Study on Key Biological Indicators of Diabetes Based on Statistical Tests

Normality testing is a fundamental hypothesis test in the statistical analysis of key biological indicators of diabetes.If this assumption is violated,it may cause the test results to deviate from the true value,leading to incorrect inferences and conclusions,and ultimately affecting the validity and accuracy of statistical inferences.Considering this,the study designs a unified analysis scheme for different data types based on parametric statistical test methods and non-parametric test methods.The data were grouped according to sample type and divided into discrete data and continuous data.To account for differences among subgroups,the conventional chi-squared test was used for discrete data.The normal distribution is the basis of many statistical methods;if the data does not follow a normal distribution,many statistical methods will fail or produce incorrect results.Therefore,before data analysis and modeling,the data were divided into normal and non-normal groups through normality testing.For normally distributed data,parametric statistical methods were used to judge the differences between groups.For non-normal data,non-parametric tests were employed to improve the accuracy of the analysis.Statistically significant indicators were retained according to the significance index P-value of the statistical test or corresponding statistics.These indicators were then combined with relevant medical background to further explore the etiology leading to the occurrence or transformation of diabetes status.

STATISTICAL TESTS FOR THE COMPARISON OF THE INCIDENCE OR MORTALITY RATES IN CANCER REGISTRY AND DESCRIPTIVE EPIDEMIOLOGY──A MICROCOMPUTER PROGRAM IN BASIC

This paper describes the statistical methods of the comparison of the incidence or mortality rates in cancer registry and descriptive epidemiology, and the features of microcomputer program (CANTEST) which was designed to perform the methods. The program was written in IBM BASIC language. Using the program CANTEST we presented here the user can do several statistical tests or estimations as follow: 1. the comparison of the adjusted rates which were calculated by directly or indirectly standardized methods, 2. the calculation of the slope of regression line for testing the linear trends of the adjusted rates, 3. the estimation of the 95% or 99%conndence intervals of the directly adjusted rates, of the cumulative rates (0-64 and 0-74), and of the cumulative risk. Several examples are presented for testing the performances of the program.

A Comprehensive Guide for Selecting Appropriate Statistical Tests: Understanding When to Use Parametric and Nonparametric Tests

Choosing appropriate statistical tests is crucial but deciding which tests to use can be challenging. Different tests suit different types of data and research questions, so it is important to choose the right one. Knowing how to select an appropriate test can lead to more accurate results. Invalid results and misleading conclusions may be drawn from a study if an incorrect statistical test is used. Therefore, to avoid these it is essential to understand the nature of the data, the research question, and the assumptions of the tests before selecting one. This is because there are a wide variety of tests available. This paper provides a step-by-step approach to selecting the right statistical test for any study, with an explanation of when it is appropriate to use it and relevant examples of each statistical test. Furthermore, this guide provides a comprehensive overview of the assumptions of each test and what to do if these assumptions are violated.

A Comprehensive Guide for Selecting Appropriate Statistical Tests: Understanding When to Use Parametric and Nonparametric Tests

Choosing appropriate statistical tests is crucial but deciding which tests to use can be challenging. Different tests suit different types of data and research questions, so it is important to choose the right one. Knowing how to select an appropriate test can lead to more accurate results. Invalid results and misleading conclusions may be drawn from a study if an incorrect statistical test is used. Therefore, to avoid these it is essential to understand the nature of the data, the research question, and the assumptions of the tests before selecting one. This is because there are a wide variety of tests available. This paper provides a step-by-step approach to selecting the right statistical test for any study, with an explanation of when it is appropriate to use it and relevant examples of each statistical test. Furthermore, this guide provides a comprehensive overview of the assumptions of each test and what to do if these assumptions are violated.

A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

A New Statistical Approach to Assess Climate Variability in the White Bandama Watershed,Northern Cote d’Ivoire

The population of northern C&#244te d’Ivoire, especially in the white Bandama watershed, lives for majority in rural areas and depends on farming, which is mainly linked to climate variability. This study evaluates the trends within watershed’s hydro-climatic variables and their level of significance over the period 1950-2000. The methodological approach consists in applying successively standardized indexes to detect trends and breaks in hydro-climatic long-term data. The Mann-Kendall statistical test lets us know the trends significance and the Kendall-Theil Robust Line test reveals their magnitude. The Student’s t test underlines break years. Results show that although rainfall has decreased, this decline is not statistically significant. However, temperature and potential evapotranspiration have strongly rised and discharge was submitted to high decline. These changes in hydrometeorological variables appeared from 1970 to 1980. This study is different from others conducted on climate variability in the northern C&#244te d’Ivoire by the methodological statistical framework implemented and the understanding of significance level of climate trends. Until now, authors used the standardized index to detect trends in hydro-climatic parameters. For this work, we added the Mann-Kendall statistical test to assess the significance level of these trends at α = 5% and 10%. Then, the Kendall-Theil statistical test was used to highlight the trends magnitude and the student’s t test to know the break years.

Statistical analyses for NANOGrav 5-year timing residuals

In pulsar timing, timing residuals are the differences between the observed times of arrival and predictions from the timing model. A comprehensive timing model will produce featureless resid- uals, which are presumably composed of dominating noise and weak physical effects excluded from the timing model （e.g. gravitational waves）. In order to apply optimal statistical methods for detecting weak gravitational wave signals, we need to know the statistical properties of noise components in the residuals. In this paper we utilize a variety of non-parametric statistical tests to analyze the whiteness and Gaussianity of the North American Nanohertz Observatory for Gravitational Waves （NANOGrav） 5- year timing data, which are obtained from Arecibo Observatory and Green Bank Telescope from 2005 to 2010. We find that most of the data are consistent with white noise; many data deviate from Gaussianity at different levels, nevertheless, removing outliers in some pulsars will mitigate the deviations.

Reply to 'Statistical testing and distribution for lead chloride toxicity'

We are very grateful for the letter written by Dr Lange,and indeed apologize for the mistakes noted in the word-ing of our text regarding statistical analysis.This wasdue to changes carried out while revising the manuscriptat the request of reviewers,whom we thank for,point-ing out several issues that were actually similar to thosenoted by Dr.Lange.Unfortunately,we were unable todescribe and discuss our findings properly in the context

Effectiveness Assessment of the Search-Based Statistical Structural Testing

Search-based statistical structural testing(SBSST)is a promising technique that uses automated search to construct input distributions for statistical structural testing.It has been proved that a simple search algorithm,for example,the hill-climber is able to optimize an input distribution.However,due to the noisy fitness estimation of the minimum triggering probability among all cover elements(Tri-Low-Bound),the existing approach does not show a satisfactory efficiency.Constructing input distributions to satisfy the Tri-Low-Bound criterion requires an extensive computation time.Tri-Low-Bound is considered a strong criterion,and it is demonstrated to sustain a high fault-detecting ability.This article tries to answer the following question:if we use a relaxed constraint that significantly reduces the time consumption on search,can the optimized input distribution still be effective in faultdetecting ability?In this article,we propose a type of criterion called fairnessenhanced-sum-of-triggering-probability(p-L1-Max).The criterion utilizes the sum of triggering probabilities as the fitness value and leverages a parameter p to adjust the uniformness of test data generation.We conducted extensive experiments to compare the computation time and the fault-detecting ability between the two criteria.The result shows that the 1.0-L1-Max criterion has the highest efficiency,and it is more practical to use than the Tri-Low-Bound criterion.To measure a criterion’s fault-detecting ability,we introduce a definition of expected faults found in the effective test set size region.To measure the effective test set size region,we present a theoretical analysis of the expected faults found with respect to various test set sizes and use the uniform distribution as a baseline to derive the effective test set size region’s definition.

Remote Sensing Image Retrieval Based on 3D-Local Ternary Pattern(LTP)Features and Non-subsampled Shearlet Transform(NSST)Domain Statistical Features

With the increasing popularity of high-resolution remote sensing images,the remote sensing image retrieval(RSIR)has always been a topic of major issue.A combined,global non-subsampled shearlet transform(NSST)-domain statistical features(NSSTds)and local three dimensional local ternary pattern(3D-LTP)features,is proposed for high-resolution remote sensing images.We model the NSST image coefficients of detail subbands using 2-state laplacian mixture(LM)distribution and its three parameters are estimated using Expectation-Maximization(EM)algorithm.We also calculate the statistical parameters such as subband kurtosis and skewness from detail subbands along with mean and standard deviation calculated from approximation subband,and concatenate all of them with the 2-state LM parameters to describe the global features of the image.The various properties of NSST such as multiscale,localization and flexible directional sensitivity make it a suitable choice to provide an effective approximation of an image.In order to extract the dense local features,a new 3D-LTP is proposed where dimension reduction is performed via selection of‘uniform’patterns.The 3D-LTP is calculated from spatial RGB planes of the input image.The proposed inter-channel 3D-LTP not only exploits the local texture information but the color information is captured too.Finally,a fused feature representation(NSSTds-3DLTP)is proposed using new global(NSSTds)and local(3D-LTP)features to enhance the discriminativeness of features.The retrieval performance of proposed NSSTds-3DLTP features are tested on three challenging remote sensing image datasets such as WHU-RS19,Aerial Image Dataset(AID)and PatternNet in terms of mean average precision(MAP),average normalized modified retrieval rank(ANMRR)and precision-recall(P-R)graph.The experimental results are encouraging and the NSSTds-3DLTP features leads to superior retrieval performance compared to many well known existing descriptors such as Gabor RGB,Granulometry,local binary pattern(LBP),Fisher vector(FV),vector of locally aggregated descriptors(VLAD)and median robust extended local binary pattern(MRELBP).For WHU-RS19 dataset,in terms of{MAP,ANMRR},the NSSTds-3DLTP improves upon Gabor RGB,Granulometry,LBP,FV,VLAD and MRELBP descriptors by{41.93%,20.87%},{92.30%,32.68%},{86.14%,31.97%},{18.18%,15.22%},{8.96%,19.60%}and{15.60%,13.26%},respectively.For AID,in terms of{MAP,ANMRR},the NSSTds-3DLTP improves upon Gabor RGB,Granulometry,LBP,FV,VLAD and MRELBP descriptors by{152.60%,22.06%},{226.65%,25.08%},{185.03%,23.33%},{80.06%,12.16%},{50.58%,10.49%}and{62.34%,3.24%},respectively.For PatternNet,the NSSTds-3DLTP respectively improves upon Gabor RGB,Granulometry,LBP,FV,VLAD and MRELBP descriptors by{32.79%,10.34%},{141.30%,24.72%},{17.47%,10.34%},{83.20%,19.07%},{21.56%,3.60%},and{19.30%,0.48%}in terms of{MAP,ANMRR}.The moderate dimensionality of simple NSSTds-3DLTP allows the system to run in real-time.

Estimation of Daily Global Solar Radiation with Different Sunshine-Based Models for Some Burundian Stations

Sunshine duration (S) based empirical equations have been employed in this study to estimate the daily global solar radiation on a horizontal surface (G) for six meteorological stations in Burundi. Those equations include the Ångström-Prescott linear model and four amongst its derivatives, i.e. logarithmic, exponential, power and quadratic functions. Monthly mean values of daily global solar radiation and sunshine duration data for a period of 20 to 23 years, from the Geographical Institute of Burundi (IGEBU), have been used. For any of the six stations, ten single or double linear regressions have been developed from the above-said five functions, to relate in terms of monthly mean values, the daily clearness index () to each of the next two kinds of relative sunshine duration (RSD): and . In those ratios, G0, S0 and stand for the extraterrestrial daily solar radiation on a horizontal surface, the day length and the modified day length taking into account the natural site’s horizon, respectively. According to the calculated mean values of the clearness index and the RSD, each station experiences a high number of fairly clear (or partially cloudy) days. Estimated values of the dependent variable (y) in each developed linear regression, have been compared to measured values in terms of the coefficients of correlation (R) and of determination (R2), the mean bias error (MBE), the root mean square error (RMSE) and the t-statistics. Mean values of these statistical indicators have been used to rank, according to decreasing performance level, firstly the ten developed equations per station on account of the overall six stations, secondly the six stations on account of the overall ten equations. Nevertheless, the obtained values of those indicators lay in the next ranges for all the developed sixty equations:;;;, with . These results lead to assert that any of the sixty developed linear regressions (and thus equations in terms of and ), fits very adequately measured data, and should be used to estimate monthly average daily global solar radiation with sunshine duration for the relevant station. It is also found that using as RSD, is slightly more advantageous than using for estimating the monthly average daily clearness index, . Moreover, values of statistical indicators of this study match adequately data from other works on the same kinds of empirical equations.

Association between people’s attitudes towards human-elephant conflict and their locational,demographic,and socio-economic characteristics in Buxa Tiger Reserve and its adjoining area,India

“Human-elephant conflict(HEC)”,the alarming issue,in present day context has attracted the attention of environmentalists and policy makers.The rising conflict between human beings and wild elephants is common in Buxa Tiger Reserve(BTR)and its adjoining area in West Bengal State,India,making the area volatile.People’s attitudes towards elephant conservation activity are very crucial to get rid of HEC,because people’s proximity with wild elephants’habitat can trigger the occurrence of HEC.The aim of this study is to conduct an in-depth investigation about the association of people’s attitudes towards HEC with their locational,demographic,and socio-economic characteristics in BTR and its adjoining area by using Pearson’s bivariate chi-square test and binary logistic regression analysis.BTR is one of the constituent parts of Eastern Doors Elephant Reserve(EDER).We interviewed 500 respondents to understand their perceptions to HEC and investigated their locational,demographic,and socio-economic characteristics including location of village,gender,age,ethnicity,religion,caste,poverty level,education level,primary occupation,secondary occupation,household type,and source of firewood.The results indicate that respondents who are living in enclave forest villages(EFVs),peripheral forest villages(PFVs),corridor village(CVs),or forest and corridor villages(FCVs),mainly males,at the age of 18–48 years old,engaged with agriculture occupation,and living in kancha and mixed houses,have more likelihood to witness HEC.Besides,respondents who are illiterate or at primary education level are more likely to regard elephant as a main problematic animal around their villages and refuse to participate in elephant conservation activity.For the sake of a sustainable environment for both human beings and wildlife,people’s attitudes towards elephants must be friendly in a more prudent way,so that the two communities can live in harmony.

Temperature Variation and Mutation Analysis over the Past 59 Years in Shenyang

In this paper,based on the observation data of air temperature during 1951-2009 in Shenyang,the interannual and interdecadal variation of annual average temperature,maximum and minimum temperature in Shenyang were conducted the statistical analysis by means of linear trend estimation and mutation detection by using Mann-Kendall method.As was demonstrated in the results,the annual average temperature,maximum and minimum temperature in Shenyang showed an upward trend,whose linear tendency rate was 0.231,0.181 and 0.218 respectively.The increment trend of annual average temperature,maximum and minimum temperature was extremely clear.The increase in minimum temperature was more significant than that in mean temperature and maximum temperature.The abrupt change point of annual mean temperature in Shenyang appeared in 1981;the abrupt change point of annual mean maximum temperature appeared in 1994;the annual mean minimum temperature underwent mutation in 1978.

Statistic Analysis of the Annual Extreme Wave Parameters for the Coast of China

According to the need of project design for offshore engineering and coastal engineering, this paper statistically analyses the annual extreme data of waves acquired at 19 observation stations along the coast of China. Five kinds of distribution curves are adopted: Pearson III (P-III), Log-Extreme I (LE), Log-Normal(LN), Weibull(W) and Exponential Γ(EΓ) to check the adaptability to the long-term distribution of annual extreme of wave in the China Sea areas. The New Curve Fitting Method (NFIT) method and Probability Weighted Moments (PWM) method are used to estimate the distribution parameters and thereby to derive the design wave parameters with different return periods at 19 observation stations. The test results show that by combining EΓ distribution and NFIT parameter estimation and optimum seeking by computer, the design wave parameters can be estimated with high accuracy, high speed and high efficiency, and the randomness of the estimated results can be avoided.

INFLUENCE FACTORS AND PREDICTION METHOD ON FLOOD/DROUGHT DURING THE ANNUALLY FIRST RAINY SEASON IN SOUTH CHINA

By using the significance test of two-dimensional wind field anomalies and Monte Carlo simulation experiment scheme, the significance features of wind field anomalies are investigated in relation to flood/drought during the annually first rainy season in south China. Results show that westem Pacific subtropical high and wind anomalies over the northeast of Lake Baikal and central Indian Ocean are important factors. Wind anomalies over the northem India in January and the northwest Pacific in March may be strong prediction signals. Study also shows that rainfall in south China bears a close relation to the geopotential height filed over the northern Pacific in March.

Impact of Climate Variability on Water Resources: The Case of Marc Delorme-Cnra Station, Southeast of Ivory Coast

This study aims to characterize the climatic variability in the South-East of Ivory Coast and to show its impact on the supply of water resources. To do this, statistical and hydrological methods were applied to climatic data collected at the Marc DELORME Research Station of the CNRA. The statistical trend tests on this data revealed a significant decrease in precipitation and an increase in temperature, insolation and evaporation. Statistical break methods indicate a rainfall break in 1982 which marks a modification of the rainfall regime thus translating a drop in rainfall of 15%, a recession in the frequency of rainy days in general and in particular in rainfall heights between 10 and 30 mm and greater than 50 mm. This break is accompanied by a shortening of the rainy seasons, with average rainfall durations ranging from 54 days (short rainy season) to 104 days (great rainy season). Despite the disturbances in the different seasons of the year, the monthly rainfall regimes in the area have not changed. The assessment of the effects of drought on water resources using the Standardized Precipitation and Evapotranspiration Index (SPEI) for three-time scales (1 month, 3 months and 12 months) indicates a severe drought ranging from 3% to 7% over the period 1961 to 2018. However, despite the presence of this severe drought, the intensity of the drought was found to be moderate on all time scales. The Thorrnthwaite method was used to highlight the impacts of this climatic variability on the region’s water resources. The average annual recharge estimated at 402 mm, has been reduced to 153 mm during a deficit period, a decrease of about 62%. The average annual runoff, which was 294 mm, fells to 257 mm, a decrease of about 13%. This recorded decrease in the water infiltrated after the rainfall break (1983-2018), explains the heterogeneous decrease in the depth of the water table.

Exact Bayesian and Fiducial Limits for the Mean of Lognormal Distribution

In this paper, the exact Bayesian limits, taking conjugate and noninformative prior distribution, and the exact fiducial limits for the mean of the lognormal distribution are presented. They can be found iteratively by one-dimension integral on a finite interval. The new algorithm is very convenient and with high accuracy. It can meet the practical engineering need excellently. However, the primitive algorithm is rather cumbersome. By the way, the very close approximate limits with a simple algorithm are derived. They can be applied immediately to engineering. Otherwise, they can also be used as a search interval to find the root of equation for the exact limits.

Path Planning of Quadrotors in a Dynamic Environment Using a Multicriteria Multi-Verse Optimizer

Paths planning of Unmanned Aerial Vehicles(UAVs)in a dynamic environment is considered a challenging task in autonomous flight control design.In this work,an efficient method based on a Multi-Objective MultiVerse Optimization(MOMVO)algorithm is proposed and successfully applied to solve the path planning problem of quadrotors with moving obstacles.Such a path planning task is formulated as a multicriteria optimization problem under operational constraints.The proposed MOMVO-based planning approach aims to lead the drone to traverse the shortest path from the starting point and the target without collision with moving obstacles.The vehicle moves to the next position from its current one such that the line joining minimizes the total path length and allows aligning its direction towards the goal.To choose the best compromise solution among all the non-dominated Pareto ones obtained for compromise objectives,the modified Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)is investigated.A set of homologous metaheuristics such as Multiobjective Salp Swarm Algorithm(MSSA),Multi-Objective Grey Wolf Optimizer(MOGWO),Multi-Objective Particle Swarm Optimization(MOPSO),and Non-Dominated Genetic Algorithm II(NSGAII)is used as a basis for the performance comparison.Demonstrative results and statistical analyses show the superiority and effectiveness of the proposed MOMVO-based planning method.The obtained results are satisfactory and encouraging for future practical implementation of the path planning strategy.

Computation Analysis of Brand Experience Dimensions: Indian Online Food Delivery Platforms

Online Food Delivery Platforms(OFDPs)has witnessed phenomenal growth in the past few years,especially this year due to the COVID-19 pandemic.This Pandemic has forced many governments across the world to give momentum to OFD services and make their presence among the customers.The Presence of several multinational and national companies in this sector has enhanced the competition and companies are trying to adapt various marketing strategies and exploring the brand experience(BEX)dimension that helps in enhancing the brand equity(BE)of OFDPs.BEXs are critical for building brand loyalty(BL)and making companies profitable.Customers can experience different kinds of brand experiences through feeling,emotions,affection,behavior,and intellect.The present research work is taken up to analyze the factors affecting BEX and its impact on BL and BE of the OFDPs and analyze the mediating role of BL in the relationship between BEX and BE of the OFDPs in the Indian context.A survey of 457 Indian customers was carried out.A questionnaire was used for data collection and a mediation study was used to test hypothesized relationships.Our computational analysis reveals that BEX influences the BL and BE of OFDPs.The study further indicates that BL mediates the relationship between BEX and BE of OFDPs.The effective marketing and relationship management practices will help company to enhance BEX that will enable in enhancing BL and raising BE of their product.It therefore provides a more thorough analysis of BEX constructs and their consequences than previous research.Some of the managerial implication,limitations,and scope of future research are also presented in the study.

Economic Consequences of Gender Equality in Europe

This paper shows influence of gender equality on economy where it analyzed how gender equality in Europe has affected on the development of the frozen food industry and services related to childcare. The development of these industries has given a positive impulse to the development of the whole economy. In this analysis, it is used multiple regressions as one of the most important statistical methods. In the first part of this paper, it shows the connection among the growth of female employment, growth in frozen food expenditure and growth of GDP in United Kingdom. In the second part of paper, it shows the relationship among the growth of labor force participation of women, growth of number of kindergarten and growth of GDP in Hungary. To proof these relationships, it used a multiple regression model. This statistical model was tested by using the T schedule which showed that the model in both the analyses is correct. At the end of the paper, it presents that employment rate and GDP behaves in the same way in European Union. These analyses show that it is necessary to continue to strengthen gender equality if the policy makers want to achieve even greater economic growth. The issue of gender equality is a very important factor in creating employment policy, and statisticians should be more involved in process of employment policy and gender equality