Answer:
that's my answer please
and I hope it helps you my good friend
Answer:
a. When drawing conclusions, make sure you summarize and explain your findings.
b. Tips for writing recommendations:
A. Your recommendations should always be the result of prior logical analysis.
B. Your recommendations should never be in the form of a command.
Explanation:
A good conclusion touches the theme or main topic, summarizes the main points, and connects with the introduction, but with a sense of closure. Conclusions should be sound and logical. Irrelevant conclusions are annoying to the senses. Without a conclusion, the report will sound like one illogical move without clear direction and purpose.
Recommendations should address improvement efforts based on the problem(s) presented in the body of the report.
I think the answer is a that is what i think
Answer:
Gross Domestic Product
= $500
<em>GDP is the final value of goods and services. The haircut is valued at $500 so is GDP. </em>
Net National Product:
= GDP - Depreciation
= 500 - 80
= $420
National Income
= $420
<em>This is the income that a resident of the country earns and $420 is what Barry earned in net income.</em>
Personal Income
= National income - Retained earnings
= 420 - 120 - 50
= $250
Disposable Personal Income (Dollars)
= Personal income - income taxes
= 250 - 90
= $160
Answer:
Multiple regression
Explanation:
With regards to the above, multiple regression can be used to determine one educational background, interest and gender so as to see if there is a variation in terms of individual's annual income as it relates to their educational background.
Multiple regression basically is a mathematical model, which is used when one value is matched with two or more variables. Here, the value is a stand alone, which is why we study, while the variables are dependent; hence are factors that required to be checked and why the whole analysis is being conducted.
In the above scenario, the value being represented is 'annual income' which is independent, while educational background, interest and gender are variables which are independent.
