The h.w. grant corporation used regression analysis to predict the annual cost of indirect materials. the results were as follo
ws: 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
Answer: The cost estimation equation is y=11.25x+15,640
<span>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 </span> 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
