Vector: First choose either police or firefighter data and create one vector for the $6$ variables you created above for that data set. For more information about this step, reference the sample data analysis report. Consider naming it FirefighterV or PoliceV $($depending on which youchose$).$

Matrix: For the other data set not used in the vector $($either police or firefighter$),$ create a $6-$by$-1$ matrix $(6$ rows, $1$ column$)$ with row names for the $6$ variables you created above for that data set. Consider naming it FirefighterM or PoliceM.

Combine Data Into Single Matrix: Then, create another column matrix with the column names included for the vector and matrix above. Consider naming this one CompensationM.

Data Frame: Create a data frame using the second matrix $($CompensationM$)$ as the first construct and the matrix$-$vector as the second. Note that matrix$-$ vector is the difference between the matrix and vector, so if you used Firefighter as the matrix and Police as the vector, the data frame column would be df$<-($CompensationM$,$FirefighterM$-$PoliceV$).$ Include column names for the data frame. Print the resulting data frame to share your findings based on two variables.

Findings: Briefly summarize $(35$ sentences$)$ your findings from the calculations you ran in the above sections.

Informed Conclusion: Based on the calculations and commands you have run above, what were you able to discern from the information? Clearly state your informed conclusion.

Looking Ahead: If you had additional salary information for other occupations, such as EMTs, fire chiefs, or police chiefs, how might you use the constructs you created above $($vectors$,$ matrices, data frames$)$ to incorporate this additional data and further the analysis with the new information?