Answer:
Workplace discrimination prevents the firm from using the full potential of those employees that are being discriminated against.
Explanation:
For example, if the firm discriminates against a specific group of people when hiring (for example, it can discriminate against older people), the firm could lose valuable potential employees that could have provided great skill and experience for the firm.
If the firm practices discrimination against employees, the operation in the company will not be as streamlined as it could be against discrimination because those who are being treated poorly will be less motivated and have lesser output.
Answer:
The answer is: B)The adjustment for prepaid insurance was omitted.
Explanation:
The adjusted trial balance is the last step before producing the financial statements of a company. Its format is identical to unadjusted balances, it has three columns: account names, debits and credits. The debit and credit columns are calculated at the bottom and should always be equal. If they aren’t equal, the trial balance was prepared incorrectly.
The only error that would cause the adjusted trial balance to be unequal (debts ≠ credits) is; The adjustment for prepaid insurance was omitted. Prepaid insurance should be debited and cash (or accounts payable) should be credited.
Answer:
The property's distribution channels.
Explanation:
The marketing mix is commonly performed through the 4 P’s of marketing which are:
Price.
Product.
Promotion and
Place.
And the term 'Place' or 'Placement' in the 4 P’s of the marketing mix has to do with how the service will be rendered to the customer. And this refers to the physical location of the hotel and distribution channels, that is, how the service can be rendered to the customer and help assess what channel is the most suited to a service.
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.
