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In accounting, adjusting entries are journal entries usually made at the end of an accounting period to allocate income and expenditure to the period in which they actually occurred. The revenue recognition principle is the basis of making adjusting entries that pertain to unearned and accrued revenues under accrual-basis accounting .
A general journal entry would typically include the date of the transaction (which may be dispensed with after the first entry of the day), the names of the accounts to be debited and credited (which should be the same as the name in the chart of accounts), the amount of each debit and credit, and a summary explanation of the transaction ...
Journal entries can record unique items or recurring items such as depreciation or bond amortization. In accounting software, journal entries are usually entered using a separate module from accounts payable, which typically has its own subledger, that indirectly affects the general ledger. As a result, journal entries directly change the ...
The reason for this is to limit the number of entries in the nominal ledger: entries in the daybooks can be totalled before they are entered in the nominal ledger. If there are only a relatively small number of transactions it may be simpler instead to treat the daybooks as an integral part of the nominal ledger and thus of the double-entry system.
Closing entries are journal entries made at the end of an accounting period to transfer temporary accounts to permanent accounts. An "income summary" account may be used to show the balance between revenue and expenses , or they could be directly closed against retained earnings where dividend payments will be deducted from.
Variations include: simple, cumulative, or weighted forms. Mathematically, a moving average is a type of convolution. Thus in signal processing it is viewed as a low-pass finite impulse response filter. Because the boxcar function outlines its filter coefficients, it is called a boxcar filter. It is sometimes followed by downsampling.
The arithmetic mean (or simply mean or average) of a list of numbers, is the sum of all of the numbers divided by their count.Similarly, the mean of a sample ,, …,, usually denoted by ¯, is the sum of the sampled values divided by the number of items in the sample.
Depending on the analysis method, these data can still induce parameter bias in analyses due to the contingent emptiness of cells (male, very high depression may have zero entries). However, if the parameter is estimated with Full Information Maximum Likelihood, MAR will provide asymptotically unbiased estimates.