scholarly journals Traveling Abroad: Motivations and Factors

2019 ◽  
Author(s):  
Kacie R. Lorenson
Keyword(s):  
Test Statistic ◽  
Chi Squared ◽  
Group Travel

This study examines motivations, factors and influences for individuals either traveling alone, with others they know, or with a group travel company. The method used was a self-administered survey that had a total of 17 questions. There were 141 individuals who completed the survey. Once the data was gathered a chi-squared test was used to understand if there is a difference in factors to be included in a tour, in motivations desired for individuals traveling and in influences that affected individual’s decisions in traveling. Once the test statistic was performed and analyzed, the study was able to conclude if there was a difference in factors, motivations, and influences and their ranking.

scholarly journals Demographic Seasonality

2019 ◽  
Vol 22 (03) ◽  
pp. 187-194 ◽  
Author(s):  
Johan Fellman
Keyword(s):  
At Risk ◽  
Goodness Of Fit ◽  
Extreme Values ◽  
Demographic Data ◽  
Climatic Conditions ◽  
Data Sets ◽  
Test Statistic ◽  
Population At Risk ◽  
Chi Squared ◽  
Number Of Births

AbstractThe seasonality of demographic data has been of great interest. It depends mainly on the climatic conditions, and the findings may vary from study to study. Commonly, the studies are based on monthly data. The population at risk plays a central role. For births or deaths over short periods, the population at risk is proportional to the lengths of the months. Hence, one must analyze the number of births (and deaths) per day. If one studies the seasonality of multiple maternities, the population at risk is the total monthly number of confinements and the number of multiple maternities in a given month must be compared with the monthly number of all maternities. Consequently, when one considers the monthly rates of multiple maternities, the monthly number of births is eliminated and one obtains an unaffected seasonality measure of the rates. In general, comparisons between the seasonality of different data sets presuppose standardization of the data to indices with common means, mainly 100. If one assumes seasonality as ‘non-flatness’ throughout a year, a chi-squared test would be an option, but this test calculates only the heterogeneity and the same test statistic can be obtained for data sets with extreme values occurring in consecutive months or in separate months. Hence, chi-squared tests for seasonality are weak because of this arbitrariness and cannot be considered a model test. When seasonal models are applied, one must pay special attention to how well the applied model fits the data. If the goodness of fit is poor, nonsignificant models obtained can erroneously lead to statements that the seasonality is slight, although the observed seasonal fluctuations are marked. In this study, we investigate how the application of seasonal models can be applied to different demographic variables.

scholarly journals Nonparametric test for spatial geometric anisotropy

2010 ◽  
Vol 51 ◽  
Author(s):  
Kęstutis Dučinskas ◽  
Lina Dreižienė
Keyword(s):  
Spatial Data ◽  
Nonparametric Test ◽  
Test Statistics ◽  
Simple Test ◽  
Test Statistic ◽  
Simulation Experiments ◽  
Chi Squared ◽  
Geometric Anisotropy ◽  
Chi Squared Distribution

Paper deals with a problem of testing isotropy against geometric anisotropy for Gaussian spatial data. The original simple test statistic based on directional empirical semivariograms is proposed. Under the assumption of independence of the classical semivariogram estimators and for increasing domain asymptotics, the distribution of test statistics is approximated by chi-squared distribution. The simulation experiments demonstrate the efficacy of the proposed test.

Multiway Contingency Tables: Monte Carlo Resampling Probability Values for the Chi-Squared and Likelihood-Ratio Tests

2010 ◽  
Vol 107 (2) ◽  
pp. 501-510 ◽  
Author(s):  
Michael A. Long ◽  
Kenneth J. Berry ◽  
Paul W. Mielke
Keyword(s):  
Monte Carlo ◽  
Likelihood Ratio ◽  
Contingency Table ◽  
Contingency Tables ◽  
Likelihood Ratio Tests ◽  
Ratio Test ◽  
Test Statistics ◽  
Test Statistic ◽  
Resampling Methods ◽  
Chi Squared

Monte Carlo resampling methods to obtain probability values for chi-squared and likelihood-ratio test statistics for multiway contingency tables are presented. A resampling algorithm provides random arrangements of cell frequencies in a multiway contingency table, given fixed marginal frequency totals. Probability values are obtained from the proportion of resampled test statistic values equal to or greater than the observed test statistic value.

A Modified Score Test Statistic Having Chi-Squared Distribution to Order n -1

Biometrika ◽  
1991 ◽  
Vol 78 (3) ◽  
pp. 573 ◽  
Author(s):  
Gauss M. Cordeiro ◽  
Silvia L. de Paula Ferrari
Keyword(s):  
Score Test ◽  
Test Statistic ◽  
Chi Squared ◽  
Chi Squared Distribution

A modified score test statistic having chi-squared distribution to order n−1

Biometrika ◽  
1991 ◽  
Vol 78 (3) ◽  
pp. 573-582 ◽  
Author(s):  
GAUSS M. CORDEIRO ◽  
SILVIA L. DE PAULA FERRARI
Keyword(s):  
Score Test ◽  
Test Statistic ◽  
Chi Squared ◽  
Chi Squared Distribution

scholarly journals USP: an independence test that improves on Pearson’s chi-squared and the G -test

2021 ◽  
Vol 477 (2256) ◽  
Author(s):  
Thomas B. Berrett ◽  
Richard J. Samworth
Keyword(s):  
Simulated Data ◽  
Real Data ◽  
Minimum Variance ◽  
Test Statistic ◽  
Test Of Independence ◽  
Exact Test ◽  
Cell Counts ◽  
Chi Squared ◽  
Measure Of Dependence ◽  
Zero Cell

We present the U -statistic permutation (USP) test of independence in the context of discrete data displayed in a contingency table. Either Pearson’s χ 2 -test of independence, or the G -test, are typically used for this task, but we argue that these tests have serious deficiencies, both in terms of their inability to control the size of the test, and their power properties. By contrast, the USP test is guaranteed to control the size of the test at the nominal level for all sample sizes, has no issues with small (or zero) cell counts, and is able to detect distributions that violate independence in only a minimal way. The test statistic is derived from a U -statistic estimator of a natural population measure of dependence, and we prove that this is the unique minimum variance unbiased estimator of this population quantity. The practical utility of the USP test is demonstrated on both simulated data, where its power can be dramatically greater than those of Pearson’s test, the G -test and Fisher’s exact test, and on real data. The USP test is implemented in the R package USP .

A National Reassessment: Exploring Variables that Predispose People with Disabilities to Vocational Rehabilitation Acceptance

2009 ◽  
Vol 23 (3) ◽  
pp. 159-170
Author(s):  
Keith B. Wilson ◽  
Jason E. Gines
Keyword(s):  
Vocational Rehabilitation ◽  
Marital Status ◽  
People With Disabilities ◽  
Demographic Variables ◽  
Rehabilitation Services ◽  
Test Statistic ◽  
Research Teams ◽  
Vocational Rehabilitation Services ◽  
Chi Squared ◽  
Automatic Interaction Detector

Vocational rehabilitation (VR) acceptance has been explored by many research teams over the last 30 years. However, none of the prior studies explored the multitude of demographic variables that may influence VR acceptance and the possible interactions of those variables with VR acceptance. Extrapolating demographic variables from the national RSA-911 database of persons who sought vocational rehabilitation services, a Chi-squared Automatic Interaction Detector (CHAID) and the backwards elimination method of entry were used as the test statistic. Race, education, monthly public assistance at application, and marital status, respectively, were significantly correlated to VR acceptance. Implications for VR counselors and educators are discussed.

scholarly journals TESTING FOR NEGLECTED NONLINEARITY USING EXTREME LEARNING MACHINES

2013 ◽  
Vol 21 (supp02) ◽  
pp. 117-129
Author(s):  
KYULEE SHIN ◽  
JIN SEO CHO
Keyword(s):  
Monte Carlo ◽  
Sample Size ◽  
Linear Models ◽  
Regularity Conditions ◽  
Extreme Learning Machines ◽  
Test Statistic ◽  
Learning Machines ◽  
Monte Carlo Experiments ◽  
Chi Squared ◽  
Chi Squared Distribution

We introduce a statistic testing for neglected nonlinearity using extreme learning machines and call it ELMNN test. The ELMNN test is very convenient and can be widely applied because it is obtained as a by-product of estimating linear models. For the proposed test statistic, we provide a set of regularity conditions under which it asymptotically follows a chi-squared distribution under the null. We conduct Monte Carlo experiments and examine how it behaves when the sample size is finite. Our experiment shows that the test exhibits the properties desired by our theory.

scholarly journals Specific Direction-Based Outlier Detection Approach for GNSS Vector Networks

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1836 ◽  
Author(s):  
Yufeng Nie ◽  
Ling Yang ◽  
Yunzhong Shen
Keyword(s):  
Outlier Detection ◽  
Three Dimensional ◽  
Unit Vector ◽  
Test Statistic ◽  
Height Measurement ◽  
Maximum Test ◽  
Detection Approach ◽  
Specific Direction ◽  
Chi Squared ◽  
Gnss Network

In this paper we propose an outlier detection approach for GNSS vector networks based on the specific direction (i.e., SD approach), along which the test statistic constructed reaches the maximum. We derive the unit vector of this specific direction in detail, and prove that the unit vector is the same as that determined by the outlier estimates in three-dimensional (3D) approach, while the distribution of the maximum test statistic in this direction is the square root of Chi-squared distribution. Therefore, eliminating an outlier along this specific direction can get the same result as that of eliminating all three components of outlier vector in 3D approach. The mathematical equivalence of SD approach and 3D approach is further demonstrated by a real GNSS network. Moreover, preliminary application of the SD approach to detect the abnormal antenna height measurement is carried out in terms of numerical simulations of multiple baseline solutions, and it shows that the SD approach can effectively detect baselines that are directly infected by corresponding receiver antenna height errors.

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