Prior knowledge of Quantitative Methods, MS Office and Paint is desired.
Predictive modelling course aims to provide and enhance predictive modelling skills across business sectors/domains. Quantitative methods and predictive modelling concepts could be extensively used in understanding the current customer behavior, financial markets movements, and studying tests and effects in medicine and in pharma sectors after drugs are administered. The course picks theoretical and practical datasets for predictive analysis. Implementations are done using SPSS software. Observations, interpretations, predictions and conclusions are explained then and there on the examples as we proceed through the training. The course also emphasizes on the higher order regression models such as quadratic and polynomial regressions which aren’t covered in other online courses
Essential skillsets – Prior knowledge of Quantitative methods and MS Office, Paint
Desired skillsets — Understanding of Data Analysis and VBA toolpack in MS Excel will be useful
The course works across multiple software packages such as SPSS, MS Office, PDF writers, and Paint.
Regression modelling forms the core of Predictive modelling course. The core objective of this course is to provide skills in understand the regression model and interpreting it for predictions. The associated parameters of the regression model will be interpreted and tested for significance and test the goodness of fit of the given regression model.
Through this course we are going to understand
• Interpretation of regression attributes such as R-Squared (correlation coefficient), t and p values
• m (slope) and c (intercept),
• dependent (Y) and independent (X) variables
• Examining the significance of independent (X) variable to check the fitness of regression model
• Predicting Y-variable based on varying values of X-variable
• Implementation on sample datasets using SPSS and output simulation in MS Excel
Who this course is for:
- Students and Quantitative and Predictive Modellers and Professionals
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