-12"></a><span class="fu">ts_error</span>(<span class="at">n =</span> <span class="dv">100</span>, <span class="at">sd =</span> <span class="fu">c</span>(<span class="fl">0.5</span>, <span class="dv">2</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code># A tibble: 200 × 3
-       t sd         X_t
-   &lt;int&gt; &lt;glue&gt;   &lt;dbl&gt;
- 1     1 sd_0.5 -0.105 
- 2     2 sd_0.5 -0.161 
- 3     3 sd_0.5  0.132 
- 4     4 sd_0.5 -0.460 
- 5     5 sd_0.5  0.0920
- 6     6 sd_0.5 -0.711 
- 7     7 sd_0.5  1.27  
- 8     8 sd_0.5  0.443 
- 9     9 sd_0.5  0.155 
-10    10 sd_0.5  0.337 
+       t sd          X_t
+   &lt;int&gt; &lt;glue&gt;    &lt;dbl&gt;
+ 1     1 sd_0.5  0.136  
+ 2     2 sd_0.5 -0.705  
+ 3     3 sd_0.5  0.00306
+ 4     4 sd_0.5 -0.519  
+ 5     5 sd_0.5 -0.758  
+ 6     6 sd_0.5 -0.301  
+ 7     7 sd_0.5  0.658  
+ 8     8 sd_0.5 -0.339  
+ 9     9 sd_0.5  0.240  
+10    10 sd_0.5 -0.0384 
 # ℹ 190 more rows</code></pre>
 </div>
 </div>
@@ -8685,7 +8685,7 @@ <h2>Procesos lineales</h2>
 <h2>Procesos MA</h2>
 <p><span class="math display">\[X_t = \sum_{j = -\infty}^{j=\infty} \Psi_j \varepsilon_{t-j}, \quad \sum_{j = -\infty}^{j=\infty} \left| \Psi_j \right| &lt; \infty, \quad  \text{proceso lineal}\]</span></p>
 <p>Uno de los tipos de procesos lineales más importantes son los conocidos como <span class="hl-yellow"><strong>procesos de medias moviles (MA)</strong></span> definidos como</p>
-<p><span class="math display">\[X_t = \varepsilon - \theta_1 \varepsilon_{t-1} - \ldots - \theta_q \varepsilon_{t-q} \quad q \geq 1, \quad \left\lbrace \varepsilon_{t} \right\rbrace \text{ ruido blanco}\]</span></p>
+<p><span class="math display">\[X_t = \varepsilon_t - \theta_1 \varepsilon_{t-1} - \ldots - \theta_q \varepsilon_{t-q} \quad q \geq 1, \quad \left\lbrace \varepsilon_{t} \right\rbrace \text{ ruido blanco}\]</span></p>
 <p>donde <span class="math inline">\(q\)</span> será el <strong>orden</strong> (lo llamaremos <span class="hl-yellow"><strong>procesos de medias moviles de orden q o MA(q)</strong></span>). Si te fijas es un <strong>caso particular de proceso lineal</strong> donde todos los coeficientes son 0 salvo <span class="hl-green"><strong><span class="math inline">\(\left(\Psi_0 = 1, \Psi_1 = -\theta_1,\ldots, \Psi_q = -\theta_q \right)\)</span></strong></span>.</p>
 </section>
 <section id="procesos-ma-1" class="slide level2">

diff --git a/time-series/diapos/index.qmd b/time-series/diapos/index.qmd
@@ -8563,7 +8563,7 @@ $$X_t = \sum_{j = -\infty}^{j=\infty} \Psi_j \varepsilon_{t-j}, \quad \sum_{j =
 
 Uno de los tipos de procesos lineales más importantes son los conocidos como [**procesos de medias moviles (MA)**]{.hl-yellow} definidos como
 
-$$X_t = \varepsilon - \theta_1 \varepsilon_{t-1} - \ldots - \theta_q \varepsilon_{t-q} \quad q \geq 1, \quad \left\lbrace \varepsilon_{t} \right\rbrace \text{ ruido blanco}$$
+$$X_t = \varepsilon_t - \theta_1 \varepsilon_{t-1} - \ldots - \theta_q \varepsilon_{t-q} \quad q \geq 1, \quad \left\lbrace \varepsilon_{t} \right\rbrace \text{ ruido blanco}$$
 
 donde $q$ será el **orden** (lo llamaremos [**procesos de medias moviles de orden q o MA(q)**]{.hl-yellow}). Si te fijas es un **caso particular de proceso lineal** donde todos los coeficientes son 0 salvo [**$\left(\Psi_0 = 1, \Psi_1 = -\theta_1,\ldots, \Psi_q = -\theta_q \right)$**]{.hl-green}.
 
@@ -8962,3 +8962,10 @@ $$\begin{eqnarray}\rho_h = \frac{\gamma_h}{\gamma_0} = \begin{cases} 1 \quad & &
 * simula varios y observa sus ACF
 
 # Clases 18: [procesos MA]{.flow} {#clase-18}
+
+
+
+
+
+
+
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