ANLY620

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.