For a bivariate data set on (x, y), if the means, standard deviations and correlation coefficient are x̅ = 1.0, y̅ = 2.0, s x= 3.0, s y= 9.0, r = 0.8 Then the regression line of y on x is:
y = 2 + 2.4(x - 1)
The y-on-x slope is r(sy/sx)=0.8(9/3)=2.4. The line passes through (x̄,ȳ)=(1,2), so y−2=2.4(x−1), or y=2+2.4(x−1).
The data about the sales and advertisement expenditure of a firm is given below
| Sales (in crore of Rs.) | Advertisement exp (in crores of Rs.) | |
| Means | 40 | 6 |
| Standard Daviation | 10 | 1.5 |
The standard deviation of Y is double of standard deviation of x. The correlation coefficient between X and Y is 0.5.
The acute angle between lines of regression is
For the variables X, Y and Z, r xy = 0.80, r xz = 0.64, and r yz = 0.79, the square of multiple correlation coefficient \(\rm \mathop R\nolimits_{xyz}^2 \) is:
If x and y are uncorrelated variables then this implies:
If a constant 60 is subtracted from each of the values of X and Y, then the regression coefficient is
| $x$ | 2 | 3 | 4 | 5 |
| $y$ | 8 | 9 | 7 | 16 |
| $x$ | 1 | 2 | 3 | 4 |
| $y$ | 2 | 6 | 7 | 10 |
The graph below shows a plot of annual rainfall on the X axis and number of plant species on the Y axis. Based on the pattern in the graph, which one of the following statistical methods would be most appropriate to model the relationship between annual rainfall and the number of plant species?
