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 ...
y = (100 -24)e^(-0.12·20) + 24 ≈ 30.9
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(b) The temperature of sample 2 is ...
y = (100 -4)e^(-0.12·10) +4 ≈ 32.9
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:


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:

Answer:
t = 14
Step-by-step explanation:
log (7t + 2) = 2
Raise each side to the power of 10
10 ^log (7t + 2) =10^ 2
7t+2 = 100
Subtract 2 from each side
7t+2-2 = 100-2
7t = 98
Divide each side by 7
7t/7 = 98/7
t = 14
Step-by-step explanation:
1×100000+2×10000+9×1000+9×100+8×10+2×1
=100000+20000+9000+900+80+2