Answer:
(1) Shen spends $200 to purchase legal service from Rowan and Martin. Associates - Dollars
(2) Valerie spends $8 to order a mojito cocktail - Dollars.
(3) Shen earns $375 per week working for Little Havana - Inputs.
Explanation:
<em>(1) & (2) statements in the "Answer" above</em> are <em>purchase on cash </em>transactions. Hence, they imply the flow of <em>dollars</em> from the household to the firm.
<em>(3) statement in the</em> <em>"Answer" above</em> implies giving of <em>factor input labor services</em> by Shen to Little Havana. Hence, it indicates the flow of <em>inputs </em>from the household to the firm.
Answer:
a positive incentive I think
Explanation:
When you're in middle school or younger, so you can save up money for college, a car, or whatever you need.
Answer:
see below
Explanation:
<u>1. COGS</u>
Expenses incurred for manufacturing or obtaining the products and materials sold during a given period.
COGS are the direct expenses in the production process. They include labor, materials, and direct overheads.
<u>2. Gross profit </u>
Balance arrived at after deducting the expenses incurred on the goods sold from the revenue earned by selling the goods.
The revenues must exceed the expenses for a business to realize a gross profit. Otherwise, it will be a loss.
3<u>. Operating expenses</u>
Expenses that a business incurs to carry out its daily operations. They are the indirect cost of production. Examples include insurance, administrative, and security costs.
4. <u>Selling expenses </u>
Money spent on advertising, traveling, and promotions. These are the costs incurred in the selling process.
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.