As of August 2020, the keyword "can" triggered SERPs with featured snippet results that also contained a People Also Ask (PAA) box in approximately ** percent of searches. In ** percent of U.S. searches, the keyword "can" also triggered search engine result pages with PAA that also contained a featured snippet.

Question Paper Solutions of Statistics (ST),Question Paper,Graduate Aptitude Test in Engineering,Competitive Exams

Measuring the quality of Question Answering (QA) systems is a crucial task to validate the results of novel approaches. However, there are already indicators of a reproducibility crisis as many published systems have used outdated datasets or use subsets of QA benchmarks, making it hard to compare results. We identified the following core problems: there is no standard data format, instead, proprietary data representations are used by the different partly inconsistent datasets; additionally, the characteristics of datasets are typically not reflected by the dataset maintainers nor by the system publishers. To overcome these problems, we established an ontology---Question Answering Dataset Ontology (QADO)---for representing the QA datasets in RDF. The following datasets were mapped into the ontology: the QALD series, LC-QuAD series, RuBQ series, ComplexWebQuestions, and Mintaka. Hence, the integrated data in QADO covers widely used datasets and multilinguality. Additionally, we did intensive analyses of the datasets to identify their characteristics to make it easier for researchers to identify specific research questions and to select well-defined subsets. The provided resource will enable the research community to improve the quality of their research and support the reproducibility of experiments.

Here, the mapping results of the QADO process, the SPARQL queries for data analytics, and the archived analytics results file are provided.

Up-to-date statistics can be created automatically by the script provided at the corresponding QADO GitHub RDFizer repository.

OpenAI's o1 had the highest share of questions answered when attempted in SimpleQA benchmark in 2025. Claude-3 had the highest share of simply not attempting questions, though whether this is due to lack of data or other reasons is unknown.

This dataset contains the Department of Finance Performance Statistics on Assembly Written Questions .

Question Paper Solutions of year 2021 of Statistics, Question Paper , Graduate Aptitude Test in Engineering

"What to watch" was the most frequently asked question on Google in 2023. This question generated an average of *** million online search queries per month. "What is my ip" and "do a barrel roll" followed as the most popular Google search questions worldwide with an average of *** million monthly searches each.

The solutions of mysteries can lead to salvation for those on the reference desk dealing with business students or difficult questions.

We study a nonparametric test procedure based on order statistics for testing the null hypothesis of equality of two continuous distributions. The exact null distribution of the proposed test statistic is obtained using an enumeration method and a novel combinatorial argument. A recurrence relation for the probability generating function and a sequential approach for computing the mean and variance of the distribution are given. Critical values and characteristics of the distribution for selected small sample sizes are presented. For the Lehmann alternative family, the exact power function of the new test is derived, and its power performance is examined. We also study the power performance of the proposed test under the location-shift and scale-shift alternatives using Monte Carlo simulations and observe its superior performance when compared to commonly used nonparametric tests under various scenarios. A generalization of the proposed procedure for unequal sample sizes is discussed. An illustrative example and some concluding remarks are provided.

This dataset contains the Department of Justice Performance Statistics on Assembly Written Questions

n =  Numbers of responses. Test statistics for Wilcoxon rank sum test (W), Student's t-test (t), and χ2-test (χ2). Mean, median and ranges calculated from raw data before imputation.

Questions asked by library patrons and responded to by library staff. This assistance may be requested in person or remotely and from a variety of public desks. Data is provided by a monthly administration report created by the Library and Recreation Services management staff.

As of August 2020, the People Also Ask (PAA) feature box on Google's search results in the United States usually had * questions ***** percent of the time. In comparison, only **** percent of search results had * questions in the PAA feature box.

Question Paper Solutions of year 2019 of Statistics, Question Paper , Graduate Aptitude Test in Engineering

Data for stats.stackexchange question: https://stats.stackexchange.com/q/454499/101464

jooni22/dst-question-plus-que-vs-stat dataset hosted on Hugging Face and contributed by the HF Datasets community

Context

A team's mean seasons statistics can be used as predictors for their performance in future games. However, these statistics gain additional meaning when placed in the context of their opponents' (and opponents' opponents') performance. This dataset provides this context for each team. Furthermore, predicting games based on post-season stats causes data leakage, which from experience can be significant in this context (15-20% loss in accuracy). Thus, this dataset provides each of these statistics prior to each game of the regular season, preventing any source of data leakage.

Content

All data is derived from the March Madness competition data. Each original column was renamed to "A" and "B" instead of "W" and "L," and the mirrored to represent both orderings of opponents. Each team's mean stats are computed (both their stats, and the mean "allowed" or "forced" statistics by their opponents). To compute the mean opponents' stats, we analyze the games played by each opponent (excluding games played against the team in question), and compute the mean statistics for those games. We then compute the mean of these mean statistics, weighted by the number of times the team in question played each opponent. The opponents' opponent's stats are computed as a weighted average of the opponents' average. This results in statistics similar to those used to compute strength of schedule or RPI, just that they go beyond win percentages (See: https://en.wikipedia.org/wiki/Rating_percentage_index)

The per game statistics are computed by pretending we don't have any of the data on or after the day in question.

Next Steps

Currently, the data isn't computed particularly efficiently. Computing the per game averages for every day of the season is necessary to compute fully accurate opponents' opponents' average, but takes about 90 minutes to obtain. It is probably possible to parallelize this, and the per-game averages involve a lot of repeated computation (basically computing the final averages over and over again for each day). Speeding this up will make it more convenient to make changes to the dataset.

I would like to transform these statistics to be per-possession, add shooting percentages, pace, and number of games played (to give an idea of the amount uncertainty that exists in the per-game averages). Some of these can be approximated with the given data (but the results won't be exact), while others will need to be computed from scratch.

The requests we receive at the Reference Desk keep surprising us. We'll take a look at some of the best examples from the year on data questions and data solutions.

This survey shows the expected response time for social media questions or complaints in the United States and worldwide in 2018. During the survey, 31 percent of respondents from the United States, stated that they expect a response in 24 hours or less.

The reference desk is the common challenge for all of us. We will be taking some great moments of the past year on the reference desk (yours included) and look at tools, referrals, and colleagial education that will make your life easier.