Data driven fault detection in chemical processes: Application to Tennessee Eastman Plant
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Updated
Jul 19, 2020 - Python
Data driven fault detection in chemical processes: Application to Tennessee Eastman Plant
A simple framework for ANOVA on various types of Julia statistical models
Implementation of various feature selection methods using TensorFlow library.
Conduct one-way and multi-way anova in Julia with GLM.jl
Examples of using the test statistics to test hypotheses
ASIS is a web application developed for the compilation of impact report PDF documents for the tutorial programme (A-STEP) at the University of the Free State.
OLS. R and Python. In this project, we study fundamental concepts of Supervised ML models, such as Regression Analysis: Coefficient of Model Adjustment (R²), Parameters Estimation ,Statistical Significance of the Model (F test, T test) ,Multiple Regression , Qualitative Explanatory Variables (X) , heteroscedasticity and etc.
Performed graphical and numerical EDA, correlation and residual error analysis, log normalization
Assessment of UK healthcare performance in 2011 compared to healthcare in other OECD Countries: University of Sheffield, MAS223 Practical 4 code and files
Implementation of some r programs using R studio.
We present a detailed study of the asymptotic behavior of the distribution of the tails of these, perhaps, most commonly used statistical tests under non-standard conditions, that is, releasing the underlying assumptions of normality, independence and identical distribution and considering a more general case where one only assumes that the vect…
Wine dataset statistical analysis using Hypothesis testing (F-test, T-test, ANOVA, ANCOVA). Using machine learning to predict wine quality. Using regularization with 10 fold cross validation to overcome overfitting. Created different visualizations on the dataset.
Conduct one-way and multi-way anova in Julia with MixedModels.jl
Create barplots or boxplots with significant level annotations.
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