RStudio
A management consultant is studying the roles played by experience and training in a system administrator’s ability to complete a set of tasks in a specified amount of time. In particular, she is interested in discriminating between administrators who are able to complete given tasks and those who are not. Data are collected on the performance of 75 randomly selected administrators. They are stored in the file SystemAdminstrations.csv.The variable Experience measures the months of full-time system administrator experience, while Training measures the number of relevant training credits. The dependent variable Completed is either Yes or No, according to whether or not the administrator completed the tasks. 1. Create a variable named isCompleted, which takes the value 1 if Completed == “Yes” and 0 otherwise. Use the entire dataset as training data (i.e., no need to separate training data and validation data). Fit a logistic regression using isCompleted as the outcome variable, and Experience and Training as predictors. Report the following: The logistic regression model result (Put a screenshot of the regression summary and write out how the logistic regression model looks like). Logit(isCompleted) = [Intercept value] + [coefficient1]*Experience + [coefficient2]*Training Based on the result, does having more experience increase, decrease, or have no impact on completing the task? Why? Based on the result, does earning more training credits increase, decrease, or have no impact on completing the task? Why? Provide the confusion matrix from the logistic regression using the cutoff value of 0.5 (in other words, an administrator is predicted to success if the predicted probability is higher than 0.5). You can put a screenshot of the results from R. Based on the regression results, among those who successfully completed the task, what is the percentage of administrators who are incorrectly classified as failing to complete the task? Show the calculation steps that you use to answer this question. If we want to decrease the percentage in question (4), should the cutoff level be increased or decreased? Why? And please include the R scripts code