Answer:
b. $103,345
Explanation:
Assets = Liabilities + Owner's Equity
Owner's Equity (Year 1) = $908,100 - $267,845
= $640,255
Owner's Equity (Year 2) = $980,279 - $233,892
= $746,387
increase in Owner's Equity = Owner's Equity (Year 2) - Owner's Equity (Year 1)
= $746,387 - $640,255
= $106,132
Net income during Year 2 = Increase in Owner's Equity - Additional investment + Withdrawals
= $106,132 - $28,658 + $25,871
= $103,345
Therefore, the amount of net income during Year 2 is $103.345.
Answer:
Answer:B Place the decimal point after 2
Explanation:
All you have to do is multiply 3.12 times 4
Answer:
net income during 2019 = $109,045
Explanation:
total stockholder equity 2018 = assets - liabilities = $293,500 - $79,245 = $214,255
total stockholder equity 2019 = assets - liabilities = $497,512 - $177,212 = $320,300
change in equity from 2018 to 2019 = $106,045
$33,000 can be explained by additional capital invested, and the remaining $73,045 corresponds to change in retained earnings
change in retained earnings = net income - dividends distributed
$73,045 = net income - $36,000
net income = $109,045
Answer:
S/n General Journal Debit Credit
a Insurance expense $1,200
Prepaid Insurance $1,200
(To record insurance expired)
b Supplies expense $6,200
Supplies $6,200
($5,000 + $2,000 - $800)
(To record supplies used)
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
