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Question
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
Options
7
30
15
1.875
Solution
15
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RELATED QUESTIONS
For bivariate data. `bar x = 53, bar y = 28, "b"_"YX" = - 1.2, "b"_"XY" = - 0.3` Find estimate of Y for X = 50.
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You are given the following information about advertising expenditure and sales.
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For a certain bivariate data
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Given the following information about the production and demand of a commodity obtain the two regression lines:
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The following data about the sales and advertisement expenditure of a firms is given below (in ₹ Crores)
Sales | Adv. Exp. | |
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If the two regression lines for a bivariate data are 2x = y + 15 (x on y) and 4y = 3x + 25 (y on x), find
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- bXY
- r [Given `sqrt0.375` = 0.61]
Find the line of regression of X on Y for the following data:
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Choose the correct alternative:
|byx + bxy| ≥ ______
Choose the correct alternative:
bxy and byx are ______
State whether the following statement is True or False:
Regression coefficient of x on y is the slope of regression line of x on y
If the sign of the correlation coefficient is negative, then the sign of the slope of the respective regression line is ______
Given the following information about the production and demand of a commodity.
Obtain the two regression lines:
ADVERTISEMENT (x) (₹ 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?
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.
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`
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`
For a bivariate data:
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Find:
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