0
H_0:μ_1-μ_2=d assuming heterogeneity t=((x ̅_1-x ̅_2 )-(μ_1-μ_2 ))/√((s_1^2)/n_1 +(s_2^2)/n_2 ) Student t(υ)
ν=((s_1^2)/n_1 +(s_2^2)/n_2 )^2/[((s_1^2)/n_1 )^2/(n_1-1)+((s_2^2)/n_2 )^2/(n_2-1)]
H_0:μ_1-μ_2=d assuming homogeneity t=((x ̅_1-x ̅_2 )-(μ_1-μ_2 ))/(s_p √(1/n_1 +1/n_2 )) Student t(υ) ν=n_1+n_2-2
H_0:μ_d=0 t=(d ̅-μ_d)/(s_d⁄√n) Student t(υ) ν=n-1
H_0:p_1-p_2=0 z=((p ̂_1-p ̂_2 )-(p_1-p_2 ))/√((p ̅(1-p ̅))/n_1 +(p ̅(1-p ̅))/n_2 ) N(0,1) NA
H_0:σ_1^2=σ_2^2 F=(s_Larger^2)/(s_Smaller^2 ) F_(ν_1,ν_2 ) ν_i=n_i
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What does it means if the standard deviation is large?
that you have a large variance in the population and/or your sample size is too small
What are adverse variances?
Adverse variances means unfavourable variance which is actual expenses are more than budgted variance.
An analysis of variance differs from a t test for independent means in that an analysis of variance?
Analysis of Variance (ANOVA) compares 3 or more means. The t-test would only compare 2 means.
What does homogeneity of variance mean?
It means that the variance remains the same across the range of values of the variable.
What is the difference between heterogeneity and homogeneity in statistics?
Homogeneity means that the statistical properties of the variable which is being studied remain the same across the population. Heterogeneity means that they do not: it could be that the mean changes between different subsets of the population or the variance does.
