This online application gives manufacturers the ability to compare Iowa to other states on a number of different topics including: business climate, education, operating costs, quality of life and workforce.

Statistical comparison of multiple time series in their underlying frequency patterns has many real applications. However, existing methods are only applicable to a small number of mutually independent time series, and empirical results for dependent time series are only limited to comparing two time series. We propose scalable methods based on a new algorithm that enables us to compare the spectral density of a large number of time series. The new algorithm helps us efficiently obtain all pairwise feature differences in frequency patterns between M time series, which plays an essential role in our methods. When all M time series are independent of each other, we derive the joint asymptotic distribution of their pairwise feature differences. The asymptotic dependence structure between the feature differences motivates our proposed test for multiple mutually independent time series. We then adapt this test to the case of multiple dependent time series by partially accounting for the underlying dependence structure. Additionally, we introduce a global test to further enhance the approach. To examine the finite sample performance of our proposed methods, we conduct simulation studies. The new approaches demonstrate the ability to compare a large number of time series, whether independent or dependent, while exhibiting competitive power. Finally, we apply our methods to compare multiple mechanical vibrational time series.

Nursing Home Compare has detailed information about every Medicare and Medicaid nursing home in the country. A nursing home is a place for people who can’t be cared for at home and need 24-hour nursing care. These are the official datasets used on the Medicare.gov Nursing Home Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at every Medicare and Medicaid-certified nursing home in the country, including over 15,000 nationwide.

Comparison of OR tables between the interaction of rs7522462 and rs11945978 in the WTCCC data with the shared controls (left) and the interaction of the proxy SNPs, rs296533 and rs2089509 in the IBDGC data (right). The legend to this table is the same as that of Table 3.

The purpose of this report is to compare alternative methods for producing measures of SEs for regression models for the MHSS clinical sample with the goal of producing more accurate and potentially smaller SEs.

This data set contains data associated with MODIS fire maps generated using two different algorithms and compared against fire maps produced by ASTER. These data relate to a paper (Morisette et al., 2005) that describes the use of high spatial resolution ASTER data to evaluate the characteristics of two fire detection algorithms, both applied to MODIS-Terra data and both operationally producing publicly available fire locations. The two algorithms are NASA's operational Earth Observing System MODIS fire detection product and Brazil's National Institute for Space Research (INPE) algorithm. These data are the ASCII files used in the logistic regression and error matrices presented in the paper.

o compare Austin’s diversion methodology and goals to those of its peers, Burns & McDonnell collected data from 13 benchmark cities regarding diversion calculation methods, recyclables processing contract terms, and policy implementation. Based on analysis on this compiled data, Burns & McDonnell determined various key findings based on a preliminary comparison, and comparisons of diversion material type considerations, methodology and policy considerations, and effective programming.

Comparison data form literature.

This dataset provides values for WORLD reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Diagnostic inference involves the detection of anomalous system behavior and the identification of its cause, possibly down to a failed unit or to a parameter of a failed unit. Traditional approaches to solving this problem include expert/rule-based, model-based, and data-driven methods. Each approach (and various techniques within each approach) use different representations of the knowledge required to perform the diagnosis. The sensor data is expected to be combined with these internal representations to produce the diagnosis result. In spite of the availability of various diagnosis technologies, there have been only minimal efforts to develop a standardized software framework to run, evaluate, and compare different diagnosis technologies on the same system. This paper presents a framework that defines a standardized representation of the system knowledge, the sensor data, and the form of the diagnosis results – and provides a run-time architecture that can execute diagnosis algorithms, send sensor data to the algorithms at appropriate time steps from a variety of sources (including the actual physical system), and collect resulting diagnoses. We also define a set of metrics that can be used to evaluate and compare the performance of the algorithms, and provide software to calculate the metrics.

Number of SNPs refers to the ingroup dataset (29 samples) after filtering, but not filtering for linkage.

Dataset used to compare the assessment results on the levels of Findability, Accessability, Interoperability and Reusability of datasets in a sample of repositories, by means of the F-UJI FAIR data assessment tool and FAIR-Enough assessment tool. Assessment carried out for the European Research Data Landscape study.

The NADAC Weekly Comparison identifies the drug products with current NADAC rates that are replaced with new NADAC rates. Other changes (e.g. NDC additions and terminations) to the NADAC file are not reflected in this comparison. Note: Effective Date was not recorded in the dataset until 6/7/2017

People make a city, making each city as unique as the combination of its inhabitants. However, some cities are similar and some cities are inimitable. We examine the social structure of 10 different cities using Twitter data. Each city is decomposed to its communities. We show that in many cases one city can be thought of as an amalgamation of communities from another city. For example, we find the social network of Manchester is very similar to the social network of a virtual city of the same size, where the virtual city is composed of communities from the Bristol network. However, we cannot create Bristol from Manchester since Bristol contains communities with a social structure that are not present in Manchester. Some cities, such as Leeds, are outliers. That is, Leeds contains a particularly wide range of communities, meaning we cannot build a similar city from communities outside of Leeds. Comparing communities from different cities, and building virtual cities that are comparable ...

Comparison of vacation and sick days portion of the employee benefits package. The overall data is based on policy and statue for the current fiscal year. The market estimate for sick days/leave is based on the 2011 Towers Watson Survey General Industry Benefit Policies and Practices Survey Report.

Crime Statistics Comparison

There is interest in using social media content to supplement or even substitute for survey data. O’Connor et al. (2010) report reasonably high correlations between the sentiment of tweets containing the word “jobs” and survey-based measures of consumer confidence in 2008-2009. Other researchers report a similar relationship through 2011 but after that time it is no longer observed, suggesting such tweets may not be as promising an alternative to survey responses as originally hoped. But, it’s possible that with the right analytic techniques, the sentiment of “jobs” tweets might still be an acceptable alternative. We explore this possibility by attempting to strengthen the original relationship and then extending the most successful approaches to more recent years. We classify “jobs” tweets into categories whose content is related to employment and categories whose content is not, to see if sentiment of the former correlates more highly with a survey-based measure of consumer sentiment. We use five sentiment-scoring tools, calculate daily sentiment three different ways, and use a measure of association less sensitive to outliers than correlation. None of these approaches improved the size of the relationship in the original or more recent data. We discuss the possibility that weighting and better understanding why users tweet might help recover the original relationship between the sentiment of tweets and survey responses. However, despite the earlier promise of tweets as an alternative to survey responses, we find no evidence that the original relationship was more than a chance occurrence.

Modern statistics provides an ever-expanding toolkit for estimating unknown parameters. Consequently, applied statisticians frequently face a difficult decision: retain a parameter estimate from a familiar method or replace it with an estimate from a newer or more complex one. While it is traditional to compare estimates using risk, such comparisons are rarely conclusive in realistic settings. In response, we propose the “c-value” as a measure of confidence that a new estimate achieves smaller loss than an old estimate on a given dataset. We show that it is unlikely that a large c-value coincides with a larger loss for the new estimate. Therefore, just as a small p-value supports rejecting a null hypothesis, a large c-value supports using a new estimate in place of the old. For a wide class of problems and estimates, we show how to compute a c-value by first constructing a data-dependent high-probability lower bound on the difference in loss. The c-value is frequentist in nature, but we show that it can provide validation of shrinkage estimates derived from Bayesian models in real data applications involving hierarchical models and Gaussian processes. Supplementary materials for this article are available online.

These data are night level totals of bat calls by species and phonic group recorded at Fort Knox, Kentucky during the summer of 2017. Data contain recordings from zero-crossing frequency division and full spectrum receiving units.