# Question

With reference to Example 14.1, show that the regression equation of X on Y is

Also sketch the regression curve.

In Example 14.1

Also sketch the regression curve.

In Example 14.1

## Answer to relevant Questions

Show that if µY|x is linear in x and var(Y|x) is constant, then var(Y|x) = σ22 (1 – ρ2). Given the random variables X1, X2, and X3 having the joint density f(x1, x2, x3), show that if the regression of X3 on X1 and X2 is linear and written as Then Where µi = E(Xi), σ2i = var(Xi), and σij = cov(Xi,Xj). ...Using se (see Exercise 14.18) instead of , rewrite (a) The expression for t in Theorem 14.4; (b) The confidence interval formula of Theorem 14.5. Exercise 14.18 Show that (a) ∑2, the random variable corresponding to ...Solve the double inequality –tα/2,n–2 < t < tα/2,n–2 with t given by the formula of Exercise 14.25 so that the middle term is y0 and the two limits can be calculated without knowledge of y0. Although the resulting ...Use the t statistic of Theorem 14.8 to construct a (1 – α)100% confidence interval formula for βi for i = 0, 1, . . . , k. Theorem 14.8 Under the assumptions of normal multiple regression analysis, Are values of random ...Post your question

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