Show your work — that is, your R commands and output — for the data analytics regression you just did. From partitioning the data through calculating the summary measures. I will review this work for partial credit if your answer to the three previous questions are incorrect.

What is the specificity of the model? That is, given that the individual actually did not have a stroke, what’s the probability your model predicts no stroke? Again, provide your answer to four decimal places.

Report here the mean absolute percent error you calculated. Provide your answer in the form of a fraction, to four decimal places.

Do your answers to the previous two questions tend to support the notion that recent internationalization has made countries more vulnerable to economic conditions of other countries? Briefly support your response.

Enter below the commands to create a scatterplot of Salary vs. Certificates, with the best straight line through the data (in red) included.

Enter below the R command to create the test data set. Call this test data frame “testStroke”.

Enter a command below that will output the column totals for tab.stroke.test.

Enter below the R command to create the training data set. Call this training data frame “trainStroke”.

Enter below an R command to create the confusion matrix for your naive bayes model applied to the test set. (What you just did.) You should call this table “tab.stroke.test”. (That is, the command you enter below should assign the table you create to a variable called tab.stroke.test.) Your table should include labels indicating which values are actual and which are predicted.)

Which of the following is the value for smoking_status for the individual in row 800 of the testSroke DF?