Financial statements include assets listed at historical costs. Hence, the assets are recorded at their historical cost.
<h3>What do you mean by historical costs?</h3>
The price paid when an asset was purchased is known as the historical cost. On a company's balance sheet, the majority of long-term assets are recorded at their historical cost.
One of the fundamental accounting principles outlined by generally accepted accounting principles is historical cost (GAAP). The use of historical cost is consistent with conservative accounting because it avoids overstating an asset's value.
Hence, Financial statements include assets listed at historical costs. Hence, the assets are recorded at their historical cost.
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Answer:
b. $400,000
Explanation:
According to the historical cost principle, the land or fixed assets should be reported in the financial statement with the purchase price or historical price.
In the given situation, the land receiving value is $400,000 and its fair market value or FMV is $500,000 and exchange value is $300,000
So, here the land should be recorded at $400,000. Hence, we ignored the fair market value and the exchanged value
Answer:
The options for this question are the following:
a. 1
b. 2
c. 0.5
d. 1.5
The correct answer is a. 1
.
Explanation:
Group analysis or grouping is the task of grouping a set of objects in such a way that the members of the same group (called a cluster) are more similar, in some sense or another. It is the main task of exploratory data mining and is a common technique in the analysis of statistical data. It is also used in multiple fields such as machine learning, pattern recognition, image analysis, information search and retrieval, bioinformatics, data compression and graphic computing.
Group analysis is not in itself a specific algorithm, but the task pending solution. Clustering can be done using several algorithms that differ significantly in your idea of what constitutes a group and how to find them efficiently. Classical group ideas include small distances between members of the group, dense areas of the data space, intervals or particular statistical distributions. Clustering, therefore, can be formulated as a multi-objective optimization problem. The appropriate algorithm and the values of the parameters (including values such as the distance function to use, a density threshold or the number of expected groups) depend on the set of data analyzed and the use that will be given to the results. Grouping as such is not an automatic task, but an iterative process of data mining or interactive multi-objective optimization that involves trial and failure. It will often be necessary to pre-process the data and adjust the model parameters until the result has the desired properties.
