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Single Sample Z-Test for a Population Proportion
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import scipy.stats as stats | |
import math | |
def one_sample_ztest_pop_proportion(tail, p, pbar, n, alpha): | |
#Calculate test stat | |
sigma = math.sqrt((p*(1-p))/(n)) | |
z = round((pbar - p) / sigma, 2) | |
if tail == 'lower': | |
pval = round(stats.norm(p, sigma).cdf(pbar),4) | |
print("Results for a lower tailed z-test: ") | |
elif tail == 'upper': | |
pval = round(1 - stats.norm(p, sigma).cdf(pbar),4) | |
print("Results for an upper tailed z-test: ") | |
elif tail == 'two': | |
pval = round(stats.norm(p, sigma).cdf(pbar)*2,4) | |
print("Results for a two tailed z-test: ") | |
#Print test results | |
print("Test statistic = {}".format(z)) | |
print("P-value = {}".format(pval)) | |
print("Confidence = {}".format(alpha)) | |
#Compare p-value to confidence level | |
if pval <= alpha: | |
print("{} <= {}. Reject the null hypothesis.".format(pval, alpha)) | |
else: | |
print("{} > {}. Do not reject the null hypothesis.".format(pval, alpha)) | |
#one_sample_ztest_pop_proportion('upper', .20, .25, 400, .05) | |
#one_sample_ztest_pop_proportion('two', .64, .52, 100, .05) |
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