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NEW QUESTION: 1
An organization accumulated the following data for the prior fiscal year:
Value of
Percentage of
Quarter
Output Produced
Cost X
1
$4,750,000
2.9
2
$4,700,000
3.0
3
$4,350,000
3.2
4
$4,000,000
3.5
Based on this data, which of the following describes the value of Cost X in relation to the value of Output Produced?
A. Cost X is a semi-fixed cost.
B. Cost X and the value of Output Produced are unrelated.
C. Cost X is a fixed cost.
D. Cost X is a variable cost.
Answer: C

NEW QUESTION: 2
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are creating a model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.
You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution: Use the following metrics: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.
Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.
Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.
Relative squared error (RSE) similarly normalizes the total squared error of the predicted values by dividing by the total squared error of the actual values.
Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In other words: ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.
Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
AUC.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

NEW QUESTION: 3
Mark works as a System Analyst for Blue Well Inc. He uses Microsoft Outlook for task reque sting and emailing. MS Outlook has been configured to use an IMAP account. Mark has to prepare a business report of the company. He assigns the task of writing the report to another employee of the company.
However, after assigning the task, he is not receiving updates of the assigned task.
Which of the following is the most likely cause of the issue?
A. He has created an unassigned copy of the task.
B. He has cleared the Send me a status report when this task is complete check box.
C. He has cleared all the filters that are applied on the task.
D. He has cleared the Keep an updated copy of this task on my task list check box.
Answer: A

NEW QUESTION: 4
Was sind einige Anforderungen für die automatische Wellenerstellung? Es gibt 2 richtige Antworten auf diese Frage.
A. Wave-Vorlagen sind definiert
B. Die Planung der Transporteinheiten ist abgeschlossen
C. Bedingungen und Konditionssätze existieren
D. Die Kartellierungsplanung ist abgeschlossen
Answer: A,C