• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.593-601
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• 2013

We often need to include categorial variables as explanatory variables in regression models. The categorial variables in regression models can be quantified through dummy variables. In this study, we provide an education tool using Excel VBA for displaying regression lines along with test results for regression models with a continuous explanatory variable and one or two categorical explanatory variables. The regression lines with test results are provided step by step for the model(s) with interaction(s), the model(s) without interaction(s) but with dummy variables, and the model without dummy variable(s). With this tool, we can easily understand the meaning of dummy variables and interaction effect through graphics and further decide which model is more suited to the data on hand.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.581-589
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• 2005

The purpose of this thesis is to develop and introduce Add-in program which we can systematically, visually and dynamically study discrete probability distribution of binomial distribution, poisson distribution and hypergeometric distribution, and continuous probability distribution of normal distribution, exponential distribution, and the definition and characteristics of t distribution, F distribution and ${\chi}^2$ distribution to be driven from normal distribution, and graphs, the computation process of probability by using VBA which is the device of Excel.

• 한국데이터정보과학회:학술대회논문집
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• 2004.10a
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• pp.17-23
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• 2004

At the conclusion from the hypothesis testing, there is a possibility of making Type I error and Type II error. The purpose of this article is to use this program in statistics teaching through developing the program for studying on the concept about these two errors, two kinds of the probability of errors by the variation of rejection region, two kinds of the probability of errors by the variation of sample size, the relations of the probability $\alpha$ and $\beta$ by these two errors, and power function, power curve.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.543-554
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• 2004

In teaching statistics, the part which is very important but difficult to understand to the students is estimation and hypothesis testing. This paper introduces the developed program about estimation and hypothesis testing by using Excel Macro. This program will help learners to study and use a statistical inference conveniently, and to get a good learning effect.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.185-193
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• 2005

This study develops and introduces the program for letting learners study statistics in a systematic and efficient way by using Excel tools such as VBA and Macro, when they study statistical inference at two populations. This study helps learners understand the steps on statistical inference at two populations by utilizing the systematic and visual techniques.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.19-30
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• 2006

The purpose of this article is to develop a program for teaching Type I and Type II errors in one sample. In this program, the concepts of two errors are visually explained, and the probabilities of two errors are also visually displayed as the rejection region changes or the sample size changes. Also, in this program, the power curve and the operating characteristic curve are visually displayed in terms of the parameter value of interest.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.403-410
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• 2014

Binary logistic regression analysis is a statistical technique that explains binary response variable by quantitative or qualitative explanatory variables. In the binary logistic regression model, the probability that the response variable equals, say 1, one of the binary values is to be explained as a transformation of linear combination of explanatory variables. This is one of big barriers that non-statisticians have to overcome in order to understand the model. In this study, an educational tool is developed that explains the need of the binary logistic regression analysis using Excel VBA. More precisely, this tool explains the problems related to modeling the probability of the response variable equal to 1 as a linear combination of explanatory variables and then shows how these problems can be solved through some transformations of the linear combination.

• The Journal of Korean Society for Radiation Therapy
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• v.25 no.1
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• pp.87-90
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• 2013

Purpose: Introducing CR (Computed Radiography) system created a process of printing therapy irradiation images and converting the degree of enlargement. This is to increase job efficiency and contribute to work improvement using a computerized method with home grown software to simplify this process, work efficiency. Materials and Methods: Microsoft EXCEL (ver. 2007) and VISUAL BASIC (ver. 6.0) have been used to make the software. A window for each shield block was designed to enter patients' treatment information. Distances on the digital images were measured, the measured data were entered to the Excel program to calculate the degree of enlargement, and printouts were produced to manufacture shield blocks. Results: By computerizing the existing method with this program, the degree of enlargement can easily be calculated and patients' treatment information can be entered into the printouts by using macro function. As a result, errors in calculation which may occur during the process of production or errors that the treatment information may be delivered wrongly can be reduced. In addition, with the simplification of the conversion process of the degree of enlargement, no copy machine was needed, which resulted in the reduction of use of paper. Conclusion: Works have been improved by computerizing the process of block production and applying it to practice which would simplify the existing method. This software can apply to and improve the actual conditions of each hospital in various ways using various features of EXCEL and VISUAL BASIC which has already been proven and used widely.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.137-143
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• 2021

In the case of residential facilities, general fire scenarios cannot be applied. Becauseit is difficult to quantify due to the types of combustibles and various fire loads. Existing research conducting surveys of combustibles, but research on fire characteristics is insufficient. Therefore, in this study, an Excel macro that can be quantified by experimenting with the HRR experiments of sofa, drawer, mattress, chair, desk and TV, which are typical combustibles. As a result of experimenting 6 loading combustibles in domestic residential facilities by using a furniture calorimeter, values of 2,391.26kW appeared from the sofa, 1,891.80kW from the drawer, 1,778.95kW from the mattress, 1,104kW from the chair, 291kW from the desk, and 135.09kW from the TV. Also, by applying the α value of the fire growth rate by classifying fire-growing speeds at NFPA 72 (National Fire Alarm Code 2007, Annex B), the mattress can be defined as Very Fast, the sofa and drawer Fast, the TV Slow, the desk Slow, and the chair Medium.