Correlated variables can have zero correlation coeffficient. True or False?

Related MCQs

• If two variables are correlated, is it necessary that they have a linear relationship?
• �If two variables are correlated, is it necessary that they have a linear relationship?
• If two variables are correlated, is it necessary that they have a linear relationship?
•  If two variables are correlated, is it necessary that they have a linear relationship?
• Suppose that we have N independent variables (X1,X2� Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. You found that correlation coefficient for one of it�s variable(Say X1) with Y is -0.95.Which of the following is true for X1?
• Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95.Which of the following is true for X1?
• A term used to describe the case when the independent variables in a multiple regression model are correlated is
• Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider? 1. I will add more variables 2. I will start introducing polynomial degree variables 3. I will remove some variables
• Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. You found that correlation coefficient for one of it’s variable(Say X1) with Y is 0.95.
• Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model. (i) Models which overfit are more likely to have high bias (ii) Models which overfit are more likely to have low bias (iii) Models which overfit are more likely to have high variance (iv) Models which overfit are more likely to have low variance