Answer and Explanation:
The computation is shown below:
a. For the percentage of failures is
= Number of failures ÷ number of pacemakers tested
= 4 ÷ 90
= 4.4%
b. For Number of failures per unit-hour of operating time
= Number of failure ÷ total time - non-operating time
= 4 ÷ (5,000 × 90) - (5,000 ÷ 2 × 4)
= 4 ÷ (450,000 - 10,000)
= 4 ÷ 440,000
= 9.09 × 10^-6
= 0.00000909 failure per unit-hour
c. For Number of failures per unit-year is
= Failure ÷ unit year
= 0.0000090909 × 24 hours × 365 days
= 0.07963 failure per unit-year
Answer:
$30.59
Explanation:
<em>Note that the FIFO method is used for this question</em>
Equivalent Units
Materials = 5,200 x 100 % + 300 x 100 % = 5,500
Conversion Costs = 400 x 55 % + 5,200 x 100 % + 300 x 35 % = 5,525
Total Costs
Materials = $25,200
Conversion Costs = $143,700
Cost per Equivalent unit
Materials = $25,200/5,500 = $4.58
Conversion Costs = $143,700/5,525 = $26.01
Total Cost = $4.58 + $26.01 = $30.59
<u>Conclusion</u>
The cost of completing a unit during the current period was $30.59
Answer:
The E.E.O.C: Equal Employment Opportunity Commission
Explanation:
Hope This Helps!!
Answer:
The correct option is Increase and Decrease respectively
Explanation:
Answer:
d. It is best measured using the statistic variance inflation factor (VIF).
Explanation:
Multicollinearity is an important issue in multiple regression model, having many independent/ explanatory variables. Multicollinearity is the situation in which two or more independent variables are highly correlated. It is problematic because it increases the standard error of independent variable coefficient & undermines its statistical significance
Variance Inflation Factor [VIF] is a check & corrective measure of multicollinearity.
- VIF as a multicollinearity check : It quantifies the correlation between one explanatory variable with other explanatory variables.VIF = 1 implies there is no multicollinearity (correlation between independent variables); VIF upto 5 implies there is moderate multicollinearity (correlation between independent variables). VIF > 5 implies high multicollinearity (correlation between independent variables)
- VIF as a multicollinearity correction : Calculating
= σ^2 /
; where TSS = total sum of square of variable j , σ^2 = j variance, R^2 j = R^2 from regressing all other independent variable on variable j