library(ggplot2)
library(dplyr)
library(broom)
library(ggpubr)
library(tidyverse)
## `geom_smooth()` using formula = 'y ~ x'
Statistical analysis of effectiveness against PEOU
mdl <- lm(PEOU ~ EFFECTIVENESS, data = data_neverlang)
summary(mdl)
##
## Call:
## lm(formula = PEOU ~ EFFECTIVENESS, data = data_neverlang)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.41298 -0.22307 -0.01554 0.36464 1.04696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4227 1.1128 3.076 0.0152 *
## EFFECTIVENESS 0.3204 1.3820 0.232 0.8225
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7349 on 8 degrees of freedom
## Multiple R-squared: 0.006676, Adjusted R-squared: -0.1175
## F-statistic: 0.05377 on 1 and 8 DF, p-value: 0.8225
Statistical analysis of effectiveness against PU
mdl <- lm(PU ~ EFFECTIVENESS, data = data_neverlang)
summary(mdl)
##
## Call:
## lm(formula = PU ~ EFFECTIVENESS, data = data_neverlang)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.69946 -0.11462 -0.05646 0.16244 0.72708
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.04175 0.59196 6.828 0.000134 ***
## EFFECTIVENESS 0.01962 0.73512 0.027 0.979356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3909 on 8 degrees of freedom
## Multiple R-squared: 8.907e-05, Adjusted R-squared: -0.1249
## F-statistic: 0.0007127 on 1 and 8 DF, p-value: 0.9794
Statistical analysis of effectiveness against ITU
mdl <- lm(ITU ~ EFFECTIVENESS, data = data_neverlang)
summary(mdl)
##
## Call:
## lm(formula = ITU ~ EFFECTIVENESS, data = data_neverlang)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.02970 -0.16247 -0.03488 0.33356 0.77762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1236 0.8441 3.701 0.00604 **
## EFFECTIVENESS 0.5414 1.0482 0.517 0.61945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5574 on 8 degrees of freedom
## Multiple R-squared: 0.03228, Adjusted R-squared: -0.08869
## F-statistic: 0.2668 on 1 and 8 DF, p-value: 0.6194
## `geom_smooth()` using formula = 'y ~ x'
Statistical analysis of efficiency against PEOU
mdl <- lm(PEOU ~ EFFICIENCY, data = data_neverlang)
summary(mdl)
##
## Call:
## lm(formula = PEOU ~ EFFICIENCY, data = data_neverlang)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7582 -0.3380 -0.1332 0.4405 0.8481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.5774 0.4077 11.227 3.55e-06 ***
## EFFICIENCY -23.1375 9.4278 -2.454 0.0397 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.557 on 8 degrees of freedom
## Multiple R-squared: 0.4295, Adjusted R-squared: 0.3582
## F-statistic: 6.023 on 1 and 8 DF, p-value: 0.03968
Statistical analysis of efficiency against PU
mdl <- lm(PU ~ EFFICIENCY, data = data_neverlang)
summary(mdl)
##
## Call:
## lm(formula = PU ~ EFFICIENCY, data = data_neverlang)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.25950 -0.16254 -0.06558 0.05527 0.74050
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.5137 0.2233 20.211 3.75e-08 ***
## EFFICIENCY -11.7043 5.1644 -2.266 0.0532 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3051 on 8 degrees of freedom
## Multiple R-squared: 0.391, Adjusted R-squared: 0.3149
## F-statistic: 5.136 on 1 and 8 DF, p-value: 0.05319
Statistical analysis of efficiency against ITU
mdl <- lm(ITU ~ EFFICIENCY, data = data_neverlang)
summary(mdl)
##
## Call:
## lm(formula = ITU ~ EFFICIENCY, data = data_neverlang)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.53331 -0.30444 -0.09617 0.22681 0.84169
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.201 0.327 12.847 1.27e-06 ***
## EFFICIENCY -16.691 7.562 -2.207 0.0583 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4467 on 8 degrees of freedom
## Multiple R-squared: 0.3785, Adjusted R-squared: 0.3008
## F-statistic: 4.872 on 1 and 8 DF, p-value: 0.05834
wilcox.test(data_neverlang$PEOU, mu = 3, alternative = "greater")
##
## Wilcoxon signed rank test with continuity correction
##
## data: data_neverlang$PEOU
## V = 49, p-value = 0.01588
## alternative hypothesis: true location is greater than 3
wilcox.test(data_neverlang$PU, mu = 3, alternative = "greater")
##
## Wilcoxon signed rank test with continuity correction
##
## data: data_neverlang$PU
## V = 55, p-value = 0.002881
## alternative hypothesis: true location is greater than 3
wilcox.test(data_neverlang$ITU, mu = 3, alternative = "greater")
##
## Wilcoxon signed rank test with continuity correction
##
## data: data_neverlang$ITU
## V = 42, p-value = 0.01188
## alternative hypothesis: true location is greater than 3
## `geom_smooth()` using formula = 'y ~ x'
Statistical analysis of effectiveness against efficiency
mdl <- lm(EFFECTIVENESS ~ EFFICIENCY, data = data_neverlang)
summary(mdl)
##
## Call:
## lm(formula = EFFECTIVENESS ~ EFFICIENCY, data = data_neverlang)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.34241 -0.03197 0.01415 0.12222 0.17192
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6436 0.1255 5.127 0.0009 ***
## EFFICIENCY 3.6891 2.9031 1.271 0.2395
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1715 on 8 degrees of freedom
## Multiple R-squared: 0.1679, Adjusted R-squared: 0.06394
## F-statistic: 1.615 on 1 and 8 DF, p-value: 0.2395
## `geom_smooth()` using formula = 'y ~ x'
Statistical analysis of effectiveness against PCU
mdl <- lm(EFFECTIVENESS ~ PCU, data = data_neverlang)
summary(mdl)
##
## Call:
## lm(formula = EFFECTIVENESS ~ PCU, data = data_neverlang)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.29353 -0.06159 0.02917 0.07492 0.19081
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1111 0.3053 0.364 0.7254
## PCU 0.8696 0.3880 2.241 0.0553 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1474 on 8 degrees of freedom
## Multiple R-squared: 0.3858, Adjusted R-squared: 0.309
## F-statistic: 5.024 on 1 and 8 DF, p-value: 0.0553
## `geom_smooth()` using formula = 'y ~ x'
Statistical analysis of efficiency against PCU
mdl <- lm(EFFICIENCY ~ PCU, data = data_neverlang)
summary(mdl)
##
## Call:
## lm(formula = EFFICIENCY ~ PCU, data = data_neverlang)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.021523 -0.010164 -0.002435 0.006315 0.040627
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01674 0.03841 -0.436 0.674
## PCU 0.07167 0.04881 1.468 0.180
##
## Residual standard error: 0.01854 on 8 degrees of freedom
## Multiple R-squared: 0.2123, Adjusted R-squared: 0.1138
## F-statistic: 2.156 on 1 and 8 DF, p-value: 0.1802