A)
ΣX | ΣY | Σ(x-x̅)² | Σ(y-ȳ)² | Σ(x-x̅)(y-ȳ) | |
total sum | 4380.00 | 39.73 | 504.00 | 9.81 | 46.62 |
mean | 182.50 | 1.66 | SSxx | SSyy | SSxy |
Sample size, n = 24
here, x̅ = Σx / n= 182.500
ȳ = Σy/n = 1.655
SSxx = Σ(x-x̅)² = 504.0000
SSxy= Σ(x-x̅)(y-ȳ) = 46.6
estimated slope , ß1 = SSxy/SSxx =
46.615/504= 0.0925
intercept,ß0 = y̅-ß1* x̄ = 1.6554- (0.0925
)*182.5= -15.2240
Regression line is, Ŷ= -15.2240 +
( 0.0925 )*x
............
B)
Predicted Y at X= 185
is
Ŷ= -15.22402 +
0.09249 *185= 1.8866
....
C)
X | Y | PREDICTED Y | RESIDUAL | |||
185 | 0.84 | 1.886642 | -1.05 | |||
185 | 1.85 | 1.886642 | -0.04 | |||
185 | 1.92 | 1.886642 | 0.03 | |||
185 | 2.62 | 1.886642 | 0.73 |
OPTION D
...............
D)
R² = (SSxy)²/(SSx.SSy) = 0.439
Approximately 0.439 Of variation in observations of variable Y, is
explained by variable x
..............
Please let me know in case of any doubt.
Thanks in advance!
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The efficiency for a steel specimen immersed in a phosphating tank is the weight of the...
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