Answer:
Explanation:
Forecast usage = 50 %
Actual Usage = 52%
smoothing constant = 0.10
⇒ 50 + 0.10 (52 - 50)
⇒ 50 + 0.10 (2)
⇒ 50 + 0.2 = 50.20
Answer:
16.54%
Explanation:
We have to applied the rate formula that is shown in the attachment.
The NPER shows the time period.
Given that,
Present value = $2,500
Future value or Face value = $5,375
PMT = $0
NPER = 6 years - 1 years = 5 years
The formula is shown below:
= Rate(NPER,PMT,-PV,FV,type)
The present value come in negative
So, after solving this, the annual rate of return is implied is 16.54%
Answer:
Larger-sq and small Se.
Explanation:
Regression line is a line that clearly describes the behavior of a given set of data.
Regression lines are very essential for forecasting processes. The importance of the line is to describe the interrelation of a dependent variable (Y variable) with one or many independent variables (X variable).
An analyst can forecast future behaviors of the dependent variable by making use of the equation gotten the regression line. This is done by inputting different values for the independent ones. Regression lines are frequently employed in the financial sector.
Financial analysts make use of linear regressions to forecast stock prices, commodity prices and also to carry out valuations for many different securities. Companies use regressions for the purpose of forecasting sales, inventories and a lot of other variables that are needed for strategy and planning. The regression line formula is represented below:
(Y = a + bX + u)
Answer:
(a) $700
(b) $5.50
Explanation:
Weekly fixed costs = $6,000
Weekly Total meals = Average customers per day × No. of days
= 500 × 6
= 3,000
Fixed cost per meal = Weekly fixed costs ÷ Weekly Total meals
= $6,000 ÷ 3,000
= $2
(a) Lowest price in total = Number of customers × Variable costs for each meal
= 200 × $3.50
= $700
(b) Lowest price = Variable costs for each meal + Fixed cost per meal
= $3.50 + $2
= $5.50
