The computer output from a set of data and a transformed set of data are below. Regression
1: Coefficients Standard Error t Stat P-value Intercept -413.7688464 197.6508511 -2.093433163 0.074590956 5.996971463 0.000543899 Volume 3.532763971 0.589091343 R-sq. = 83.7% R-sq.(adj) = 81.4% Regression
2: Coefficients Standard Error Intercept 0.002014396 0.001293176 t Stat P-value 1.55771221 0.163260187 /Volume 0.021538226 3.85426E-06 5588.154718 1.55202E-24 R-sq. = 99.9% R-sq.(adj) = 99.9% Which LSRL is a better fit?
Explain. O Regression 1 is a better fit because r-squared is farther from 1 than r-squared in regression 2. O Regression 2 is a better fit because r-squared is closer to 1 than r-squared in regression
1. O Regression 2 is a better fit because the intercept is positive. O Regression 1 is a better fit because the intercept is farther away from zero than the intercept in regression
2. O Unable to determine which is a better fit.