IDEAS home Printed from https://ideas.repec.org/p/osf/inarxi/t4b7f.html
   My bibliography  Save this paper

Regresi Logistik untuk Pemodelan Indeks Pembangunan Kesehatan Masyarakat Kabupaten/Kota di Pulau Kalimantan

Author

Listed:
  • Fathurahman, Muhammad

    (Mulawarman University)

Abstract

Regresi logistik merupakan model regresi yang paling sering digunakan untuk pemodelan data kategorik. Pada penelitian ini dilakukan pemodelan regresi logistik dan penerapannya pada Indeks Pembangunan Kesehatan Masyarakat (IPKM) kabupaten/kota di Pulau Kalimantan tahun 2013. Metode Maximum Likelihood Estimation (MLE) digunakan untuk penaksiran parameter. Metode Likelihood Ratio Test (LRT) dan uji Wald digunakan untuk pengujian parameter. Hasil penelitian menunjukkan bahwa penaksir parameter dengan metode MLE berbentuk fungsi yang tidak eksplisit. Sehingga digunakan pendekatan numerik dengan metode Fisher Scoring. Berdasarkan metode LRT dan uji Wald, statistik uji untuk pengujian parameter mendekati distribusi chi-square dan distribusi normal standar. Berdasarkan model regresi logistik terbaik, faktor-faktor yang berpengaruh terhadap IPKM kabupaten/kota di Pulau Kalimantan tahun 2013 adalah Indeks Pembangunan Manusia (IPM), tingkat kepadatan penduduk dan persentase penduduk miskin.

Suggested Citation

  • Fathurahman, Muhammad, 2017. "Regresi Logistik untuk Pemodelan Indeks Pembangunan Kesehatan Masyarakat Kabupaten/Kota di Pulau Kalimantan," INA-Rxiv t4b7f, Center for Open Science.
    • Handle: RePEc:osf:inarxi:t4b7f
    DOI: 10.31219/osf.io/t4b7f
    as

    Download full text from publisher

    File URL: https://osf.io/download/5b763e3125dd720017f6954c/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/t4b7f?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

    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:inarxi:t4b7f. 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://ios.io/preprints/inarxiv/discover .

    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.