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Six Sigma
Published in James William Martin, Operational Excellence, 2021
A contingency table answers a practical question, “Are two variables related to each other based on an observed count or frequency?” In Figure 9.25, the null hypothesis states the observed counts of defective invoices are the same regardless of the type of form used or the shift using a form. The observed counts are shown in Figure 9.25 to be 5, 8, and 20 for Form A and shifts 1, 2, and 3, respectively. The observed counts for Form B were 20, 30, and 25 for shifts 1, 2, and 3, respectively. The calculated or expected counts (rounded) are shown to be 7.6, 11.5, and 13.8 for Form A and shifts 1, 2, and 3, respectively. The expected counts for Form B are 17.4, 26.4, and 31.3 for shifts 1, 2, and 3, respectively. Contingency tables help answer the question, “Are the observed counts close enough to the expected counts to be considered a random pattern?” If the p value of the calculated test statistic is less than 0.05 (or 5%), the null hypothesis with its assumption of equality is rejected and we conclude the counts differ by the type of form or shift with 1–p confidence of not making a Type I error.
Greedy Search Methods
Published in Max Kuhn, Kjell Johnson, Feature Engineering and Selection, 2019
When screening individual categorical predictors, there are several options depending on the type of outcome data: When the outcome is categorical, the relationship between the predictor and outcome forms a contingency table. When there are three or more levels for the predictor, the degree of association between predictor and outcome can be measured with statistics such as χ2 (chi-squared) tests or exact methods (Agresti, 2012). When there are exactly two classes for the predictor, the odds-ratio can be an effective choice (see Section 5.6).When the outcome is numeric, and the categorical predictor has two levels, then a basic t-test can be used to generate a statistic. ROC curves and precision-recall curves can also be created for each predictor and the area under the curves can be calculated.83 When the predictor has more than two levels, the traditional ANOVA F-statistic can be calculated.
Quality of the Final Product and Classification of Green Coffee
Published in Hii Ching Lik, Borém Flávio Meira, Drying and Roasting of Cocoa and Coffee, 2019
Mario Roberto Fernandez Alduenda
A methodology named “descriptive cupping” was developed in 2012–2014 at the University of Otago, New Zealand (Fernandez Alduenda et al., 2014; Fernandez Alduenda, 2015) to interpret descriptive data entered through a cupping interface, using statistical tools borrowed from other sensory applications (Lawrence et al., 2013). The aim of this methodology was to assess the use of a cupping panel to obtain descriptive coffee flavor profiles. Specifically, descriptive cupping focuses on characterizing the sensory profile of coffee using descriptive data generated using the SCAA (2009a) cupping protocol. In this protocol, quality scores are used to grade coffee while the descriptive data is usually disregarded. However, the descriptive data collected by the SCAA cupping protocol may provide a means to obtain a coffee flavor profile in origin countries that lack advanced sensory facilities and descriptive panels. Descriptors from the cuppers are grouped into categories. A contingency table is constructed with samples as columns and categories as rows. The descriptive space is visualized using non-symmetric correspondence analysis (NSCA) (Fernandez Alduenda et al., 2014). Descriptive cupping data can be integrated with other types of data such as cupping scores or even qualitative data using multiple factor analysis (MFA) (Fernandez Alduenda, 2015).
Landsat-based vegetation abundance and surface temperature for surface urban heat island studies: the tale of Greater Amman Municipality
Published in Annals of GIS, 2018
Finally, correlation analysis using contingency tables (Everitt 2000) was performed to characterize the relationships between normalized and reclassified vegetation abundance and normalized and reclassified surface temperature maps for both summer and winter seasons in the years 1987 and 2016 in Greater Amman Municipality. Contingency tables are two-way frequency tables used to present categorical data in terms of frequency counts. They show the observed frequency of two variables. There is a row for each factor level and a column for each response level. By computing expected frequencies for the cells in the contingency tables and comparing these expected values with the observed ones a Pearson Chi-Square (χ2) test is calculated. This test was used in this study to determine the significance of the contingency tables. The null hypothesis is whether the two variables are independent. In addition, the measure of association represented by Gamma (G) test was applied to describe the general direction and strength of the relationships. G is a measure of ordinal association that consider whether the first variable tends to increase as the second variable increases. It classifies pairs of observations as concordant or discordant. A pair is concordant if an observation with a larger value of the first variable also has a larger value of the second variable. A pair is discordant if an observation with a larger value of the first variable has a smaller value of the second variable. G values range from −1 (perfect negative association) to +1 (perfect positive association), while zero means no association. It appears with its standard error and confidence interval.
How does Industry 4.0 contribute to operations management?
Published in Journal of Industrial and Production Engineering, 2018
Diego Castro Fettermann, Caroline Gobbo Sá Cavalcante, Tatiana Domingues de Almeida, Guilherme Luz Tortorella
The classification of each of the cases was performed based on a consensus reached by the authors. The frequencies checked are analyzed according to the Chi-square test of independence in contingency tables. The Chi-Square test of independence is a non-parametric test that determines whether there is an association between categorical variables. This test is used to verify the hypothesis that frequencies in the contingency table are independent [96]. The significant associations are those with an adjusted residue value greater than |1,96|. The results obtained are presented in the following section.
A Multi-Stage predictive model for missed appointments at outpatient primary care settings serving rural areas
Published in IISE Transactions on Healthcare Systems Engineering, 2021
Laith Abu Lekham, Yong Wang, Ellen Hey, Sarah S. Lam, Mohammad T. Khasawneh
The chi-square test of independence is used to analyze frequencies of contingency tables (Everitt, 1992; Bal et al., 2009; Mehta & Patel, 2011). The chi-square test checks the association between two categorical variables to see if they are independent. The null hypothesis of this test assumes no association between the categorical variables which means they are independent. An association in this test does not mean causality.