Answer:
first - 25 degrees, second - 65 degrees
Step-by-step explanation:
For a smoothing constant of 0.2
Time period – 1 2 3 4 5 6 7 8 9 10
Actual value – 46 55 39 42 63 54 55 61 52
Forecast – 58 55.6 55.48 52.18 50.15 52.72 52.97 53.38 54.90
Forecast error - -12 -.6 -16.48 – 10.12 12.85 1.28 2.03 7.62 -2.9
The mean square error is 84.12
The mean forecast for period 11 is 54.38
For a smoothing constant of 0.8
Time period – 1 2 3 4 5 6 7 8 9 10
Actual value – 46 55 39 42 63 54 55 61 52
Forecast – 58 48.40 53.68 41.94 41.99 58.80 54.96 54.99 59.80
Forecast error - -12 6.60 -14.68 0.06 21.01 -4.80 0.04 6.01 -7.80The mean square error is 107.17
The mean forecast for period 11 is 53.56
Based on the MSE, smoothing constant of .2 offers a better model since the mean forecast is much better compared to the 53.56 of the smoothing constant of 0.8.
1/6p + (-4/5) is the equivalent expression. You have to add like terms, meaning constants are added to constants, variables are added to variables, etc. the result you get from adding like variables leaves you with 1/6p + (-4/5) or 1/6p - 4/5
Answer:
zero.
Step-by-step explanation:
. Here are the steps. h(x) = -5x - 10. h(-2) = -5(-2) - 10. h(-2) = 10 - 10. h(-2) = 0. Have a great day