Formula_ttests comparison-1

Formula_ttests comparison-1 - Comparison among t Tests 1...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Comparison among t Tests 1 sample ∑X M= n s 2 independent samples M1 = Mean Estimation of σ SS (Sum of Squares) ∑X n1 1 ; M2 = sp 2 ∑X n2 2 2 dependent samples ∑D MD = n s Definitional formula: SS = ∑ (D − M D ) 2 Computational formula: (∑ X ) Computational formula: SS = ∑ X − n 2 Definitional formula: SS = ∑ ( X − M ) 2 (∑ D) SS = ∑ D − n 2 2 Standard Deviation Variance Standard Error Statistic s= 2 s= SS n −1 SS n −1 sp = sp = 2 SS1 + SS 2 df1 + df 2 SS1 + SS 2 df1 + df 2 s= 2 s= SS n −1 SS n −1 sM = s n t= s( M 1 − M 2 ) = s2 p n1 + s2 p n2 sM D = t= s n t= M −μ sM ( M 1 − M 2 ) − ( μ1 − μ 2 ) s( M 1 − M 2 ) M D − μD sM D Reference Cohen’s d r 2 t Table Level of significance (α) Two-tailed or one-tailed Degrees of freedom (df) = n-1 M −μ d= s t Table Level of significance (α) Two-tailed or one-tailed Degrees of freedom (df) = df1 + df2 = n1 + n2 -2 ( M1 − M 2 ) − (μ1 − μ2 ) d= sp t Table Level of significance (α) Two-tailed or one-tailed Degrees of freedom (df) = n-1 M − μD d= D s r2 = t2 t 2 + df Estimate for Population Mean μ = X ± ts M or μ = M ± tsM μ1 − μ 2 = X 1 − X 2 ± ts( M − M ) or μ1 − μ 2 = M 1 − M 2 ± ts( M − M ) 1 2 1 2 μ D = D ± tsM D or μ D = M D ± ts M D ...
View Full Document

This note was uploaded on 04/20/2011 for the course PSY 207 taught by Professor Pfordesher during the Spring '07 term at SUNY Buffalo.

Ask a homework question - tutors are online