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Introduction
Published in Przemyslaw Biecek, Tomasz Burzykowski, Explanatory Model Analysis, 2021
Przemyslaw Biecek, Tomasz Burzykowski
Figures and tables have been created mostly in the R language for statistical computing (R Core Team, 2018) with numerous libraries that support predictive modelling. Just to name a few packages frequently used in this book: randomForest (Liaw and Wiener, 2002), ranger (Wright and Ziegler, 2017), rms (Harrell Jr, 2018), gbm (Ridgeway, 2017), or caret (Kuhn, 2008). For statistical graphics, we have used the ggplot2 package (Wickham, 2009). For model governance, we have used archivist (Biecek and Kosinski, 2017). Examples in Python were added thanks to the fantastic work of Hubert Baniecki and Wojciech Kretowicz, who develop and maintain the dalex library. Most of the presented examples concern models built in the sklearn library (Pedregosa et al., 2011). The plotly library (Plotly Technologies Inc., 2015) is used to visualize the results.
Evaluation of physicochemical composition and bioactivity of a red seaweed (Pyropia orbicularis) as affected by different drying technologies
Published in Drying Technology, 2020
Elsa Uribe, Antonio Vega-Gálvez, Vivian García, Alexis Pastén, Katia Rodríguez, Jéssica López, Karina Di Scala
A one-way of variance analysis (ANOVA) was performed using Statgraphics Centurion XVI (Statistical Graphics Corp., Herdon, VA) to determine significant differences among the different treatments. Differences between the media were analyzed using the least significant difference (LSD) test with a significance level of α0.05 and a confidence interval of 95% (p < .05). In addition, the multiple range test (MRT) included in the statistical program was used to demonstrate the existence of homogeneous groups within each of the parameters.
Synthesis and photocatalytic studies of TiO2-clinoptilolite on spent caustic wastewater treatment
Published in Particulate Science and Technology, 2018
Amin Ahmadpour, Ali Haghighi Asl, Narges Fallah
In this research, the experimental data analysis was performed using the statistical graphics software system Design Expert (Version 8.0.4.1, USA). A four-factor three-level Box–Behnken design having six central points was used to determine the operating conditions for maximizing the COD removal. The method comprised defining a minimum or low level (symbolized as −1), a central or medium level (symbolized as 0) and a high or maximum level (symbolized as 1) for all experimental factors (Table 2).
Effect of drying methods on bioactive compounds, nutritional, antioxidant, and antidiabetic potential of brown alga Durvillaea antarctica
Published in Drying Technology, 2020
E. Uribe, C. M. Pardo-Orellana, A. Vega-Gálvez, K. S. Ah-Hen, A. Pastén, V. García, S. P. Aubourg
Analysis of variance (ANOVA) was performed to determine significant differences among treatments using the software Statgraphics Centurion XVI (Statistical Graphics Corp., Herndon, USA). Means were compared by least significant difference (LSD) test at p < 0.05. Furthermore, the multiple range test (MRT) was used to demonstrate the existence of homogeneous groups within each of the parameters. Data were average values from three treatment replicates and reported as mean ± standard deviation.