Analysis of imprecise measurement data utilizing z-test for correlation

The conventional Z-test for correlation, grounded in classical statistics, is typically employed in situations devoid of vague information. However, real-world data often comes with inherent uncertainty, necessitating an adaptation of the Z-test using neutrosophic statistics. This paper introduces a...

Full description

Saved in:
Bibliographic Details
Published in:Journal of big data Vol. 11; no. 1; pp. 4 - 10
Main Author: Aslam, Muhammad
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.12.2024
Springer Nature B.V
SpringerOpen
Subjects:
Ambiguity
Big Data
Communications Engineering
Computational Science and Engineering
Computer Science
Correlation
Correlation analysis
Data
Data Mining and Knowledge Discovery
Database Management
Decision making
Hypotheses
Imprecise data
Information Storage and Retrieval
Mathematical Applications in Computer Science
Measurement
Medical data
Networks
Normal distribution
Random variables
Science
Simulation
Statistical methods
Statistical tests
Statistics
Tests
Uncertainty
Z-test
Medical data
Z-test
Correlation
Statistics
Imprecise data
ISSN:2196-1115, 2196-1115
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The conventional Z-test for correlation, grounded in classical statistics, is typically employed in situations devoid of vague information. However, real-world data often comes with inherent uncertainty, necessitating an adaptation of the Z-test using neutrosophic statistics. This paper introduces a modified Z-test for correlation designed to explore correlations in the presence of imprecise data. We will present the simulation to check the effect of the measure of indeterminacy on the evolution of type-I error and the power of the test. The application of this modification is illustrated through an examination of heartbeat and temperature data. Upon analyzing the heartbeat and temperature data, it is determined that, in the face of indeterminacy, the correlation between heartbeat and temperature emerges as significant. This highlights the importance of accounting for imprecise data when investigating relationships between variables.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2196-1115
2196-1115
DOI:10.1186/s40537-023-00873-7