Answer:
A. $52,020
B. $0
C. $208,080
Explanation:
a. Computation of Rafael's realized gain on the exchange
Using this formula
Realized gain=Fair market value -Adjusted basis
Let plug in the formula
Realized gain= $190,740-$138,720
Realized gain=$52,020
Therefore a. Rafael's realized gain on the exchange is $52,020
b. Based on the information given Rafael's recognized $1031 gain is $0 reason been that
NO BOOT WAS RECEIVED
c. Computation for Rafael's $1245 depreciation recapture Amount
Using this formula
Depreciation recapture Amount=Equipment originally cost -Adjusted basis
Let plug in the formula
Depreciation recapture=$346,800-$138,720
Depreciation recapture=$208,080
Therefore Rafael's $1245 depreciation recapture of $208,080 is carried over to the replacement property
It can easily show a lot of the ups and downs to careers that one may be interested. You can see people's personal experiences and how people view the job compared to what it is. You can get a lot of insight which could sway your choice.
Answer:
. when an organized body of workers withholds its labor to force the employer to comply with its demands.
Explanation:
Answer:
The question you are asking is very <u><em>unclear</em></u>. There is no statement given, therefore I cant give a proper answer.
Explanation:
Answer:
Answer is the one which produces values which compare well with actual values based on a standard measure of error.
Explanation:
Exponential smoothing is one means of preparing short-term sales forecasts on a routine basis. To use exponential smoothing, however, one must decide the proper values for the smoothing constants in the forecasting model. One method for selecting the smoothing constants involves conducting a grid search to evaluate a wide range of possible values.
Exponential smoothing forecasting methods use constants that assign weights to current demand and previous forecasts to arrive at new forecasts. Their values influence the responsiveness of forecasts to actual demand and hence influence forecast error. Considerable effort has focused on finding the appropriate values to use.
One approach is to use smoothing constants that minimize some function of forecast error. Thus, in order to select the right constants for forecasting, different values are tried out on past time series, and the ones that minimize an error function like Mean Absolute Deviation (MAD) or Mean Squared Error (MSE) are the ones used for forecasting
