Problem:Analyze at least two pairs of attributes via linear regression method. Visualize the results and elaborate on the meaning of the uncovered relationships in the dataset and how are these...

Problem:Analyze at least two pairs of attributes via linear regression method. Visualize the results and elaborate on the meaning of the uncovered relationships in the dataset and how are these related to the microcosm of the studied phenomenon.

How to proceed:

1. 
   

   The regression analysis should be focused on attributes exhibiting high correlation. In order to investigate that, develop attribute correlation matrix, heat map, density plots, box-plots and histograms (as shown inExampleAnalysis.pdf). Each function/visualization must be supported by a short narrative to elaborate on the meaning of the function and possible deductions. The meaning of the attributes is also provided inReadme.txt. Don’t be shy to do additional research to facilitate your analysis.

   
   


2. 
   

   Upon finding the best correlated pairs of attributes, conduct the regression analysis, visualize and analyze.

   
   

All of the homework must be coded in Python (help file on the regression is provided). The homework must be formatted with Introduction, Method, Data set, Results - visualizations, interpretations, linear regressions and, finally, Conclusions (example file is provided of the format, albeit missing the attribute analysis -ExampleRegression.pdf).

Grading:

Attribute analysis - correlation matrix, heat map, box-plots and histograms – 20 points. Interpretations to the above – 20 points.
Justification linear regression attribute pairs selection – 10 points.
Two pairs of selected attributes regression – 20 points (10 points per pair.)

Analysis of regression results, meanings and data set conclusions – 40 points (20 points per regression.)