A. It is decreased by 50,000 (I'm 50% sure)
6% of 50,000 is 3,000
Answer:
The correct answer is letter "C": When both the fair value of a reporting unit and its associated implied goodwill fall below their respective carrying values.
Explanation:
Impairment Loss is the decrease in an asset's net carrying value that exceeds the future undisclosed cash flow it should generate. The net carrying value is an asset's acquisition cost minus depreciation. Impairment occurs when a company sells or abandons an asset that is no longer beneficial.
Thus, <em>a goodwill impairment loss is recognized when the goodwill's net carrying value is below its fair value and the expected cash flow it was to generate.</em>
Answer and Explanation:
Forecast error is a difference between Estimated data and real data, here Estimated data is referred to as forecast data.
According to rational expectations principles, expected forecast error's average always near to be zero.
Expected forecast error may be forecast or predict in future.
So, Expected forecast error will be zero (0%)
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.
Cut of alcohol
Explanation:
If they drink mre they are likely to becom drunk and migjt get into an accident
