sleepdate

.gitattributes

@@ -0,0 +1,2 @@
+build/* linguist-generated
+docs/* linguist-generated

docs/CNAME

@@ -1 +0,0 @@
-uni.steffo.eu

docs/push-manifest.json

@@ -1 +1 @@
-{"/":{"style.5336e.css":{"type":"style","weight":1},"bundle.7a595.js":{"type":"script","weight":1}}}
+{"/":{"style.5336e.css":{"type":"style","weight":1},"bundle.24f07.js":{"type":"script","weight":1}}}

docs/ssr-build/ssr-bundle.js

@@ -10802,7 +10802,7 @@ var statistica__templateObject = statistica__taggedTemplateLiteralLoose(['P(E) =
     statistica__templateObject71 = statistica__taggedTemplateLiteralLoose(['E(X) = overline{k} cdot P(X < k) + k cdot P(X geq k)'], ['E(X) = \\overline{k} \\cdot P(X < k) + k \\cdot P(X \\geq k)']),
     statistica__templateObject72 = statistica__taggedTemplateLiteralLoose(['epsilon'], ['\\epsilon']),
     statistica__templateObject73 = statistica__taggedTemplateLiteralLoose(['\frac{Var(X)}{epsilon^2}'], ['\\frac{Var(X)}{\\epsilon^2}']),
-    statistica__templateObject74 = statistica__taggedTemplateLiteralLoose(['\forall epsilon > 0, P([ -epsilon leq left( X - E(X) \right) leq epsilon]) leq \frac{Var(X)}{epsilon^2}'], ['\\forall \\epsilon > 0, P([ -\\epsilon \\leq \\left( X - E(X) \\right) \\leq \\epsilon]) \\leq \\frac{Var(X)}{\\epsilon^2}']),
+    statistica__templateObject74 = statistica__taggedTemplateLiteralLoose(['\forall epsilon > 0, P([ left| X - E(X) \right| geq epsilon]) leq \frac{Var(X)}{epsilon^2}'], ['\\forall \\epsilon > 0, P([ \\left| X - E(X) \\right| \\geq \\epsilon]) \\leq \\frac{Var(X)}{\\epsilon^2}']),
     statistica__templateObject75 = statistica__taggedTemplateLiteralLoose(['mu_k = E ( X^k ) = \begin{cases}\n                                sum_i x_i^k p_X (x_i) qquad nel discreto\\\n                                \\\n                                int_{-infty}^{+infty} x^k f_X (x) dx qquad nel continuo\n                                end{cases}'], ['\\mu_k = E ( X^k ) = \\begin{cases}\n                                \\sum_i x_i^k p_X (x_i) \\qquad nel\\ discreto\\\\\n                                \\\\\n                                \\int_{-\\infty}^{+\\infty} x^k f_X (x) dx \\qquad nel\\ continuo\n                                \\end{cases}']),
     statistica__templateObject76 = statistica__taggedTemplateLiteralLoose(['m_X (t) = E( e^{t cdot X} )'], ['m_X (t) = E( e^{t \\cdot X} )']),
     statistica__templateObject77 = statistica__taggedTemplateLiteralLoose(['H_X (t) = E ( e^{i cdot t cdot X} )'], ['H_X (t) = E ( e^{i \\cdot t \\cdot X} )']),

docs/sw.js

@@ -1 +1 @@
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src/pages/statistica.js

@@ -726,7 +726,7 @@ export default class Statistica extends Component {
                             Se la variabile aleatoria <Latex>X</Latex> ha media e varianza, allora la probabilità che essa abbia un valore a più di <Latex>{r`\epsilon`}</Latex> di distanza dal valore medio è minore o uguale a <Latex>{r`\frac{Var(X)}{\epsilon^2}`}</Latex>.
                         </p>
                         <p>
-                            <Latex>{r`\forall \epsilon > 0, P([ -\epsilon \leq \left( X - E(X) \right) \leq \epsilon]) \leq \frac{Var(X)}{\epsilon^2}`}</Latex>
+                            <Latex>{r`\forall \epsilon > 0, P([ \left| X - E(X) \right| \geq \epsilon]) \leq \frac{Var(X)}{\epsilon^2}`}</Latex>
                         </p>
                         <Example>
                             Serve per semplificare i calcoli quando la funzione di ripartizione è difficile da calcolare!