Answer:
Accommodation
Engagement Refinement
Explanation:
In the entrepreneurial ecosystem, networks still remain a popular and important aspect which is often thought and seen as a stepping stone to achieving entrepreneurial greatness. This network simply entails the creation of a circle or set of skilled individuals usually in different strategic areas of specialization relevant to a certain business line or sector. This way embarking on projects tends to be much easier as these networks of people can offer help, tips or together engage in to proffer solution on time. Networks are created usually through meetups and good interpersonal relationships. Having professionals around can speed up processes and. However, networks has to be properly managed usy be being accommodating and warm when approcached; frequent engagement topics and trending issues, including the desire to learn more and measure up to new trends.
Answer:
15.45%
Explanation:
Expected return of portfolio = (R1*W1) + (R2*W2 ) + (R3*W3) (Where R means Expected Return of stock and W means Weight of stock)
Expected return of portfolio = (15%*0.25)+(18%*0.45)+(12%*0.30)
Expected return of portfolio = 3.75% + 8.1% + 3.6%
Expected return of portfolio = 15.45%
So, the expected return of the portfolio above is 15.45%
<span>1. </span>Employ
a business that caters the needs of people such as owning a gymnasium. That way
it improves the quality of life of a person.
<span>2. </span>Basing
on the example above, it does not engage into any harmful practice.
<span>3. </span>Your
decision will depend on increasing the fee of gym users or improving the
interior design of your gym
<span>4. </span><span>It
provides support to the employee because they can interact with the gym users
and can use the gym too</span>
And the answer is A. Credit <span>refers to the money that a bank pays an account holder for putting money in the bank for a certain period.</span>
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.