• Journal of Korean Home Economics Education Association
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• v.30 no.3
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• pp.151-173
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• 2018

The purposes of this study was to identify the magnitude and direction of the effects of Practical Reasoning Instruction (PRI) on students' achievements in Home Economics classes using the meta-analysis method and to examine whether the effects of PRI vary across publication status, study design, year of the studies, school level, gender of students, type of students' achievements, content area, location where the interventions of PRI were conducted, and duration. Thirty-four primary studies with 44 effect sizes were analyzed with calculation method of Becker(1988). A funnel plot method result revealed no publication bias. The results of this meta-analysis are as follows. First, PRI was more effective than traditional instruction on students' achievements. A summary statistic was 0.60 with a standard error of .074, which means that an increase of about two-third of a standard deviation beyond what would be expected from traditional instruction was gained from PRI intervention. Second, categorical and regression analyses were employed to find the sources of variance and moderators that predict the effects of PRI. The moderator analyses revealed no statistically significant effects of publication status, study design, school level, gender of students, type of students' achievements, and duration. Content area, location where the interventions of PRI were revealed to be moderators. It was concluded that PRI was effective in improving students' achievements regardless of publication status, study design, year of the studies, school level, gender of students, type of student achievement, and duration.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.83-90
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• 2000

The chi-squared test statistic is usually employed for testing independence of two categorical variables in a two-way contingency table. It is well known that, under independence, the test statistic has an asymptotic chi-squared distribution under multinomial or product-multinomial models. For the case where both margins fixed, the sampling model of the contingency table is a multiple hypergeometric distribution and the chi-squared test statistic follows the same limiting distribution. In this paper, we study the difference between the small sample and large sample distributions of the chi-squared test statistic for the case with fixed margins. For a few small sample cases, the exact small sample distribution of the test statistic is directly computed. For a few large sample sizes, the small sample distribution of the statistic is generated via a Monte Carlo algorithm, and then is compared with the large sample distribution via chi-squared probability plots and Kolmogorov-Smirnov tests.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Animal Science and Technology
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• v.55 no.5
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• pp.373-380
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• 2013

The study aimed to develop prediction models of primal cut yield using body measurements of Hanwoo steers in Korea. The progeny of 874 steers at Hanwoo Improvement Main Center from 2008 to 2010 were recorded. Pearson's correlation coefficients for primal cuts and other traits were estimated. Primal cuts were adjusted for slaughter date and age using the SAS GLM procedure. Afterwards, a stepwise regression was performed on each primal cut by fitting body measurement traits. An independent covariable was selected at the highest coefficient of determination with the greater fitness model using Mallows's Cp statistic. Results showed that primal cuts were significantly influenced by slaughter date (P<0.01). The age at slaughter, however, was only significant for the top round (P<0.05). There was a moderate to high correlation between chest girth and tenderloin (0.54), loin (0.74), and rib (0.80). Most primal cut percentages were negatively related to BFT. Similar negative to low positive correlations were observed for primal cut percentage and body size traits. In addition, a correlation of 0.21 was observed between rib percentage and chest girth. The regression of body measurements on the adjusted primal cuts were significant for later traits. Regression estimates revealed that wither height, body length, rump length, hip bone width, and chest girth are important for primal cut weight and percentage determination. In particular, chest girth was always important for primal cut weight estimates.