A Box-Plot and Outliers Detection Proposal for Histogram Data: New Tools for Data Stream Analysis

Box and whisker plots for the distribution of herbivory between native and invasive genotypes in North America, and between European genotypes in their native (EU) and invasive (NA) range.

Water quality is a critical environmental issue because all forms of life depend on the water. The present study primarily focused on the spatiotemporal trends of water quality in a section of the Bhagirathi-Hugli River, West Bengal, using geospatial technology and integrated statistical methods. For this purpose, 83 samples of 7 water parameters were analysed and compared them with Indian Standards (IS 2004), Environmental Protection Agency (EPA 2001) and World Health Organization (WHO 1993) for the protection of aquatic life and human consumption. Correlation, box and whisker plots, paired sample t test, water quality index (WQI), cluster analysis (CA) and principal component analysis (PCA) were applied as an integrated multivariate statistical approach to understanding the nature of water quality. Pollution sources were identified by PCA indicating different origins both naturally and anthropogenic sources. The box and whisker plots displayed the significantly spatiotemporal variations and concentration of the variables. The paired sample t test identified that the surface water quality varied significantly between the seasons with significant value p < 0.05. Cluster analysis grouped 83 monitoring sites into 4 clusters to identify the pollution status such as low, moderate, high and very high pollution sites. Principal component analysis confirmed that the first three PCs with eigenvalues are higher than 1 contributing 90.83% of total variability for various parameters. The conductivity, total dissolved solids (TDS), salt and pH were expressively influenced by the anthropogenic effect while the temperature, oxidation-reduction potential (ORP) and dissolved oxygen (DO) were affected by seasonal factors. Results of WQI ranged from 45.04 to 83.79, and an average value was 69.55 with 69% samples representing poor water quality for drinking and domestic purposes. It also indicates that the water quality of rural sites was better than industrial and urban sites in both seasons and also shows that it was better for the duration of the post-monsoon than pre-monsoon.

The variation exhibited within three species of Barleria (B. bechuanensis, B. irritans and B. jubata) was studied to establish whether it was discrete or continuous. Morphological characters were examined and recorded in matrices. Cluster analysis was employed to impose a hierarchical non-overlapping association among operational taxonomic units (OTUs) while ordination was used to establish whether the variation was discrete or continuous. Discrete characters were determined from quantitative morphological data using box and whisker plots. Locality information for the OTUs was obtained from herbarium labels and used to generate maps to illustrate geographic distribution of taxa. Cluster analysis and ordination demonstrated that there was discrete variation within Barleria bechuanensis, B. irritans and B. jubata, which each split into two distinct clusters, although box and whisker plots illustrated that many quantitative characters overlapped within and between species. Since clear morphological gaps between clusters are assumed to be indicators of breaks in gene flow, the distinct clusters were recognised at species level.

In this paper, a convenient and parsimonious family of the quantile functions is proposed for modeling time-to-event data. Detailed mathematical arguments are presented for deriving the explicit expressions of the quantile density, quantile hazard and quantile mean remaining life functions. Their functional properties are also explored. Some important members of the family are provided. A special case, the so-called additive power logarithmic quantile family, is discussed with detailed properties, parameter estimation and application to time-to-event data. Measures of skewness and kurtosis for the proposed quantile function are derived. Important statistical measures useful for reliability analysis are also provided with their explicit expressions. Stochastic ordering, tail weight and order statistics are also discussed. L-moments are explicitly derived for the proposed quantile function. Estimation is approached using the likelihood function and percentiles. A real data set consists of times between occurrence of the coal-mining accidents is analyzed for illustration purposes.

To generate random variates from an unknown continuous distribution via the inverse transform method, we present a flexible, computationally tractable procedure for estimating the associated inverse distribution function based on sample data. Previously proposed methods for estimating inverse distribution functions can fail in either the distribution-fitting or variate-generation stages of application. To avoid these difficulties, we have developed the procedure IDPF for estimating an Inverse Distribution with a Polynomial Filter. After a first-cut or reference distribution has been obtained by some standard technique, a front-end polynomial filter for the inverse of the reference distribution is estimated by constrained nonlinear regression so that the resulting inverse distribution has minimum distance from the empirical inverse distribution. The constraints on the regression ensure that the fitted inverse distribution function is nondefective and monotonically nondecreasing. A specific implementation of this procedure is based on well-known techniques for obtaining a reference fit from the Johnson translation system of distributions. We present the results of a Monte Carlo study to demonstrate the effectiveness of the method. Compared to the reference fit, procedure IDPF yields significantly better approximations not only to the empirical inverse distribution function but also to the underlying theoretical inverse distribution function.

This work is devoted to the study of rates of convergence of the empirical measures μ n = 1 n ∑ k = 1 n δ X k \mu _n = \frac {1}{n} \sum _{k=1}^n \delta _{X_k} , n ≥ 1 n \geq 1 , over a sample ( X k ) k ≥ 1 {(X_k)}_{k \geq 1} of independent identically distributed real-valued random variables towards the common distribution μ \mu in Kantorovich transport distances W p W_p . The focus is on finite range bounds on the expected Kantorovich distances E ( W p ( μ n , μ ) ) \mathbb {E}(W_p(\mu _n,\mu )) or [ E ( W p p ( μ n , μ ) ) ] 1 / p \big [ \mathbb {E}(W_p^p(\mu _n,\mu )) \big ]^{1/p} in terms of moments and analytic conditions on the measure μ \mu and its distribution function. The study describes a variety of rates, from the standard one 1 n \frac {1}{\sqrt n} to slower rates, and both lower and upper-bounds on E ( W p ( μ n , μ ) ) \mathbb {E}(W_p(\mu _n,\mu )) for fixed n n in various instances. Order statistics, reduction to uniform samples and analysis of beta distributions, inverse distribution functions, log-concavity are main tools in the investigation. Two detailed appendices collect classical and some new facts on inverse distribution functions and beta distributions and their densities necessary to the investigation.

Facial expressions in infrared imaging have been introduced to solve the problem of illumination, which is an integral constituent of visual imagery. The paper investigates facial skin temperature distribution on mixed thermal facial expressions of our created face database where six are basic expressions and rest 12 are a mixture of those basic expressions. Temperature analysis has been performed on three facial regions of interest (ROIs); periorbital, supraorbital and mouth. Temperature variability of the ROIs in different expressions has been measured using statistical parameters. The temperature variation measurement in ROIs of a particular expression corresponds to a vector, which is later used in recognition of mixed facial expressions. Investigations show that facial features in mixed facial expressions can be characterized by positive emotion induced facial features and negative emotion induced facial features. Supraorbital is a useful facial region that can differentiate basic expressions from mixed expressions. Analysis and interpretation of mixed expressions have been conducted with the help of box and whisker plot. Facial region containing mixture of two expressions is generally less temperature inducing than corresponding facial region containing basic expressions.

A Fast Fuzzy C-Means (FFCM) clustering algorithm, optimized by the Particle Swarm Optimization (PSO) method, referred to as PSOFFCM, has been introduced and applied on liver CT images. Compared to FFCM, the proposed approach leads to higher values in terms of Jaccard Index and Dice Coefficient, and thus, indicating higher similarity with the ground truth provided. Based on ANOVA analysis, PSOFFCM showed better results in terms of Dice Coefficient. It also showed better mean values in terms of Jaccard Index and Dice Coefficient based on the box and whisker plots.

The combination of the two-dimensional (2D) materials g-C3N4 and MXenes in photocatalysis offers several advantages. The g-C3N4 can serve as a visible light-absorbing material, while MXenes can enhance the charge separation and transfer processes leading to improved photocatalytic efficiency. A critical review of 77 already published articles in the field of photocatalytic reactions using g-C3N4 and MXenes, such as hydrogen evolution, the reduction of carbon dioxide, the degradation of organic compounds, the redox reactions of nitrogen, was conducted. For the purpose of greater objectivity, the published results were analysed by non-parametric Mann–Whitney, Kolmogorov–Smirnov, and Mood´s median tests and visualised by box and whisker plots. It was found that MXenes can significantly improve the photocatalytic activity of g-C3N4. Adding other co-catalysts to the MXene/g-C3N4 composites does not bring a significant improvement in the photocatalytic performance. Promising results were obtained especially in the fields of hydrogen evolution and the reduction of carbon dioxide. Since the MXenes are relatively a new class of materials, there is still a big challenge for finding new photocatalytic applications and for the enhancement of existing photocatalytic systems based on g-C3N4, especially in terms of the MXenes and g-C3N4 surface and in the heterojunction engineering.

Over 170 municipalities in Colombia have been invaded by Lissachatina fulica, an African snail that can carry larvae of nematodes of interest in human and veterinary health. Nematodes enter the host snail as larvae L1 and then change to L2 and L3, the infectious form for vertebrates. To standardize culture in vitro of L3 carried by L. fulica from Santa Fe de Antioquia. Between July and November, 2014, 10 snails were collected, killed, and conserved with HCl 0.7%. Larvae were recovered using the Baermann technique and cultured for 36 days in Schneider, DMEM and RPMI media, with and without SFB 20% and distilled water with SFB 20%. Replacements were made every 36 hours; larvae were measured with an ocular micrometer on a microscope. Summary statistics were estimated; box and whisker plots were made; the t Student test was performed in SPSS 18™. A p-value below 0.05 was assumed as significant. Fifty per cent of the larvae survived. The highest survival and growth was 85% in supplemented DMEM. The final average length of larvae in supplemented media exceeded the initial one. There were significant differences between the average length of larvae cultured in supplemented media and the initial length. The initial width of larvae did not change. The best medium for the culture of L3 larvae was supplemented DMEM. The length provided more information than the width for the larval growth evaluation. The larvae studied did not correspond to Angiostrongylus cantonensis, A. costaricensis or Aelurostrongylus abstrusus.

Decision tree analysis for efficient CO2 utilization in electrochemical systems

This book is about statistics, so you will have learned something about that subject. You know how to collect data, because you gathered information in the real world on house prices. You understand that data can be classified in different ways, numerically or by categories. You have also gained practical skills in recording data, using a stem and leaf plot or a tally mark chart. These methods of recording data which you learned are also used for presenting data. In addition you learned how to present your data in graphical forms, using box and whisker plots and histograms. Learning how to carry out these methods of presenting data enables you to understand and interpret other peoples' statistical presentations.

Clinical management of abdominal aortic aneurysms (AAA) can benefit from patient-specific computational biomechanics-based assessment of the disease. Individual variations in shape and aortic material properties are expected to influence the assessment of AAA wall mechanics. While patient-specific geometry can be reproduced using medical images, the accurate individual and regionally varying tissue material property estimation is currently not feasible. This work addresses the relative uncertainties arising from variations in AAA material properties and its effect on the ensuing wall mechanics. Computational simulations were performed with five different isotropic material models based on an ex-vivo AAA wall material characterization and a subject population sample of 28 individuals. Care was taken to exclude the compounding effects of variations in all other geometric and biomechanical factors. To this end, the spatial maxima of the principal stress (σ max), principal strain (e max), strain-energy density (ψ max), and displacement (δ max) were calculated for the diameter-matched cohort of 28 geometries for each of the five different constitutive materials. This led to 140 quasi-static simulations, the results of which were assessed on the basis of intra-patient (effect of material constants) and inter-patient (effect of individual AAA shape) differences using statistical averages, standard deviations, and Box and Whisker plots. Mean percentage variations for σ max, e max, ψ max, and δ max for the intra-patient analysis were 1.5, 7.1, 8.0, and 6.1, respectively, whereas for the inter-patient analysis these were 11.1, 4.5, 15.3, and 12.9, respectively. Changes in the material constants of an isotropic constitutive model for the AAA wall have a negligible influence on peak wall stress. Hence, this study endorses the use of population-averaged material properties for the purpose of estimating peak wall stress, strain-energy density, and wall displacement. Conversely, strain is more dependent on the material constant variation than on the differences in AAA shape in a diameter-matched population cohort.

Box and Whisker Plot

&lt;p&gt;Auranofin synergizes with cisplatin to reduce tumor burden in an OVCAR3 CDX mouse model. &lt;b&gt;A,&lt;/b&gt; Weekly i&lt;i&gt;n vivo&lt;/i&gt; imaging system spectrum &lt;i&gt;in vivo&lt;/i&gt; bioluminescent images showing tumor burden in OVCAR3 CDX mice treated with placebo, cisplatin, auranofin, or both drugs, according to the regimen shown. &lt;b&gt;B,&lt;/b&gt; Tumor growth over time shown as mean normalized bioluminescence ± SEM for each treatment group (&lt;i&gt;n&lt;/i&gt; = 5 mice per group at baseline). &lt;b&gt;C,&lt;/b&gt; Raw &lt;i&gt;P&lt;/i&gt; values plotted over time comparing cisplatin monotherapy with auranofin–cisplatin combination treatment. Statistical significance (red dots) was determined using multiple unpaired &lt;i&gt;t&lt;/i&gt; tests. &lt;b&gt;D,&lt;/b&gt; Tumor burden of individual mice at the end of two treatment cycles (week 7). Unpaired &lt;i&gt;t&lt;/i&gt; tests were used for statistical comparison. *, &lt;i&gt;P&lt;/i&gt; &lt; 0.05; **, &lt;i&gt;P&lt;/i&gt; &lt; 0.01; ***, &lt;i&gt;P&lt;/i&gt; &lt; 0.001. &lt;b&gt;E,&lt;/b&gt; AUC analysis of normalized tumor burden over time (weeks 3–13) shown as box and whisker plots. Both tumor growth and shrinkage phases were quantified. Statistical comparisons were performed using one-way ANOVA with Holm–Šídák multiple comparisons. &lt;b&gt;F,&lt;/b&gt; Kaplan–Meier survival curves. Statistical significance was determined with unpaired &lt;i&gt;t&lt;/i&gt; tests. *, &lt;i&gt;P&lt;/i&gt; &lt; 0.05; ***, &lt;i&gt;P&lt;/i&gt; &lt; 0.001.&lt;/p&gt;