Answer:
-$30,250 favorable
Explanation:
labor efficiency variance = (standard quantity - actual quantity) x standard labor cost
- actual quantity = 7,700 hours
- standard quantity = 9.9 hours x 1,000 units = 9,900
- standard labor cost = $13.70
labor efficiency variance = (7,700 - 9,900) x $13.70 = -$30,250 favorable variance
the variance is favorable, because less hours were actually used than forecasted
Answer:
The answer is A
Explanation:
Taxes on goods with INELASTIC demand curves will tend to raise more tax revenue for the government than taxes on goods with ELASTIC.
Goods with inelastic demand are insensitive to price. An increase price of the goods for example from an increase in tax on the goods will have no significant effect in the quantity demanded. Consumers will still buy it with an higher. So taxing this goods is a good source of revenue for the government.
Whereas goods with elastic demand are very sensitive to rice. Any slight increase in price will result in a significant decrease in quantity demanded. So government increasing tax on this good will be bad for its tax revenue because consumers won't be it
To further sell the car and make it seem more desirable, aswell as to be adding benefits constantly
Answer:
1) $240 warranty expense
2) $240 warranty liaiblity
3) zero as decreases the warranty laibility
4) 240 beginning - 209 used = 31 ending
5)
cash 6,000 debit
sales revenues 6,000 credit
--to record sale--
warranty expense 240 debit
warranty liability 240 credit
--to record prevision for warranty expenses--
warranty liability 209 debit
inventory 209 credit
--to record use of the warranty from the customer--
Explanation:
1) sales x expected warranty = 6,000 x 0.04 = 240
2) it will be for the 240 as the accounting works with double-entry
Answer:
B. they involve the use of expert judgement do develop forecasts
Explanation:
A time series is a series of events that is spaced equally in time. It is a statistical technique used to identify a time based trend of events and them make forecast using data from the trend/time series.
Time series requires certain processes which include discovering of a pattern in the historical data, projection of the historical data into the future, assumption that the pattern will remain the same(constant) as the time goes by, etc.
In time series method, since historical data is the point of reference for making a forecast, no expert judgements is required to develop forecasts. This is because once the data of the series from the past has been taken and a trend/pattern has been identified, that becomes the basis for future forecasts.
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