Formulas_exam3 - Statistics 101 Formulas Final Exam y= yi n s2 = y se = s n y = n z= y t(se t= y 0 se z= 0 = 0(1 0 n z(se se = n =(1 n = 2 z M(2 1

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Statistics 101: Formulas – Final Exam y = Σ y i n s 2 = Σ( y - y ) 2 n - 1 σ y = σ n z = y - μ σ y ± t ( se ) se = s/ n t = y - μ 0 se z = ˆ π - π 0 σ ˆ π σ ˆ π = s π 0 (1 - π 0 ) n ˆ π ± z ( se ) se = q ˆ π (1 - ˆ π ) /n n = π (1 - π ) ± z M ² 2 n = σ 2 ± z M ² 2 π 2 - ˆ π 1 ) ± z ( se ) , se = s ˆ π 1 (1 - ˆ π 1 ) n 1 + ˆ π 2 (1 - ˆ π 2 ) n 2 t = ( y 2 - y 1 ) /se, ( y 2 - y 1 ) ± t ( se ) se = s s 2 1 n 1 + s 2 2 n 2 χ 2 = Σ ( f 0 - f e ) 2 f e , df = ( r - 1)( c - 1) , f e = (row total)(col. total) /n standardized residual = f o - f e q f e (1 - row prop . )(1 - col . prop . ) 1
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Bivariate regression models E ( y ) = α + βx ˆ y = a + bx r = b ( s x /s y ) r 2 = ( T SS - SSE ) / ( T SS ) b ± t ( se ) t = b se ( df = n - 2) , se = s q ( x - x ) 2 = s s x n - 1 s = s SSE ( n - 2 s = q SSE/ ( n - 2) = Root MSE Multiple regression models E ( y ) = α + β 1 x 1 + β 2 x 2 + ··· + β k x k ˆ y = a + b 1 x 1 + b 2 x 2 + ··· + b k x k R 2 = ( T SS - SSE ) / ( T SS ) T SS = X ( y - ¯ y ) 2 SSE = X ( y - ˆ y ) 2 F = R 2 /k (1 - R 2 ) /
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This note was uploaded on 07/14/2011 for the course STA 101 taught by Professor Alan during the Fall '10 term at University of Florida.

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Formulas_exam3 - Statistics 101 Formulas Final Exam y= yi n s2 = y se = s n y = n z= y t(se t= y 0 se z= 0 = 0(1 0 n z(se se = n =(1 n = 2 z M(2 1

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