The role of linear regression in IT service requests:

Problem statement: IT service requests relief time can be plotted all over the chart ranging from a few seconds to well over months. The nature of the service request, it’s priority and severity determine the response towards remediation. Since they come from all quarters, the relief time does not necessarily follow a line. Linear regression is suitable for data that lies somewhat coherently on the chart and linear along the line. Let us see how to apply linear regression.  

Solution: IT service requests demonstrate such distribution in specific categories. Even when the requests come demanding different resolutions in the same category, the relief times are bounded and can be plotted along the timeline. One of the best advantages of linear regression is the prediction about time as an independent variable. When the data point has many factors contributing to their occurrence, a linear regression gives an immediate ability to predict where the next occurrence may happen. This is far easier to do than come with up a model that behaves like a good fit for all the data points. It gives an indication of the trend which is generally more helpful than the data points themselves. Also, a scatter plot is only changing in one dependent variable in conjunction with the independent variable. This lets us pick the dimension we consider fitting the linear regression independent of others. Lastly, the linear regression also gives an indication of how much the data is adhering to the trend via the estimation of errors.  

To determine the best parameters for the slope and intercept of the line, we calculate the partial derivatives with respect to them and set them to zero. This yields two equations to be solved for two unknowns. The standard error of the estimate quantifies the standard deviation of the data at a given value of the independent variable. The standard error of slope and intercept can be used to place confidence intervals.