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7.7

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Sub sections and their related questions

Introduction to bivariate distributions.

Covariance and (population) product moment correlation coefficient \(\rho \).

Proof that \(\rho = 0\) in the case of independence and \( \pm 1\) in the case of a linear relationship between \(X\) and \(Y\).

Definition of the (sample) product moment correlation coefficient \(R\) in terms of n paired observations on \(X\) and \(Y\). Its application to the estimation of \(\rho \).

Informal interpretation of \(r\), the observed value of \(R\). Scatter diagrams.

Topics based on the assumption of bivariate normality: use of the \(t\)-statistic to test the null hypothesis \(\rho = 0\) .

Topics based on the assumption of bivariate normality: knowledge of the facts that the regression of \(X\) on \(Y\) (\({E\left. {\left( X \right)} \right|Y = y}\)) and \(Y\) on \(X\) (\({E\left. {\left( Y \right)} \right|X = x}\)) are linear.

Topics based on the assumption of bivariate normality: least-squares estimates of these regression lines (proof not required).

Topics based on the assumption of bivariate normality: the use of these regression lines to predict the value of one of the variables given the value of the other.