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<body>
<h1>Estadística Descriptiva con R</h1>
<p><a href="http://bioinfo.cipf.es/" title="webpage of department">Francisco García García</a> <em>(2014-12-08)</em> </p>
<hr/>
<h3>0. Introducción</h3>
<h3>1. ¿Cómo leemos los datos en R?</h3>
<h3>2. Descripción numérica de una variable</h3>
<h3>3. Descripción gráfica de una variable</h3>
<h3>4. Descripción de relaciones entre parejas de variables</h3>
<h3>5. Ejercicios complementarios</h3>
<h3>6. Bibliografía y enlaces de interés</h3>
<hr/>
<p><br />
<br /></p>
<h2>0. Introducción</h2>
<p>En cualquier análisis estadístico siempre debemos realizar un descriptivo de la muestra con un doble objetivo:</p>
<ol>
<li><p>Comprobar la calidad de los datos</p></li>
<li><p>Conocer detalladamente los datos. Esto nos ayudará a decidir la estrategia de análisis más adecuada.
<br />
<br /></p></li>
</ol>
<h2>1. ¿Cómo leemos los datos en R?</h2>
<p>Existen varias funciones de lectura de datos en R según el formato del archivo. Cuando tenemos un fichero en excel, es aconsejable
guardarlo previamente en formato csv y posteriormente lo leemos desde R con la función <strong>read.csv</strong>:</p>
<pre><code class="r">datos <- read.csv("riesgos.csv", header = T, sep = "\t")
</code></pre>
<p>Para obtener información detallada de esta función de lectura:</p>
<pre><code class="r">?read.csv
</code></pre>
<p>Algunas funciones para explorar los datos:</p>
<pre><code class="r">head(datos)
</code></pre>
<pre><code>#> id contrato jornada turno carfisi carpsiqui expquímica edad peso talla
#> 1 2 3 completa 1 no no no 33 74 155
#> 2 3 1 completa 1 no no no 37 74 170
#> 3 6 5 completa 1 sí no no 35 67 170
#> 4 7 1 completa 1 sí no no 30 57 164
#> 5 8 3 completa 1 sí no sí 30 69 160
#> 6 10 2 completa 1 no sí no 32 56 160
</code></pre>
<pre><code class="r">tail(datos)
</code></pre>
<pre><code>#> id contrato jornada turno carfisi carpsiqui expquímica edad peso talla
#> 86 73 5 parcial 1 no no no 32 65 159
#> 87 75 1 parcial 1 no no no 33 87 185
#> 88 76 1 parcial 1 sí no no 30 76 178
#> 89 80 4 parcial 1 no no no 29 65 153
#> 90 82 1 parcial 1 no no no 35 78 190
#> 91 86 4 parcial 4 no no no 28 65 167
</code></pre>
<pre><code class="r">colnames(datos)
</code></pre>
<pre><code>#> [1] "id" "contrato" "jornada" "turno" "carfisi"
#> [6] "carpsiqui" "expquímica" "edad" "peso" "talla"
</code></pre>
<pre><code class="r">dim(datos)
</code></pre>
<pre><code>#> [1] 91 10
</code></pre>
<pre><code class="r">summary(datos)
</code></pre>
<pre><code>#> id contrato jornada turno carfisi
#> Min. : 1.0 Min. :1.000 completa:67 Min. :1.000 no:57
#> 1st Qu.:23.5 1st Qu.:1.000 parcial :24 1st Qu.:1.000 sí:34
#> Median :46.0 Median :1.000 Median :1.000
#> Mean :46.0 Mean :1.857 Mean :1.209
#> 3rd Qu.:68.5 3rd Qu.:3.000 3rd Qu.:1.000
#> Max. :91.0 Max. :5.000 Max. :4.000
#>
#> carpsiqui expquímica edad peso talla
#> no:46 no:86 Min. :23.00 Min. :50.00 Min. :150.0
#> sí:45 sí: 5 1st Qu.:29.00 1st Qu.:62.00 1st Qu.:160.0
#> Median :31.00 Median :69.00 Median :165.0
#> Mean :31.45 Mean :70.34 Mean :166.3
#> 3rd Qu.:34.00 3rd Qu.:76.00 3rd Qu.:171.0
#> Max. :39.00 Max. :98.00 Max. :190.0
#> NA's :2
</code></pre>
<pre><code class="r">attach(datos)
</code></pre>
<p><br />
<br /></p>
<h2>2. Descripción numérica de una variable</h2>
<h3>2.1. Variables cuantitativas:</h3>
<pre><code class="r">summary(edad)
</code></pre>
<pre><code>#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 23.00 29.00 31.00 31.45 34.00 39.00
</code></pre>
<pre><code class="r">sd(edad)
</code></pre>
<pre><code>#> [1] 3.442427
</code></pre>
<pre><code class="r">sum(edad)
</code></pre>
<pre><code>#> [1] 2862
</code></pre>
<pre><code class="r">is.na(edad)
</code></pre>
<pre><code>#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [78] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [89] FALSE FALSE FALSE
</code></pre>
<p>También es posible pedir un descriptivo de varias variables cuantitativas al mismo tiempo:</p>
<pre><code class="r">summary(datos[, c("edad","peso","talla")])
</code></pre>
<pre><code>#> edad peso talla
#> Min. :23.00 Min. :50.00 Min. :150.0
#> 1st Qu.:29.00 1st Qu.:62.00 1st Qu.:160.0
#> Median :31.00 Median :69.00 Median :165.0
#> Mean :31.45 Mean :70.34 Mean :166.3
#> 3rd Qu.:34.00 3rd Qu.:76.00 3rd Qu.:171.0
#> Max. :39.00 Max. :98.00 Max. :190.0
#> NA's :2
</code></pre>
<h3>2.2. Variables categóricas:</h3>
<p>Obtenemos tablas de frecuencias absolutas con el comando <strong>table</strong>:</p>
<pre><code class="r">table(jornada)
</code></pre>
<pre><code>#> jornada
#> completa parcial
#> 67 24
</code></pre>
<p>También es posible obtener tablas de frecuencias relativas combinando los comandos <strong>table</strong> con <strong>margin.table</strong>:</p>
<pre><code class="r">table(jornada) / margin.table(table(jornada))
</code></pre>
<pre><code>#> jornada
#> completa parcial
#> 0.7362637 0.2637363
</code></pre>
<h3>2.3. Ejercicios:</h3>
<p>Tenemos una base de datos llamada <strong>estrés</strong> que incluye información sobre 90 trabajadores acerca de su nivel
de estrés y diversas variables relacionadas con el puesto de trabajo. </p>
<p><strong>A</strong>. Lee los datos en R utilizando la función <strong>read.csv</strong>. </p>
<p><strong>B</strong>. ¿Qué variables contiene esta base de datos? (utiliza la función <strong>head</strong>)</p>
<p><strong>C</strong>. Clasifica las variables según sean cuantitativas o categóricas. A continuación descríbelas utilizando los
procedimientos que hemos visto anteriormente: tablas de frecuencias y descriptivos (en función del tipo de
variable) </p>
<p><br />
<br /></p>
<h2>3. Descripción gráfica de una variable</h2>
<h3>Introducción a los gráficos en R:</h3>
<p>Los comandos de R son funciones que incluyen un conjunto de argumentos separados por comas
y todos ellos entre paréntesis:</p>
<pre><code class="r"># boxplot(datos, main = "primer gráfico", xlab = "nivel de colesterol")
</code></pre>
<pre><code class="r"># hist(datos, main = "primer gráfico", xlab = "nivel de colesterol")
</code></pre>
<p>Argumentos generales que utilizaremos en los gráficos:</p>
<ul>
<li><p><strong>main</strong>: <em>Título principal del gráfico</em></p></li>
<li><p><strong>xlab</strong>: <em>Etiqueta para el eje X</em></p></li>
<li><p><strong>ylab</strong>: <em>Etiqueta para el eje Y</em></p></li>
<li><p><strong>col</strong>: <em>vector de colores</em> </p></li>
</ul>
<h3>3.1. Variables cuantitativas:</h3>
<p>Hay varias opciones: box-plots, histogramas,…
Con un interrogante delante del nombre de la función obtenemos ayuda sobre su uso: <strong>?boxplot</strong> </p>
<p>Es posible incluir o modificar elementos en el gráfico:</p>
<p>Incluimos color en la caja</p>
<pre><code class="r">boxplot(edad, col = "red")
</code></pre>
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<p>Añadimos un título principal y subtítulo para el eje X</p>
<pre><code class="r">boxplot(edad, col = "red", main = "Evaluación de riesgos laborales", xlab = "Edad")
</code></pre>
<p><img src="data:image/png;base64,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alt="plot of chunk unnamed-chunk-11"/> </p>
<p>Modificamos la escala del eje Y</p>
<pre><code class="r">boxplot(edad, col = "red", main = "Evaluación de riesgos laborales", xlab = "Edad",
ylimit = c(0,45))
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAMAAACR9g9NAAAC+lBMVEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAOAAARAACMAACaAACoAACqAAC2AAC7AADMAADSAAD/AACcMcf8AAAA/nRSTlMAAQIDBAUGBwgJCgsMDQ4PEBESExQVFhcYGRobHB0eHyAhIiMkJicoKSorLC0uLzEyMzQ1Njc4OTo7PD0+P0BBQkNERUZHSUpLTE1OT1BRUlNUVVZXWFlaXF1eX2BhYmNkZWZnamtsbW5vcHFyc3R1dnd4eXp9fn+AgYKDhIWGh4iJiouMjY6PkJKTlJWWl5mbnJ2en6ChoqOkpaanqKmqq6ytrq+wsbKztLW2t7i6u7y9vr/AwcLDxMXGx8jJysvMzc7P0NHS09TV1tfY2drb3N3e3+Dh4uPk5ebn6Onq6+zt7u/w8fLz9PX29/j5+/z9/v///////////////+bsKc8AAAAJcEhZcwAACxIAAAsSAdLdfvwAAA3nSURBVHic7d1/fFVlHcDx5+4Hs+EQ3ESaYPgTkkyZhYbMGphKaQmoaanTZahl1ozCtFITRStDppCVg7RsiuFUNCR/hJvYEBxDYWyGMsfkhzAHT79/vV6dc3bPfpzt2mPnXu7Z8/28//Byn+ee5xzuZ9zzXH2pSgEAAAAAAAAAAAAAAAAAAAAA7HbatQen+xLw/2nQnkf7TOynOxIfFZ/MeGVhv9Pj9NrEB70HPY8YoxuMX4v/rUE31jnm95l41zcyp26V+zClPq/f6f7Dxw96DwifOg16av8TYd7I/sO/d4RPna7wC/U3lLpZ36ImP71962PHe2/kOPfNnqBXKn9Q5d/VuC0+qQbPqd9R84WYGqFbPlO3/cH43X74/VtXXeKGH/1AS0vlSG/MecWJK6d5B/mjk5Zv3/LgOKWG3dv67GnuGfzVumZUPGb81E74qxobb8zqdV5n1Z7X22d1JNQV/nT9W6Ve0BNGvd2x7Bm9IdYrvD+Y8Zx+cbluOdidjD2qNy3Zpa90AnRse36PvtNbJnOV3lS7xwm//2vvVC3Vrwx2B0foHY3aC++PHrxj7wNP6ubc2HL9Wu025wxdq/kz7nHuEf6px+g97St26tt7ntddtef1BldP03s6IMQ3d9PUoNaOYcN1Y6x46TfV0Hd0Qa/w/uCpel2Wuqej1J2cpJuGqk/orRkjtP6w+rx+0VvvTP3S4Nh8J/wVuqKg4HE9wx10XnFNfo57kD86RTccqubed+QkvfGA2A+cM3St5s+4x7lH+Kceo50f0eP37s7reV5n1Z7XG1w9je9r5MU3d6cqNV/P+JzzSa+Ovenx7VqP6P1RHx/8ir7LO8qdnKnvcH7VpA8doVuVGhu/BV+rr1Oq2Al/Z+dP1LfcwRF6V0bnQf5o/gat/3DLEepK54+w+pBzhq7V/Bn/NP6px+g3nftArT6hx3ndVXteb3B1JNa9uZui775bf1R9rKP11rO3+OHXK3WSk8UfvFrf6r3WnbzcC9CoD3Putd17r+v1t5U62Qm/UN822XGUO+i9wjuoazTngvu26d3HfE3PdX9oVnav5s/4p/FP3Rm+Rhf1Pm+v6w2uvg/fxwGnO3zWG5ubNsbUHH2jGrW38408Ur9zYGyWk8UfPEOvHqQW7rjA+wzWmw5Qp+i2jF7hp+nVubF5TvhrtPMV8YtzjncHu8P7o2fNn6FyluovfUo37O9sKVd2r+bPqPgR/qmdj/rT1XF7d+/f+7y9rje4+j5/NweQ+Ee9+w37x1rPUeqrevuDf9S60H0js7forev3OFn8wcwX9EvL9Wbvhhp7XDc+9La3yWrpDp+9Xm+s0U74glb9i4f0eu+Lfnd4f7RE71ryQPue47LrdePze93Nnb+aP6PiR/indsLvXr7Dqdz7vL2uN7h6Wt7RASK+uXO/A5+inU96Nfin2xquWKEv9O6vJat3rrjYyeIPquH3NL+1dFznzTdvbsPO+NeqHl+zR1a1rS13v86NXbr1jZ8c4o11h+8aPfe5t3bWfNb5ArZ024vnursIf7WumfgR/qnH6Fdv2Nz0nczAeXtfb3B1RFXhWSXuT9zSdF8H9rEj2vX3Lq7XF6f7OrCvFT/RuqP2kli6LwMAAAAAAAAAAAAAAAAAAAxM+ZeWIbouykxV+Bk/S/fvDe/iqZT9e5kzLkvVykiC+YSXifBCEV4owgtFeKEILxThhSK8UIQXivBCEV4owgtFeKEILxThhSK8UIQXivBCEV4owgtFeKEILxThhSK8UIQXivBCEV4owgtFeKEILxThhSK8UIQXivBCEV4owgtFeKEILxThhSK8UIQXivBCEV6oZISPDcnoZ5TwkRY6fO6s+nbdsW52TnCC8JEWOvyC6uL87PyJVRXBCcJHWujwbYXeQ15LcILwkRY6fG2p9zC9JjhB+EgLHb5ow5rFFYvqmscHJwgfaeF39VklpeVlJVndA0d3/r8vFt4QdmWkUJK+x0/osak/errnoaqkrIzUSFL4lpF9huZWJmVlpEbo8Ds6XHpPR3CC8JEWOvzY3y0+vKCg9diC4AThIy38R33ml9eezkf9gJOMe/wRy+7ZRvgBJimbu4yyyvw+g4SPtNT9Y9kBE/7YaiNrjV71m7x0/3YMEd7UsnRfQHIR3hThDdkWflG6LyC5CC8U4YUivKmZ6b6A5CK8KTZ3hggfaYQ3xa7ekG3hLUN4oQhvil29IdvCs7kzRPhII7wpdvWGbAtvGcILRXhT7OoN2RaezZ0hwkca4U2xqzdkW3jLEF4owptiV2/ItvBs7gwRPtIIb4pdvSHbwluG8EIR3hS7ekO2hWdzZ4jwkUZ4U+zqDdkW3jKEF4rwptjVG7ItPJs7QxEIf96NSbQxmYtNSvdbY3f4J/6VRP9I4lp/uz3db43d4Zf9J6L+TfiUInxihE8HwqcW4RMjfDoQPrUInxjh04HwqUX4xAifDoRPLcInRvh0IHxqET4xwqcD4VOL8IkRPh0In1qET4zw6UD41CJ8YoRPB8KnFuETszr8M3+PqL/MS/dbY3f4l3VUpf+9SUb42JCMfkYjEH7NnyPqTz9P91sTPnzurPp23bFudk5wIgLhuccnFjr8guri/Oz8iVUVwQnCJ2ZD+LZC7yGvJThB+MRsCF9b6j1MrwlOED4xG8IXbVizuGJRXfP44AThE7MhvMoqKS0vK8nqM074xKwI78rJ7PFkarVn4yPJWDkUwicWOvwHH14weln7rsqC4AR/4hOzIfyKiu9u+X7+qIX3BicIn5gN4XcdNFTnKnXQ1uAE4ROzIfzrY2PnOA8T6oIThE/MhvDXbzxBqUNve/3C4AThE7MhfGzyYUod9fWiPhOET8yG8AlFIPyT/4yovxI+pc4uT6IfJXOxvh+Q+5rV4ZOK/8ChIcJHGuFNEd6QbeGPSvcFJBfhhSK8qRPTfQHJRXhT3OMNET7SCG+K8IZsC8+u3pBt4S1DeFPs6g3ZFp57vCHCRxrhTRHekG3h2dUbsi28ZQhvil29IdvCc483RPhII7wpwhuyLTy7ekO2hbcM4U2xqzdkW3ju8YYIH2mEN0V4Q7aFZ1dvyLbwliG8KXb1hmwLzz3eEOEjjfCmCG/ItvDs6g3ZFt4yhDfFrt6QbeG5xxsifKQR3hThDdkWnl29IdvCW4bwptjVG7ItPPd4Q4SPNMKrUWVGXjZ72X7p/u0YIrwaPt3IFWYvy073b8cQ4YUivFCEF4rwQhFeKMILRXihCC8U4YUivFCEF4rwQhFeKMILRXihkhE+NiSjn1HCR1ro8Lmz6tt1x7rZOcEJwkda6PALqovzs/MnVlUEJwgfaaHDtxV6D3ktwQnCR1ro8LWl3sP0muAE4SMtdPiiDWsWVyyqax4fnCB8pIXf1WeVlJaXlWR1D3yk3LN8SdiVkUJJ+Do3LOb8JbOg6/nIyZ77fxl6ZaRO6PDHrN67fqpSo3Vwgo/6SAsd/qnrBk1qKiL8QBM6/M4hSp25MpPwA0zo8C9MUyr2q+8Sf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alt="plot of chunk unnamed-chunk-12"/> </p>
<h3>3.2. Variables categóricas:</h3>
<p>Comandos en R:</p>
<ul>
<li><strong>table(x)</strong> para obtener la tabla de frecuencias</li>
<li><strong>pie(table(x))</strong> genera un gráfico de sectores</li>
<li><strong>barplot(table(x))</strong> genera un diagrama de barras</li>
</ul>
<p>Los diagramas de barras son preferibles a los diagramas de sectores.</p>
<p><strong>3.2.1 Gráficos circulares o de sectores</strong>:
La variable <strong>contrato</strong> indica el tipo de contrato de cada trabajador: 1, 2, 3, 4, 5, 6</p>
<p>Preparamos una tabla de frecuencias</p>
<pre><code class="r">table(contrato)
</code></pre>
<pre><code>#> contrato
#> 1 2 3 4 5
#> 59 6 12 8 6
</code></pre>
<p>Generamos el diagrama de sectores</p>
<pre><code class="r">pie(table(contrato))
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-14"/> </p>
<p>Seleccionamos colores que nos gusten más para cada sector del gráfico:</p>
<pre><code class="r">pie(table(contrato), col = c("red", "blue", "pink", "green", "grey", "yellow"))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-15"/> </p>
<p>Además incluimos un título </p>
<pre><code class="r">pie(table(contrato), col = c("red", "blue", "pink", "green", "grey", "yellow"),
main = "Tipos de contratos")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-16"/> </p>
<p><strong>3.2.2. Gráficos de barras:</strong>
Este tipo de gráficos son útiles para representar variables categóricas o variables cuantitativas discretas</p>
<p>La variable <strong>contrato</strong> indica el tipo de contrato de cada trabajador: 1, 2, 3, 4, 5, 6</p>
<p>Preparamos una tabla de frecuencias</p>
<pre><code class="r">table(contrato)
</code></pre>
<pre><code>#> contrato
#> 1 2 3 4 5
#> 59 6 12 8 6
</code></pre>
<p>Generamos el diagrama de barras</p>
<pre><code class="r">barplot(table(contrato))
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-18"/> </p>
<p>Incluimos título del gráfico y títulos para los ejes X,Y.</p>
<pre><code class="r">barplot(table(contrato), xlab = "contratos", ylab = "frecuencias",
main = "Evaluación de riesgos")
</code></pre>
<p><img src="data:image/png;base64,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alt="plot of chunk unnamed-chunk-19"/> </p>
<p>Cambiamos el modo de visualización y añadimos color a las barras</p>
<pre><code class="r">barplot(table(contrato), horiz = TRUE,
ylab = "contratos", xlab = "frecuencias",
main = "Evaluación de riesgos",
col = "green")
</code></pre>
<p><img 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alt="plot of chunk unnamed-chunk-20"/> </p>
<h3>3.3. Ejercicios:</h3>
<p>Continuamos con la base de datos llamada <strong>estrés</strong>:</p>
<p><strong>A</strong>. Describe gráficamente la variable <strong>nivel de estrés</strong> utilizando un <strong>diagrama de barras</strong>. </p>
<ul>
<li>Incluye un título principal para el gráfico y también subtítulos para los ejes X e Y.</li>
<li>Las barras las colorearemos en verde (pista: col = “green”) </li>
</ul>
<p><strong>B</strong>. Describe gráficamente la variable <strong>sueldo</strong> utilizando un diagrama de cajas (<strong>boxplot</strong>). </p>
<ul>
<li>Colorea la caja en azul.</li>
<li>Incluye como título príncipal “Salario de trabajadores del Hospital Dr. Peset”. El nombre del eje Y será
“euros brutos / año”.</li>
<li>Comenta cada uno de los elementos del gráfico: cuartiles, rango de la variable, presencia de valores anómalos.</li>
<li>¿La variable tiene una distribución normal?</li>
</ul>
<p><strong>C</strong>. Describe gráficamente la variable <strong>jornada</strong> utilizando un <strong>diagrama pastel o de sectores</strong>.</p>
<ul>
<li>¿Cuántas tipos de jornadas presenta esta variable? Escoge un color para cada una de ellas en el gráfico. </li>
<li>Incluye un título para el gráfico.</li>
</ul>
<p><br />
<br /></p>
<h2>4. Descripción de relaciones entre parejas de variables</h2>
<h3>4.1. Varias variables categóricas</h3>
<p>Obtenemos <strong>tablas de contingencia</strong> para dos variables categóricas utilizando comando <strong>table</strong>:</p>
<pre><code class="r">table(jornada, carfisi)
</code></pre>
<pre><code>#> carfisi
#> jornada no sí
#> completa 38 29
#> parcial 19 5
</code></pre>
<p>También es posible estratificar la información anterior por una tercera variable:</p>
<pre><code class="r">table(jornada, carfisi, carpsiqui)
</code></pre>
<pre><code>#> , , carpsiqui = no
#>
#> carfisi
#> jornada no sí
#> completa 14 14
#> parcial 14 4
#>
#> , , carpsiqui = sí
#>
#> carfisi
#> jornada no sí
#> completa 24 15
#> parcial 5 1
</code></pre>
<p>La relación entre <strong>jornada</strong> y <strong>carga física</strong> se puede representar gráficamente:</p>
<pre><code class="r">plot(jornada, carfisi)
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAMAAACR9g9NAAAC6FBMVEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAQEDAwMHBwcICAgJCQkKCgoLCwsMDAwODg4PDw8REREWFhYgICAhISEiIiIwMDAyMjJISEhNTU1aWlpra2uKioqPj4/m5uZD7yqTAAAA+HRSTlMAAQIDBAUGBwgJCgsMDQ4PEBESExQVFhcYGRobHB0eHyAhIiMkJSYnKCkqKywuLzAzNDU2Nzg5PD0+P0BCQ0RFRkpMTU9RUlNUVVZXWVpbXF1eX2BiY2VmZ2hpa2xtcHFyc3R1dnd4enx9fn+AgYKEhYeIiYuMjo+QkpOUlpeYmZqbnJ2en6ChoqOkp6ipqqusra+wsbKztLW2t7i5uru8vb6/wMHCw8TFxsfIycvMzc7P0NHS1NXW19jZ2tvc3d7f4OHi5OXm5+jp6uvs7e7v8PHy8/T19vf4+fr7/P3+/////////////////////////////////9olunMAAAAJcEhZcwAACxIAAAsSAdLdfvwAAAuZSURBVHic7d1rdBTlHcfxZ3MxgA2CmygkaiVKCSVWUosIdlGiYlKtqDWmXkq9NEWL2ha11mJRGxN7IamoWHuRarRyESGRxgaJtqGg2AabxNAaiab2fr9f3nayu3hogHOG3c0zz+T3/bzY2TOcw/6f+TITMuckYwwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAINwi47OCHgHWjVu8tX9woGNJXtCDwK6G1XOjudHZq+qCHgR2dRfFN/ldAc8By1qr45uqloDngGXlne1NdY1tO2YGPQgsy4lV19bEcoIeAyPqnMtqhlwaHf4HxRXp/tX//Dd8eiJ5yDbY+8g/3BoPXzN5eLbKvnTD/+e/8Kk5ecia7X3kX05Pty/h00d4UYQX5UT40r0Ib40T4b8+uHt7HOGtcSK8uXtZuskJf4jcCB+7hvCWuRE+UwjvG+FFEV4U4UURXhThRRFeFOFFEV4U4UURXhThRRFeFOFFEV4U4UURXhThRRFeFOFFEV4U4UURXhThRRFeFOFFEV4U4UURXhThRRFeFOFFEV4U4UURXhThRRFeFOFFEV4U4UURXhThRRFeFOFFEV4U4UURXhThRRFeFOFFEV4U4UURXhThRRFeFOFFEV4U4UW5E/6ICYS3yInw0x+7L/rQawOPFRHeGifCP7Hslq6b88Z+7gHCW+NE+J8enf/mWGMKeghvjRPht8VmDc4y5ozvEd4aJ8Jf/nrP5S9+ob77HMJb40R4c+JkU3rtx9+dbnfC+xdA+D+fNXHI/t+9FVcQ3poAwv+xuSlu+vBslX3+8o4hfPrcuNQfmuXm+ATCp86V8JHxWb7DXx/rTCB86vaGf/kta34+b3ivcYu39g8OdCzJ81n+oNd6wvv29hn/D2t+u98Z37B6bjQ3OntVnc/w3rcBh4298pJcwqfOifDdiZv0+V1+u3/itYLrN2y+g/CpcyJ8a3V8U9XiN/yukyMvHVey/78TwvvmRPjyzvamusa2HTP9hu8+/pRnzKRewqfOifAmJ1ZdWxPL8dvdLN/ywiVTnlpJ+NS5Ef5Q5VRVZU+9+h2ET104wx8M4X0jvCjCiyK8KMKLIrwowosivCjCiyK8KMKLIrwowosivCjCiyK8KMKLIrwowosivCjCiyK8KMKLIrwowosivCjCiyK8KMKLIrwowosivCjCiyK8KMKLIrwowosivCjCiyK8KMKLIrwowosivCjCiyK8KMKLIrwowosivCjCiyK8KMKLIrwowosivCh3whdOILxFToSf1nzSsesHXm8+hvDWOBH+qc/krVw2Ju+2rxHeGifCv3q06ZhqTMH+j4cm/EhxIvw3rorcdZkxF2wivDVOhC/a1P7gG82P7zyF8NY4Ed5E3vuha6+oyEu3O+H9cyN8QnEF4a0JIPwvNq6OmzE8W2Uf4a1x6YxPH+F9cyV8ZHwW4W1yIvy4xVv7Bwc6lqT9vzvC++ZE+IbVc6O50dmr6ghvjRPhu4vim/wuwlvjRPjW6vimqoXw1jgRvryzvamusW3HTMJb40R4kxOrrq2J5aTbnfD+uRE+UwjvG+FFja7wf/8XfGofVeH/9Df4tG5Uhf+rvVWE3ei61BPeN8KLIrwowosivCjCiyK8KMKLIrwowosivCjCiyK8KMKLIrwowosivCjCiyK8KMKLcjb87XOyCT+CnA1/4+add7z/UH+oivC+ORvemJKrvrPzrjNzCT8iHA4/oar+lbZ1288m/EhwNvy1j/d96yNTjDn9JcKPBGfD37MgP749/BzCjwRnw6eE8L4RXhThRRFeFOFFEV4U4UURXhThRRFeFOFFEV4U4UURXhThRRFeFOFFEV4U4UURXhThRRFeFOFFEV4U4UURXpRD4Wel/RRxwvvnUPiuYwhvjxPh+waGDL4xQHhrnAg/fV1TSUHBKycVEN4aJ8Kb7EVb5nOpt8qN8Mac0PylXsJb5Ep4k1WzIkp4e5wJ7ymuILw1AYT/1Z21Qz42eXi2yj7CWxNA+F9fPS9uQrqZCZ8GVy71kfFZhLfJifDjFm/tHxzoWJL2PVvC++ZE+IbVc6O50dmr6ghvjRPhu4vim/wuwlvjRPjW6vimqoXw1jgRvryzvamusW3HTMJb40R4kxOrrq2JHeovKSd8GtwInymE943woggvivCiCC+K8KIIL4rwoggvivCiCC+K8KIIL4rwoggvivCiCC+K8KIIL4rwoggvivCiCC+K8KIIL4rwoggvKqzhsycd6CftCO9bOMMXNu7p29NYSPjUhTP8/fWFpvDelYRPXTjD9070XqI9hE9dOMNvnee9zNtC+NSFM/y53Q03NHQvIHzqwhnelCxauqhk/92E9y2k4Q+C8L6FM3xs3ZYhhE9dOMNvW1pW6iF86sIZ/sWxB95PeN/CGf6a2mzCpyec4df09TzH1/i0hDN8aQLhUxfO8AdDeN8IL4rwoggvivCiCC+K8KIIL4rwoggvivCiCC+K8KIIL4rwoggvivCiHAk/MeK9ZBcQ3honws949s0XKow5fpDw1jgRfs1Nh83ZXk54m5wIv3u8MWdvzCa8RU6Ef6bSmMj9nyK8RU6EP+PV9UeZgpZWwtvjRHgz+bx8Y/LOu5nw1gQQ/jfnlwyZst9PQRVXEN6aAML/8qE746YOz1bZR3hr3LjUZwrhfXMlfGR8FuFtciL8uMVb+wcHOpbkEd4aJ8I3rJ4bzY3OXlVHeGucCN9dFN/kdxHeGifCt1bHN1UthLfGifDlne1NdY1tO2YS3honwpucWHVtTexAv4ic8CPEjfCZQnjfCC+K8KIIL4rwoggvivCiCC+K8KIIL4rwoggvivCiCC+K8KIIL4rwoggvivCiCC+K8KIIL4rwoggvivCiCC+K8KIIL4rwoggvivCiCC+K8KIIL4rwoggvivCiCC+K8KIIL4rwoggvivCiCC+K8KIIL4rwoggvivCiCC+K8KIIL4rwoggvivCiCC+K8KJcCc/z4y1zIjzPj7fPifA8P94+J8Lz/Hj7nAjP8+PtcyI8z4+3z4nwPD/ePjfCJxRXpBv+d7+HT19JHrJH7X3kW89uHPL0jOHZKvvSDQ8gHDJz5w7hkrE7dwiXjN25Q7hk7M4dwiVjd+4QLhm7c4eQydSdOwAAAAAAgAw4ue3ttwOj5YZF+ca97/ZZHf7faAwfnb/3nXb4D3Ts+nyeWfBcz8pCU7r21q41pz796mfNwru/2L12WvzQnNa6+5vF5pHBbYdf+oOfPTUt6Hl9Sy7BJKYue/y67ybWOnTGJ/ZJh39X16klG6undJ9xZP0KU/rGB4/c+PI7Zw0WLtxzeeHNz0S8QxPtOmvisuahM/7Y/jkF9XcGPbBvySUkpy7rrp+RWKsXPrlPOvx1txvznjmLGowpfD27dLsxS5cb03niws3G5O6a6h2ai+8zZszubC/8mOPM4besCHpg35JLSE5d1p+XXKsXPrlPOvzyq4del97gvfRNKt1izCe9t8+fuPABb8em071Dc11vZ2fnTyZ74XNuevrbD4cof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alt="plot of chunk unnamed-chunk-23"/> </p>
<h3>4.2. Variable categórica vs. variable cuantitativa</h3>
<p><em>¿Qué relación hay entre la edad y la jornada?</em> Lo vemos gráficamente:</p>
<pre><code class="r">boxplot(edad ~ jornada)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-24"/> </p>
<p><em>¿Qué relación hay entre la edad y la carfisi?</em></p>
<pre><code class="r">boxplot(edad ~ carfisi)
</code></pre>
<p><img 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alt="plot of chunk unnamed-chunk-25"/> </p>
<h3>4.3. Variable cuantitativa vs. variable cuantitativa</h3>
<p><em>¿Qué relación hay entre el peso y la talla?</em> </p>
<pre><code class="r">plot(peso, talla)
</code></pre>
<p><img src="data:image/png;base64,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alt="plot of chunk unnamed-chunk-26"/> </p>
<p>representamos cada sujeto con triángulos azules </p>
<pre><code class="r">plot(peso, talla, pch = 2, col = "blue", main = "peso vs. talla")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-27"/> </p>
<p>un poco más grandes! </p>
<pre><code class="r">plot(peso, talla, pch = 2, col = "blue", main = "peso vs. talla", cex=2)
</code></pre>
<p><img 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OCUfNltwPkQX6pZ8QrgXNI13pK6yAQvqRumAvcA769ZtinwLPB3I+qdADxTs+z+nkcnDSETvKQuyB4C3rbyeWwJnA7Ze1etF2sBVwO7Q7asf/FJw8dz8JJ64WTgmNUXZ0uBs0mz3EnqIRO8pC6L3YGnIPtFnQrnA7tBeN5d6iETvKQuivHA8aTz7HVkkdf55/7EJHUixkMcXHQU7TDBS+qmQ4BLIXtm7GrZbcA4iJ37EZTUgcOA4yHe1rCmCjObNIWmpJ6IqRA35r34ZupPh7gGIuttXFK7YhrEdRAbQtwAsUYbG5kDbNHtyJphD15StxwPnLTqHPRjyR4Ffgx8uHchSR35PHACZE8AVwIfKzielniZnKQuiA2AjwCTIQ5oVLvGZOCjpHvESyUSOwJTILslX/BV4DqIuZAtLDCwppngJXXDU8BOba7bZI9f6pfIgFOAj69clr0A8UXSkaqji4mrNSZ4SV2QvQz8tugopC7ZD7gVst+tujj7PsShEFtB9qtCImuB5+AlSXpFTAaOAE6tU+FYUu++9EzwkiStdCRwJmR/HL04ux+YB7FnH2NqiwlefRCbQdwCMaHoSMop/hriRYjX1Cm3/aS2tfL5ic2AWcAFDSqeRLpz4qs6j693TPDqh1OBO4C/LzqQkvoOsAC4uU657Se1r5XPzz8Bn81nWxxDtpD0JeCTnQbXSyZ49VjsTkpeRwB7Ov/4SDEHeB6YBmwJ8e4R5baf1LZWPj8xC3gZsrua3PjZwD4Q63UapTrnTHZ9F+Pzmc3yQ88xE+LcYmMqk1gL4oX8elsgvgCxqKbc9pPa1srnJ8ZBXJ9mrGtpH7Mgvt6gUmEz2XmZnHppxLzk2W0Qh6X5x7PbC42sHK4H7oPszvQ0+yzEpyFOguwfsf2kTrTy+VkfeAn4NjQ4Or+6xZ0Gqs7Zg++revOSO/94Em/Ne+9rjVi+J8TyNDOc7Se1p1T/f5yLXgOnzrzk2aPAjcCHCoipTK4CzoVs6aqLs6uB3wH/i+0ntcv/P0PGHnzfxDYQl41RPgniZog1+xdTmcThEHWusQWIXSBehphRp3zI208aS+n+/xTWgx8mJvi+iasgGryhY790rnkYxfMQY9xBLa7Kb1H58Bh1hrj9pLGU7v+Ph+g1KGIv4JeQPTh2vewS4K31e6mDKr4DPAPZd+qUr2i/3YBp6R/RaIa1/aSx+P9nWNmD77mYmB/6WrvJ+ttBfLe3MZVJrJ/PWLdlnfIR7RdHQzw7xvaGrP2ksZT2/489eA2ETwHnQbakuerZvcCidM55KNwA/C9kv65TPqL9stOA5yHOGL360LWfNBb//4wwTJfazAY2Aj5QdCCDKdYFfkq6y9JLLaw4Ddgbsp17ElZpxJ+S2ucORm+fNYBtgT+w6oW4awNTgU/U2fCQtJ80llL//5lDiqvBaYPuc6Ibdcsy4Kg21lsANDs1ZJU9CXx/jPJxwM9HWb4A+FX+czTD0n7SWPz/MwoTvLokWwbMLTqK8sqeAPYuOgppMPn/ZzSeg5ckaQCZ4CVJGkAmeEnqSEyB+GrRUUgjmeAlqTMnAG+COKDoQKRaJnhJaltsA7we2As43PsDqExM8JLUvlOBo/NR3F8Bjik4HukVJnhJasvIec+zuTi/uUrEBC9JLYuJwLHAyDuSHQt8of/xSKszwUtS6+rMe57dCywe5PnNVR3OZCdJLYlppEF1u9epcAJwMcRfQtbKvOhSV9mDl/oi1ik6AnXN54ETIHt59OJsPnAl8LE+xlRxsWnREQwiE7zUc7ET8ADEq4uORJ2KHYEpkN3SoOJXgYPyu5xpTHEQMA/itUVHouqaDVxYdBAaNjEO4nqI90P8W9HRqBORQfyo+d5mvAfitN7GVHUxHuIZiC9D/KzoaHpkDrBFETu2By/11kHAjyC7CFgPYtuiA1Lb9gNuhex3zVXPfgC8HmKrXgZVcWcCt0P2D8A6EPsVHdAgcZCd1DMxBTgE2C1fcCzwb8B7CgtJbYqJwPHAEflh+mZ9jzQZzr49CavSYjpwADA9X3AgcEn+UBeY4KXeOQY4HbLl6Wn2EMTdEPtCdlmhkalVk4GrgT3aWPfuLscyKC4GzoRsaXqa3QTxa4hTITu20MhUOZ6DVx/FlhBXjrJ8LYibISb1PyapLGJ3iMdHWb4hxKIBG5zoOXhpwJzMqPOSZ0uBs4HP9DkeqUzOAQ5dfXH2BKkjdlGf4xlIJnip62J34CnIflGnwvnAbhAb9zEoqSTiGGARZKMc4QLgMGAniO37GJQqzkP06oMYD3EjxGsa1JsJ8c3+xCSVRUyCWAjR4JB1HJLOxw8ED9FLA+IQ4FLInhm7WnYbMA5i534EJZXEucB1K+/AV082B8ggDu5HUKo+e/DqsZia996bvDolpkNckyZQkQZdbJn33pscYBo7QjydJouqNHvw0gA4HjgJshebq549CvwY+HDvQpJK4yLgZMiWNVc9uxO4izR3hNrgdfBSV8QGwEeAyRAHtLDiZOCjwLd7EZVUDvEm4C3AJIhWbsIzCdiFNPBOLTLBS93xFLBTm+s22eOXKusB4J1trvt8NwMZJu0k+INIl/lIekX2MvDboqOQyil7Ebix6CiGzVgJfmvSZBy197H+E+DP6G2Cz4C1gaVAnfstS5KksYw1yO48YALwO1LCvQqYBny8B3FMBj5LOoyzDFgELAd+DXwOmNiDfUqSNJSWAVOBNYFr8mUbAjf3YF//ke9jl3yfr8p/zgKuIE1r2CkvkytMzIRYnOZhL6NO44vNIG6BmNDduMqiUfs0+vvjryFebDz5j8qp7J/f0ivsMrmxzAP+Iv/9DuDVpEP0T/dgXwuBetN2rk0awNQpE3xhYh7EPRBXFB3J6DqNLy6AOAPiqK6GVRqN2qfR3x9LIeZD3NezENVDZf/8ll4pr4P/AnAtsBnpNonXAj8kJftuewjYs07ZHsAjPdin+iKOAZYAOwBvL9/80p3GF7sDC4AjgD0Hb375Ru3T6O+POaRR0NOALSHe3fOQ1UVl//yqE5uSrkMcD3wI+DQwpQf72Yl0xOCXpHsEn0OaFOHnwKPAjl3Yhz34vhs573QcAvFAsTHV6jS+kfPOx0yIc7sfZ1EatU+jvz/WgnghzUgGEF9ItwJVNZT981sZpTxE32/jgd1Ig/iOAf42f96ta/VN8H0XF0BcOmLZb8ozv3Sn8cUnIA4fsexbgzO/fKP2afT3x+3p0O4q5YshTupNvOqusn9+K6NUCf7nTTy6bRbp/H4vmeD7qt6802WZX7rT+OrNOz8o88s3bJ/1G/z9O+e99xEDs2JPiOUQa/Q2fnWm7J/fSilVgn9LE49uC+A+4B092PYKJvi+irsgjq5Tdi3E1/sbz2oxdBhfzIbYrU7Z8RAVn1++Yfvc2+DvXwRxVp3yB9OXAJVX2T+/lVKqBF+EII3Y/zbp3Puf9mAfJvi+iX0g/jBG+bp5AtigfzGtsv8O44ttIC4bY/1JEDdDrNlZnEVp2D5vy3vn9drnKIiX6v/9sSHpsrkZHYeqHij757dySpXgizhEH8CM/PeZwH+T7rJ1KGmgX7N2Bc6q8/gp6WoA9Vw8AdFg3uk4DeKm/sSz2r47jC+u4pWBR3XX36+655obtU9cBTFnjPZ5HuIrY//9cQXEw53Fqd4o++e3ckqV4Is6RD+j5nlGmvTmy6TR9Y82uZ21gdfVeZwLzO1KtBpDnAhxe5N150PM6mk4q+/zxM7ii70gvtTk+ldXr5faqH1q//5R2+c7EI/lvzf4++P59EVI5VH2z28llSrBN3JQD7Y5MsHXyujOlwoP0fdcTMkP3W3UZP39+9uL6zS+mJgfel+7yfW3g/hu63EWpVH7jPz7V2uf9fND71vmzxv8/XE0xLPdiV2dK/vnt7JKmeC3Jh3avrDmcTnweA/2dTSr3tSmF0zwPReXQ5zX4jr3sdqlVr3SaXxxJMShLa7/7xC7tLZOURq1z2h//yrt83OIW0eUN/j74xmIM9qLV91V9s9vZRWW4Me6lOd20sj2p4A3Ad8F/h44Cfh+70PrutnARsAHig5kMMXmwIOk0yCt3N98OrAzZD2e57rT+NiENI7jFOClFtafBuwNWcmvjW/YPhOAvUljcKJm+bqko29fBP4ReJhV7wL5KtI01/XG7mwAbA6Zl80Vquyf30qbQ/q/8WC/dzzWJDLbkqaPXQb8J2mE+zXAJVQzwau3FgHt9MTm05sbGI3UaXzLgHbmml8A3NXGev3WqH3GAzeMsvwp4Geku07W+zsfo/79JJ4C/qfJGNU7Zf/8qsvm0b+bzWzdxKNTHqKXJPVbYYfox+rBr7jZzJasvNnMUnpzs5nTSUcLniP1eEazSQ/2K0nSQBorwX+HlNjnA58n3QjmNcClY6zTrvcAZ5PuOvXpHmxfkqShMtp8wuPzxy9I585WDLi4mHQe/lc9iuVC0mkBlU5Mgfhq0VFUV0yHuLfoKIoTW0PUOwcvqUdGS/DL8sdmNb+veDwF/KBHsVwHnNajbaszJwBvgjig6EAq6jJgU4jZRQdSkBuAdSAcnCuVxI+KDqDLHGTXlhXzrsckiJ9Q2fnVi7JiXu9hnb87Dof4I84/r+FVyoluBo0Jvi21867H/lR2fvWi1M7rHaczdPN3x/O8cme9uMqZzzSETPB9YIJv2WjzrldxfvWijDav9zDN3107L/0ry5x/XsPGBN8HJviW1Jt3PbaDuKCYmKqk3rzesT/EvEJC6quR89K/svxoiKXFxCQVorAEP9ogOwngU8B5kC1ZdXF2L7C4OvOrF+Z84HLIRvRgs7nAs0Mwf/cNwP9C9utVF2enAcudf15SN9mDb1pMg7gOos4XwFgf4gYI5w8fVWyb30SlzjwTsTXEAogJ/Y2rX+JdEC/U//viz/PyP+lvXFIh7MGrVD4PnADZy6MXZ/OBK4GP9TGmHotNu7ixC4HjIKtz047sfuAm0gd/QETt3SAvBWZDtnz0utktpPk0ru99XJKGgT34psSOY9/D+5V6r0ojwmPd3sfUa3EQxEsQr+3Ctg5sbqR4TIZYmN/Fq+Jip/xqgVdDnEBT93iPdfJe/J/2Pj6pUA6y6wMTfEORQfyo+d5svAei4pMTxfj8cPqXIX7W4bbGpRnb4s+arP85iP/tbJ9Fi3EQ10O8H+LrEMsh/qrJdb8N8Xhv45MKZ4LvAxN8Q7E/xOdbXOc/IbbqTTz9EGdD/DD//ZHOLuGK0yFubHGdxyD2aH+fRYuPQByb//6H9Ghp/WUQh3Q/Lqk0CkvwWRE7LchsYCPgA0UHUk4xEbgdOAJY0qByrTcD+0K2b0/C6qmYTrrnwnTIlkK8A7gEsjZmm4spwBPAcdS/9/lo/gJ4L2Qbt77PosUU4PvAbsA6pL//EeCcFjbyLmAmZA6406CaA5wCPNjvHY91NzkNl8mkuwe205u8u8ux9MvFwJkpuQNkN0H8GuJUyI5tcVtTgXuA97cRx/1trFMGxwCnp8F0MZ10c6qpwD+Q7l3RrMcaV5Gk+jxErxqx++jnf2PDfIKaARg82EuxJcSVoyxfK58gaVL/Y5JKycvkpD47Bzh09cXZE6Qvghf1OZ6qOZnUgx8hWwqcDXymz/FIGsEEryEUxwCLIBulBwrAYcBOENv3MagKid2BpyD7RZ0K5wO7QVRwXIGkKvIQvUiHjmMhr9whr269Q9L5eK0qxqcrBeI1DerNhPhmf2KSSs1D9FKfnAtcB1mDEa3ZHCCDOLgfQVXIIcClkD0zdrXsNmAcxM79CErScLMHP/Riy7z33uQAsNgR4un6c/IPm5ia996bvPompkNckyZQkoaWPXipDy4CToasyUu4sjuBu4B/62FMVXI8cFL9OfZHyh4Ffgx8uHchSarH6+A1JOJNwFuASRCt3CRnErALaeDdEIsNgI8AkyEOaGHFycBHgW/3IipJ9ZngNSweAN7Z5rrPdzOQinoK2KnNdZvs8UvqJhO8hkT2ItDiPPFaKXsZ+G3RUUhqnufgJUkaQCZ4SZIGkAm+L2IziFsgJjBPB8IAABueSURBVLS5/kyIxWmeb3Vfp69P1fffa77/e6tR+9p+w8oE3x+nAncAf9/m+heSzn9e0LWIVKvT16fq++813/+91ah9bb8hZYLvudgdWEC6z/qerc/PHceQ7s++A/B250fvtk5fn6rvv9d8//dWo/a1/TQcCpjJbuS83TET4twW1h8xb3ocAvFA9+McVp2+PlXff6/5/u+tRu1r+5WEM9kNqBHzdme3AWu0MD/3iHnTszmk+b2dH707On19qr7/XvP931uN2tf209Docw++3rzdzc7PXW/edOdH745OX5+q77/XfP/3VsP2fYPtVxqF9eCHSb8T/GyI3eqUHQ/RYH7uuAvi6Dpl10J8vbP4hl2nr0/V999rvv97q2H7PmT7lYYJvg/6mOBjG4jLxiifBHEzxJp1yveB+MMY668LsYg0P7ha1unrU/X995rv/95q2L77Qzw/RvsOefv1nQm+D/qZ4K9aObClbp39IE6qU/YExIh502PEPOBxGsRNLcTUYB7xRuWDpNPXp+r777VevP9Xq9Pi+7/sWvn8NWrfeALixLHfP4PWfqVmgu+DPiX42AviS03WvRpixohlJ0LcPmLZPvl5s9eNWD4fYlYT+6mzfrPlg6TT16fq+++1Xrz/667f5Pu/7Fr5/DVq39r2a/T+GZT2Kz0TfB/0IcHHxPzQ49pN1t8O4rs1z6fkh842qlk2GeInENtDfG/E+vtDPNxgH2Os30z5IOn09an6/nutF+//Mddv4v1fdq18/hq178j2a/T+GYT2qwQvkxsQnwLOg2xJc9Wze4FFELvkC84HLofssZpKRwJnQnY38DDEHjXrzwWWQhw+xk7GWL+p8kHS6etT9f33Wi/e/2Ot38z7v+xa+fw1at8R7dfo/TMQ7acxVPxSnJbMBjYCPtCbzce6wE+BU4CXWlhxGrA3sD/wIDCXlffPXhP4M+AGIIAJwK7AtcDLeZ3pwM6QjTLPdGwGfAPYE7LIY7wC2A2yFxqXD5JOX5+sw2vTi95/r/Xk/d+MMd7/ZdfK569h+04FTgbuZOX/BoBJwCbA1XWCqHD7VcYc0uv2YL937P3gu2cZcFQb6y0A7gIWAWeMKNuBlNyfrFn2M2Bj4O78+Xzg5jrb/ifgs+mfB0C2EOIC4JPAV5ooHySdvj5V33+v9eL934yx3v9l18rnr1H7Lqd+Ep9HaqfRVLn9pFcUMFVtJ2IWxDdHWb4GxI8h1utw/b06276k9nX6+VaFOMiuDyqU4GMcxPUQG9YpfydjTlTRcP13Qfy+/e1Lal+nn29VjIPstIqDgB9B9sToxdkNwFSIbdtbn02Bp4E6/2Aabl9S+zr9fEsaoSI9+JiSXwozoUG910L8oPX1Xyl/w+jrN9q+pPZ1+vlWBdmD1yuOAU6HbPnY1bKHgLsg3tfi+ivKH6izfqPtS2pfp59vSaOoQA8+toS4soX6a+WTZExqbv2R5SPXb7R9Se3r9POtinKQXR9UIcFfBPGmFtf5vxDHNbf+aOW16zfavqT2dfr5VkUVluCd6KY0YhppkopftLjiONLf9a4G608gTZrz7ChlE4Fbx95+1uI/Jkkrdfr59vNXYU50o+xx0qxSneh0fUk90ZXPt9QSB9lJkjSATPCSJA0gE7wkDb1Yp+gI1H0meEkaarET8ADEq4uORN1lgpekoRXjgH8BDifdblYDxAQvScNrxbz4FwHrOf/9YDHBS9JQiinAIcDp+YJjgVOLi0fdZoKXpOE0Yl787CHgboh9iwxK3WOCl6ShE1sC20J22YiCU4Ajnf9+MJjgJWn4nEzqwY+QLQXOBj7T53jUAyZ4SRoqsTvwFGT15sU/H9gNYuM+BqUeMMELiPEQBxcdhaRei/HA8cAJ9etkkdf55/7EpF4xwQvgMOB4iLcVHYiknjoEuBSyZ8ault0GjIPYuR9BSZ2qwP3gixDTIK6D2BDiBog1io5IUi/EVIgb8158M/WnQ1wDMUy3Fe+Fwu4Hbw9enwdOgOwJ4ErgYwXHI6k3jgdOguzF5qpnjwI/Bj7cu5DUS94PfqjFjsAUyG7JF3wVuA5iLmQLCwxMUlfFBsBHgMkQB7Sw4mTgo8C3exGVessEP7QiI13z+vGVy7IXIL5I+qZ/dDFxSeqBp4Cd2ly3yR6/ysYEP7z2A26F7HerLs6+D3EoxFaQ/aqQyCR1WfYy8Nuio1B/lfEcfAZMoZyxDYiYDBxB/XmnjyX17iVJFVWWJDoZ+CzwALAMWAQsB34NfA6YWFxoA+lI4EzI/jh6cXY/MA9izz7GJEnqorIk+K8D7wQOBTYGJgAbAgcDO+blBYrNIG6BmFDQ/v8a4kWI17S5fk38sRkwC7igwUonAcdBvKq9fZZJzIRYDLFWj7bfxddHkgbLQlJiH83apAEinergOvi4AOIMiKO6EEc7+18KMR/ivjbXr4k/vgWxQ5PrfQLiiPb2WSYxD+IeiCt6tP0uvj6SBszQXwf/EFDvcPAewCN9jGWE2B1YQDpnvWf/52eOOcDzwDRgS4h3t7h+bfwfBCZBdleTK58N7AOxXmv7LJM4BlgC7AC8HWL7Lm+/m69PAe8vSeqtnYB5wC+Bi4FzgIuAnwOPkg7Td6qNHnyMz2d+yg+9xkyIc7sQS7P7Xwvihfx6dSC+ALGohfVr4o9xEHdAfLfFGGZBFHyKpF0xCWIhRP7tOQ6BeKCL2+/i6wP9f39J6oPCevBlmoJwPLALMANYj9SreRC4ke5chzkb2Aj4QPOrxCeACZCdUbPsW8C/QXZ7F2JqtP/b8/2/pWbZYuDLkP1jE+vXxB8bkiareDPwO2BxC4EshuxvWqhfEnEBMHHV2OM3wD9D9s0ubL+Lr88ry/r4/pLUB3NIVyU9WHQgZbI+sG4Xt9diD77evM39mp853pr3DkcMDIs9IZY3njO+6PiLFlvmvfdJI5bvCPF0OqLR0fZ9fSQ1o7AefFlsBdwAbAtsCtxK6rUvz5dv0oV9tJrgZ0PsVqfseIgez88cT0CcVafswZQExly/4PiLFndB1JmNL67t/LSDr4+kpgx9gr8NOJ10vfulwNeASfnz2cBVXdhHCwk+toG4bIzySRA3Q6zZhbhG2/7hEHWuUYd0uD1ehJhRp7zg+IsW+0D8YYzyddO58tigze37+khq1tAn+CWk694hTW6zZU3ZejR/vnhX4Kw6j58CVze3mbhq5cCsV5aNmMc59oM4qcm4WhTPN+7BxRUQD9cpGyX+1er0MP6ixRMQ72xQ5zSIm2qeH9TC9n19+q6V10cqlaFP8FeTZlfLgG8An6gp+zBwZ5PbWRt4XZ3HucDcxpuIvSC+NGLZPvl529eNWH51/V5au+I7EI81Wff5lAhWWTZK/HXX70H8RYsTSYPfmqk7n3SVwCkQL0Ps0sQ6vj5918rrI5XO0Cf4jUlJ/JfA5cBLpHPvPwYeB3buwj6aOEQfE/NDo2vXLJsM8ROI7SG+N6L+drR82dmY+18/P7S7ZeO6kM4xx7M1z0eJf8z1uxx/0WJKfuh9oybr7w/xu3ydD0I82qC+r0/fxXrNvz5SKQ19gofUe38r6d7D/x/wKWBvujcPfTMJ/kiIQ0csqxnwFLMh9hhR/u/d61nEzyFubXGdZyDyy6xGi7/h+l2Mv2hxOcR5La6zNCVdIA3MO36Mur4+fVc7ILLR6yOVktfB90GD6+BjXdJ5+lNIRxAAXgMcCHwVCNJNcT6Zb2tFnWnA3pB1eJQh/jTf/x01227GBsDmwNRR4m9Gl+IvWmxOus50Ls3Pm7A+8C5gOWRr5du4F9gYsudGbN/Xp+9iJnAlsGG63elYr49UWoVdB+/94FdaBoycC/xDwHnAM/nzBaTTBjsDP6hZ1uzUr2N5Evh+G+s9BfwPo8ffjG7FX7RFwBkNa63qvcC1wP+mp9nDED8gTQj0vhF1fX3679vAEfm9zGnw+kgqqa2beHSq1evgZ0GMMttZrAHxYyo9P7sgPgnx61GWT4BYAPGG/seklXx9NDCG/hz8D0iHwJ8Ffl/n0alWroMfB3E9aXrX0crfSWXnZxdpDvhnILapU/6ZdL5dxfD10UAZ+gQP6c5lX+vh9ltJ8B+BOLZBnYsgtu04KhUgzob4YYM6j7DaJW7qD18fDRQTPLAbUGdq0a5oMsHHlPxSpgkN6r02Px+oSonp+WVXazWo9w6IJ/sTU9XFFIivdmlbvj4aNEN/P3iA64DTig4COAY4HbLlY1fLHgLugnCwT7VcDJwJ2dKxq2U3AQ9A/Es/gqq4E4A3QRzQhW35+khqWTPXwW8JcWXzm4y1SJPgTGpcV8WL3SEeb6H+hnlvspt3NRwwK+bVj0n5Z6GD+fN9fTSQPETfB80k+Isg3tTaZuP/QhzXdlTqo3gE4r0trnMWxH/1Jp5BUDuvfuxPR/Pn+/poIBWW4L0O/hUxDZgFfCUN6G/aONIEOqf0Iip1S2xLmhL5P1p8fTPSBEdaTewF/BKyfAKPbC7ExyBmQDavxW35+khqW4vXwUuqr968+rEdxAXFxCSVkoPsJFXKp4DzIFuy6uLsXmAxQz1/vlQOHqKX1KKYBuwF7F6nwgnAxRB/CVkr8+5L6iJ78JJa9XnghJVzxI+UzSfdJOZjfYxJ0ggmeEktiB2BKZDd0qDiV4GDvIRNKo4JXlKTIiNdLfL/N66bvQCcCnj/dqkgJnhJzdoPuBWy3zVXPfsB8HqIrXoZlKTROchOUhNiIqk3fkR+mL5Z3yP15PftSViS6jLBS2rGZOBqYI821r27y7FIaoIJXlITsgVAg1soSyoTz8FLkjSATPCSJA0gE7wkSQPIBC9J0gAywUuSNIBM8JIkDSATvCRJA8gEL0nSADLBS5I0gEzwkiQNIBO8JEkDyASvCoiZEIsh1io6kuEUm0HcAjGh6EgkNc8Eryq4EPgtcEHRgQypU4E7gL8vOhBJzTPBq+TiGGAJsAPwdojtCw5oyMTuwALgCGBPiI0LDkiSVjOb1BNUZcQkiIUQW+TPD4F4oNiYhkmMh7gR4jX585kQ5xYbk1Q5c4AtitixPXiV2bnAdZA9mJ5mc4BxEAcXGdQQOQS4FLJn0tPsNmANiJ2LDEqSRrIHXymxZd57nzRi+Y4QT0P45bSnYmreex8/Yvl0iGsgsmLikirHHrw0wkXAyZAtW3VxdidwN/C1AmIaJscDJ0H24qqLs0eBG4EPFRCTJI3KHnxlxD4QfxijfF2IRRAb9C+mYRLbQFw2RvkkiJsh1uxfTFJl2YOXapwFfLh+cbYQOBu4pE/xDJtTgaPrF2fLgK8Ax/QpHkltMMGrZOJE4GHIbhi7XnY08EaIWX0IaojEXsAvVw5srCe7BHgrxIw+BCVJY/IQfenFlPzQ+0ZN1t8f4uHexjRMYmJ+6H3tJutvB/Hd3sYkVZ6H6CXgfOByyB5rrno2F1gKcXgvgxoinwLOg2xJc9Wze4FFELv0MihJ7RmmS11mAxsBHyg6EI0mNgceBOYCLzaoXGs6sDNkzlPfkVgX+ClwCvBSCytOA/aGzGvjpdHNIX2uGpz26r7xjatIfbEIOKON9eYDN3c5lmG0DDiqjfUWAHd1ORZJXWCCV0lkC4Eji45ieGXLSEdPJA0Iz8FLkjSATPCSJA0gE7y6IKZD3Ft0FO2revyStDoTvLrhMmBTiNlFB9KmqscvSasxwatDsQ+wCfA64GPVmx++6vFL0uhM8OpUPm98thA4h+rND1/1+CVp6DlVbdfFiRC3j1g2vzrzw1c9fkkVUNhUtcPEBN9V9eaNj/0h5hUSUkuqHr+kinAuelVOnXnjs7nAsxWYH77q8UuScvbguya2hXgGos5MiLE1xAKICf2Nq1lVj19ShdiDV6VcCBwHWZ2bwmT3AzeR3thlVPX4JUk17MF3RRzY3D3YYzLEwvwucSVS9fglVYw9eFVBjAO+DBzQuG72HOlLVYkuO6t6/JLUPBO8WnEacB9k/9Nc9ezzwCYQe/QyqBZUPX5Japq3i1WTYgpwGHBcOszdtCuAc4GNexJW06oevyS1xgSvZk0F7gHe38a693c5lnZUPX5JaokJXk3KHgLeVnQU7at6/JLUGs/BS5I0gEzwkiQNIBO8JEkDyAQvSdIAMsFLkjSATPCSJA0gE7wkSQPIBC9J0gAywUuSNIDKmOAzYArljE2SpEooSxKdDHwWeABYBiwClgO/Bj4HTCwuNEmSqqcsCf7rwDuBQ0l37ZoAbAgcDOyYl6ttsRnELRAT6pTPhFgMsVZ/4yqLRu1TtLLHJ0n1LaT+7TjXBp7qwj5mAxd2YTsVFBdAnAFxVJ3yeRD3QFzR17BKo1H7FK3s8UkawxxgiyJ2XJYe/EPAnnXK9gAe6WMsAyZ2BxYARwB7Qoz4IhXHAEuAHYC3Q2zf7wiL1ah9ilb2+CRpbDsB84BfAhcD5wAXAT8HHiUdpu/UEPbgYzzEjRCvyZ/PhDi3pnwSxEKI/NtlHALxQP/jLEqj9ila2eOT1ISh78HfAbwe+DTwX6TBdteRei2bA3cWF1qlHQJcCtkz6Wl2G7AGxM55+bnAdZA9mJfPAcZBHNz3SIvRqH2KVvb4JKkt6wPrdnF7Q9aDj6l572/8iOXTIa6BeEPee580onxHiKchyvLlr0catk9WTFyvxFHy+CQ1aeh78D8kjZoHmA78N/A48CRwLbBRQXFV2fHASZC9uOri7FHgRtKRkpMhWzai/E7gbuBr/QiyQI3a50MFxFSr7PFJUlMCmJH/fiFwPrAm6XK5fwUu7cI+hqgHH9tAXDZG+QEQz0OsWad8XYhFEBv0Jr6iNWyfSRA312+fXit7fJJaUFgPvixqE/yDwFY1ZeuRLqNrxq7AWXUe9wI/6DzUKoirVg6cG7X8CYjPQZw0Rp3TIG7qfmxl0Kh9AGK/sdunl8oen6QWFJbgxzeu0jcbA78H7iMl+1/ly98MPNHkNu6k/iV17wXW6CC+ioi9gF+uHDi3WvmJwMOQfR7iaogZkM1bvV52NMR8iFmQ/aRn4fZdo/ZZIbsE4uP126dXyh6fJLXmRlJiXka65veefPk78ud/24V9HAB8sgvbKbGYmB+6XbtO+ZT80Hs+piG2g/juGNvbH+Lh7sdZlEbts1r9Bu3TbWWPT1Ibhn6Q3S7AZqSbzLwVOCxf/kdSz/sbBcVVNZ8CzoNsSZ3y84HLIXssPc3uBRZB7DJ69WwusBTi8K5HWoxG7TNCo/bpurLHJ6lChulSmwNI5/MHdF77WBf4KXAK8NIoFaYCJ5NOY7xcs/xPSFcuXF1nw9OBnSGr+Dz1DdunnmnA3pD1+NrzsscnqU1zSJ/rBqfduq9M5+DVmWXAWHOVP0/9JP4QML9O2Xzg5g7iKotG7VPPAuCuLscymrLHJ6liypLgt26izv09j6LSsmXA3AaVvtWPSMqpqfYpUNnjk1Q1ZUnwp5NuNvMcqUcymk36F44kSdVWlgT/HuBs0mHkTxcciyRJlVeWUfSQZpmbV3QQkiQNgrL04CHdPe66ooOQJGkQlKkHL0mSusQEL0nSADLBS5I0gIZpJrv/A3wVWNzm+m9m1Rng1JpXAS8UHUSF2X6dsf068zIrbwCm1qwDvB14suhAVN8NRQdQcbZfZ2y/zth+nbH9KshD9JIkDSATvCRJA8gEL0nSADLBS5I0gEzwkiQNIBN8854vOoCKs/06Y/t1xvbrjO2ngTap6AAqzvbrjO3XGduvM7afJEmSJEmSJEmSJEmSJEmSJEmSJEmSJElSl90IRM3jhzVlOwF3AQuAbwJ/0vfoym8j4AfAYuA2YKuaMttvbEez6ntvxWNWXm77NfZ3wDzgOeDHwNY1ZbZfY/sC9wPPAOcBa9aU2X6qvEeAPwdm5I8N8+XjSf84DgGmA9cCn+17dOWWAXcCRwHTgNOBG/Iy26+xdVj5vpsB/AVwH+kfqe3X2OuB5cBupPff14Dr8zLbr7GtScl7V2A94HvAl/My20+VNwFYBqwxStluwC9rnu8KPNCHmKrkraQ2yvLnE4Ft899tv9Z9G3hH/rvt19hGpCNHfwasDXwJuCQvs/0aO5rUM19hK+Cp/Hfbr4K82cyqNgP+CFwG/Aa4gPRtFVKP6mc1dX8GbM7KZCZ4M/Ar4CzSh/87pB4B2H6tmkn6wnlT/nwGtl8jjwHHkE4NLQIOJh2yB9uvGROBF2ueLwemkg7Tz8D2qxwT/KrWBx4GzgT2Bl4CLsrL1gOW1NRdTPoHvHY/Ayy59YG/Au4mtd8T2H7tyIB/Bf6pZpnt19jWwAmkU2xrAueyskdq+zV2DfA3pCNxU4FT8+WTsf0qaXzRAZTMrcBbap4fRuoJrEfqia5VUzaF9G13ad+iK7/nSD3OM/PnR5P+Kdh+rXkH8AKr9phsv8beSxrgeWv+/ATS53cdbL9m3E46r34ZKTecQUr4z2D7VZI9+FW9lZXnPCEdonqJ9Eb+LekQ9ApvJA06eblfwVXAwyOev0waBW77tWZf0umNWrZfY2uw6viZLH+eYfs1Y23gSmAT0iDFG4BfkP4H2n6qvFmkb6rvIB2iOo00WhTSN9pHgf1I32QvBz5XQIxlNhF4HPgwqf1OJ12qBLZfszLSP87NRiy3/Rp7M7AQ+EvS++8MVh1Fb/uN7Y2kHvks0vvvRtJRTLD9NAAy0qCcB0iH9r5HGpm7wk7APcDTpHN7E/sdYAXsTLpUbgnpcOkmNWW2X2NvIf0jHW3wku3X2PtI13EvBq5g5SBZsP2a8SngSeD3pARe+z60/SRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkkrtQOBs4DxgIfDfwFY15X8B3A08C/wQmJ4vHw+cCSwAngJOqFnnfcCvgEXApcD6vQtfkiSN5kDgBeAwUiL+InAPkAFTScn7vcCrga8BN+Tr7Q/cD8wAtgeWAVsAryV9UdgdeA3wTeDCvvwlkiTpFQcC99Y8fxXwNLAl8BHgkpqySaSe/BqkBP8gsDPpy8B6wATgH4D/qFlnfWB5vo6kgowvOgBJhXio5vcXgHmkQ/GbAu/On6+wHNgA+B4wBfgGsCHwb8C/AtNG1J+fr7M+8HgPYpfUBBO8NJxeW/P7eGAz4LH8cQ3wN3nZGqTD8Y8DrweuB87J688lHZp/HNiuZnvrAxNJh/olSVKfHAgE8Hekw+ynkA7ZjyP1xp8E3pOXfQn4Sb7eUcBtpN77a0nn7T9OOg+/CNiNdN7+W8DF/flTJEnSCgcC3yf1wBcDtwJvrCl/N/Bz0rn364HX5cvXAi4DlpLO2Z9JOn8Pqcf/q3x7l5EO6UuSpD46EEe5SwNvXNEBSJKk7jPBS5IkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSRoa/w9D+hdf+NY72QAAAABJRU5ErkJggg==" alt="plot of chunk unnamed-chunk-28"/> </p>
<p><em>¿Se podría cuantificar la relación entre estas dos variables cuantitativas?</em> Sí, mediante un coeficiente
de correlación:</p>
<pre><code class="r">cor.test(peso,talla)
</code></pre>
<pre><code>#>
#> Pearson's product-moment correlation
#>
#> data: peso and talla
#> t = 5.3627, df = 87, p-value = 6.67e-07
#> alternative hypothesis: true correlation is not equal to 0
#> 95 percent confidence interval:
#> 0.3237866 0.6402328
#> sample estimates:
#> cor
#> 0.4984336
</code></pre>
<h3>4.4. Ejercicios:</h3>
<p>Continuamos con la base de datos llamada <strong>estrés</strong>:</p>
<p><strong>A</strong>. ¿Qué relación hay entre las variables <strong>sueldo</strong> y <strong>edad</strong>? Primero utiliza un gráfico que nos
informe de la relación entre ambas variables y luego cuantifícala mediante un coeficiente de correlación.</p>
<p><strong>B</strong>. <strong>Salario vs. género</strong>. ¿Quién cobra más en promedio, los hombres o las mujeres? Representa gráficamente ambas variables para
describir la distribución de los salarios en cada uno de estos dos grupos.</p>
<p><strong>C</strong>. <strong>Exposición a riesgos vs. género</strong>. ¿La exposición de riesgos laborales (carga física, psíquica y ruido) es igual para hombres que para
mujeres?</p>
<p><strong>D</strong>. <strong>Estrés vs. género</strong>. ¿El nivel de estrés es igual en hombres que en mujeres?</p>
<p><br />
<br /></p>
<h2>5. Ejercicios complementarios</h2>
<p>En la base de datos <strong>exposiciones</strong> se incluye información sobre la exposición a determinados riesgos laborales en
un grupo de trabajadores. </p>
<ul>
<li>Realiza una descripción detallada de cada una de las variables incluidas en la base</li>
<li>Describe la relación de las siguientes variables por parejas: <strong>carfisica, carpsiquica, ruido,
jornada, sueldo, tipo de contrato, edad y grupo de trabajo</strong>.</li>
</ul>
<p><br />
<br /></p>
<h2>6. Bibliografía y enlaces interesantes:</h2>
<ul>
<li><p><a href="http://www.antonibosch.com/system/downloads/487/original/EC-MOORE2_Contenido.pdf?1358332517">David S. Moore. Estadística aplicada básica. Editorial Antoni Bosch.</a> Manual de Estadística Básica.</p></li>
<li><p><a href="http://www.statmethods.net/">Quick-R</a> Web con recursos para trabajar con R.</p></li>
<li><p><a href="http://www.r-tutor.com/">r-tutor</a> An R Introduction to Statistics.</p></li>
</ul>
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