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        <title type="main">Introduzione alla Statistica Computazionale con R</title>
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            <forename>Bruno</forename>
            <surname>Bertaccini</surname>
            <placeName type="affiliation">University of Florence, Italy</placeName>
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        <publisher>Firenze University Press</publisher>
        <pubPlace>Florence</pubPlace>
        <date when="2018">2018</date>
        <idno type="DOI">https://doi.org/10.36253/978-88-6453-674-3</idno>
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          <p>Available for academic research purposes</p>
          <p>Open Access</p>
          <p>Copyright Author(s)</p>
          <licence source="text" target="https://creativecommons.org/licenses/by/4.0/legalcode" notBefore="2018-05-08">
            <p>Content licence CC BY 4.0</p>
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        <title>Strumenti per la didattica e la ricerca</title>
        <idno type="ISSN" subtype="print">2704-6249</idno>
        <idno type="ISSN" subtype="electronic">2704-5870</idno>
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          <edition n="1">Digital edition PDF</edition>
          <date>2018</date>
          <idno type="ISBN" subtype="electronic">978-88-6453-674-3</idno>
          <biblScope unit="page">182 pages</biblScope>
          <extent>39,56 MB</extent>
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            <p>This is original content, published in Open Access. It is also available to read for free online at <ref target="https://media.fupress.com/files/pdf/24/3581/3581_14308">https://media.fupress.com/files/pdf/24/3581/3581_14308</ref></p>
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          <date>2018</date>
          <idno type="ISBN" subtype="electronic">978-88-9273-117-2</idno>
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            <p>It is available to read for free online</p>
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          <edition n="3">Print edition</edition>
          <date>2018</date>
          <idno type="ISBN" subtype="print">978-88-6453-673-6</idno>
          <biblScope unit="page">182 pages</biblScope>
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        <tag>peer-reviewed</tag>
        <rs type="FUP_policy" source="https://doi.org/10.36253/fup_best_practice">Firenze University Press Best Practice in Scholarly Publishing</rs>
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      <abstract xml:lang="en">
        <p>Nowadays, the information transfer speed (on the web, but not only) requires the predisposition of ever more adequate analysis tools for working with data and increasingly faster algorithms, in order to allow the so-called decision maker to make decision based on information which can become obsolete very quickly with time. In the current situation, analysing this information in order to simulate complex decision-making scenarios could prove fundamental to secure an advantage over competitors. This text introduces the true art of Statistical Computing. In other words, it illustrates how computer programming skills in the development of algorithms can be used within Statistics for the virtual simulation and replication of realities and experiments of varying complexity. In order to do so, it uses the excellent development environment “R”.</p>
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        <p>La velocità con cui si trasferiscono oggi le informazioni (sul web, ma non solo) richiede la predisposizione di strumenti di analisi sempre più adeguati nel trattare moli di dati, di algoritmi sempre più veloci che permettano ai cosiddetti decision maker  di operare scelte basate su informazioni che il trascorrere del tempo può rendere obsolete molto velocemente. Nella realtà attuale, analizzare tali informazioni per poter simulare scenari decisionali complessi potrebbe rivelarsi di fondamentale importanza nell’assicurarsi un vantaggio nei confronti della concorrenza. Questo testo introduce alla vera arte della Computazione Statistica, ovvero illustra come le competenze di programmazione informatica nello sviluppo di algoritmi possano essere messe al servizio della Statistica per la simulazione e replicazione virtuale di realtà ed esperimenti più o meno complessi. E lo fa avvalendosi dell’ottimo ambiente di sviluppo R.
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      <p>It is available online at https://doi.org/10.36253/978-88-6453-674-3<ref target="https://doi.org/10.36253/978-88-6453-674-3" /></p>
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