Experience versus training using color or symbol


dentifying Good System Administrators. 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 within a specified time and those who are not. Data are collected on the performance of 75 randomly selected administrators. They are stored in the file SystemAdministrators.xls.
The variable Experience measures 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.
a. Create a scatterplot of Experience versus Training using color or symbol to differentiate programmers who complete the task from those who did not complete it. Which predictor(s) appear(s) potentially useful for classifying task completion?
b. Run a logistic regression model with both predictors using the entire dataset as training data. Among those who complete the task, what is the percentage of programmers who are incorrectly classified as failing to complete the task?
c. To decrease the percentage in part (c), should the cutoff probability be increased or decreased?
d. How much experience must be accumulated by a programmer with 4 years of training before his or her estimated probability of completing the task exceeds 50%? 

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Basic Computer Science: Experience versus training using color or symbol
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The logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. It can have any one of an infinite number of possible values. In logistic regression, the outcome (dependent variable) has only a limited number of possible values. Logistic regression is used when the response variable is categorical in nature

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