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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
  या प्रश्नात किंवा उत्तरात काही त्रुटी आहे का?
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

For bivariate data. `bar x = 53, bar y = 28, "b"_"YX" = - 1.2, "b"_"XY" = - 0.3` Find estimate of Y for X = 50.


From the data of 7 pairs of observations on X and Y, following results are obtained.

∑(xi - 70) = - 35,  ∑(yi - 60) = - 7,

∑(xi - 70)2 = 2989,    ∑(yi - 60)2 = 476, 

∑(xi - 70)(yi - 60) = 1064

[Given: `sqrt0.7884` = 0.8879]

Obtain

  1. The line of regression of Y on X.
  2. The line regression of X on Y.
  3. The correlation coefficient between X and Y.

Bring out the inconsistency in the following:

bYX = 1.9 and bXY = - 0.25


Bring out the inconsistency in the following:

bYX = 2.6 and bXY = `1/2.6`


For a certain bivariate data

  X Y
Mean 25 20
S.D. 4 3

And r = 0.5. Estimate y when x = 10 and estimate x when y = 16


Two samples from bivariate populations have 15 observations each. The sample means of X and Y are 25 and 18 respectively. The corresponding sum of squares of deviations from respective means is 136 and 150. The sum of the product of deviations from respective means is 123. Obtain the equation of the line of regression of X on Y.


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.

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?


For certain bivariate data the following information is available.

  X Y
Mean 13 17
S.D. 3 2

Correlation coefficient between x and y is 0.6. estimate x when y = 15 and estimate y when x = 10.


The equations of two regression lines are
2x + 3y − 6 = 0
and 3x + 2y − 12 = 0 Find 

  1. Correlation coefficient
  2. `sigma_"X"/sigma_"Y"`

For a bivariate data, `bar x = 53`, `bar y = 28`, byx = −1.5 and bxy = −0.2. Estimate y when x = 50.


The equations of two regression lines are x − 4y = 5 and 16y − x = 64. Find means of X and Y. Also, find correlation coefficient between X and Y.


Regression equations of two series are 2x − y − 15 = 0 and 3x − 4y + 25 = 0. Find `bar x, bar y` and regression coefficients. Also find coefficients of correlation. [Given `sqrt0.375` = 0.61]


The two regression lines between height (X) in inches and weight (Y) in kgs of girls are,
4y − 15x + 500 = 0
and 20x − 3y − 900 = 0
Find the mean height and weight of the group. Also, estimate the weight of a girl whose height is 70 inches.


Find the line of regression of X on Y for the following data:

n = 8, `sum(x_i - bar x)^2 = 36, sum(y_i - bar y)^2 = 44, sum(x_i - bar x)(y_i - bar y) = 24`


Choose the correct alternative:

bxy and byx are ______


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 = ______


If u = `(x - "a")/"c"` and v = `(y - "b")/"d"`, then bxy = ______ 


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


If u = `(x - 20)/5` and v = `(y - 30)/4`, then byx = ______


If n = 5, Σx = Σy = 20, Σx2 = Σy2 = 90 , Σxy = 76 Find Covariance (x,y) 


x y `x - barx` `y - bary` `(x - barx)(y - bary)` `(x - barx)^2` `(y - bary)^2`
1 5 – 2 – 4 8 4 16
2 7 – 1 – 2 `square` 1 4
3 9 0 0 0 0 0
4 11 1 2 2 4 4
5 13 2 4 8 1 16
Total = 15 Total = 45 Total = 0 Total = 0 Total = `square` Total = 10 Total = 40

Mean of x = `barx = square`

Mean of y = `bary = square`

bxy = `square/square`

byx = `square/square`

Regression equation of x on y is `(x - barx) = "b"_(xy)  (y - bary)`

∴ Regression equation x on y is `square`

Regression equation of y on x is `(y - bary) = "b"_(yx)  (x - barx)`

∴ Regression equation of y on x is `square`


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`


The following results were obtained from records of age (x) and systolic blood pressure (y) of a group of 10 women.

  x y
Mean 53 142
Variance 130 165

`sum(x_i - barx)(y_i - bary)` = 1170


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