The following data were collected from several hospitals and reflect ER visits, bed days, and total expenditures per period of time.
|
ER Visits
|
Bed Days
|
Total Expenditures
|
|
1,567
|
554
|
$ 10,945,664
|
|
1,621
|
283
|
$ 6,844,832
|
|
3,463
|
331
|
$ 8,203,411
|
|
5,033
|
2,137
|
$ 13,020,058
|
|
5,357
|
2
|
$ 7,033,959
|
|
6,433
|
81
|
$ 1,973,125
|
|
7,369
|
207
|
$ 1,757,830
|
|
7,750
|
121
|
$ 2,326,256
|
|
8,228
|
74
|
$ 820,835
|
|
8,955
|
726
|
$ 13,611,527
|
|
11,374
|
153
|
$ 2,206,398
|
|
13,362
|
1,093
|
$ 10,124,680
|
1. Generate a correlation matrix and a scatterplot matrix.
2. Test the statistical significance of all bivariate correlations at the .05 level. Interpret
3. Test normality of the Total Exp variable. If not normal, transform it.
4. Run a regression model for Total Exp as a function of Total ER Visits and Average Monthly Bed Days.
5. Test the assumptions of regression. Do they hold? If not, what might you do.
6. Interpret all results.