of X on Y (a) b xy = N dy dy N dx dy dy - ] ^ g (b) b yx = N dy dy N dx dy dy - ] ^ g (c) b xy = N N dxdy dy - ] ^ g (d) b y = N x N y N xy . The regression coefficient of Y on X (a) b xy = N dy dy N dx dy dy - ] ^ g (b) b yx = N dy dy N dxdy dy - ] ^ g (c) b yx = N dx N dx dy dy - ] ^ g (d) b xy = N x N y N xy . When one regression coefficient is negative, the other would be (a) Negative (b) Positive (c) Zero (d) None of them - - Correlation and Regression analysis . If X and Y are two variates, there can be atmost (a) One regression line (b) two regression lines (c) three regression lines (d) more regression lines .
The lines of regression of X on Y estimates (a) X for a given value of Y (b) Y for a given value of X (c) X from Y and Y from X (d) none of these . Scatter diagram of the variate values ( X , Y ) give the idea about (a) functional relationship (b) regression model (c) distribution of errors (d) no relation . If regression co-efficient of Y on X is , then the regression co-efficient of X on Y is (a) (b) (c) > (d) . If two variables moves in decreasing direction then the correlation is (a) positive (b) negative (c) perfect negative (d) no correlation .
The person suggested a mathematical method for measuring the magnitude of linear relationship between two variables say X and Y is (a) Karl Pearson (b) Spearman (c) Croxton and Cowden (d) Ya Lun Chou . The lines of regression intersect at the point (a) ( X , Y ) (b) X Y (c) ( , ) (d) (σ x ,σ