As of May 12, 2025, Newcastle United had the most VAR decisions work in their favor, with a net score of +9. Meanwhile, AFC Bournemouth had the league's lowest net VAR decision score with -7.

Despite being a source of controversy in recent years, most VAR interventions in the Premier League from 2022/23 to 2024/25 were found to be justified, with accuracy improving season by season. Following the 23rd matchweek of the 2024/25 season, only ************** VAR interventions were found to be incorrect, with an additional **** missed intervention opportunities taking the total number of errors to **. By comparison, the number of errors at the same stage of the 2023/24 season totaled **.

Statistics on Products in the Services Sector: Sampling errors by variable class and main activity. National.

Statistics on R&D Activities in the Business Sector: Commercial exchange coverage rate for the manufacturing industry por type of variable y years. National.

At the beginning of the ********* season, the English Premier League introduced Video Assistant Referees (commonly known as VAR) to all matches. This statistic presents the positions of British adults who watch the Premier League very or fairly frequently, on the proposed changes in the application of VAR within English Premier League football matches. Encouraging the on-field referee to consult pitch-side VAR screens is strongly supported by ** percent of respondents.

Feature preparation Preprocessing was applied to the data, such as creating dummy variables and performing transformations (centering, scaling, YeoJohnson) using the preProcess() function from the “caret” package in R. The correlation among the variables was examined and no serious multicollinearity problems were found. A stepwise variable selection was performed using a logistic regression model. The final set of variables included: Demographic: age, body mass index, sex, ethnicity, smoking History of disease: heart disease, migraine, insomnia, gastrointestinal disease, COVID-19 history: covid vaccination, rashes, conjunctivitis, shortness of breath, chest pain, cough, runny nose, dysgeusia, muscle and joint pain, fatigue, fever ,COVID-19 reinfection, and ICU admission. These variables were used to train and test various machine-learning models Model selection and training The data was randomly split into 80% training and 20% testing subsets. The “h2o” package in R version 4.3.1 was employed to implement different algorithms. AutoML was first used, which automatically explored a range of models with different configurations. Gradient Boosting Machines (GBM), Random Forest (RF), and Regularized Generalized Linear Model (GLM) were identified as the best-performing models on our data and their parameters were fine-tuned. An ensemble method that stacked different models together was also used, as it could sometimes improve the accuracy. The models were evaluated using the area under the curve (AUC) and C-statistics as diagnostic measures. The model with the highest AUC was selected for further analysis using the confusion matrix, accuracy, sensitivity, specificity, and F1 and F2 scores. The optimal prediction threshold was determined by plotting the sensitivity, specificity, and accuracy and choosing the point of intersection as it balanced the trade-off between the three metrics. The model’s predictions were also plotted, and the quantile ranges were used to classify the model’s prediction as follows: > 1st quantile, > 2nd quantile, > 3rd quartile and < 3rd quartile (very low, low, moderate, high) respectively. Metric Formula C-statistics (TPR + TNR - 1) / 2 Sensitivity/Recall TP / (TP + FN) Specificity TN / (TN + FP) Accuracy (TP + TN) / (TP + TN + FP + FN) F1 score 2 * (precision * recall) / (precision + recall) Model interpretation We used the variable importance plot, which is a measure of how much each variable contributes to the prediction power of a machine learning model. In H2O package, variable importance for GBM and RF is calculated by measuring the decrease in the model's error when a variable is split on. The more a variable's split decreases the error, the more important that variable is considered to be. The error is calculated using the following formula: 𝑆𝐸=𝑀𝑆𝐸∗𝑁=𝑉𝐴𝑅∗𝑁 and then it is scaled between 0 and 1 and plotted. Also, we used The SHAP summary plot which is a graphical tool to visualize the impact of input features on the prediction of a machine learning model. SHAP stands for SHapley Additive exPlanations, a method to calculate the contribution of each feature to the prediction by averaging over all possible subsets of features [28]. SHAP summary plot shows the distribution of the SHAP values for each feature across the data instances. We use the h2o.shap_summary_plot() function in R to generate the SHAP summary plot for our GBM model. We pass the model object and the test data as arguments, and optionally specify the columns (features) we want to include in the plot. The plot shows the SHAP values for each feature on the x-axis, and the features on the y-axis. The color indicates whether the feature value is low (blue) or high (red). The plot also shows the distribution of the feature values as a density plot on the right.

In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg et al., 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022).

It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter et al., 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (e.g., Naoki et al., 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running:

In the first instance, we generated the background points and extracted the values of the variables (Code2_Extract_values_DWp_SC.R). Barbet-Massin et al. (2012) recommend the use of 10,000 background points when using regression methods (e.g., Generalized Linear Model) or distance-based models (e.g., DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.). Then, we extracted the values of predictor variables (e.g., bioclimatic, topographic, demographic, habitat) in function of presence and background points (e.g., Hijmans and Elith, 2017).

Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009).

After that, we ran the code for the GBM method (Gradient Boost Machine; Code3_GBM_Relative_contribution.R and Code4_Relative_contribution.R), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (e.g., Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable.

Subsequently, the correlation of the variables is run by Pearson's method (Code5_Pearson_Correlation.R) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (e.g., Awan et al., 2021).

Once the above codes were run, we uploaded the same subgroups (i.e., presence and background groups with 75% training and 25% testing) (Code6_Presence&backgrounds.R) for the GLM method code (Code7_GLM_model.R). Here, we first ran the GLM models per variable to obtain the p-significance value of each variable (alpha ≤ 0.05); we selected the value one (i.e., presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (e.g., Fielding and Bell, 1997; Allouche et al., 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included.

On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017).

Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission).

We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche et al., 2006).

The last code (i.e., Code8_DOMAIN_SuitHab_model.R) is for species distribution modelling using the DOMAIN algorithm (Carpenter et al., 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model.

Regarding the model evaluation and estimation, we selected the following estimators:

1) partial ROC, which evaluates the approach between the curves of positive (i.e., correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020).

2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long et al., 1988).

Statistics on Products in the Services Sector: Sample errors by type of variable and activity principal. National.

Vietnam GSO Projection: Population: Var: High: Urban data was reported at 65,228.000 Person th in 2049. This records an increase from the previous number of 64,208.000 Person th for 2048. Vietnam GSO Projection: Population: Var: High: Urban data is updated yearly, averaging 47,340.500 Person th from Dec 2014 (Median) to 2049, with 36 observations. The data reached an all-time high of 65,228.000 Person th in 2049 and a record low of 29,939.000 Person th in 2014. Vietnam GSO Projection: Population: Var: High: Urban data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G002: Population: Projection: General Statistics Office.

Suppose we observe a random vector X from some distribution in a known family with unknown parameters. We ask the following question: when is it possible to split X into two pieces f(X) and g(X) such that neither part is sufficient to reconstruct X by itself, but both together can recover X fully, and their joint distribution is tractable? One common solution to this problem when multiple samples of X are observed is data splitting, but Rasines and Young offers an alternative approach that uses additive Gaussian noise—this enables post-selection inference in finite samples for Gaussian distributed data and asymptotically when errors are non-Gaussian. In this article, we offer a more general methodology for achieving such a split in finite samples by borrowing ideas from Bayesian inference to yield a (frequentist) solution that can be viewed as a continuous analog of data splitting. We call our method data fission, as an alternative to data splitting, data carving and p-value masking. We exemplify the method on several prototypical applications, such as post-selection inference for trend filtering and other regression problems, and effect size estimation after interactive multiple testing. Supplementary materials for this article are available online.

At the beginning of the 2019-2020 season, the English Premier League introduced Video Assistant Referees (commonly known as VAR) to all matches. This statistic shows the results of a representative survey of the British Public in relation to the English Premier League, presenting the opinions of British adults who watch the Premier League very or fairly frequently, on the future use of VAR within Premier League football matches.

The majority of respondents indicated that VAR should continue to be used within the English Premier League however the way in which it is used should be changed. ** percent of respondents categorized as 35-44 and **+ indicated that the English Premier League should stop using VAR entirely.

Using the light-curve time-series data for more than 11.7 million variable sources published in the Gaia Data Release 3, the average magnitudes, colors, and variability parameters have been computed for 0.836 million Gaia CRF objects, which are mostly quasars and active galactic nuclei (AGNs). To mitigate the effects of occasional flukes in the data, robust statistical measures have been employed: namely, the median, median absolute deviation, and Spearman correlation. We find that the majority of the CRF sources have moderate amplitudes of variability in the Gaia G band just below 0.1 mag. The heavy-tailed distribution of variability amplitudes (quantified as robust standard deviations) does not find a single analytical form, but is closer to Maxwell distribution with a scale of 0.078 mag. The majority of CRF sources have positive correlations between G magnitude and G_BP_-G_RP_ colors, meaning that these quasars and AGNs become bluer when they are brighter. The variations in the G_BP_ and G_RP_ bands are also mostly positively correlated. Dependencies of all variability parameters with cosmological redshift are fairly flat for the more accurate estimates above redshift 0.7, while the median color shows strong systematic variations with redshift. Using a robust normalized score of magnitude deviations, a sample of the 5000 most variable quasars is selected and published. The intersection of this sample with the ICRF3 catalog shows a much higher rate of strongly variable quasars (mostly blazars) in ICRF3.

At the beginning of the ********* season, the English Premier League introduced Video Assistant Referees (commonly known as VAR) to all matches. This statistic shows the results of a representative survey of the British Public in relation to the English Premier League, presenting the opinions of British adults who watch the Premier League very or fairly frequently, on the future use of VAR within Premier League football matches. Responses have subsequently been categorized by the NRS social grade of the panelist.

The NRS social grades are a system of demographic classification used in the United Kingdom. The grades are grouped here into ABC1 and C2DE; these are taken to equate to middle class and working class, respectively.
Although the majority of panelists indicated that VAR should continue to be used within the English Premier League, ** percent of panelists within the ABC1 social grade and ** percent of panelists within the C2DE social grade indicated that the way in which it is used should be changed. The share of panelists within the C2DE social grade that indicated that it should continue being used without any changes was significantly higher than the share of panelists within the ABC1 social grade with this preference.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Vietnam GSO Projection: Population: Var: High: Whole Country data was reported at 112,123.000 Person th in 2049. This records an increase from the previous number of 111,774.000 Person th for 2048. Vietnam GSO Projection: Population: Var: High: Whole Country data is updated yearly, averaging 104,871.000 Person th from Dec 2014 (Median) to 2049, with 36 observations. The data reached an all-time high of 112,123.000 Person th in 2049 and a record low of 90,493.000 Person th in 2014. Vietnam GSO Projection: Population: Var: High: Whole Country data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G002: Population: Projection: General Statistics Office.

Financial overview and grant giving statistics of Var Stilla

Financial overview and grant giving statistics of Rigg W Var Char

Vietnam GSO Projection: Population: Var: High: Southeast data was reported at 19,594.000 Person th in 2034. This records an increase from the previous number of 19,504.000 Person th for 2033. Vietnam GSO Projection: Population: Var: High: Southeast data is updated yearly, averaging 18,203.000 Person th from Dec 2014 (Median) to 2034, with 21 observations. The data reached an all-time high of 19,594.000 Person th in 2034 and a record low of 15,721.000 Person th in 2014. Vietnam GSO Projection: Population: Var: High: Southeast data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G002: Population: Projection: General Statistics Office.

The Static Var Compensators (SVCs) market is witnessing significant growth, driven by the increasing demand for efficient power quality management in various industries. SVCs are crucial devices used in electrical power systems for enhancing voltage stability and regulating reactive power, thereby improving the over

The Value-Added Reseller (VAR) software market plays a pivotal role in the technology ecosystem, facilitating the enhancement of core software solutions through additional features, services, and customization tailored to meet the unique needs of businesses. VARs provide more than just software; they deliver integra