Improving Accuracy of Multiple Regression Analysis for Effort Prediction Model

Iwata, Kazunori; Nakashima, Toyoshiro; Anan, Yoshiyuki; Ishii, Naohiro

doi:10.1109/icis-comsar.2006.46

Cited by 10 publications

(3 citation statements)

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“…F statistic value of regression equation is 61.559, P value is 0.000, and they show that equation is extremely suitable [4] . By testing the regression equation coefficients, we can see that the constant term is 210.628 when it has not been standardized, t statistic is 13.392, the corresponding P value is 0.000, the constant term of equation is significant, and it should be elected into the equation [5] .…”

Section: A Aanalysis Of the Primary Industrymentioning

confidence: 98%

Empirical Study on Economic Transformation and Industry Development in Tianjin City

Zheng

Zhang

Zhou

2009

2009 Second International Conference on Future Information Technology and Management Engineering

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China's economic restructuring on the industry plays a vital role. This article is intended to study the degree of the impact on China's economic restructuring in various industries and their inter-relationship. In this paper, multiple linear regression method is used; multiple regression equations are established with the four main indicators of economic restructuring and the three industries output value by filtrating variables, then their causalities is analyzed. Through nearly a decade of economic data of Tianjin, economic restructuring and industrial development are empirical analyzed, and found that the opening extend to the outside world has a great role in promoting the output value of all the industries, in addition, the output value of the third industries are influenced by the degree of government intervention in the economy.

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Section: A Aanalysis Of the Primary Industrymentioning

confidence: 98%

Empirical Study on Economic Transformation and Industry Development in Tianjin City

Zheng

Zhang

Zhou

2009

2009 Second International Conference on Future Information Technology and Management Engineering

View full text Add to dashboard Cite

show abstract

“…This requires improving further the quality and cost during software development. Hence, we have already studied costs of the processes by using analysis (Iwata et al (2006b); Nakashima et al (2006)) and collaborative filtering (Iwata et al (2006a)). To cope with this situation, the tools that can manage the progress status or results in the database are used to improve the quality and productivity.…”

Section: Software Project Management and Issuesmentioning

confidence: 99%

“…However, there is little research on the relationship between the scale of the development and the number of errors, based of data accumulated from past projects. As a result, previously we described the prediction of the total scale using multiple regression analysis (Iwata et al (2006b); Nakashima et al (2006)) and collaborative filtering (Iwata et al (2006a)). In this Chapter we therefore, propose a method for creating effort and errors prediction model using an Artificial Neural Network (ANN) for complementing missing values (Iwata et al (2006a)).…”

Section: Introductionmentioning

confidence: 99%