IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/z48hc.html
   My bibliography  Save this paper

Ukuran Gejala Pusat Data Belum Dikelompokan(Kebakaran di Provinsi-Provinsi Tertentu di Indonesia Tahun 2015-2016)

Author

Listed:
  • Santoso, Riska Nurhapsari
  • Utomo, Yudis Satrio
  • Luturmasse, Yuliani

Abstract

- Statistics is a framework of theories and methods that have been developed to collect, analyze, and write sample data in order to obtain useful conclusions. Statistics is the science of ways of collecting, classifying, analyzing, and searching for information related to the collection of data that investigations and conclusions based on evidence in the form of figures. Based on the results of the study can be concluded as follows: the size of the symptoms of the data center has not been grouped is the data compiled into the frequency distribution so that it does not have class intervals and midpoints of the class. Symptom Size Un-Grouped Data Center The size of the data center included in the statistical analysis is the calculated average (mean), median, mode, and fractil (quartile, decile, percentile)

Suggested Citation

  • Santoso, Riska Nurhapsari & Utomo, Yudis Satrio & Luturmasse, Yuliani, 2020. "Ukuran Gejala Pusat Data Belum Dikelompokan(Kebakaran di Provinsi-Provinsi Tertentu di Indonesia Tahun 2015-2016)," OSF Preprints z48hc, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:z48hc
    DOI: 10.31219/osf.io/z48hc
    as

    Download full text from publisher

    File URL: https://osf.io/download/5fe9700de3acd102254a5d6d/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/z48hc?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:z48hc. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.