scholarly journals Improving Customers Satisfaction through Significance of Technical Attribute in QFD Studies

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Zafar Iqbal ◽  
Lubna Waheed ◽  
Waheed Muhammad ◽  
Rajab Muhammad
Keyword(s):  
Quality Function Deployment ◽  
Statistical Significance ◽  
P Value ◽  
Relationship Matrix ◽  
The Novel ◽  
Test Statistic ◽  
P Values ◽  
Weighted Matrix ◽  
Importance Ratings ◽  
Selection Of

Purpose: Quality Function Deployment, (QFD) is a methodology which helps to satisfy customer requirements through the selection of appropriate Technical Attributes (TAs). The rationale of this article is to provide a method lending statistical support to the selection of TAs.  The purpose is to determine the statistical significance of TAs through the derivation of associated significance (P) values.   Design/Methodology/Approach: We demonstrate our methodology with reference to an original QFD case study aimed at improving the educational system in high schools in Pakistan; and then with five further published case studies obtained from literature. Mean weights of TAs are determined. Considering each TA mean weight to be a Test Statistic, a weighted matrix is generated from the VOCs’ importance ratings, and ratings in the relationship matrix. Finally using R, P-values for the means of original TAs are determined from the hypothetical population of means of TAs.  Findings: Each TA’s P-value evaluates its significance/insignificance in terms of distance from the grand mean. P-values indirectly set the prioritization of TAs. Implications/Originality/Value: The novel aspect of this study is extension of mean weights of TAs, to also provide P-values for TAs. TAs with significant importance can be resolved on priority basis, while other can be fixed with appropriateness.

Abstract MP11: Circulating Plasma Biomarkers Associated With Brain Arteriovenous Malformations

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Sarah E Wetzel-Strong ◽  
Shantel M Weinsheimer ◽  
Jeffrey Nelson ◽  
Ludmila Pawlikowska ◽  
Dewi Clark ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Statistical Significance ◽  
Protein Profiling ◽  
P Value ◽  
P Values ◽  
Plasma Biomarkers ◽  
Standard Curve ◽  
Disease States ◽  
Heparin Plasma ◽  
Circulating Levels

Objective: Circulating plasma protein profiling may aid in the identification of cerebrovascular disease signatures. This study aimed to identify circulating angiogenic and inflammatory biomarkers that may serve as biomarkers to differentiate sporadic brain arteriovenous malformation (bAVM) patients from other conditions with brain AVMs, including hereditary hemorrhagic telangiectasia (HHT) patients. Methods: The Quantibody Human Angiogenesis Array 1000 (Raybiotech) is an ELISA multiplex panel that was used to assess the levels of 60 proteins related to angiogenesis and inflammation in heparin plasma samples from 13 sporadic unruptured bAVM patients (69% male, mean age 51 years) and 37 patients with HHT (40% male, mean age 47 years, n=19 (51%) with bAVM). The Quantibody Q-Analyzer tool was used to calculate biomarker concentrations based on the standard curve for each marker and log-transformed marker levels were evaluated for associations between disease states using a multivariable interval regression model adjusted for age, sex, ethnicity and collection site. Statistical significance was based on Bonferroni correction for multiple testing of 60 biomarkers (P< 8.3x10 - 4 ). Results: Circulating levels of two plasma proteins differed significantly between sporadic bAVM and HHT patients: PDGF-BB (P=2.6x10 -4 , PI= 3.37, 95% CI:1.76-6.46) and CCL5 (P=6.0x10 -6 , PI=3.50, 95% CI=2.04-6.03). When considering markers with a nominal p-value of less than 0.01, MMP1 and angiostatin levels also differed between patients with sporadic bAVM and HHT. Markers with nominal p-values less than 0.05 when comparing sporadic brain AVM and HHT patients also included angiostatin, IL2, VEGF, GRO, CXCL16, ITAC, and TGFB3. Among HHT patients, the circulating levels of UPAR and IL6 were elevated in patients with documented bAVMs when considering markers with nominal p-values less than 0.05. Conclusions: This study identified differential expression of two promising plasma biomarkers that differentiate sporadic bAVMs from patients with HHT. Furthermore, this study allowed us to evaluate markers that are associated with the presence of bAVMs in HHT patients, which may offer insight into mechanisms underlying bAVM pathophysiology.

On “Field Significance” and the False Discovery Rate

2006 ◽  
Vol 45 (9) ◽  
pp. 1181-1189 ◽  
Author(s):  
D. S. Wilks
Keyword(s):  
False Discovery Rate ◽  
Statistical Power ◽  
Statistical Significance ◽  
Significance Test ◽  
P Value ◽  
Test Statistic ◽  
Global Test ◽  
Additional Advantage ◽  
Counting Procedure ◽  
False Discovery

Abstract The conventional approach to evaluating the joint statistical significance of multiple hypothesis tests (i.e., “field,” or “global,” significance) in meteorology and climatology is to count the number of individual (or “local”) tests yielding nominally significant results and then to judge the unusualness of this integer value in the context of the distribution of such counts that would occur if all local null hypotheses were true. The sensitivity (i.e., statistical power) of this approach is potentially compromised both by the discrete nature of the test statistic and by the fact that the approach ignores the confidence with which locally significant tests reject their null hypotheses. An alternative global test statistic that has neither of these problems is the minimum p value among all of the local tests. Evaluation of field significance using the minimum local p value as the global test statistic, which is also known as the Walker test, has strong connections to the joint evaluation of multiple tests in a way that controls the “false discovery rate” (FDR, or the expected fraction of local null hypothesis rejections that are incorrect). In particular, using the minimum local p value to evaluate field significance at a level αglobal is nearly equivalent to the slightly more powerful global test based on the FDR criterion. An additional advantage shared by Walker’s test and the FDR approach is that both are robust to spatial dependence within the field of tests. The FDR method not only provides a more broadly applicable and generally more powerful field significance test than the conventional counting procedure but also allows better identification of locations with significant differences, because fewer than αglobal × 100% (on average) of apparently significant local tests will have resulted from local null hypotheses that are true.

The Conundrum of P-Values: Statistical Significance is Unavoidable but Need Medical Significance Too

2020 ◽  
Author(s):  
Abhaya Indrayan
Keyword(s):  
Type I Error ◽  
Dominant Role ◽  
Statistical Significance ◽  
Empirical Studies ◽  
P Value ◽  
Selective Reporting ◽  
Type I ◽  
Practical Application ◽  
P Values ◽  
Zero Effect

Background: Small P-values have been conventionally considered as evidence to reject a null hypothesis in empirical studies. However, there is widespread criticism of P-values now and the threshold we use for statistical significance is questioned.Methods: This communication is on contrarian view and explains why P-value and its threshold are still useful for ruling out sampling fluctuation as a source of the findings.Results: The problem is not with P-values themselves but it is with their misuse, abuse, and over-use, including the dominant role they have assumed in empirical results. False results may be mostly because of errors in design, invalid data, inadequate analysis, inappropriate interpretation, accumulation of Type-I error, and selective reporting, and not because of P-values per se.Conclusion: A threshold of P-values such as 0.05 for statistical significance is helpful in making a binary inference for practical application of the result. However, a lower threshold can be suggested to reduce the chance of false results. Also, the emphasis should be on detecting a medically significant effect and not zero effect.

scholarly journals P values: from suggestion to superstition

2016 ◽  
Vol 64 (7) ◽  
pp. 1166-1171 ◽  
Author(s):  
John Concato ◽  
John A Hartigan
Keyword(s):  
Clinical Research ◽  
Statistical Significance ◽  
Historical Context ◽  
P Value ◽  
Threshold Probability ◽  
Conceptual Approach ◽  
P Values ◽  
Clinical Investigations ◽  
Current Usage ◽  
Genomic Studies

A threshold probability value of ‘p≤0.05’ is commonly used in clinical investigations to indicate statistical significance. To allow clinicians to better understand evidence generated by research studies, this review defines the p value, summarizes the historical origins of the p value approach to hypothesis testing, describes various applications of p≤0.05 in the context of clinical research and discusses the emergence of p≤5×10−8 and other values as thresholds for genomic statistical analyses. Corresponding issues include a conceptual approach of evaluating whether data do not conform to a null hypothesis (ie, no exposure–outcome association). Importantly, and in the historical context of when p≤0.05 was first proposed, the 1-in-20 chance of a false-positive inference (ie, falsely concluding the existence of an exposure–outcome association) was offered only as a suggestion. In current usage, however, p≤0.05 is often misunderstood as a rigid threshold, sometimes with a misguided ‘win’ (p≤0.05) or ‘lose’ (p>0.05) approach. Also, in contemporary genomic studies, a threshold of p≤10−8 has been endorsed as a boundary for statistical significance when analyzing numerous genetic comparisons for each participant. A value of p≤0.05, or other thresholds, should not be employed reflexively to determine whether a clinical research investigation is trustworthy from a scientific perspective. Rather, and in parallel with conceptual issues of validity and generalizability, quantitative results should be interpreted using a combined assessment of strength of association, p values, CIs, and sample size.

scholarly journals Mind your gaps: Overlooking assembly gaps confounds statistical testing in genome analysis

2018 ◽  
Author(s):  
Diana Domanska ◽  
Chakravarthi Kanduri ◽  
Boris Simovski ◽  
Geir Kjetil Sandve
Keyword(s):  
Statistical Tests ◽  
Statistical Significance ◽  
Null Model ◽  
Statistical Testing ◽  
Slight Reduction ◽  
Test Statistic ◽  
Experimental Conditions ◽  
P Values ◽  
Localization Analysis ◽  
The Right

AbstractBackgroundThe difficulties associated with sequencing and assembling some regions of the DNA sequence result in gaps in the reference genomes that are typically represented as stretches of Ns. Although the presence of assembly gaps causes a slight reduction in the mapping rate in many experimental settings, that does not invalidate the typical statistical testing comparing read count distributions across experimental conditions. However, we hypothesize that not handling assembly gaps in the null model may confound statistical testing of co-localization of genomic features.ResultsFirst, we performed a series of explorative analyses to understand whether and how the public genomic tracks intersect the assembly gaps track (hg19). The findings rightly confirm that the genomic regions in public genomic tracks intersect very little with assembly gaps and the intersection was observed only at the beginning and end regions of the assembly gaps rather than covering the whole gap sizes. Further, we simulated a set of query and reference genomic tracks in a way that nullified any dependence between them to test our hypothesis that not avoiding assembly gaps in the null model would result in spurious inflation of statistical significance. We then contrasted the distributions of test statistics and p-values of Monte Carlo simulation-based permutation tests that either avoided or not avoided assembly gaps in the null model when testing for significant co-localization between a pair of query and reference tracks. We observed that the statistical tests that did not account for the assembly gaps in the null model resulted in a distribution of the test statistic that is shifted to the right and a distribu tion of p-values that is shifted to the left (leading to inflated significance).ConclusionOur results shows that not accounting for assembly gaps in statistical testing of co-localization analysis may lead to false positives and over-optimistic findings.

scholarly journals Statistical Approach for Biologically Relevant Gene Selection from High-Throughput Gene Expression Data

2020 ◽  
Author(s):  
Samarendra Das ◽  
Shesh N. Rai
Keyword(s):  
Gene Expression ◽  
Statistical Approach ◽  
Gene Selection ◽  
Statistical Significance ◽  
High Dimensional ◽  
Support Vector ◽  
Expression Data ◽  
Test Statistic ◽  
Biologically Relevant ◽  
Selection Of

Selection of biologically relevant genes from high dimensional expression data is a key research problem in gene expression genomics. Most of the available gene selection methods are either based on relevancy or redundancy measure, which are usually adjudged through post selection classification accuracy. Through these methods the ranking of genes was done on a single high-dimensional expression data, which leads to the selection of spuriously associated and redundant genes. Hence, we developed a statistical approach through combining Support Vector Machine with Maximum Relevance and Minimum Redundancy under a sound statistical setup for the selection of biologically relevant genes. Here, the genes are selected through statistical significance values computed using a non-parametric test statistic under a bootstrap based subject sampling model. Further, a systematic and rigorous evaluation of the proposed approach with nine existing competitive methods was carried on six different real crop gene expression datasets. This performance analysis was carried out under three comparison settings, i.e. subject classification, biological relevant criteria based on quantitative trait loci, and gene ontology. Our analytical results showed that the proposed approach selects genes that are more biologically relevant as compared to the existing methods. Moreover, the proposed approach was also found to be better with respect to the competitive existing methods. The proposed statistical approach provides a framework for combining filter, and wrapper methods of gene selection.

scholarly journals Visualization Strategies for Regression Estimates with Randomization Inference

2019 ◽  
Author(s):  
Marshall A. Taylor
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Regression Models ◽  
Statistical Significance ◽  
Permutation Tests ◽  
P Value ◽  
P Values ◽  
Alpha Level ◽  
Significance Levels ◽  
Nonprobability Sample

Coefficient plots are a popular tool for visualizing regression estimates. The appeal of these plots is that they visualize confidence intervals around the estimates and generally center the plot around zero, meaning that any estimate that crosses zero is statistically non-significant at at least the alpha-level around which the confidence intervals are constructed. For models with statistical significance levels determined via randomization models of inference and for which there is no standard error or confidence intervals for the estimate itself, these plots appear less useful. In this paper, I illustrate a variant of the coefficient plot for regression models with p-values constructed using permutation tests. These visualizations plot each estimate's p-value and its associated confidence interval in relation to a specified alpha-level. These plots can help the analyst interpret and report both the statistical and substantive significance of their models. Illustrations are provided using a nonprobability sample of activists and participants at a 1962 anti-Communism school.

scholarly journals A Probabilistic Model For Co-Occurrence Analysis in Bibliometrics

2021 ◽  
Author(s):  
Xiaobei Zhou ◽  
Miao Zhou ◽  
Desheng Huang ◽  
Lei Cui
Keyword(s):  
Probabilistic Model ◽  
Statistical Significance ◽  
Low Frequency ◽  
Critical Threshold ◽  
P Value ◽  
Model Framework ◽  
Practical Applications ◽  
Mesh Terms ◽  
Occurrence Matrix ◽  
Selection Of

Abstract The co-occurrence analysis of Medical Subject Heading (MeSH) terms in the bibliographic database is popularly used in bibliometrics. Practically for making the result interpretable, it is necessary to apply a certain filter procedure of co-occurrence matrix for removing the low-frequency items that should not appear in the final result due to their low representativeness for co-occurrence analysis. Unfortunately, there is rare research referring to determine a critical threshold to remove noise of data for co-occurrence analysis. Here, we propose a probabilistic model for co-occurrence analysis that can calculate statistical significance (p-values) of co-occurred items. With help of this model, the dimensionality of co-occurrence network could be conveniently reduced according to selection of different levels of p-value thresholds. The conceptual model framework, simulation and practical applications are illustrated in the manuscript. Further details (including all reproducible codes) can be downloaded from the project website: https://github.com/Miao-zhou/Co-occurrence-analysis.git.

A Novel Web-Based Approach to Quality Function Deployment

2003 ◽  
Author(s):  
Hong-jun Wang ◽  
Xin Chen ◽  
Ping Zhang ◽  
De-tao Zheng ◽  
Jian Sun
Keyword(s):  
Quality Function Deployment ◽  
Relationship Matrix ◽  
Manufacturing Companies ◽  
Customer Requirements ◽  
Web Based ◽  
Quality Function ◽  
Engineering Characteristics ◽  
Novel Approach ◽  
Importance Ratings ◽  
The Relationship

Quality Function Deployment (QFD), as an effective tool for customer-driven product development, has been widely adopted in manufacturing companies for many years. The main difficulties in using QFD lie in capturing the actual needs of customers, and in the conflicting conclusions provided by different experts while establishing the relationship matrix between customer requirements and engineering characteristics. This paper describes a novel approach to applying QFD. Firstly, the customer requirements are captured through Web-based technology. Secondly, the relationship matrix between customer requirements and engineering characteristics is established by handling coordinately fuzzy knowledge. Thirdly, the translation of a customer’s importance ratings into engineering weightings is carried out by fuzzy mapping technology. In order to reduce the complexity of HOQ, the less important customer requirements and engineering characteristics are identified and eliminated from the model by using α-cuts. Finally, an example using a refrigerator illustrates that the proposed approach is both available and practicable.

P543Cardiac resynchronization therapy in left ventricular non-compaction: long-term results in a series of 40 patients

EP Europace ◽  
2020 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
D A Radu ◽  
C N Iorgulescu ◽  
S N Bogdan ◽  
A I Deaconu ◽  
A Badiul ◽  
...  
Keyword(s):  
Statistical Significance ◽  
Systolic Dysfunction ◽  
Serum Levels ◽  
Left Ventricular ◽  
Long Term Results ◽  
P Value ◽  
P Values ◽  
Mean Differences

Abstract Background Left ventricular non-compaction (LVNC) is a structural cardiomyopathy (SC) with a high probability of LV systolic dysfunction. Left bundle branch block (LBBB) frequently occurs in SCs. Purpose We sought to analyse the evolution of LVNC-CRT (LC) patients in general and compare it with the non-LVNC-CRT group (nLC). Methods We analysed 40 patients with contrast-MRI documented LVNC (concomitant positive Petersen and Jacquier criteria) implanted with CRT devices in CEHB. The follow-up included 7 hospital visits for each patient (between baseline and 3 years). Demographics, risk factors, usual serum levels, pre-procedural planning factors, clinical, ECG, TTE and biochemical markers were recorded. Statistical analysis was performed using software. Notable differences were reported as either p-values from crosstabs (discrete) or mean differences, p-values and confidence intervals from t-tests (continuous). A p-value of .05 was chosen for statistical significance (SS). Results Subjects in LC were younger (-7.52 ys; &lt;.000; (-3.617;-11.440)), with no sex predominance, more obese (45.9 vs. 28.3%; &lt;0.24) and had less ischaemic disease (17.9 vs. 39.7%; &lt;.007). LC implants were usually CRT-Ds (91 vs. 49.5%; &lt;.000) and more frequently MPP-ready (35.8 vs. 8.4%; &lt;.000). At baseline, sinus rhythm was predominant in LC (97.4 vs. 79.8%; &lt;.007) and permitted frequent use of optimal fusion CRT (75.5 vs. 46.6%; &lt;.002). Although initial LVEFs were similar, LCs had much larger EDVs (+48.91 ml; &lt;.020; (+7.705;+90.124)) and ESVs (+34.91; &lt;.05; (+1.657;+71.478)). After an initial encouraging ⁓ 1 year evolution the LC-CRT group crashed its performance in terms of both LVEF and volumes. Thus, at 1 year follow-up, when compared to nLCs, LVEFs were far lower (-22.02%; &lt;.000; (-32.29;-11.76)) while EDVs and ESVs much higher – (+70.8 ml; &lt;.037; (+49.27;+ 189.65)) and (+100.13; &lt;.039; (+5.25;+195)) respectively – in LCs in spite of similarly corrected dyssynchrony. The mean mitral regurgitation (MR) degree at 1 year was much higher in LCs (+1.8 classes; &lt;.002; (+0.69;+2.97)) certainly contributing to the poor results. The cumulated super-responder/responder (SR/R) rates were constantly lower and decreasing at both 1 year (37.5 vs. 72.4; &lt;.040) and 2 years of follow-up (10.1 vs. 80%; NS). Conclusions CRT candidates with LVNC are significantly more severe at the time of implant. After an initial short-term improvement (probably due to acute correction of dyssynchrony) most patients fail to respond in the long term. Severe dilation with important secondary MR probably plays an important role.

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