[img]data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAOEAAADhCAMAAAAJbSJIAAABKVBMVEX///8AAAAGqunm5ub7sDv/AADp6entHCSnp6cGru65ubnv7+8GmtTs7OxISEhkZGQnJyeSkpIEbpdubm7AwMDkoDbf399RUVGKioqBgYHX19cXFxeodij/tj3c3NzlGyM9PT0EgbHLy8uXl5cbGxsGntgDR2FcXFwjIyMPDw8vLy9ra2t7e3sDWntCQkKurq7BAAAFjcEBKjk3NzffnDR/WR4OCgMADBC7gywCNUkBHytBAADIjC9wTxqicSZUOxQEZ40BGSI8Kg4FhbYuAADpAADVAAAjAAACQVnSGSCaEhg+CApNCQx7DhOJEBWyFRteCw7YGiEaAwSqFBpKNBImGglkRhiQZCIXEAV8Vx00JAxtTBrRkjGTAACjAABkAACBAABhAAB4AABEDgwiAAASDUlEQVR4nN1d+UMbtxLG2DG2twYcG3yAicE4xpwFcgAvR0nSNmnTK02bJqVNX///P+Ktbc1Iu6vVjHa1Bt73E4ll7X4eaS5Jo7m5mWJruNNszPaRs0UpN0b/ul8jO+RzU2xcy8MbJjh6pUXBsOemOzuUcia0HT+j5KS/pE/XoeXiEd5Idph30aEdZsCwrnS45KJDO2TPcCvQ4+xNRvYMt4NdOujRDpkzrIW6dKO8LJC1Ll2N9Nlx8NY28DomFFP33wRib+CPQwdvfYNwBLy+W/gV/rwOo5gZPGD1/cLCwlv4h3fdr+UQaArf+AxxnA6v+7VcwcujGvthYYwfcZwWb70YNyrtpZ6iPxemeCf/p7fUcuXXzxxbrcWdkIH4STD8KfT/O4v9rczfx230tFXbjdi/XO7nBcBnzaen/WwtpDuL7zX2Y3r5BRnee6dtsLbfyG5iuvLatpZGcZ18tyDxa1yjtXpW49UJw432WnwfrxZUvI1vuNbOJHp0wLCxaOzjTYDhG2PbLPJxaRl6/bDiDOGHhSB+NDff6buekekYei3t7GvWGhAVvrsXYrjwSnwyatS0wh+13HJMo0urOuXZmyh/jAp/ChOURnHc+Vb/UPdYlxyriaMnrxV5s+09ofUxKvw5QlAxisIONurbkZ5qN8CtK0XGZ10aNYwKf9EwvAcfYqToNfbCnY2uO8baCvsup0fKr65EhTroIkWv0gz3mL1DF4/qUuht9gOWDKPCVwv3dJBGMTALqu1Qr3vXNlRD2mnUqgY/r+e4CEWKXn85+PnR7EiprxEaThEDFkyQmhGx8EfB4d+8BjEeBd5gTaMQQnIwYhT9eiPIMbUY85bhUnAG6hReOEFqhs7iBtV02oWAEv1ABZ2AfOpVTZNogtQMXXDoBXTOcrr4McTQ7KYFGu/qtXlY6VPoaXtZDXSTyjbaMKwznhqcphzErH03VEenPhOGnuon12N0HJrCF+sU3kNT3VgfQ/1BF5PrVDbDDVXFxSo4fKurLoUH0DQ2fVpRnri8mjXDDutpaAofdgsUuh+gcWzYW1SHTVJ9w9SlDaXNfnxvIOdHJL8xfhOtt+OHYF95bsL4v8oKl1T9YTDBaArPaBH6eEL9rj4626wnp4Ui6FPDfIBtM7nfWQQL3aeMEVhVcueZRVQKwaapHc6aFRbBQmHlkfjCrqnbYeYUFYJGu4RD+RlPhL4Q1+Erxg1h7YwpKnPQ6A/gtpkXXII+RTSKxlSp8htnMBcVLWreeYdpqSs2wUIBjaLZv1YCMucZVcUOVngvwTCFihBpoxjs3flOByVSINImkBj+zYLfGC+gf3P3CsXE3o0OnnTViNFhaQqlEDlGcQw5W3Zdxv0yhiFmOMqaaQoVihyjOIakaDRZdpA5bWp7L/4UXFOoAL7aI54hKRr8RjtIO0GtX9ibQkWIZ/BlytjJWMORzZBahgpAZVRoL0Efz+HrcZEiQK4juNE2p9Aduf8Fc/I2plCCjhQB6MCduiCIk3CZapnMFEpIo2g2uXMyPEuV1xCQ85o0sfDYPxLxG+OT6GGHMgQYvqT3beTmbNLZxSjV0hQquIIuyM0f8odPaxVxapHDIbkplOh+ZA8Y/D1TZopRMZOTUEaFT1aS4woiRVqFoOlNZTLkGCXVsn2CVAtgSE+KIrQcpRmnqEfJ38kLv2pqUEZR/qYpXJsN6IPeCcpfK+SCfiauDiUPpHBqkZuUbNYKuSCNYhVa9pISRDVTI5vqtiKmxTb5VBynSZXNLvtRdmuFXNDzC7w3WtNrgXkfcjNEPuYV04KcX2iDkx3ahG/Trh+aprtu8Fh01yOfjAnGJBYDnQa+2v6q7Aab0CFpFFHZ0KoiCvYAQLfgojzvBuVzeDipxFEO9gT5X0VTeOyM4RfQJW0U4ee1n4mQFSTFj6bwwBVBn+JX0CkZHMEUWbOdiahIyZaw7HU5cEbQxwX7+bAmZbuUAd8jRYimcNOdCH0hHkO3ZKQIfollQgOGHum3o0V67ZKgT/E+dEwaRdjbY7eHEZxacoM3mkKnY3QM6Jg8pwg60SpBjGaGEiGawrtuRegLcRO6piYYvqzNIQb4WfaIdhgVnjjmN6b4GjqnXA4wVzZWnzu03ZtChSHbKIIu2OETxMVCol0WplChyDaKEMfydQ0kLyg/AaPCDPiNKV6K7g0bbSaA1CI/PQyDlJi62ZhChSHbKMIw5fo1MEgJ9YtRoWNTqFDkGkXI6nKHKSztEIkSPOXyRVYM5wcQKRo32shhyj0nKaYX4ctmZwolpFE0qwQI4Oh8ywSgfM1KGqPCk+wI8iNF8MF4x3khrDD7EpgszsAUKgzRKJqXJyqsdwY0Ob9HtqZQoYhG0agVwLlaZDEUjdeMjcCgXM5nzHD+RDxJcyRDARh9DkGwFcZsJZrCu4MvssUAlY1RUYInzbEXMA1NoyKrBKkZJqMIs4aTroE8sslW2B6mcIOe6bVFG06QKKyhKfDsxL5EtjANQZF2YeT3YQCaontHq6HWMA1BcMRoiwgD2mRZGnGvkDFMUVSF0WYKUEpGXzeLxTQaxiAKchl0oA9hu9EpXSXKC2SCHXOAIX51KvGCqpRw5ucapVmDsnTixUmvxhPSuYbChinBGnxzcjjPvCReaoCbRdWWg9CJ3CZw4wDKlMqRN5jtbh7ADaF2LYBX6nQj/EwArgrlmcJovoYisWkhdCS11CKW/qkM5U2E8EyppGmdZw5vIkTuj1oDEHazN4M3cg0R0lGrcYtMz+AGQgiHWp/Z5Yn6JgIyikSzNd50vYmo8xiObqvTJjO4RLNbzLD1f8+wz2N4a0ML6Y4RzW6xDNs8hiJb7+xk3wzB1KXCubt9IT5afGoNUbg+Dg+gzgxNnku9dHu9tkOeX7rP8byzD608W8yxJ5gwm7r0v9cp1YYyUbo9rJW2MuFaTLLw04azrpQZEGZzFNpLlq/sn2o73q0fuU54eNEinxwcih+fSnpDxkp97Y2Wnh2ybDvNW/WNDyNBLeXDwgy+s1fiLFLs9NNfgQFIeUiMyoMWg+024qqPR1F3JchwyVBLUJMGNjVMMlYdu4cN3dRSTcmQPAAjrMqepv4qg6OLJGQ6hsukekeTrzuP9ujT86cfnq2frT/78PD5pz80LRy47OINTrmLUv1AiUw6/yI89LVI9dH3H59crQSq5RUePHn4PtxslHrFA35jr8rDaqDU/Skpw0r4lSf482yloDuE3i2snP0Zaps2LkGGeR42gtWoe1T/mn0Wn9YLXcMZ+27hLCjJnXSXjqRkSB4tiXoUf2ilFyR5FRRkqmJjaRnmesRAjRa5/8AoktBd+ah+Jc01ZkkZSqKHZooat5cmOJGjOlZTJFyTMqxJM2McqIoT8xj2IH9k1j88eyS/nDzCDDNcjUOYoVJ30KBRFYLnclsgs+pTt/C7A4pBhtX4UGq/GmLoSSnGDlSF4MF8GQ88PGWXK1mXHSTNhIQYKgUhQ2hHGHqycQxFpfbi+AyF3Ej+gMuweyVHKr15xzlDRYto56KsvHg5PWGAG8mfs4XYXcHqeQltfxqGnuLVaoyGLG5xOZjubS6/hP+xqW2FNbuSVXNIxdAzzUVZgOViAJu38YicRalOlWKSDEdKhspc7IV6xlTFCRJUjh5ZFX9Cy2hxYC6OYbwujTAMdhChiNHS44Gy/R6PHn2yIKhUskygbYIMi5XYsKlRDDBsttoC8kdQB6r0uINHRPBIx7pVgSvUqPbVxkIWvxgbNhWDFl8LxSrjGA0dYyofJBFiF+uS2Y/T1J53ALhFCsdo5AzMAD6xqvYoix9bR/1uGUJuG/XoefTUCp7LsRFioYvxlK0+tWWYN2eQRa+mc1p4CJATRSmALm13rtgyDF94o2WIlRF057SkEK3KrspxaplItWVYNB//mHYKp0wv9aerQIh2NS2xSLelEK1HabGztLscxu62yhBF+FJ7EC2BAz4B1iO1E6I1w3zRq0b+rxrYuAA7Uc7jTtpBLRV+FDURIhQ/tjP79gx1CDI0i9CBEK2WGTNgCMeY4k/0JoiiJkIEi2GVe8uAIVTbiS8ckCyKkkK02pDrniHqGcN5VxTie4sLD7qFJ+Ce2uSI3TOEyP4rE0N0wJlRVLewsi7DRKv8aRKGxaAzHmIILoGxNAJGUZxS3d3Cg2cKvZzdruoEDItb9Qn2tpCiwhDOAV0Y+M0rURQlxG736sOLXBgWC4tJGMJ6UkXHED4kDtZjFGW81sGfeg91y4o2YaJzhhARU7URyCiq2105wwsOwrBIuzln2GQNUkWI2ijKp7euJLwj6F0jQ/EHo8gMvG0kiup2HzyLLAZP8e4/4g++W+OaIRyIoutblO/CaweiqG7h6gPcKxbCq7++ufOl+JsfBxMMixNYMITPOJWQolGUT+9hDL23f399x8c34p/85LCZIdybxmcI9p5RhCXkgI81y58x9D5P6fn4WvwPv/qmkSHcvtgMSNHIUOwie0xL0AfUGXk63omx/lxZLlTx75d3FIg71PlLpmaGsMWXz1DsIrvkFGGRQnwSp1m+/eebO0GIi5v5y4lshsUizEkjQyF1XoVcWdRIi7d/hen5+O/0M354wWVY3Cod+SiVCIZiZ1BseB9kiMsYUfyMUy+If6cfmwvoJGGI93evmhmKhA2z5lqcEOPo+fgHnuScISRfCIZiy9R9JkONEL/9/GUcuzH+uiEMuQWtZBm8Kb6PaJYw/r5uhiMrGSqh8ATvjOK7GTIUDNm1D2UGfIpXFMdrn4dWulRHMfcfM8fsdCmTobCHFrUBywOMozgcP0/b8C9oiDLcWOyNcVqrJmEolixYPk0ijiJ84i9eRBl24MxgIobQzK5adZRj7Hz8dvo5P9umYbiWhqFFbJFIjhBb8NPerhnCZ3FrFmk5QnzI3/7tmiHE+KZ8MJ/j9xGOYPDTxPjpGEKeJmoulEtDUshRqFKLi7WcMxRfeRxRNcebiOPkY1UEwBbrwM4ZwiaFsKqRVd8Jr9XIERSNxcKFc4ZQGSrMQvWxqXx4vO2AaWhxJMo5Q1i3CBf/t2BokCOkSy0O7zlniGtPoWFqxTCOIwxSmx3R7hmCzT9IxVBvO2CQ2myldc8Q7wBMyVDHEWCzVcE9QywfH9yVyNalDI5W2/YzYAh7MULLTwMFTIJxHK1O7GXAEPfTuLnFIcqRH/1mxRASj65uiwlztLsxLAuGuN8kMBM1tcgTcrSrvJAFQ3kEXn3JUN5wDD5FlaPlwRIoJuuUIdZYV5KKKRkqq+Lk+XEFG3gzvFuGuj3COoY2OhU3ivFn4Wq/J5/lmCHu85Y34KVkOLjMBR9BSq8UPL3qmKEst4Gp4XQM5bc5O2lWj5YiG+5dM8QNipjOSMVQ7mogVkY9b6O0F31QFgzldnBh91WvDcDVNHIS5ka7w3qt0lmNnCPLdxq1+jBS2CA7hkqViKm2KW9Gr7HlEtSswW3vnvYOm8O9/b1h87B3ukyW2XHPUI5TsUtRc40tk2BwfcoWo8wYyvOHj1PdUVXGIygJ0Czl9zJjqF6QnuKKo/LxZVJ6w6NxsiMTr01AngNOHmbIHSl2GNUrQhllyVA9y50kBz6vmolcbmmRd0nE9mKtoajaTBmq5/FfD+w5lueVkGJ8anWj0dpbjFebO8299lEnZEhcr67FU7y0nozllxfy67JumlftVEq1Vr25OLYSu73mUr3davUrq1VtWKVZIV0ejZFr+QyHucmfw+KYw+S/RxOG0xYMhirF3H2rC7nK86oXlOSy8ziG+aLcclmM/h36k2KozkU/JGZzLM9vqkbC/iJwE8MEMDBUNWoud7LJ4ujzO1G/lqZYVPYMVbvo48KXI7FsUS7fvVC/MkpVRmkGDOe8XoBj7uA4nqT/wXEot5ayvOssGM5FirVdHLwcRBzT8X8Mjg8uQ21TTMEZMpzrhMTo4/xg83iguOGD482D84gL2kxdds+2XpseHl3eW1dzzzeSFyfn9++/Pj+51LvXqap8BRju1NOBceej19ZyMCFNia8wQ1cwPitvV0q0bZM2vBkMfY76sapDzVWN1tky9FE5pHvJNe1LmMSCXxSWA/OFsQL5o6GpuMb28Mjp3Uklw7PswT0p7221ltY0399earmveO30llOrueM1Sv394em4JsNOb9juZ1TO25fiYqQIRDL0WtW5/wE8Wk2XSeLodQAAAABJRU5ErkJggg==[/img]
Si la probabilidad de que un individuo sufra una reacción por una inyección de un determinado suero es de 0.001 determinar la probabilidad de que de un total de 2,000 individuos.[br][br][color=#ff7700][b]a)[/b]Exactamente 3 tengan reacción.[/color][br]La variable aleatoria: x el numero de individuos que sufre una reacción. Suponemos que X tiene una distribución de Poisson.[br][br][math]P\left(x=k\right)=\frac{e^{-\lambda}.\lambda^k}{k!}[/math][br][color=#ff7700]Donde:[/color][br][math]\lambda=np=2000.0,0001=2[/math][br][br][math]p\left(x=3\right)=\frac{e^{-2.}2^3}{3!}=0,180[/math] la probabilidad es 18 %[br][br][br][br]
[color=#ff7700]b) Más de dos individuos tengan reacción.[br][math]P\left(x>2\right)=1-P\left(x\le2\right)[/math][/color] [br] [math]=1-\left[P\left(x=0\right)+P\left(x=1\right)+P\left(x=2\right)\right][/math][br] [math]=1-\left\langle\frac{e^{-2}x2^0}{0!}+\frac{e^{-2}x2^1}{1!}+\frac{e^{-2}x2^2}{2!}\right\rangle[/math][br] [math]=1-e^{-2}\left[1+2+2\right][/math][br] [math]=1-5e^{-2}[/math][br] [math]=0,323[/math] la probabilidad es 32,33%