Outline

  • Abstract
  • Keywords
  • 1. Introduction
  • 2. Literature Review
  • 3. Statistical Analysis with Soft Computing
  • 3.1. Questionnaire with Fuzzy Set Theory
  • 3.2. the Nature of Fuzzy Answering
  • 3.3. Measurement with Fuzzy Data
  • 3.4. Two-Dimensional Questionnaires and Answering
  • 4. an Empirical Study
  • 4.1. Characteristics
  • 5. Conclusions
  • References

رئوس مطالب

  • چکیده
  • کلید واژه ها
  • 1. مقدمه
  • 2. مرور منابع
  • 3. آنالیزهای آماری با محاسبه نرم
  • 3.1. پرسشنامه با تئوری مجموعه فازی
  • 3.2. طبیعت پاسخ دهی فازی
  • 3.3. اندازه گیری با داده فازی
  • 3.4. پرسشنامه ها و پاسخ های دو بعدی
  • 4. یک مطالعه تجربی
  • 4.1. ویژگی ها
  • 5. نتیجه گیری

Abstract

This research proposes new statistical methods for marketing research and decision making. The study employs a soft computing technique and a new statistical tool to evaluate people’s thinking. Because the classical measurement system has difficulties in dealing with the non-real valued information, the study aims to find an appropriate measurement system to overcome this problem. The main idea is to decompose the data into a two-dimensional type, centroid and its length (area). The two-dimensional questionnaires this study proposes help reaching market information.

Keywords: - - -

Conclusions

Soft computing techniques are growing as a new discipline responding to the necessity to deal with vague samples and imprecise information that human thought causes in certain experimental environments. In this context, available observed data are coarse in a specific sense. The necessity to conduct research for this case is two-fold: (1) Providing multivariate model building and dependence analysis for fuzzy data observations, and (2) providing the legitimate framework for an important (engineering) domain of applications, generalizing Bayesian statistics, namely, the theory of belief functions (for fusion or combination of evidence).

The proposed techniques can render a more sophisticated and detailed interpretation of the data than the conventional ones, especially when the data could not show a clear cut human thought. In addition, the data triggers the question for constructing continuous fuzzy data which truthfully explains the flow of human ideology.

The crucial question is: what is the optimal, best fitted model for a set of fuzzy data? Is the SSE or the AIC still available to choose the best fitted model? Obviously, open research is on a comprehensive theory of finite random samples, comparable with that of random vectors, including elements such as “covariance of random samples” and “expected random sets”. This theory can apply to the case of big data random samples.

The study carefully reveals how to use fuzzy statistics in people’s time management effectiveness of fuzzy time allocation and management assessments. Empirical studies demonstrate how to measure fuzzy data that can deal with trapezoid, triangular, and interval-valued data simultaneously and how to perform the nonparametric hypothesis testing.

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