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All Journal Fraction: Jurnal Teori dan Terapan Matematika Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika
Azwar Habibi
Institut Agama Islam Negeri Madura
Economics, Econometrics & Finance Mathematics

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KAJIAN SIMULASI DISTRIBUSI SAMPLING, TEOREMA LIMIT PUSAT DAN ESTIMASI PARAMETER Azwar Habibi
Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika Vol. 3 No. 1 (2023): Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (494.334 KB) | DOI: 10.46306/bay.v3i1.42

Abstract

Hypothesis testing is a very important part of inferential statistics.  The results of this hypothesis are later used to decide or establish something in planning or in other interests. In everyday life there are often problems that require a decision to find a way out. The decision taken must be right so that the problems experienced can be resolved properly. Because if not, the decision taken will be detrimental. Decision in the sense of a hypothesis that is positive for the decision maker itself needs to be tested whether the hypothesis taken can be beneficial or even detrimental, so that a hypothesis test is needed.  Based on the results of research conducted on sampling distribution, central limit theorem and parameter estimation using simulated data, several things can be concluded as follows: Hypothesis Testing for the Average of One Population, Hypothesis Testing for the Average Difference of Two Populations, Hypothesis Testing for the Proportion of One Population.   In hypothesis testing for the proportion of one population, for n = 10, the proportion of sample values less than 10 is 0.4, for n = 20, the proportion of sample values less than 10 is 0.5, and for n = 30, the proportion of sample values less than 10 is 0.466667. Based on this information, it can be concluded that the proportion of the sample closest to the proportion of the actual population is at the time n = 30, because for n = 30, the proportion of sample values less than 10 is 0.466667, where the value is equal to the value of the proportion of the population.  Hypothesis Testing For the Difference in Proportion of Two Populations.  In hypothesis testing the difference in the proportion of the two populations, for n 1 = 20 and n 2 = 35, the difference in the proportion of sample values less than 10 is 0.507143, for n 1 = 15 and n 2 = 25, the proportion of sample values less than 10 is 0.426667, and for n 1 = 30 and n 2 = 40, the proportion of sample values less than 10 is 0.711  
PENERAPAN MODEL LOG LINIER TERHADAP PERSEPSI MAHASISWA PADA SINETRON RELIGIUS : APPLICATION OF THE LINEAR LOG METHOD TO STUDENT PERCEPTION ON RELIGIOUS SINETRON Azwar Habibi
Fraction: Jurnal Teori dan Terapan Matematika Vol. 3 No. 2 (2023): Fraction: Jurnal Teori dan Terapan Matematika
Publisher : Jurusan Matematika, Fakultas Teknik, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/fraction.v3i2.46

Abstract

The purpose of this study is to find out which variables in the cell cause the contingency to be dependent in the case of student perceptions in religious soap operas. The data taken from this study are the results of a survey of IAIN Madura students with the variables having experienced events, gender and deepening religious knowledge. The data analysis method used is Linear Log Model. The conclusions in this study are as follows based on the estimated parameter values ​​presented in the table above, it can be seen that male and female IAIN MADURA students believe that religious soap operas can deepen religious knowledge, and also male and female students believe that religious soap operas cannot deepen religious knowledge. Based on the Chi Square values ​​presented in the table above, it can be seen that the opinion variable, deepening religious knowledge and having experienced an incident have no relationship or each variable is independent, so it cannot be continued to a linear logistic test.
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