Part A:
For the data listed below,
Month
Tired Sold
January
515
February
385
March
1413
April
1925
May
1146
June
894
July
826
August
635
September
2165
October
1535
November
707
December
639
1. Create a forecast using a three period simple moving average
2. Create a forecast using a three month weighted moving average using weights 0.6, 0.3 and 0.1 assigning higher weight for the most recent period.
3. Create an exponential smoothing forecast using a smoothing constant of 0.4.
4. Calculate RMSE for each of the methods, compare values and identify the method yielding a forecast with better accuracy.
Part B:
For the data listed below,
Purchase
Income ($ ‘000)
Age
Gender
0
71.9
42
2
0
100.4
42
1
0
105.6
44
1
1
83.1
39
2
0
114.2
43
1
1
113.5
44
1
0
115.2
42
1
0
100.4
35
2
0
92.6
43
2
0
123.8
42
1
0
122.8
45
1
1
98.6
46
2
0
107.6
41
2
0
108.4
42
2
1
138.8
41
1
1
109.9
44
2
1
136.2
47
1
1
117.6
38
2
1
122.8
43
2
0
121.8
45
2
1
126.6
41
2
1
125.8
46
2
1
138.8
42
2
0
149.6
37
1
1
159.5
33
2
Code definitions: Purchase 0 Not purchased and 1 Purchased; Gender 1 Male and 2 Female
Fit a logistic regression model to predict purchase decision. Identify significant predictors and comment on classification accuracy.
Submit a word doc including key results and their interpretation for both parts A and B. Attach Excel files to support your results which is a must to get credit for the assignment.
