Learn in Identifying the collinearity problem with 2 inputs; Function for Scatter diagram and the correlation coefficient values in one visualization; Identifying and removing influential records; Analysis of Diagnostic plots - Cook's Distance, Studentized residual, Bonferroni p - values, hat values; Variance Inflation Factor & Added Variable Plots for identifying the column that needs to be removed from the regression modelling; Multiple R squared value vs Adjusted R squared value; Evaluating the LINE Assumptions using Plots
This is the bite size course to learn R Programming for Applied Statistics. In CRISP DM data mining process, Applied Statistics is at the Data Understanding stage. This course also covers Data processing, which is at the Data Preparation Stage. You will need to know some R programming, and you can learn R programming from my "Create Your Calculator: Learn R Programming Basics Fast" course.
In this course for the Big Data Specialty Certification, we learn how to identify the appropriate data processing technologies needed for big data scenarios. We explore how to design and architect a data processing solution, and explore and define the operational characteristics of big data processing. Intended audience: This course is intended for students wanting to extend their knowledge of the data processing options available in AWS.