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Journal : Sciencestatistics: Journal of Statistics, Probability, and Its Application

Analysis of Linear Log Models on Covid-19 Data in Indonesia Indah Suciati; Warsono Warsono; Mustofa Usman
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol 1 No 1 (2023): JANUARY
Publisher : Universitas Muhammadiyah Metro

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Abstract

Covid-19 is still a concern of the world, including Indonesia. The transmission of Covid-19 is very fast and has a wide impact on all people around the world, especially Indonesia. In everyday life, we find a lot of data that looks into a certain category. Categorical analysis of data can be done using the log linear model. The log linear model is used to analyze the relationship between categorical variables that form a contingency table of arbitrary dimensions. The analysis used in this study is to make descriptive statistics and three-way contingency tables, then perform the analysis with the help of SPSS 25.0 software where the goodness of fit test is used to see which models can be used or suitable. The purpose of this study is to analyze a log linear model, so that a log linear model is obtained that is suitable for Covid-19 data based on gender, province, and age group. The conclusion of this study is that of the 9 models used, the model is the most suitable model to be used, with a value of 18,885 and the equation of the log linear model is , which means that there is a relationship between the two factors for the variables gender and province, gender and age group and province and age group in Covid-19 cases in Covid-19 in Indonesia by gender, province, and age group.
Goodness Of Fit Test In Structural Equation Modeling with Unweighted Least Square (ULS) Estimation Method Ani Amanathi; Eri Setiawan; Mustofa Usman
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol 1 No 2 (2023): JULY
Publisher : Universitas Muhammadiyah Metro

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Abstract

Structural equation model (SEM) is a multivariate analysis method that is used to describe a linear relationship simultaneously between indicator variables and latent variables. There are several estimation methods in SEM, one of them is Unweighted Least Square (ULS). The method doesn‟t have specific assumptions about the distribution of variables. This study aims to estimate the model using the ULS method and see the influence of employee competency variables and library facilities on the quality of service at the University of Lampung library. Survey of quality of service in the library of Lampung University is used in the research. Based on the results of the study, it is found that from the three suitability tests, namely the overall model test, the structural model test and the measurement model test using ULS estimation give good results in explaining the compatibility between the model and observation results.
Bayesian Structural Time Series Model for Forecasting the Composite Stock Price Index in Indonesia Indah Suciati; Mustofa Usman
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol 1 No 2 (2023): JULY
Publisher : Universitas Muhammadiyah Metro

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Abstract

One of the models that can be used to predict time series data is the Bayesian Structural Time Series (BSTS) model. The BSTS model is a more modern model and can handle data movement better. In the BSTS model, the Markov Chain Monte Carlo (MCMC) sampling algorithm is used to simulate the posterior distribution, which smoothes the forecasting results over a large number of potential models using Bayesian averaging models. The purpose of this study was to obtain the best BSTS model for Composite Stock Price Index (CSPI) data in Indonesia based on the state component and the number of MCMC iterations, and obtain forecasting results for CSPI value in Indonesia for the next 24 months, namely the period July 2023 to June 2024. The results obtained are based on a comparison of the R-square values in the model, the BSTS model with local linear trend and seasonal state components, and the number of MCMC iterations n = 5 00 is the best BSTS model that can be used for forecasting the CSPI value in Indonesia with an R-square value of 99.96%. The results of forecasting the CSPI value in Indonesia for the period July 2023 to June 2024 range from 6589 to 6760, with the lowest forecasting value in October 2023 and the highest in March 2023.