The two regression lines are given by : 3x + 2y = 6 and 7x +5y = 12, estimate y when x is 10

(i) Given equations of regression are

3x + 2y - 26 = 0

i.e., 3x + 2y = 26       ….(i)

and 6x + y - 31 = 0

i.e., 6x + y = 31       ….(ii)

By (i) - 2 × (ii), we get

3x + 2y = 26

12x + 2y = 62

-      -       -    
- 9x = - 36

∴ x = `(-36)/-9 = 4`

Substituting x = 4 in (ii), we get

6 × 4 + y = 31

∴ 24 + y = 31

∴ y = 31 - 24

∴ y = 7

Since the point of intersection of two regression lines is `(bar x, bar y)`, 

`bar x` = mean of X = 4, and

`bar y` = mean of Y = 7

(ii) Let 3x + 2y 26 = 0 be the regression equation of Y on X.

∴ The equation becomes 2Y = 3X + 26

i.e., Y = `(-3)/2"X" + 26/2`

Comparing it with Y = bYX X + a, we get

`"b"_"YX" = (- 3)/2`

Now, the other equation 6x + y - 31 = 0 is the regression equation of X on Y.

∴ The equation becomes 6X = - Y + 31

i.e., X = `(-1)/6 "Y" + 31/6`

Comparing it with X = bXY Y+ a', we get

`"b"_"XY" = (-1)/6`

∴ r = `+-sqrt("b"_"XY" * "b"_"YX")`

`= +- sqrt((- 1)/6 xx (- 3)/2) = +- sqrt(1/4) = +- 1/2 = +- 0.5`

Since the values of bXY and bYX are negative,

r is also negative.

∴ r = - 0.5

(iii) The regression equation of Y on X is

Y = `(- 3)/2 "X" + 26/2`

For X = 2, we get

Y = `(- 3)/2 xx 2 + 26/2 = - 3 + 13 = 10`

(iv) Given, Var (Y) = 36, i.e., `sigma_"Y"^2` = 36

∴ `sigma_"Y" = 6`

Since `"b"_"XY" = "r" xx sigma_"X"/sigma_"Y"`

`(- 1)/6 = - 0.5 xx sigma_"X"/6`

∴ `sigma_"X" = (-6)/(- 6 xx 0.5) = 2`

∴ `sigma_"X"^2` = Var(X) = 4

The equations given of the two regression lines are 2x + 3y - 6 = 0 and 5x + 7y - 12 = 0.

Find:

(a) Correlation coefficient

(b) `sigma_x/sigma_y`

We assume that 2x + 3y - 6 = 0 to be the line of regression of y on x. 

2x + 3y - 6 = 0

⇒ `x = - 3/2y + 3`

⇒ `"bxy" = - 3/2`

5x + 7y - 12 = 0 to be the line of regression of x on y.

5x + 7y - 12 = 0

⇒ `y = - 5/7x + 12/7`

⇒  `"byx" = - 5/7`

Now,

r = `sqrt("bxy.byx") = sqrt(15/14)`

byx = `(rσ_y)/(σ_x) = - 5/7, "bxy" = (rσ_x)/(σ_y) = - 3/2`

⇒ `(σ_x^2)/(σ_y^2) =  (3/2)/(5/7)`

⇒ `(σ_x^2)/(σ_y^2) = 21/10`

⇒ `(σ_x)/(σ_y) = sqrt(21/10)`.

Concept: Lines of Regression of X on Y and Y on X Or Equation of Line of Regression

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