Answer:
The answer is below.
Explanation:
The z score is a used in statistics to determine by how many standard deviations the raw score is above or below the mean. The z score is given by:
a) Given that n = 100, μ = 2000, σ = 18
For x < 1995 millimeters:
From the normal distribution table, P(x < 1995) = P(z < -2.78) = 0.0027
b) P(z > z*) = 10% = 0.1
P(z < z*) = 1 - 0.1 = 0.9
z* = 1.28
From the normal distribution table, P(z < z
Answer:
Sweetpea have a gain of $1,000
Explanation:
When the depreciable property is sold, then the gain or loss will be computed to the extent on the difference among the selling price and the adjusted basis.
So, the adjusted basis will be
= Cost of the basis - Depreciation
= $5,000 - $2,000
= $3.000
Therefore,
Gain or Loss = Selling Price - Adjusted basis
= $4,000 - $3,000
= $1,000
Hence, it is a gain of $1,000.
Answer:
$4.00
Explanation:
To calculate the approximate overhead cost per unit of product A1 under activity - based costing we have it as
Activity 1 allocated to Product B2 line we have as
$48,000 × 4,800/6,000
= $38,400
Activity 2 allocated to Product B2 line we have it as
= $63,000 × 4,760/7,000
= $42,840
Activity 3 allocated to Product B2 line we have it as
=$80,000 × 800/8,000
= $8,000
Total overhead allocated to Product B2 = $89,240
Overhead per unit of Product B2: $89,240/22,310 = $4.00
As our overhead unit of product
Answer:
Explanation:
a. In a regression equation expressed as y= a + bx, how is the letter b best described?
Here, b is the slope and best described as the estimate of the cost when there's a visit of an additional customer.
b. How is the letter y in the regression equation best described?
The letter y is the observed store cost for that particular month.
c. How is the letter x in the regression equation best described?
The letter x is the observed customer visit for that particular month.
d. Based on the data derived from the regression analysis, what are the estimated costs for 370 customer-visits in a month?
The estimated cost for 370 customer visit will be:
Y = a + bx
where,
a =$1496
b = $2.08
x = 370 customer visit
Y = $1496 + ($2.08 × 370 customer visit)
= $1496 + $769.6
= $2265.6
e. What is the percent of the total variance that can be explained by the regression equation?
The percent of total variance which the regression equation explain will be:
R2 = 0.86814 or 86.814%
