Chapter 2, #8

* Question 8

use "(your data source)\2.ex.vonHippelLindau.dta"

codebook disease

generate logp_ne = ln( p_ne)

generate logtumorvol = ln( tumorvol)

* VonHippel-Lindau regression

regress logp_ne logtumorvol if disease == 0

regress logp_ne logtumorvol if disease == 1

* Slope estimate for vonHippel-Lindau = .242; the 95%CI = (.056, .428)

* Slope estimate for Multiple Endocrine Neoplasia = .242, 95%CI = (.196, .671).

generate s2 = (.374688289*26 + .267608258*7)/(28+9-4)

generate var_dif = s2*(.0903891^2/.374688289+.1003964^2/.267608258)

generate t = (.2418597-.4337545)/sqrt(var_dif)

generate ci95_lb = (.2418597-.4337545) - invttail(33, .025)*sqrt(var_dif)

generate ci95_ub = (.2418597-.4337545) + invttail(33, .025)*sqrt(var_dif)

list s2 var_dif t ci95_lb ci95_ub in 1/1

display 2*ttail(33, abs(t))

* The null is confirmed. These two slopes are statistically equal.

* The 95% CI = (-.486, .102)