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महाराष्ट्र राज्य शिक्षण मंडळएचएससी वाणिज्य (इंग्रजी माध्यम) इयत्ता १२ वी

The equations of two regression lines are 10x − 4y = 80 and 10y − 9x = − 40 Find: andx¯andy¯ bYXandbXYbYXandbXY If var (Y) = 36, obtain var (X) r

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प्रश्न

The equations of two regression lines are 10x − 4y = 80 and 10y − 9x = − 40 Find:

  1. `bar x and bar y`
  2. bYX and bXY
  3. If var (Y) = 36, obtain var (X)
  4. r
बेरीज
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उत्तर

(i) Given equations of regression are

10x − 4y = 80

i.e., 5x − 2y = 40        .....(i)

and 10y − 9x = −40

i.e., − 9x + 10y = −40         .....(ii)

By 5 × (i) + (ii), we get

 25x − 10y = 200

− 9x + 10y = − 40 
16x             = 160

∴ x = 10

Substituting x = 10 in (i), we get

5(10) − 2y = 40

∴ 50 − 2y = 40

∴ −2y = 40 − 50

∴ −2y = − 10

∴ y = 5

Since the point of intersection of two regression lines is `(bar x, bar y)`, `bar x = 10  and bar y = 5`

(ii) Let 10y − 9x = −40 be the regression equation of Y on X.

∴ The equation becomes 10Y = 9X − 40

i.e., Y = `9/10X − 40/10`

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

`b_(YX) = 9/10 = 0.9`

Now, the other equation 10x − 4y = 80 be the regression equation of X on Y.

∴ The equation becomes 10X = 4Y + 80

i.e., X = `4/10 Y + 80/10`

i.e., X = `2/5 Y + 8`

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

`b_(XY) = 2/5 = 0.4`

(iii) Given, Var (Y) = 36, i.e., `sigma_Y^2` = 36

∴ σY = 6

Since `b_(XY) = r xx sigma_X/sigma_Y`

`2/5 = 0.6 xx sigma_X/6`

∴ `2/5 = 0.1 xx sigma_X`

∴ `2/(5 xx 0.1) = sigma_X`

∴ `sigma_X` = 4

∴ `sigma_X^2 = 16` i.e., Var(X) = 16

(iv) r = `+-sqrt(b_(XY) *b_(YX)`

`= +-sqrt(2/5 xx 9/10) +- sqrt(9/25)`

`= +- 3/5`

 `= +- 0.6`

Since bYX and bXY are positive,

r is also positive.

∴ r = 0.6

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Properties of Regression Coefficients
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पाठ 3: Linear Regression - Miscellaneous Exercise 3 [पृष्ठ ५४]

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बालभारती Mathematics and Statistics 2 (Commerce) [English] Standard 12 Maharashtra State Board
पाठ 3 Linear Regression
Miscellaneous Exercise 3 | Q 4.11 | पृष्ठ ५४

संबंधित प्रश्‍न

Bring out the inconsistency in the following:

bYX = bXY = 1.50 and r = - 0.9 


The following data about the sales and advertisement expenditure of a firms is given below (in ₹ Crores)

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Mean 40 6
S.D. 10 1.5

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Estimate the likely sales for a proposed advertisement expenditure of ₹ 10 crores.


The following data about the sales and advertisement expenditure of a firms is given below (in ₹ Crores)

  Sales Adv. Exp.
Mean 40 6
S.D. 10 1.5

Coefficient of correlation between sales and advertisement expenditure is 0.9.

What should be the advertisement expenditure if the firm proposes a sales target ₹ 60 crores?


Two lines of regression are 10x + 3y − 62 = 0 and 6x + 5y − 50 = 0. Identify the regression of x on y. Hence find `bar x, bar y` and r.


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4y − 15x + 500 = 0
and 20x − 3y − 900 = 0
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Choose the correct alternative:

|byx + bxy| ≥ ______


Choose the correct alternative:

Find the value of the covariance between X and Y, if the regression coefficient of Y on X is 3.75 and σx = 2, σy = 8


Choose the correct alternative:

If r = 0.5, σx = 3, `σ_"y"^2` = 16, then byx = ______


Choose the correct alternative:

If r = 0.5, σx = 3, σy2 = 16, then bxy = ______


State whether the following statement is True or False:

The following data is not consistent: byx + bxy =1.3 and r = 0.75


State whether the following statement is True or False: 

If u = x – a and v = y – b then bxy = buv 


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Corr(x, x) = 0


Corr(x, x) = 1


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Cov(x, x) = Variance of x


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The value of product moment correlation coefficient between x and x is ______


The geometric mean of negative regression coefficients is ______


byx is the ______ of regression line of y on x


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Obtain the two regression lines:

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(₹ in lakhs)
DEMAND (y)
(₹ in lakhs)
Mean 10 90
Variance 9 144

Coefficient of correlation between x and y is 0.8.
What should be the advertising budget if the company wants to attain the sales target of ₹ 150 lakhs?


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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

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Mean of x = 53

Mean of y = 28

Regression coefficient of y on x = – 1.2

Regression coefficient of x on y = – 0.3

a. r = `square`

b. When x = 50,

`y - square = square (50 - square)`

∴ y = `square`

c. When y = 25,

`x - square = square (25 - square)`

∴ x = `square`


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byx = `square`

bxy = `square`

when x = 10,

`y - square = square (10 - square)`

∴ y = `square`


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4 8 32 16 64
8 7 `square` 64 49
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byx = `square/square`

∴ Regression equation of x on y is `square`

∴ Regression equation of y on x is `square`


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|bxy + byz| ≥ ______.


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