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exam1FormulaFall2011(1)

# exam1FormulaFall2011(1) - y i-¯ y 2...

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Important Formulas - Exam 1 STAT 401G ¯ Y = Y 1 + Y 2 + · · · + Y n n s 2 = n i =1 ( Y i - ¯ Y ) 2 n - 1 s = s 2 ———————————————————————————————————————— SD( ¯ Y ) = σ n SE( ¯ Y ) = s n t = ¯ Y - μ 0 s n df = n - 1 ¯ Y ± t df s n ———————————————————————————————————————— SD( ¯ Y 2 - ¯ Y 1 ) = σ s 1 n 1 + 1 n 2 SE( ¯ Y 2 - ¯ Y 1 ) = s p s 1 n 1 + 1 n 2 s p = s ( n 1 - 1) s 2 1 + ( n 2 - 1) s 2 2 n 1 + n 2 - 2 t = ( ¯ Y 2 - ¯ Y 1 ) - Δ 0 s p q 1 n 1 + 1 n 2 df = n 1 + n 2 - 2 ( ¯ Y 2 - ¯ Y 1 ) ± t df s p s 1 n 1 + 1 n 2 ———————————————————————————————————————– Source D.F. Sum of Squares Mean Square F Regression 1 SS reg = n i =1 y i - ¯ y ) 2 MSR = SS reg 1 MSR MSE Error n - 2 SSE = n i =1 ( y i - ˆ y i ) 2 MSE = SSE n - 2 Total n -
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Unformatted text preview: y i-¯ y ) 2 ————————————————————————————————————— r = SS xy p SS xx SS yy ˆ β 1 = SS xy SS xx = r s y s x ˆ β = ¯ y-ˆ β 1 ¯ x b μ ( y | x ) = ˆ β + ˆ β 1 x ˆ σ = s = √ MSE SE( ˆ β 1 ) = s √ SS xx t = ˆ β 1-β 1 SE( ˆ β 1 ) df = n-2 ˆ β 1 ± t df SE( ˆ β 1 ) SE ( b μ ( Y | X P )) = s s 1 n + ( X P-¯ X ) 2 ( n-1) s 2 X —————————————————————————————————————...
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