Answer: 21%
Explanation: The developer purchased 3 properties and he can buy each property for $20 per square foot.
Therefore: 75 × 110 =8250 square feet.
8250 × $20 = $165 000 per lot.
Each lot was sold for $200 000. Which means the developer made profits of:
$200 000 - $165 000 = $35 000 per lot.
The percentage of profit on each lot is:
Percentage of profit on cost amount:
= 
= 0.2121212 recurring × 100
= 21,21%
Percentage of profit on sale amount:
= 
= 0.175 × 100
= 17,5%
Answer:
The statement is true
Explanation:
As a fact, I agree that with large sample sizes, even the small differences between the null value and the observed point estimate can be statistically significant.
To put it differently, any differences between the null value and the observed point estimate will be material and/or significant if the samples are large in shape and form.
It's also established that point estimate get more clearer and understandable, and the difference between the mean and the null value can be easily singled out if the sample size is bigger.
Suffix to say, however, while the difference may connote a statistical importance, the practical implication notwithstanding, will be looked and studied on a different set of rules and procedures, beyond the statistical relevance.
Ideally, the Behavior Analyst should leave the business card, the name of the individual to be served, and the name of the service that will be provided. In this case, option B is the correct answer.
We can arrive at this answer because:
- The Behavior Analyst needs to show that he tried to contact the customer and show that he is interested in contacting him again.
- For this reason, he shows that the customer can get in touch with him, leaving the business card, with the contact forms.
- To make this contact more professional and thus increase the credibility of the service, the Behavior Analyst leaves the name of the person to be served and the service that will be provided.
This type of behavior shows commitment to customer service, which gives the Behavior Analyst credibility and increases the chances of a contract.
More information:
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In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product. Optimization of process parameters is one of the ways to achieve desired quality of a part.
In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product.
Optimization of process parameters is one of the ways to achieve desired quality of a part. Finite Element Method (FEM) and machine learning techniques are applied to evaluate and optimize AM process parameters. While FEM requires specific information, Powder Bed Fusion Machine Learning is based on big amounts of data. This paper provides a conceptual framework on combination of mathematical modelling and Machine Learning to avoid these issues.
Learn more about Powder Bed Fusion here
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Answer:
D hope that helps you out
