• The Journal of Korean Association of Computer Education
• /
• v.3 no.2
• /
• pp.107-114
• /
• 2000

In this study, a program for statistics education is developed. This program deals with statistical hypothesis testing which is indispensible to demonstrate the research hypothesis. Statistical inference which is the process of using data obtained from a sample to make inference about the characteristics of a population is an essential concept to peoples to learn or use statistics. But in practice, there are many cases of wrong application and peoples think that statistics is hard to understand. Therefore, it is very important subject to teach easily and rightly statistics, specially elementary statistics. This program is developed especially for non-specialist. This program under multimedia environment which includes sound, video, animation etc. can interest students greatly. It doesn't show only the result but make it possible for students to execute the program by stages. By executing it, the students can understand the method and meaning of statistical hypothesis testing naturally.

• Proceedings of the Korean Society of Medical Physics Conference
• /
• 2003.09a
• /
• pp.46-46
• /
• 2003

Several MRI studies have reported reductions in temporal lobe volumes in Alzheimer´s disease (AD). Measures have been usually obtained with regions of interest (ROI) drawn manually on selected medial and lateral portions of the temporal lobes, with variable choices of anatomical borders across different studies. We used the automated voxel based morphometry (VBM) approach to investigate gray matter abnormalities over the entire extension of the temporal lobe in 11 AD patients (MMSE 14 - 25) and 11 healthy controls. Foci of significantly reduced gray matter volume in AD patients were detected in both medial and lateral temporal regions, most significantly in the right and left posterior parahippocampal gyri. At a more flexible statistical threshold (P<0.001, uncorrected for multiple comparisons), circumscribed foci of significant gray matter reduction were also detected in the right amygdala/enthorinal cortex, the anterior and posterior borders of the superior temporal gyrus bilaterally, and the anterior portion of the left middle temporal gyrus. These VBM results confirm previous findings of temporal lobe atrophic changes in AD, and suggest that these abnormalities may be confined to specific sites within that lobe, rather than showing a widespread distribution.

• Atmosphere
• /
• v.20 no.4
• /
• pp.495-503
• /
• 2010

Satellite data have an intrinsic problem due to a number of various physical parameters, which can have a similar effect on measured radiance. Most evaluations of satellite performance have relied on comparisons with limited spatial and temporal resolution of ground-based measurements such as soundings and in-situ measurements. In order to overcome this problem, a new way of satellite data evaluation is suggested with statistical tools such as empirical orthogonal function(EOF), and singular value decomposition(SVD). The EOF analyses with OMI and OMI HCHO over northeast Asia show that the spatial pattern show high correlation with population density. This suggests that human activity is a major source of as well as HCHO over this region. However, this analysis is contradictory to the previous finding with GOME HCHO that biogenic activity is the main driving mechanism(Fu et al., 2007). To verify the source of HCHO over this region, we performed the EOF analyses with vegetation and HCHO distribution. The results showed no coherence in the spatial and temporal pattern between two factors. Rather, the additional SVD analysis between $NO_2$ and HCHO shows consistency in spatial and temporal coherence. This outcome suggests that the anthropogenic emission is the main source of HCHO over the region. We speculate that the previous study appears to be due to low temporal and spatial resolution of GOME measurements or uncertainty in model input data.

• Wind and Structures
• /
• v.19 no.6
• /
• pp.623-647
• /
• 2014

In this paper, a probability distribution which is consistent with the observed phenomenon at the roof corner and, also on other portions of the roof, of a low-rise building is proposed. The model is consistent with the choice of probability density function suggested by the statistical thermodynamics of open systems and turbulence modelling in fluid mechanics. After presenting the justification based on physical phenomenon and based on statistical arguments, the fit of alpha-stable distribution for prediction of extreme negative wind pressure coefficients is explored. The predictions are compared with those actually observed during wind tunnel experiments (using wind tunnel experimental data obtained from the aerodynamic database of Tokyo Polytechnic University), and those predicted by using Gumbel minimum and Hermite polynomial model. The predictions are also compared with those estimated using a recently proposed non-parametric model in regions where stability criterion (in skewness-kurtosis space) is satisfied. From the comparisons, it is noted that the proposed model can be used to estimate the extreme peak negative wind pressure coefficients. The model has an advantage that it is consistent with the physical processes proposed in the literature for explaining large fluctuations at the roof corners.

• The korean journal of orthodontics
• /
• v.24 no.4 s.47
• /
• pp.841-849
• /
• 1994

This study was conducted on 342 patients(male 157, female 185)aged 8 to 15 years old, who visited Dankook University Dental Hospital. Pre-orthodontic treatment orthopantomograms were used to assess the dental calcification stages of mandibular 3rd molar, 2nd molar, 2nd premolar and 1st premolar by 8 stages.(by Demirjian) Hand-wrist radiogrms were used to evaluate the skeletal maturity in 11 stages.(by Fishman) Following results were obtained after investigating the correlationship between dental calcification and skeletal maturity 1. Chronologic age showed high correlation to dental calcification and skeletal maturity. 2. Dental calcification and skeletal maturity showed high correlation and no statistical difference was observed between male and female. 3. SMI stages 1 to 4 showed high statiscal significance to mandibular 2nd molar, 2nd premolar and 1st premolar. SMI stages 5 to 8 showed high stastical significance to mandibular 2nd molar, 2nd premolar. SMI stages 9 to 11 showed high statistical significance to mandibular 3rd molar.

• Transactions on Control, Automation and Systems Engineering
• /
• v.2 no.4
• /
• pp.268-273
• /
• 2000

Plasma models are crucial to equipment design and process optimization. A radial basis function network(RBFN) in con-junction with statistical experimental design has been used to model a process plasma. A 2$^4$ full factorial experiment was employed to characterized a hemispherical inductively coupled plasma(HICP) in characterizing HICP, the factors that were varied in the design include source power, pressure, position of shuck holder, and Cl$_2$ flow rate. Using a Langmuir probe, plasma attributes were collected, which include typical electron density, electron temperature. and plasma potential as well as their spatial uniformity. Root mean-squared prediction errors of RBEN are 0.409(10(sup)12/㎤), 0.277(eV), and 0.699(V), for electron density, electron temperature, and Plasma potential, respectively. For spatial uniformity data, they are 2.623(10(sup)12/㎤), 5.704(eV) and 3.481(V), for electron density, electron temperature, and plasma potential, respectively. Comparisons with generalized regression neural network(GRNN) revealed an improved prediction accuracy of RBFN as well as a comparable performance between GRNN and statistical response surface model. Both RBEN and GRNN, however, experienced difficulties in generalizing training data with smaller standard deviation.

• Communications for Statistical Applications and Methods
• /
• v.18 no.5
• /
• pp.625-636
• /
• 2011

This paper proposes two parametric entropy estimators, the minimum variance unbiased estimator and the maximum likelihood estimator, for the lognormal distribution for a comparison of the properties of the two estimators. The variances of both estimators are derived. The influence of the bias of the maximum likelihood estimator on estimation is analytically revealed. The distributions of the proposed estimators obtained by the delta approximation method are also presented. Performance comparisons are made with the two estimators. The following observations are made from the results. The MSE efficacy of the minimum variance unbiased estimator appears consistently high and increases rapidly as the sample size and variance, n and ${\sigma}^2$, become simultaneously small. To conclude, the minimum variance unbiased estimator outperforms the maximum likelihood estimator.

• Environmental and Resource Economics Review
• /
• v.14 no.1
• /
• pp.201-220
• /
• 2005

Recently, we have been witnessing new records of crude oil price hikes. One question which naturally arises would be the possibility and accuracy of forecasting crude oil prices. This study tries to answer the relative predictability of futures prices compared to the forecasts based on experts system. Using WTI crude oil spot and futures prices, this study performs simple statistical comparisons in forecasting accuracy and a formal test of differences in forecasting errors. According to statistical results, WTI crude oil futures market turns out to be equally efficient relative to EIA experts system. Consequently, WTI crude oil futures market could be utilized as a market-based tool for price forecasting and/or resource allocation for both of petroleum producers and consumers.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.34 no.4
• /
• pp.383-390
• /
• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.984-991
• /
• 2022

The use of the sustainability rating systems in infrastructure construction projects is not as common in comparison to building construction projects. While the sustainability rating systems share some commonalities, they differ from one another in certain ways. Thus, project teams cannot make reliable decisions when choosing the best sustainability rating tools for a given infrastructure projects. The Department of Transportation (DOT) in several states are developing its own rating system to address the infrastructure sustainability, but not in the case of California. Therefore, this paper presents the statistical results on the important sustainability determinants that affects the success of meeting sustainability goals of infrastructure construction projects. The authors conducted an online survey using the structured questionnaires. The categories considered include site, water/wastewater, energy, materials/resources, environmental, and others. The statistical analyses such as Kruskal-Wallis and ANOVA are conducted using a total of 25 valid and complete data out of 59 surveys collected. The results demonstrate several factors under each of six major sustainable categories have received higher ranks than other factors. The results also show that a statistically significant difference can be found from water, energy, and environmental categories against the other category based on the pairwise comparisons.