Comparison:Article Title: Validating a model of architectural hazard visibility with low-vision observers
Article Snippet: Dev Pr(>Chi) ## NULL 249 332.03 ## x 1 8.0122 248 324.02 0.004646 ** ## --- ## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 ## Subject id: 14 , Logistic Regression Model Summary ## Call: ## glm(formula = y ~ x, family = "binomial", data = sub1) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -1.6721 -0.9424 -0.7118 1.0170 1.8054 ## ## Coefficients: ## Estimate Std. .. Error z v
Article Title: Validating a model of architectural hazard visibility with low-vision observers
Article Snippet: Dev Pr(>Chi) ## NULL 249 337.30 ## x 1 60.709 248 276.59 6.615e-15 *** ## --- ## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 ## Subject id: 5 , Logistic Regression Model Summary ## Call: ## glm(formula = y ~ x, family = "binomial", data = sub1) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -2.8466 0.1748 0.1999 0.5623 1.5099 ## ## Coefficients: ## Estimate Std. .. Error z v
Article Title: Validating a model of architectural hazard visibility with low-vision observers
Article Snippet: Dev Pr(>Chi) ## NULL 249 265.96 ## x 1 10.21 248 255.75 0.001397 ** ## --- ## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 ## Subject id: 4 , Logistic Regression Model Summary ## Call: ## glm(formula = y ~ x, family = "binomial", data = sub1) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -1.8273 -0.8351 -0.6713 0.6691 1.7893 ## ## Coefficients: ## Estimate Std. .. Error z v
Article Title: Validating a model of architectural hazard visibility with low-vision observers
Article Snippet: Dev Pr(>Chi) ## NULL 249 340.15 ## x 1 32.759 248 307.39 1.043e-08 *** ## --- ## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 ## Subject id: 15 , Logistic Regression Model Summary ## Call: ## glm(formula = y ~ x, family = "binomial", data = sub1) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -0.9192 -0.8003 -0.7345 1.4723 1.7432 ## ## Coefficients: ## Estimate Std. .. Error z v
Article Title: Validating a model of architectural hazard visibility with low-vision observers
Article Snippet: Dev Pr(>Chi) ## NULL 249 345.28 ## x 1 24.826 248 320.45 6.275e-07 *** ## --- ## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 ## Subject id: 18 , Logistic Regression Model Summary ## Call: ## glm(formula = y ~ x, family = "binomial", data = sub1) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -2.2072 -1.1478 0.4703 0.8654 1.1843 ## ## Coefficients: ## Estimate Std. .. Error z v
Article Title: Validating a model of architectural hazard visibility with low-vision observers
Article Snippet: Dev Pr(>Chi) ## NULL 249 265.96 ## x 1 87.78 248 178.18 < 2.2e-16 *** ## --- ## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 ## Subject id: 6 , Logistic Regression Model Summary ## Call: ## glm(formula = y ~ x, family = "binomial", data = sub1) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -1.7858 -0.9540 0.2510 0.9519 1.6365 ## ## Coefficients: ## Estimate Std. .. Error z v
Article Title: Validating a model of architectural hazard visibility with low-vision observers
Article Snippet: Dev Pr(>Chi) ## NULL 249 341.37 ## x 1 45.548 248 295.82 1.489e-11 *** ## --- ## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 ## Subject id: 13 , Logistic Regression Model Summary ## Call: ## glm(formula = y ~ x, family = "binomial", data = sub1) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -2.1720 -0.9813 0.4592 0.8360 1.4924 ## ## Coefficients: ## Estimate Std. .. Error z v
Article Title: Validating a model of architectural hazard visibility with low-vision observers
Article Snippet: Dev Pr(>Chi) ## NULL 219 302.36 ## x 1 45.284 218 257.08 1.705e-11 *** ## --- ## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 ## Subject id: 10 , Logistic Regression Model Summary ## Call: ## glm(formula = y ~ x, family = "binomial", data = sub1) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -1.6801 -0.8251 -0.6811 0.9160 1.8218 ## ## Coefficients: ## Estimate Std. .. Error z v
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