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Question
Given the following information about the production and demand of a commodity.
Obtain the two regression lines:
| Production (X) |
Demand (Y) |
|
| Mean | 85 | 90 |
| Variance | 25 | 36 |
Coefficient of correlation between X and Y is 0.6. Also estimate the demand when the production is 100 units.
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Solution
Given, `bar(x)` = 85, `bar(y)` = 90, `sigma_x^2` = 25, `sigma_y^2` = 36, r = 0.6
∴ `sigma_x` = 5, `sigma_y` = 6
byx = `"r" sigma_y/sigma_x = 0.6 xx 6/5` = 0.72
bxy = `"r" sigma_x/sigma_y = 0.6 xx 5/6` = 0.5
The regression equation of Y on X is given by `("Y" - bary) = "b"_(xy) ("X" - barx)`
(Y – 90) = 0.72(X – 85)
Y – 90 = 0.72X – 61.2
Y = 0.72X – 61.2 + 90
Y = 28.8 + 0.72X ......(i)
The regression equation of X on Y is given by `("X" - barx) = "b"_(xy) ("Y" - bary)`
(X – 85) = 0.5(Y – 90)
X – 85 = 0.5Y – 45
X = 0.5Y – 45 + 85
X = 40 + 05Y ......(ii)
For X = 100, from equation (i) we get
Y = 28.8 + 0.72(100)
= 28.8 + 72
= 100.8
∴ The production is 90 when demand is 100.
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Mean of x = 25
Mean of y = 20
`sigma_x` = 4
`sigma_y` = 3
r = 0.5
byx = `square`
bxy = `square`
when x = 10,
`y - square = square (10 - square)`
∴ y = `square`
| x | y | xy | x2 | y2 |
| 6 | 9 | 54 | 36 | 81 |
| 2 | 11 | 22 | 4 | 121 |
| 10 | 5 | 50 | 100 | 25 |
| 4 | 8 | 32 | 16 | 64 |
| 8 | 7 | `square` | 64 | 49 |
| Total = 30 | Total = 40 | Total = `square` | Total = 220 | Total = `square` |
bxy = `square/square`
byx = `square/square`
∴ Regression equation of x on y is `square`
∴ Regression equation of y on x is `square`
If byx > 1 then bxy is _______.
