Answer:
I. Identify what is the problem
II. Acquire the data
III. Develop the model
IV. Implement the Model.
V. Do the results look right.
Explanation:
The problem-solving process can be defined as the systematic approach used to identify and determine the solution to a particular problem.
The steps involved in the problem-solving process are;
1. Identify and define the problem: this is the first step to be taken in solving a problem. This is to ensure that, the focus is on the main issue or situation and all efforts is channeled in the right direction rather than the symptoms.
2. Gathering of information: this helps to consider the options available in solving a problem effectively.
3. Consider your options: this helps to compare the available and viable solutions to the problem.
4. Weigh disadvantages and evaluate a solution: you weigh the disadvantages of each solution, before choosing the one with the least disadvantages.
Hence, the fundamental steps of the problem solving process in the correct order are;
I. Identify what is the problem
II. Acquire the data
III. Develop the model
IV. Implement the Model.
V. Do the results look right.
Answer:
Debit Bad Debt Expense; Credit Accounts Receivable
Explanation:
Bad debts expense is related to a company's current asset accounts receivable. Bad debts expense is also referred to as uncollectible accounts expense or doubtful accounts expense.
When a cash payment is received from the debtor, cash is increased and the accounts receivable is decreased. When recording the transaction, cash is debited, and accounts receivable are credited.
Make more money and invest in the future of cars
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.
Answer:10.06 %
Explanation:
WACC = (Cost of equity × weight of equity ) + (Cost of debt × weight of debt)
Cost of equity = 0.17
Cost of debt = pretax cost of debt × (1 - tax rate )
0.06 × 0.52 = 0.0312
Weight of debt and equity = $3 / $6 = $0.5
WACC = ( 0.17 × 0.5 ) + (0.52×0.06 × 0.5) = 0.085 + 0.0156 = 0.1006 = 10.06%
