820 Words4 Pages

Analysis-
The two independent variable are Staffing and Duration.
The dependent variable is Cost.
As we can see that there are 2 independent variables it is a problem of multiple regression.
As R square= 0.4787, we can say that 47% variation in the dependent variable (Cost) can be explained by this regression model. But R square alone cannot say whether the model is good or bad. We need to see p values to determine whether the independent variables are significant or not.
P value for Staffing = 0.000385
As the p value for Staffing is very low, this means that independent variable (Staffing) is significant. When we say it is significant it means that when the value of variable (Staffing) changes, it impacts the value of the dependent variable (Cost).
P value for Duration = 0.027.
As the p value for Duration is less than 0.05, this means that independent variable (Duration) is also significant. When we say it is significant it means that when the value of variable (Duration) changes, it impacts the value of the dependent variable (Cost).
Overall, we can say that the model is good i.e prediction of the value of dependent variable (Cost) is fairly accurate. The degree to which the value of cost can be predicted given staffing and duration is 47.87 percent.
As this is Multiple Regression we also have to check the problem of co-linearity. In our model the two independent variables are fairly correlated as the correlation between them is 0.63. It means that the two variables are co-linear i.e they are redundant. So any one of the independent variables can be removed without impacting our regression model.
Analysis-
The independent variable is # Hospital Beds.
The dependent variable is # IT Projects.
The correlation between these two variables is 0.27. So they are not much correlated.
As a lot of data points are away from the best fit line , we can say that

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