Biostatistics I: Inference
Orinal Author Antoine Soetewey
Modifications by Nicholas Kaukis
Inference for:
one mean
two means
two means (paired samples)
one proportion
two proportions
Sample Mean: \(\overline{x} = \)
sample Size: \(n = \)
Population standard deviation \(\sigma\) known
\(\sigma = \)
Sample Standard Deviation: \( s \)
Differences sample mean: \(\overline{x}_D = \)
Differences Sample Size: \(n_D = \)
Differences population standard deviation \(\sigma_D\) known
\(\sigma_D = \)
Differences sample standard deviation: \( s_D \)
\(\overline{x}_1 = \)
\( n_1 \)
\(\overline{x}_2 = \)
\( n_2 \)
Population standardard deviations known.
\( s_1 \)
\( s_2 \)
Assuming
\( \sigma_1 = \sigma_2 \)
\( \sigma_1 \neq \sigma_2 \)
\(\sigma_1 = \)
\(\sigma_2 = \)
Sample size
\(n = \)
Proportion of success \(\hat{p}\)
Number of successes \(x\)
Proportion of success
\(\hat{p} = \)
Number of successes
\(x = \)
Sample size 1
\(n_1 = \)
Sample size 2
\(n_2 = \)
Proportion of success \(\hat{p}\)
Number of successes \(x\)
Proportion of success
\(\hat{p}_1 = \)
\(\hat{p}_2 = \)
Number of successes
\(x_1 = \)
\(x_2 = \)
Null hypothesis
\( H_0 : \mu = \)
\( H_0 : \mu_1 - \mu_2 = \)
\( H_0 : \mu_D = \)
\( H_0 : p = \)
\( H_0 : p_1 - p_2 = \)
Alternative
\( \neq \)
\( > \)
\( < \)
Significance level \(\alpha = \)