Test whether the proportion of successes on a two-level categorical dependent variable significantly differs from a hypothesized value.
infer_binom_calc(n, success, prob = 0.5, ...) infer_binom_test(data, variable, prob = 0.5)
n | number of observations |
---|---|
success | number of successes |
prob | assumed probability of success on a trial |
... | additional arguments passed to or from other methods |
data | a |
variable | factor; column in |
infer_binom_test
returns an object of class "infer_binom_test"
.
An object of class "infer_binom_test"
is a list containing the
following components:
expected number of successes
expected probability of success
number of successes
number of observations
assumed probability of success
lower one sided p value
upper one sided p value
binom_calc()
and binom_test()
have been deprecated. Instead use
infer_binom_cal()
and infer_binom_test()
.
Hoel, P. G. 1984. Introduction to Mathematical Statistics. 5th ed. New York: Wiley.
# using calculator infer_binom_calc(32, 13, prob = 0.5) #> Binomial Test #> -------------------------------------- #> Group N Obs. Prop Exp. Prop #> -------------------------------------- #> 0 19 0.59375 0.500 #> 1 13 0.40625 0.500 #> -------------------------------------- #> #> #> Test Summary #> -------------------------------------------- #> Tail Prob p-value #> -------------------------------------------- #> Lower Pr(k <= 13) 0.188543 #> Upper Pr(k >= 13) 0.892336 #> -------------------------------------------- # using data set infer_binom_test(hsb, female, prob = 0.5) #> Binomial Test #> --------------------------------------- #> Group N Obs. Prop Exp. Prop #> --------------------------------------- #> 0 91 0.455 0.500 #> 1 109 0.545 0.500 #> --------------------------------------- #> #> #> Test Summary #> ---------------------------------------------- #> Tail Prob p-value #> ---------------------------------------------- #> Lower Pr(k <= 109) 0.910518 #> Upper Pr(k >= 109) 0.114623 #> ----------------------------------------------