BUILD A REGRESSION MODEL. The first dataset contains cross-sectional data, that is, data collected at approximately the same point in time. This data is below:

Patient Id	Age	CRCL	Missed_Apts	Weight	Number of Meds	Sex	PCU	LOS	A1C	Fall Risk
1	64	70.41	1	138	2	M	Progressive Care	6	6.2	3
2	89	87.34	4	144	9	M	Intensive Care	11	5.8	2
3	60	75.19	5	172	10	F	Intensive Care	13	4.9	2
4	35	72.47	0	153	1	F	General Care	4	3.7	3
5	20	94.47	4	210	0	M	Progressive Care	3	4.1	3
6	59	78.63	3	227	2	M	Intensive Care	5	6.1	1
7	50	89.46	0	194	5	F	Progressive Care	6	4.9	1
8	84	50.24	5	136	7	F	Intensive Care	12	5.4	2
9	50	81.66	2	230	10	M	Intensive Care	11	6.1	3
10	26	94.19	5	194	2	M	Progressive Care	6	4.7	3

Questions:

1.) How does each independent variable in your model, affect the LOS?

2.) What is the r-squared value and what does it mean to LOS?

3.) Which variables (even if they may not end up being in the final model) required transformation into dummy variables?

4.) What does the p-values of each independent variable mean?

For your prediction, use these inputs. Some variables might not make it into your final model, so just choose the ones relevant to your regression model for your prediction:

Age 60

CRCL 70.2

Missed_Apts 2

Weight 135

Number of Meds 2

Sex F

PCU Intensive Care

A1C 5.6

Fall Risk 3

PREDICTION = ?