= Equivalence test formulation of one sample z-test =

For a population mean, $$\theta$$ estimated by a sample mean:

H0: $$\theta \leq $$-d or $$\theta \geq$$ d
and 
HA : -d $$ \leq \theta \leq$$ d

If ind equals 1 then we reject nonequivalence so -d $$\leq$$ $$\theta$$ $$\leq$$ d otherwise we accept the null hypothesis of equivalence for the given type II error, beta.


[TYPE INTO R THE DESIRED INPUTS D, N, MEAN AND BETA USING VALUES IN FORM BELOW]. 


{{{
beta <- 0.05
d <- 0.2
n <- 10
mean <- 0
}}}

[THEN COPY AND PASTE THE BELOW INTO R]

{{{
cv <- sqrt(qchisq(p=beta, df=1, ncp=n*d^2))
cv2 <- sqrt(n)*cv
ind <- 0
if (mean < cv2) ind = 1
print(ind)
}}}