Answer:
Instructions are listed below.
Explanation:
Giving the following information:
Mettel Products sells 100,000 flash drives annually to industrial distributors who resell the drives to business customers for $40 each. The distributors’ margins are 25%. Mettel Products’ cost of goods sold is $10.00 each. Mettel’s total variable costs (including selling costs) are $15.00 per drive.
Selling price= 40/1.25= $32
A) Gross margin= 32 - 15= 17
%= 53%
B) Mettel is considering increasing its annual advertising spending from $75,000 to $150,000.
Break-even point= fixed costs/ contribution margin
Break-even points= 150,000/17= 8,824 units
C) Break-even points= 75,000/14= 5,357 units
Answer:
$ 13.167 / unit
Explanation:
Data provided:
Beginning material cost = $ 126,000
Number of units in work in progress = 12,000 units
Material cost assigned = $ 32,000
thus,
the total material cost involved = $ 126,000 + $ 32,000 = $ 158,000
Now,
the material cost per equivalent unit = Total material cost involved / number of units
on substituting the values, we have
the material cost per equivalent unit = $ 158,000 / 12,000
or
= $ 13.167 / unit
Answer: Option D
Explanation: In simple words, gross profit refers to the amount of revenue that the company is left with after deduction for the expenses that are incurred to make and sell that specific product.
The low pay to supplier means that the company will have a low cost to produce the product which will result in increase in gross profit.
Hence the correct option is D.
Answer: 0.056
Explanation:
Total factor productivity is the ratio of the aggregate that is, the total output to the aggregate inputs. Total factor productivity is used to measure economic efficiency of a country.
From the question, we are informed that Burundi's observed per capita GDP, relative to the United States, is 0.01 and the predicted per capita GDP is 0.18. Then, the total factor productivity will be:
= 0.01/0.18
= 0.056
Answer:
False. See expplanation below.
Explanation:
Training error by definition is the "error that you get when you run the trained model back on the training data."
False
Sometimes if we have more predictors than the neccesary we create bias and other problems like multicolinearity between the independnet variables. The idea is have a parsimonious model with the ideal number of variables and not with too much or too low variables.
For example we can have a linear model with just one predictor adjusted to the response variable perfect. And we can have another model with the same response variable but with 10 predictors with the same correlation and significance.
Always is important to understand the context of a problem in order to select the predictors to estimate the response variable in order to don't overestimate the number of parameters neccesary to use.
