Sumarlin, Didin (2013) REGRESI PADA DATA SIRKULAR. S1 thesis, Universitas Pendidikan Indonesia.
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Abstract
Beragam model regresi dikembangkan, namun model-model yang ada masih sering bertumpu pada data linear baik jenis data pada variabel bebasnya maupun variabel terikatnya. Di lapangan banyak ditemukan juga data berupa data sirkular, misalnya sudut, arah, waktu dan sebagainya. Paradigma pembagian data didasarkan atas pembagian data linear dan sirkular membawa konsekuensi perlunya metode khusus dalam pengolahan data sirkular, khususnya dalam masalah regresi. Pada penelitian ini dibahas mengenai model regresi sirkular lebih lengkapnya regresi sirkular sirkular, dimana baik variabel bebasnya maupun variabel terikatnya berupa data sirkular. Pada model regresi ini, metode yang digunakan untuk menaksir koefisien adalah metode kuadrat terkecil. Sedangkan penentuan nilai m polinomial dilakukan dengan menguji apakah penambahan derajat m+1 berpengaruh signifikan atau tidak. Penerapan model regresi ini dilakukan terhadap variabel waktu dan arah angin di Lat=-33.06271, Lon=-66.39777 dan zona waktu= +2.5. Variabel waktu berperan sebagai variabel bebas, sedangkan variabel arah angin berperan sebagai variabel terikat. Hasil penelitian menunjukkan regresi signifikan hanya pada orde 1. Sedangkan korelasi antara variabel tersebut tidak signifikan. Kata Kunci : Data Sirkular, Regresi Sirkular, Metode Kuadrat TerKecil Various regression models were developed, but existing models are still often based on linear data both types of data on the independent variables and the dependent variable. In the field, data is found also in circular form, such as angle, direction, time and so on. The paradigm of data division based on the linear and circular data consequences need for specialized methods of processing the data in a circular, particularly in regression problems. In this study discussed the circular regression model in the full name is circular-circular regression, where both the independent variables and the dependent variable is circular. In this regression model, the method used to estimate the coefficients are the least squares method. Whereas the determination of the value of m polynomials is done by testing whether the addition of degree m +1 significant effect or not. The application of the regression model was applied on the variables of time and wind direction at the Lat = -33.06271, Lon = -66.39777 and time zone = +2.5. Time variable as the independent variable, whereas variable of wind direction as the dependent variable. The results showed significant regression only on the order of 1. Whereas the correlation between these variables was not significant . Keywords : Circular Data , Circular regression , Least Square Method
| Item Type: | Thesis (S1) |
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| Subjects: | Universitas Pendidikan Indonesia > Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Jurusan Pendidikan Matematika > Program Studi Pendidikan Matematika |
| Divisions: | Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Jurusan Pendidikan Matematika > Program Studi Pendidikan Matematika |
| Depositing User: | DAM STAF Editor |
| Date Deposited: | 23 Dec 2013 03:20 |
| Last Modified: | 23 Dec 2013 03:20 |
| URI: | http://repository.upi.edu/id/eprint/4402 |
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