Final answer:
Indicators of multicollinearity include a high R-squared value coupled with individually insignificant predictor variables, in addition to a significant F statistic. The correlation coefficient, r, is also crucial for determining the strength and direction of the relationship between variables.
Step-by-step explanation:
The detection methods for multicollinearity in regression analysis involve looking for certain patterns in statistical outputs. Options B (High R2 plus individually insignificant predictor variables) and C (High R2 and significant F statistic coupled with insignificant predictor variables) both indicate a potential multicollinearity issue.
This is because a high R2 value suggests that the model as a whole fits the data well, but the individual predictor variables are not statistically significant, suggesting that they may be providing redundant information which could be a sign of multicollinearity.
Another important statistic in detecting multicollinearity is the correlation coefficient, r, which measures the strength and direction of the linear relationship between two variables. Moreover, it is essential to look at the scatter plot to determine if a linear model is appropriate or if another model might better fit the data.
\If the correlation coefficient is not significantly different from zero, it suggests that there is no linear relationship. However, if it is significantly different from zero, the correlation is significant. The coefficient of determination, r2, helps to understand how much variation in the dependent variable is explained by the regression model.