Answer:
Liquidity
Explanation:
Liquidity is the degree to which an asset can be converted to cash. Assets that can easily be converted are described as liquid assets and include stocks and bonds.
Illiquid assets are not easy to sell. These assets may take a long time to sell or may be sold at a much cheaper price than the purchase price in order to make a quick sale. These include real estate, motor vehicles etc.
Answer:
recorded on March 31, 2021.
Explanation:
As we know that if there is an accural basis so the revenue is recognized and recorded when it is earned here the receipt of cash is not material for recording the revenue
Since in the given situation, the date of completion of the contract is considered for recording date of revenue as per the accrual basis
So March 31, 2021 should be considered
Answer:
C) Third
Explanation:
The first meal gives you 4 units of utility for every dollar spent (= 100 utility / $25).
The second meal gives you 5 units of utility for every dollar spent (= 10 utility / $2).
The third meal gives you 10 units of utility for every dollar spent (= 50 / $5). We should choose the meal that provides us with the greatest utility per dollar.
Answer:
A. $96
B. $228
C. $42
Explanation:
A. Calculation to determine the Amount of SUTA tax the company must pay to Nebraska on Porter's wages
SUTA tax =$3,000 x 3.2%
SUTA tax = $96
Therefore the Amount of SUTA tax the company must pay to Nebraska on Porter's wages is $96
B. Calculation to determine the Amount of sUTA tax the company must pay to Michiganion Porter's wages
SUTA tax =($9,000 - $3,000 )x3.8%
SUTA tax =$6,000 x 3.8%
SUTA tax = $228
Therefore the Amount of SUTA tax the company must pay to Nebraska on Porter's wages is $228
C. Calculation to determine the Amount of the net FUTA tax on Porters wages
Net FUTA tax=$7,000 limit) x 0.6%
Net FUTA tax = $42
Therefore the Amount of SUTA tax the company must pay to Nebraska on Porter's wages is $42
Answer:
Explanation:
A Supervised learning allows you to collect data or produce a data output from the previous experience while an unsupervised learning you do not need to supervise the model.
A. Deciding whether to issue a loan to an applicant based on demographic and financial data (with reference to a database of similar data on prior customers). - Supervised learning
B. In an online bookstore, making recommendations to customers concerning additional items to buy based on the buying patterns in prior transactions. - Unsupervised learning
c. Identifying a network data packet as dangerous (virus, hacker attack) based on comparison to other packets whose threat status is known - Supervised learning
d. Identifying segments of similar customers. - Unsupervised learning
e. Predicting whether a company will go bankrupt based on comparing its financial data to those of similar bankrupt and nonbankrupt firms. - Supervised learning
f. Estimating the repair time required for an aircraft based on a trouble ticket. - supervised learning
g. Automated sorting of mail by zip code scanning. - Supervised learning
H. Printing of custom discount coupons at the conclusion of a grocery store checkout based on what you just bought and what others have bought previously - Unsupervised learning
