In this assignment, you fit two regression models and interpret the result and implement diagnostic techniques to identify the issues with the model. Use the state.x77 dataset in the base package for R studio: investigate the relationship between murder rate and other features by building a multiple linear regression model (Model #1) Investigate the correlation among features and plot them (cor() function) Check for multicollinearity (vif() function) Check for outliers (outlierTest() function) Modify the first regression model based on the outcomes of the previous stages (Model # 2) Diagnose both regression models by drawing the plots out of the model to specially check the normality, linearity, and existence of outliers (par() and plot() functions) Based on the plots if necessary try a polynomial regression model. Check the AIC values for two models (AIC() function) Use the all-subsets regression method to identify the "best" model. State the preferred model in equation form (resubjects() and subsets() functions) Provide a report based on IEEE format to document your findings and put the screenshots of each step as figures in the report. Format & Guidelines Introduction Analysis Conclusion/Interpretations References