Useful Summary Tables

Symbols and R Functions

Response Explanatory Numerical_Quantity Parameter Statistic Function
quantitative - mean μ x¯ t_test()
categorical - proportion p p^ prop_test()
quantitative categorical difference in means μ1μ2 x¯1x¯2 t_test()
categorical categorical difference in proportions p1p2 p^1p^2 prop_test()
quantitative quantitative correlation ρ r cor.test()

Common Test Statistics and Approximate Distributions

Response Explanatory Numerical_Quantity Test_Statistic Distribution Assumptions
quantitative - mean x¯μos/n t(df=n1) n30 or data are normal
categorical - proportion p^pop^(1p^)n N(0,1) Ten successes, Ten failures
quantitative categorical difference in means x¯1x¯20s12n1+s22n2 t(df=min(n1,n2)1) n1,n230 or data are normal
categorical categorical difference in proportions p^1p^20p^(1p^)n1+p^(1p^)n2 N(0,1) Ten successes, Ten failures in each category
quantitative quantitative correlation r01r2n2 t(df=n2) n30

Common Distribution-Based Confidence Interval Formulae

Response Explanatory Numerical_Quantity Confidence_Interval Distribution Assumptions
quantitative - mean x¯±ts/n t(df=n1) n30 or data are normal
categorical - proportion p^±zp^(1p^)n N(0,1) Ten successes, Ten failures
quantitative categorical difference in means x¯1x¯2±ts12n1+s22n2 t(df=min(n1,n2)1) n1,n230 or data are normal
categorical categorical difference in proportions p^1p^2±zp^1(1p^1)n1+p^2(1p^2)n2 N(0,1) Ten successes, Ten failures in each category
quantitative quantitative correlation r±t1r2n2 t(df=n2) n30