Analyzing data in R: correlation and regression

In the previous section, we saw how to perform simple regression analysis in R. We also saw that multiple regression is more complex to compute but have discussed that most of what we have already seen applies to multiple regression as well.

First steps in the data analysis

In what follows, we will use a dataset of 40 cases generated from a covariance matrix obtained from a subsample of real data we collected, which is about burnout components, work satisfaction, work-family conflict, and organizational commitment in hospitals. There are six attributes in the dataset that we will analyze here; all are self-assessments made by nurses:

• Commit: Commitment to their hospital (criterion here)

• Exhaust: Emotional exhaustion (one of the three components of burnout)

• Depers: Depersonalization (one of the three components of burnout)

• Accompl: Accomplishment (one of the three components of burnout)

• WorkSat: Work satisfaction

• WFC: Work-family conflict

Our goal...