The h. w. grant corporation used regression analysis to predict the annual cost of indirect materials. the results were as follows: indirect materials cost explained by units produced constant $15,640 standard error of y estimate $3,600 r2 0.7704 number of observations 22 x coefficient(s) 11.25 standard error of coefficient(s) 2.19 what is the cost estimation equation?

Indirect material cost: y
explained by units produced: x
Linear regression. Cost estimation equation: y=mx+b
Constant: b=$15,640
Standard error of y estimate=$3,600
r^2=0.7704
Number of observations: n=22
x coeffient: m=11.25
Standard error of x coefficient=2.19

m=11.25, b=15,640 → y=11.25x+15,640

The cost estimation equation is y=11.25x+15,640

Y = $11.25X + $ 15640

Explanation:

1) This is the information given:

Indirect materials cost explained by units produced

Constant:                                     $15,640
Standard error of y estimate         $3,600
r2                                                           0.7704
Number of observations              22
X-coefficient(s)                             11.25
Standard error of coefficient(s)     2.19

2) The regresssion analysis permits to obtain a linear equation to represent the correlation between two variables and predict one in terms of the other.

The form of the equation is that of a linear function:

Y = AX + B

3) So, the cost estimation requested is the varibale times its coefficient plus the constant.

Given:
           X-coefficient = 11.25
           constant = $ 15,640

Equation:Y = $11.25X + $ 15640

The other inforamation, i.e. standard error of  Y estimate, r^2, number of observations, and standard error coefficientes are other statistical numbers that permit to understand the quality of the regression

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