Evaluate e1/2 to one decimal place.

Mathematics

2 answers:

Virty [35]4 years ago

5 0

For this case we have the following expression:

$e ^ {\frac {1} {2}}$

We must evaluate the expression, then:

$e ^ {0.5} =$

If we enter the expression in a calculator we have that the decimal form is given by:

$1.64872127$

If we round the expression we have to:

$e ^ {\frac {1} {2}} = 1.7$

The option that is closest is option A.

Answer:

Option A

Drupady [299]4 years ago

4 0

I might be 1.6 I’m not sure

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Which of the following summary measures for forecast errors does not depend on the units of the forecast variable? a. MFE (mean

Orlov [11]

Answer:

d. The mean absolute percentage error (MAPE) does not depend on the units of the forecast variable.

Step-by-step explanation:

A forecast error is the difference between the actual or real and the predicted or forecast value of a time series or any other phenomenon of interest. Here “error” does not mean a mistake, it means the unpredictable part of an observation.

There are many different ways to summarize forecast errors in order to provide meaningful information.

Scale-dependent errors. The forecast errors are on the same scale as the data. The two most commonly used scale-dependent measures are based on the absolute errors or squared errors:

$\begin{align*} \text{Mean absolute error: MAE} & = \text{mean}(|e_{t}|),\\ \text{Root mean squared error: RMSE} & = \sqrt{\text{mean}(e_{t}^2)}.\end{align*}$ $\text{Mean absolute error: MAE} & = \text{mean}(|e_{t}|),\\ \\\text{Root mean squared error: RMSE} & = \sqrt{\text{mean}(e_{t}^2)}.$

Percentage errors. Percentage errors have the advantage of being unit-free, and so are frequently used to compare forecast performances between data sets. The most commonly used measure is:

$\text{Mean absolute percentage error: MAPE} = \text{mean}(|p_{t}|).$