Provide below an R command that will output a table of the number of employees by Personality. The default is for the output to be in alphabetic order by Personality. Your command should output this information in increasing order of the number of employees in each Personality category.

Do whatever additional data exploration you wish. Then create a regression of charges vs. all the other variables in the data frame. Call this model lmChargesAll. Enter the R command to do so below.

Enter below the output of the summary(lmChargesAll) command.

Enter below an R command that will output the proportion of individuals in the data frame who had a stroke and the proportion of people who did not have a stroke.

Provide support here for your response to the previous question. Put another way, how did you arrive at your answer?

How many employees in the data set have the Explorer personality type?

How good is your model? Be sure to address how well the model is likely to predict, as well as how well it satisfies the assumptions of regression.

What is the overall accuracy of your model? Provide your answer to four decimal places.

Proceed to develop the most appropriate regression model. Provide the output of the summary() command for your regression below. (If you feel the current model is the most appropriate, then just provide the summary for that model below.) Briefly describe what you did and why to arrive at this model.

How many factor variables are in the data frame TechSales?