The following simple linear regression representing the relationship between RST and TOTWRK was specified:

= 0 + 1 +

OLS ESTIMATOR

Model 3: OLS, using observations 1-706

Dependent variable: RST

coefficient std. error t-ratio p-value

--------------------------------------------------------

const 3586.38 38.9124 92.17 0.0000 ***

TOTWRK 0.150746 0.0167403 9.005 1.99e-18 ***

Mean dependent var 3266.356 S.D. dependent var 444.4134

Sum squared resid 1.25e+08 S.E. of regression 421.1357

R-squared 0.103287 Adjusted R-squared 0.102014

F (1, 704) 81.08987 P-value(F) 1.99e-18

Log-likelihood 5267.096 Akaike criterion 10538.19

Schwarz criterion 10547.31 Hannan-Quinn 10541.71

1. A-Interpret the estimated values of (i) the intercept and (ii) the slope you obtained in Part (1) above. What do each of the two coefficients represent? Does the estimated intercept make sense? Briefly explain why or why not. Does the estimated slope make sense? Briefly explain why or why not. B-Furthermore, based on your interpretations, calculate the amount of daily rest time (in hours) for a person with zero TOTWRK.

2. By observing the GRETL output in Part (1) above, provide a detailed explanation of the coefficient of determination. Based on your analysis, is this a good model? Why or why not?

3. Construct the 95% confidence interval of the slope coefficient you obtained in Part (1). Use the estimated confidence interval to test if the slope coefficient is significant or not. Interpret your findings.