Sciencestatistics: Journal of Statistics, Probability, and Its Application

Association between Dental Caries and Body Mass Index (BMI) among Adolescents in Urban Area of North-Western Bangladesh

2026-01-30T15:03:30+07:00

This cross-sectional study aimed to investigate the potential association between body mass index (BMI) and dental caries (DMFT) in adolescents aged 12-16 years in Rangpur, Bangladesh. A total of 300 students participated in the study. Dental caries prevalence was assessed using the DMFT index, and BMI was calculated according to standardized protocols. Socioeconomic status, sugar consumption, and physical activity levels were also captured through questionnaires. Statistical analysis employed multivariate logistic regression and linear regression models to explore the relationships between BMI, dental caries, and other factors. Dental caries (DMFT > 0) was present in 54.1% of participants, while 32% were classified as overweight or obese. A statistically significant positive correlation was observed between BMI and DMFT (P = 0.008). Compared to those within the normal BMI range, obese participants were 1.79 times more likely to exhibit healthy teeth (DMFT = 0) (P = 0.02). Higher socioeconomic status (P = 0.005) and fluoridated toothpaste use (P = 0.02) were also associated with a greater likelihood of healthy teeth. Physical activity displayed a significant negative association with BMI (P < 0.001). This study demonstrated a positive association between BMI and dental caries in Rangpuri teenagers. Interestingly, obese participants were more likely to have healthy teeth, potentially signifying the influence of sociodemographic factors and oral hygiene practices in mitigating caries risk. The study highlights the importance of considering socioeconomic context and preventive measures while addressing the link between obesity and dental health in this population.

Application of the Double Exponential Smoothing Brown Method in Forecasting the Number of Poor Population

2026-03-27T15:15:02+07:00

Poverty is a socio-economic issue that requires policy planning based on accurate forecasting. This study uses the Brown Double Exponential Smoothing method to forecast the number of poor people in Metro City for the period 2026-2030, using data from 2005-2025 obtained from BPS. The analysis was conducted using a trial and error method for the alpha (α) parameter ranging from 0.1 to 0.9 based on the smallest MAD, MSE, and MAPE values. The study results indicate that the optimal alpha parameter is α = 0.5 with a MAPE of 15.91231%, which indicates good accuracy. The forecast shows an increasing trend from 321.90 thousand people (2026) to 338.87 thousand people (2030), with an average increase of 4.24 thousand people per year. The results of this study can be used as a basis for planning poverty alleviation programs in Metro City.

Modeling and Predicting Indonesia’s Inflation Using the ARIMA Model

2026-05-03T21:36:58+07:00

Inflation is one of the most important macroeconomic indicators used to evaluate the stability and performance of a country's economy. This study aims to model and predict Indonesia’s monthly inflation rate using the Autoregressive Integrated Moving Average (ARIMA) approach. The dataset consists of monthly inflation observations from January 2010 to December 2025 obtained from Bank Indonesia. The analysis begins with testing the stationarity of the series using the Augmented Dickey–Fuller (ADF) test, followed by model identification through the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots. Several candidate models are estimated, including ARIMA (0,1,1), ARIMA (1,1,0), and ARIMA (1,1,1). Model comparison based on the Akaike Information Criterion (AIC) indicates that the ARIMA (0,1,1) model provides the lowest AIC value and is therefore selected as the most appropriate model. The forecasting results suggest that Indonesia’s inflation rate is expected to remain relatively stable at around 3.63% over the next six periods. However, the prediction intervals become wider as the forecasting horizon increases, reflecting growing uncertainty in longer-term predictions.

Application of Fuzzy C-Means with Variations in Weighting Exponent for Clustering the Human Development Index

2026-05-03T22:34:32+07:00

Human development is commonly measured using the Human Development Index (HDI), which reflects the quality of life across regions. In Indonesia, disparities in HDI values indicate uneven development, requiring appropriate analytical approaches. This study aims to cluster Indonesian provinces based on HDI indicators using the Fuzzy C-Means (FCM) method with variations in the weighting exponent. The data consist of 38 provinces in 2025, including life expectancy, expected years of schooling, average years of schooling, and adjusted real expenditure per capita. The clustering results were evaluated using the Partition Coefficient Index (PCI). The optimal configuration was obtained at and , with a PCI value of 0.716399. The results show that provinces are grouped into clusters with relatively lower HDI, which are predominantly located in eastern Indonesia, and clusters with higher HDI, which are mostly found in western Indonesia. These findings demonstrate that FCM is effective in identifying regional development patterns.

Time Series Analysis in Forecasting Nickel Prices Using the ARIMA and Double Exponential Smoothing Methods

2026-05-03T22:36:02+07:00

Nickel is one of the strategic commodities that plays an important role in global industries, particularly as the primary raw material in the production of stainless steel and electric vehicle batteries. The increasing demand for nickel, driven by technological advancements and the need for more environmentally friendly energy sources, causes nickel prices to fluctuate, making it necessary to employ methods capable of forecasting future price movements. This study aims to forecast nickel prices using the Autoregressive Integrated Moving Average (ARIMA) method and the Double Exponential Smoothing method, as well as to compare the performance of both methods. The data used in this research consist of secondary daily nickel price data with 62 observation periods. The research stages include data preprocessing, stationarity testing, modeling, and model evaluation using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The results show that the best ARIMA model is ARIMA(2,1,1), which produces an MSE of 0.2797 and an RMSE of 0.5288. Meanwhile, the Double Exponential Smoothing method results in an MSE of 0.1299 and an RMSE of 0.3604. Based on these evaluation results, the Double Exponential Smoothing method demonstrates better performance than ARIMA in forecasting nickel prices in this study. This method is able to produce more accurate and stable predictions that follow the trend patterns of the data. Therefore, the Double Exponential Smoothing method is recommended as a more optimal approach for nickel price forecasting