Answer:
The answer is: the unit variable expense is $1.20 per machine hour
Explanation:
In order to calculate the unit variable cost we first take the month with the highest and lowest maintenance expense and machine hours (Highest = month 6, Lowest = month 11). We use the following formula:
unit variable cost = (highest expense - lowest expense) / (highest machine hours - lowest machine hours)
= ($3,680 - $2,780) / (2,440 - 1,690) = $1.20 per machine hour
Answer:
Because they're two different people
Explanation:
The main and major reason why, is the obvious one. The first reason that comes to the mind.
Because they are two different people. Different people have different goals, hopes and aspirations. And even those that share them go through different paths while carrying them out. The minds of the two individuals are not the same, and despite the fact that they have similar abilities, they might not necessarily get the job done on time because of other commitments either of them have. Even if they both have none, there's always a distinguishing factor, and that will always trump, thus making them have different expectancies for performing at a high level.
Answer:
$2,000 favorable
Explanation:
The computation is shown below:
= Actual overhead cost - budgeted flexible costs
where,
Actual overhead cost = $250,000
And, the budgeted flexible cost would be
= Number of units produced × variable cost per unit + fixed cost
= 9,000 units × $8 + $180,000
= $72,000 + $180,000
= $252,000
The variable cost per unit would be
= $64,000 ÷ 8,000 units
= $8
So, the difference would be
= $250,000 - $252,000
= $2,000 favorable
Answer:
The answer is C.It makes recommendations that are validated using machine learning.
Explanation:
A performance planner is a tool used by Google Ads to devise plans in relation to how a business spends on advertising and how changes on advertisement campaigns will affect key metrics and the general performance. It is mostly used as a forecasting tool, with the use of machine learning to show the possibilities or potential outcomes in Google Ads campaigns. This implies that all the conclusions arrived at, are determined by machine learning.
