The following data shows the quarterly profit (in thousands of dollars) made by a particular company in the past 3 years.
1 answer:
Answer:
208.82 ; 61.33
Step-by-step explanation:
Given the data:
Year Quarter Profit ($1000s)
1 1 45
1 2 51
1 3 72
1 4 50
2 1 49
2 2 45
2 3 79
2 4 54
3 1 42
3 2 58
3 3 70
3 4 56
The 3 period moving average :
Yr Qtr Profit ($1000s)Pt ____ Ft ____ e=(PT-Ft)²
1 _ 1 ___45 __
1 _ 2 __ 51 __
1 _ 3 __ 72 __
1 _ 4 __ 50 ______________56 ____ 36
2 _ 1 __ 49 ______________57.67__ 75.17
2 _ 2__ 45 ______________57 ____ 144
2 _ 3__ 79 ______________48 ____961
2 _ 4__ 54 ______________57.67 _ 13.47
3 _ 1__ 42 ______________59.33__300.33
3 _2 __ 58 ______________58.33__ 0.11
3 _ 3__ 70 ______________51.33__ 348.57
3 _ 4 __56 ______________56.67__0.45
MSE = Σ(Pt - Ft)² ÷ n
MSE = (36+75.17+144+961+13.47+300.33+0.11+348.57+0.75) ÷ 9
= 208.82
Forecast for next quarter :
(58 + 70 + 56) / 3 = 184 /3 = 61.33
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Answer:
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so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can said that at 5% or 1% of significance we fail to reject the null hypothesis.
Step-by-step explanation:
It's important to refresh the p value method or p value approach . "This method is about determining "likely" or "unlikely" by determining the probability assuming the null hypothesis were true of observing a more extreme test statistic in the direction of the alternative hypothesis than the one observed". Or in other words is just a method to have an statistical decision to fail to reject or reject the null hypothesis.
The significance level is not provided but we can assume it as . First we need to calculate the degrees of freedom like this:
The next step would be calculate the p value for this test. Since is a bilateral test or two tailed test, the p value would be:
If we compare the p value and using the significance level given we have
so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can said that at 5% or 1% of significance we fail to reject the null hypothesis.