Ps2 for 404

Actual historical demand for a product is:

Month (Period ) Actual Forecast

January (1) 11.1 11.9

February (2) 13.9 12.6

March (3) 16.0 14.3

April (4) 13.7 14.9

May (5) 14.3 17.5

June (6) 18.2 13.7

пÑ"? Please show all your work for each problem unless indicated otherwise.

пÑ"? Where appropriate, please round each number to 2 decimal places

пÑ"? Please format Excel spreadsheets for printing if you are submitting the assignment electronically.

1. Calculate the mean and standard deviation of actual sales.

Line number Month (Period ) Actual Forecast x - Ој (x - Ој)* (x - Ој)

10 January (1) 11.10 11.90 -3.43 11.79

20 February (2) 13.90 12.60 -0.63 0.40

30 March (3) 16.00 14.30 1.47 2.15

40 April (4) 13.70 14.90 -0.83 0.69

50 May (5) 14.30 17.50 -0.23 0.05

60 June (6) 18.20 13.70 3.67 13.44

July (7)

70 Total 87.20

80 Number of periods 6.00

90 Mean (Ој=Line 70/Line 90) 14.53

100 Sum of square deviations 28.53

110 Variance (ПÑ"2=ОЈ ((x - Ој)*(x - Ој))/(6-1)) 5.71

120 Standard deviation (ПÑ"=SQRT (ОЈ ((x - Ој)*(x - Ој))/(6-1)) 2.39

2. What is the correlation for this data set? Hint: think about what you are correlating. The correlation between which two sets of numbers would offer meaningful information? (you do not need to show your calculations for this number)

The correlation coefficient for this data set equals 0.25. That means that there is very insignificant (almost nonexistent) correlation between the forecast values and actual data, which means that the forecasts given are only 25% accurate. In other words there is very little correlation between the actual data and the forecasts, which might also imply the inaccurate forecasting model used.

3. Using the NaÐ"Їve 1 Time Series, calculate the July forecast.

According to the NaÐ"Їve 1 Time Series technique the July forecast will equal 18.2 (the same as in June)

4. Using a three-month weighted moving average with the following, calculate the July forecast where:

a. July = t

b. t-1 weight = .43

c. t-2 weight =