The coefficient of determination, R-squared, is the square of the correlation coefficient, r. If R-squared equals 1, then r squared equals 1, which implies that r must be either 1 (perfect positive correlation) or -1 (perfect negative correlation).

The correlation coefficient r is defined as the square root of the product of the two regression coefficients, r = ±√(bxy * byx). Here, the product is -0.8 * 1.2 = -0.96. Since the square root of a negative number is not a real number, and the product of regression coefficients must be non-negative for a valid correlation, the provided values are mathematically inconsistent for real-valued correlation. Thus, 'None of these' is the correct choice.

In simple linear regression, the regression coefficients (b_xy and b_yx) and the Pearson correlation coefficient (r) share the same sign. This is because they are all derived from the covariance of the two variables; a positive covariance results in a positive slope and positive correlation, while a negative covariance results in negative values for all three.

When two random variables are independent, their covariance is zero. Since the correlation coefficient is defined as the covariance divided by the product of the standard deviations, a covariance of zero results in a correlation coefficient of zero. This indicates the absence of a linear relationship between the variables.

Correlation measures the strength and direction of a linear association between two variables, but it does not imply causation. A strong correlation can exist due to coincidence, a third confounding variable, or reverse causality, meaning no direct causal link is proven by the correlation coefficient alone.

The correlation coefficient, denoted as 'r', is a measure of the strength and direction of the linear relationship between two variables. It falls between -1 and 1. Values closer to -1 indicate a strong, negative correlation; values closer to 1 indicate a strong, positive correlation; and values around 0 indicate a weak correlation. Values between -1 and 1 represent a perfect, linear relationship between the variables.

Correlation measures the strength and direction of the linear relationship between two variables. While regression also models relationships, correlation specifically refers to the existence and degree of association between variables. It is the standard term used to describe such dependencies in statistical analysis.

The coefficient of determination is the square of the correlation coefficient (r^2). Since any real number squared is non-negative, R-squared is always positive regardless of the sign of the slope. Therefore, it provides no information about the direction of the relationship.

Perfect correlation occurs when the relationship between two variables is perfectly linear, represented by a correlation coefficient of +1 or -1. The options provided do not describe this statistical concept correctly. Therefore, 'None of these' is the correct choice as the provided examples do not define perfect linear correlation.

The correlation coefficient ranges from -1 to +1. A strong negative correlation implies a value close to -1, which is indeed much smaller than 0. While the source answer is technically incomplete regarding positive correlation, it is correct in the context of identifying a strong negative relationship.