stats exam 2 note sheet

# stats exam 2 note sheet - constant (that is, c cannot be a...

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Summary of Probability Rules So Far A probability is a number between 0 and 1 0 ≤ P(A) ≤ 1 Something Has to Happen Rule P(All outcomes) = 1 Complement Rule P(A) = 1 – P(A C ) or P(A C ) = 1 –P(A) Summary of Probability Rules So Far Addition Rule for disjoint events P(A or B) = P(A) + P(B) General Addition Rule P(A or B) = P(A) + P(B) – P(A and B) Multiplication Rule for independent events P(A and B) = P(A) P(B) General Multiplication Rule P(A and B) = P(A|B) P(B) 22 Rule: VAR( X ± Y ) = VAR( X ) + VAR( Y ). So, SD( X ± Y ) = √VAR( X - Y ) = √VAR( X ) + VAR( Y ) Standard Deviation SD( x ) = σ = √ σ 2 Mean , or Expected Value µ = Σ [ x • P( x )] Variance Var( x ) = σ 2 = Σ [( x - µ) 2 • P( x )] Rule : If E(X) = µ, then E(cX) = cµ, where c is any constant (that is, c cannot be a random variable!) Rule : If Std Dev (X) = σ, Std Dev (cX) = cσ, where c is any constant Rule: E(X±Y) = E(X) ± e(Y) Rule : If E(X) = µ, then E(X + c) = µ + c, where c is any
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Unformatted text preview: constant (that is, c cannot be a random variable!) Rule : If Std Dev (X) = , Std Dev (X + c) = , where c is any constant P = probability of success, q = probability of failure, n = # of trials, x = # of success Variance: stat calc 1 var stats L1, L2 Binomial: probability of x successes in n trials Geometric: number of trials until first success: GeomCDF(p, k) Poisson: rare events: x = # of times event occurs, lambda = E(x) = np // poissonCDF(lambda, k) Binomial needs: fixed # of trials, independent trials, 2 categories of outcomes, probabilities must remain constant for each trial Mu = n * p // SD = SQR(n*p*q) // E(x) = 1/p Binom pdf = exactly x successes = BinomPDF(n, p, k) Binom cdf = x is less than or equal to k // 1 binom cdf = x or more...
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## This note was uploaded on 03/30/2008 for the course STT 200 taught by Professor Dikong during the Spring '08 term at Michigan State University.

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