Recent research into the cost of various medical procedures has shown the impact of certain complications encountered in surgery on the total cost of a patients stay in the hospital. The researchers used regression analysis and found the following results:

Total cost for patient = Constant, plus

a Length of stay (measured in days), plus

b Presence of one or more complications (= 1 if true, 0 if false), plus

c Use of a laparoscope (= 1 if true, 0 if false)

wherea, b, care coefficients of the regression model. The laparoscope is an instrument somewhat like a miniature telescope with a fiber-optic system that brings light into the abdomen. It is about as big around as a fountain pen and twice as long.

The following table shows additional regression results presented by the researchers in the study described above. There are two regressions. The right-hand column shows the results for all patients, including those treated with laparoscopic surgery. The left-hand column shows the results for the sample of patients who were treated without the laparoscopic surgery.

	Nonlaparoscopic Patients Only	All Patients
Coefficients for independent variables
Regression intercept	$ 8,043	$ 3,719
Length of stay
Coefficient*	Not significant	861
Standard error for the coefficient	Not applicable	80
Number of complications
Coefficient	3,393	1,986
Standard error for the coefficient	1,239	406
Laparascopic
Coefficient	Not applicable	908
Standard error for the coefficient	Not applicable	358
R-squared	0.11	0.53

*Note: All independent variables are significant at the level of p = 0.05 (and t-value > 2) except for the length of stay variable in the nonlaparoscopic condition. Also, the t-value for each independent variable can be calculated by dividing the coefficient of the variable by the standard error for the coefficient of that variable.

Required:

1. Which of the two regressions has the better reliability in estimating costs?

multiple choice

Regression of all patients

Regression of nonlaparoscopic patients only

2. Calculate the t-value for each of the independent variables. (Round your answers to 2 decimal places.)