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elixir [45]
3 years ago
15

What type of correlation would be expected between distance run and calories burned? Would this relationship be causal?positive

correlation, not causal
positive correlation, causal
negative correlation, not causal
negative correlation, causal
Mathematics
2 answers:
jek_recluse [69]3 years ago
7 0
It would be a positive correlation, casual.

Because as you run you burn more calories, everytime you do it.
Vlad [161]3 years ago
4 0
<h3><u>Answer:</u></h3>

The type of correlation that is expected between distance run and calories burned is:

positive correlation, causal

<h3><u>Step-by-step explanation:</u></h3>

A casual relation is one in which the occurrence of one event leads to the occurrence of the other.

A positive correlation is one when if there is a increase in the value of one variable then the value of the second variable also increases.

Now, if a person will run more distance then obviously it will lead to the burn of calories and also with the increase in distance the amount of calories burned will also increase.

Hence, the relation that holds between the two variable i.e. distance run and calories burned is:

Positive correlation, casual.

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Rasek [7]

Answer:

  c. 30.9 °C; 32.9 °C

Step-by-step explanation:

Put the given numbers into the given formula and do the arithmetic.

(a) The temperature of sample 1 is ...

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___

(b) The temperature of sample 2 is ...

  y = (100 -4)e^(-0.12·10) +4 ≈ 32.9

4 0
3 years ago
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:

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6 0
3 years ago
Solve: log (7t + 2) = 2<br>t =​
IrinaK [193]

Answer:

t = 14

Step-by-step explanation:

log (7t + 2) = 2

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10 ^log (7t + 2) =10^ 2

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Subtract 2 from each side

7t+2-2 = 100-2

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Divide each side by 7

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7 0
4 years ago
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Naily [24]

3.4-(45-9.5)-42x5

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3 0
3 years ago
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7 0
2 years ago
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