Answer:
The total gains from trade are <u>4</u> dishes of pasta and <u>4</u> pizzas an hour.
Explanation:
Before specialization, Mia and Mario each produced 4 dishes of pasta and 4 pizzas per hour. After specialization, Mia is able to produce 12 dishes of pasta, and Mario is able to produce 12 pizzas per hour.
After specialization and trade, the total maximum combined output per hour is 12 dishes of pasta and 12 pizzas. Before specialization, the total maximum combined output per hour was 8 dishes of pasta and 8 pizzas. So the net gain of specialization and trade is 4 dishes of pasta and 4 pizzas per hour.
Answer:
The correct answer is letter "B": Experienced driver with a good driving record.
Explanation:
Insurances do take into consideration the level of risk individuals represent according to the type of coverage they apply for. While talking about car insurance, <em>an experienced driver with a good driving record represents a minimum risk for the company, thus, the individual will likely pay a lower premium than someone who has had several vehicle accidents with a negative driving history.</em>
Answer:
Net Purchases = Cost of goods sold - Decrease in Inventory
= $308,000 - $16,500
= $291,500
Cash paid to Suppliers = Net Purchases + Decrease in accounts Payable
= $291,500 + $13,500
= $305,000
The summary entry is as follows:
Merchandise Inventory A/c Dr. $291,500
Accounts payable A/c Dr. $13,500
To cash $305,000
(To record the amount of cash paid to merchandise suppliers during 2018)
Prior to the studies of Hawthorne, it has been studied that
the managers had pay little attention of the role in human behavior when it
comes to making decisions because they are likely focus more about their line
of work and the progress rather than having to use their own behavior as a
human and whether which are acceptable and not.
Answer: sentiment analysis software
Explanation: The Content is produced through social networks, through Web forms, through mobile applications. Content is created by your customers, your market, by thought leaders, employees, and other enterprises. They are sharing opinions, ideas, comments, requesting information, expressing concerns, discussing technologies and products and influencing. Valuable information is trending within those conversations, threads, and post .Uncovering and translating social data into tangible insights has become one of the key challenges for Sentiment Analysis technology providers wanting to propose a real social mining solution to their users. The sentiment analysis software is a specialized classification engine used to identify and evaluate subjective patterns and expressions of sentiment within textual content. The analysis is performed at the topic, sentence, and document level and is configured to recognize whether portions of text are factual or subjective and, in the latter case, if the opinion expressed within these pieces of content are positive, negative, mixed, or neutral. The combination of machine learning with natural language processing techniques, the sentiment analysis is one of the most powerful engines available .
