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A set of experimental runs was made to determine a way of predicting cooking time y at various values of oven width x, and flue temperature x2. The coded data were recorded as follows: y X1 x2 6.4 1.32 1.15 15.05 2.69 3.4 18.75 3.56 4.1 30.25 4.41 8.75 44.85 5.35 14.82 48.94 6.2 15.15 51.55 7.12 15.32 61.5 8.87 18.18 100.44 9.8 35.19 111.42 10.65 40.40 Estimate the multiple linear regression equation.

Sagot :

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The fitted regression equation is :y = 25.228X1 + 0.861X2 + 1283

Estimate the multiple linear regression equation ?

Given the summary output :

Intercept 1283.000_ 352.000 _ 3.65

X1 _____ 25.228 ____8.63

X2 _____0.861 _____0.372

General form of a multiple regression model:

Y = B1X1+B2X2 +E

The fitted regression equation is :

y = 25.228X1 + 0.861X2 + 1283

Intercept and slope corresponding to the standard error :

Intercept = 352

Slope 1 = 8.61 ; slope 2 = 0.372

'B' coefficient represents the change in the mean response, Y, per unit increase in a version predictor variable when all the other predictors are held constant.

B1 represents the change in the mean response, U, per unit increase in x1 when x2, x3, ..., xk are held constant

B2 represents the change in the mean response, U, per unit increase in x2 when x1, x3, ..., xk are held constant

To learn more about multiple linear regression refer

https://brainly.com/question/24276942

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