Which is NOT true of simple linear regression? It can be used to identify predictors of continuous outcome variables. It can be used to predict the outcome of a binary variable (e.g., pass/fail) with continuous variables. It can be used to quantify a relationship between two continuous variables. It can be used to model a linear relationship between variables.

Q: . Calculate (a) R, 23, (b) R, 12 and (c) R,1, for the following data: X, = 6.8 S, = 1.0 "12 3.12 2.1...

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A: Givensample size(n)=25Mean(x)=3.84standard deviation(s)=0.4α=0.05

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A: Given: N=100 X12=8σ12=10.5=3.2403x1=10σ1=2N1=50

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Q: Variable X Variable X Variable X (i) (ii) (iii) Frequency Frequency Frequency

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Q: The following C.I.s were obtained for the mean of the same populat [16, 23] and [18, 25]. Identify t...

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Q: -. Calculate (a) R, 23, (b) R,12 and (c) R,1, for the following data: 1.23 3.12 2.13

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Q: Suppose x has the B(20, 0.5) distribution. Fint the P(x>10).

A: Givenn=20p=0.5x~Binomial(n=20,p=0.5)P(X=x)=nx×px×(1-p)n-x  ;   x=0,1,...,n

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