Advertisements
Advertisements
प्रश्न
The heights (in cm.) of a group of fathers and sons are given below:
| Heights of fathers: | 158 | 166 | 163 | 165 | 167 | 170 | 167 | 172 | 177 | 181 |
| Heights of Sons: | 163 | 158 | 167 | 170 | 160 | 180 | 170 | 175 | 172 | 175 |
Find the lines of regression and estimate the height of the son when the height of the father is 164 cm.
Advertisements
उत्तर
| Heights of fathers (X) |
Heights of Sons (Y) |
dx = X − 168 | dy = Y − 169 | dx2 | dy2 | dxdy |
| 158 | 163 | − 10 | − 6 | 100 | 36 | − 60 |
| 166 | 158 | − 2 | − 11 | 4 | 121 | 22 |
| 163 | 167 | − 5 | − 2 | 25 | 4 | 10 |
| 165 | 170 | − 3 | 1 | 9 | 1 | − 3 |
| 167 | 160 | − 1 | − 9 | 1 | 81 | 9 |
| 170 | 180 | 2 | 11 | 4 | 121 | 22 |
| 167 | 170 | − 1 | 1 | 1 | 1 | −1 |
| 172 | 175 | 4 | 6 | 16 | 36 | 24 |
| 177 | 172 | 9 | 3 | 81 | 9 | 27 |
| 181 | 175 | 13 | 6 | 169 | 36 | 78 |
| 1686 | 1690 | 6 | 0 | 410 | 446 | 248 |
N = 10, ∑X = 1686, ∑Y = 1690, ∑dx2 = 410, ∑y2 = 446, ∑dxdy = 248, `bar"X" = 1686/10` = 168.6, `bar"Y" = 1690/10` = 169
bxy = `("N"sum"dxdy" - (sum"dx")(sum"dy"))/("N"sum"dy"^2 - (sum"dy")^2)`
= `(10(248) - 6(0))/((10)(446) - 0^2)`
= `2480/4460`
= 0.556
Regression equation of X on Y
`"X" - bar"X" = "b"_"xy"("Y" - bar"Y")`
X – 168.6 = 0.556 (Y – 169)
X – 168.6 = 0.556Y – 93.964
X = 0.556Y – 93.964 + 168.6
X = 0.556Y + 76.636
X = 0.556Y + 74.64
byx = `("N"sum"dxdy" - (sum"dx")(sum"dy"))/("N"sum"dx"^2 - (sum"dx")^2)`
= `(10(248) - 0)/(10(410) - 6^2)`
= `2480/(4100 - 36)`
= `2480/4064`
= 0.610
Regression equation of Y on X
`"Y" - bar"Y" = "b"_"yx"("X" - bar"X")`
Y − 169 = 0.610 (X − 168.6)
Y – 169 = 0.610X – 102.846
Y = 0.610X – 102.846 + 169
Y = 0.610X + 66.154 ………(1)
To get son’s height (Y) when the father height is X = 164 cm.
Put X = 164 cm in equation (1) we get
Son’s height = 0.610 × 164 + 66.154
= 100.04 + 66.154
= 166.19 cm
APPEARS IN
संबंधित प्रश्न
From the data given below:
| Marks in Economics: | 25 | 28 | 35 | 32 | 31 | 36 | 29 | 38 | 34 | 32 |
| Marks in Statistics: | 43 | 46 | 49 | 41 | 36 | 32 | 31 | 30 | 33 | 39 |
Find
- The two regression equations,
- The coefficient of correlation between marks in Economics and Statistics,
- The mostly likely marks in Statistics when the marks in Economics is 30.
The following data give the height in inches (X) and the weight in lb. (Y) of a random sample of 10 students from a large group of students of age 17 years:
| X | 61 | 68 | 68 | 64 | 65 | 70 | 63 | 62 | 64 | 67 |
| Y | 112 | 123 | 130 | 115 | 110 | 125 | 100 | 113 | 116 | 125 |
Estimate weight of the student of a height 69 inches.
Obtain the two regression lines from the following data N = 20, ∑X = 80, ∑Y = 40, ∑X2 = 1680, ∑Y2 = 320 and ∑XY = 480.
Given the following data, what will be the possible yield when the rainfall is 29.
| Details | Rainfall | Production |
| Mean | 25`` | 40 units per acre |
| Standard Deviation | 3`` | 6 units per acre |
Coefficient of correlation between rainfall and production is 0.8.
The following data relate to advertisement expenditure (in lakh of rupees) and their corresponding sales (in crores of rupees)
| Advertisement expenditure | 40 | 50 | 38 | 60 | 65 | 50 | 35 |
| Sales | 38 | 60 | 55 | 70 | 60 | 48 | 30 |
Estimate the sales corresponding to advertising expenditure of ₹ 30 lakh.
Find the equation of the regression line of Y on X, if the observations (Xi, Yi) are the following (1, 4) (2, 8) (3, 2) (4, 12) (5, 10) (6, 14) (7, 16) (8, 6) (9, 18).
The two regression lines were found to be 4X – 5Y + 33 = 0 and 20X – 9Y – 107 = 0. Find the mean values and coefficient of correlation between X and Y.
The term regression was introduced by
Using the following information you are requested to
- obtain the linear regression of Y on X
- Estimate the level of defective parts delivered when inspection expenditure amounts to ₹ 82
∑X = 424, ∑Y = 363, ∑X2 = 21926, ∑Y2 = 15123, ∑XY = 12815, N = 10.
Here X is the expenditure on inspection, Y is the defective parts delivered.
The following information is given.
| Details | X (in ₹) | Y (in ₹) |
| Arithmetic Mean | 6 | 8 |
| Standard Deviation | 5 | `40/3` |
Coefficient of correlation between X and Y is `8/15`. Find
- The regression Coefficient of Y on X
- The most likely value of Y when X = ₹ 100.
