(* PS3.4 using DATA3-1, for Example 3.8 on conf. intervals *)
ols 1 0 2
(* generate predicted price *)
genr phat = price - uhat 
(* retrieve the number of obs. *)
genr n = $nobs 
(* retrieve degrees of freedom *)
genr df = $df 
(* retrieve sigma squared *)
genr sgmasq=$ess/df 
(* get mean of sqft *)
genr xbar=mean(sqft) 
(* get s.d. of sqft *)
genr sdx = sd(sqft) 
(* reset sample range *)
smpl 1 1 
(* calculate sxx *)
genr sxx = 13*sdx*sdx 
(* other variables for confidence interval calculation *)
genr x0 = 2000
genr temp1=((x0-xbar)^2)/sxx 
genr temp2=(temp1+(1/n)) 
(* calculate using equations 3.28 and 3.29 *)
genr sysq1=sgmasq*temp2 
genr sysq2=sgmasq*(1+temp2) 
(* take square root for standard errors *)
genr sy1=sqrt(sysq1) 
genr sy2=sqrt(sysq2) 
(* predict mean y for x0 *)
genr ymean0=52.351+(0.13875*x0) 
(* compute bounds for confidence interval using equation 3.28 *)
genr ymean1=ymean0-(2.179*sy1)
genr ymean2=ymean0+(2.179*sy1)
(* compute bounds for confidence interval using equation 3.29 *)
genr y1=ymean0-(2.179*sy2)
genr y2=ymean0+(2.179*sy2)
(* compute large sample confidence interval, that is, plus/minus 2 sigma *)
genr sgmahat = sqrt(sgmasq)
genr y3 = ymean0 - (2*sgmahat)
genr y4 = ymean0 + (2*sgmahat)
print -o n df sgmasq xbar sdx sxx sysq1 sysq2 sy1 sy2 ymean0 ymean1 \
ymean2 y1 y2 y3 y4
