Answer:
A)predict future organizational needs.
Explanation:
It is important to perform a SWOT analysis. Which helps us predict future business needs. Highlighting strengths, weaknesses, opportunities and threats.
- analyzing its internal characteristics (Weaknesses and Strengths).
- external situation (Threats and Opportunities) in a square matrix.
The two major SAS steps are data and proc.
SAS programming structure is based on two essential steps, Data and Proc.
1. The DATA step:
This involves collecting and uploading the essential data to the program memory. It is used to update, modify and edit the data in case of any errors once it has been added to a dataset. New datasets can be created from existing ones by updating, editing, and/or merging them. at the end of this step, SAS data sets are created.
2. The PROC step:
This step processes and analyses the data collected into datasets in the previous step. it is used to perform specific functions on the data. at the end of the proc step, a result or report is produced.
In a SAS code, each line of code should begin either with a DATA or PROC step.
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- Analysis: analysis is done in the PROC step.
- Content: Data or content is collected in the DATA step.
- Stat: a stat function acquires the status information regarding a specific file. Functions are performed on the datasets in the PROC step.
- Run: This command is used to execute a code.
- Import: Datasets are created by importing data from other datasets and outside.
- Print: the report produced at the end of the PROC step can be printed as a hard copy.
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B. negative attitude option
Answer:
The back propagation algorithm causes the network to settle into a stable state where it can correctly respond, to any desired degree of accuracy, to all inputs in the training set.
Explanation:
Back propagation algorithm is a part of the learning of a neural network. In a simple neural network setting at each iteration;
- The network<em> </em>assumes <em>random weights</em> at each network layer and passes the value to the next trough an <em>activation function</em>.
- Then a loss function is calculated by comparing the distace of the final value with the label value using a predetermined distance metric.
- The weights are optimized trough back propagation
Thus, back propagation algorithm used to reach the final weights of the neural network, so that it can work more accurately.
