QUESTION 1

1. Use the data about Lenovo computer chips to answer questions 1-5.

Lenovo uses the ZX-81 chip in some of its laptop computers.The prices for the chip during the past 12 months were as follows:

Month	Price Per Chip	Month	Price Per Chip
January	$1.80	July	$1.80
February	$1.67	August	$1.83
March	$1.70	September	$1.70
April	$1.85	October	$1.65
May	$1.90	November	$1.70
June	$1.87	December	$1.75

Create 2-month and 3-month moving average forecasts for the chip price data.

What is the MAD for the 2-month moving average? Enter your answer as a decimal to three places; example: 45.877

QUESTION 2

1. What is the MAD for the 3-month moving average? Enter your answer as a decimal to three places; example: 45.877

QUESTION 3

1. Which moving average forecast is better?

2-month moving average forecast

3-month moving average forecast

QUESTION 4

1. Compute the forecasts for each month using exponential smoothing, with an initial forecast for January of $1.80.Use alpha = 0.1, then alpha = 0.3, and finally alpha = 0.5.

Using MAD, which alpha is best?

alpha = 0.1 forecast

alpha = 0.3 forecast

alpha = 0.5 forecast

QUESTION 5

1. Using your best exponential smoothing forecast, what is the forecast for chip price in January? Enter your answer as a decimal to two places with no dollar signs; example: 7.71

QUESTION 6

Use the Lego World data to answer questions 6-9.

Attendance at Orlando's newest attraction, Lego World, has been as follows:

Quarter	Guests (thousands)	Quarter	Guests (thousands)
Winter Year 1	73	Summer Year 2	124
Spring Year 1	104	Fall Year 2	52
Summer Year 1	168	Winter Year 3	89
Fall Year 1	74	Spring Year 3	146
Winter Year 2	65	Summer Year 3	205
Spring Year 2	82	Fall Year 3	98

Compute seasonal indices using all data.

What is your seasonal index for spring? Enter your answer as a decimal to three places; example: 45.877

QUESTION 7

What is your seasonal index for summer? Enter your answer as a decimal to three places; example: 45.877

QUESTION 8

What is your seasonal index for fall? Enter your answer as a decimal to three places; example: 45.877

QUESTION 9

What is your seasonal index for winter? Enter your answer as a decimal to three places; example: 45.877

QUESTION

Tax_Revenue. The accompanying data file contains 57 months of tax revenue from medical and retail marijuana tax and fee collections. For cross-validation, let the training and the validation sets comprise the first 45 months and the last 12 months, respectively.

Use the training set to implement the Holt exponential smoothing method with user-supplied ( = 0.30 and = 0.20), as well as the computer-generated, smoothing parameters and compute the resulting MSE, MAD, and MAPE for the validation set.

Determine the preferred model and reimplement it with the entire data set to forecast tax revenue for the 58th month."

QUESTION 10 Build two Holt exponential smoothing forecasts: (1) alpha = 0.30 and beta = 0.20 and (2) computer generatedparameters.ComputeMSE, MAD, and MAPE for the validation data.Which forecast is better?

	Forecast 1: alpha = 0.30 and beta = 0.20
Forecast 2: computer generated parameters

QUESTION 11

1. Answer part b for exercise #36.What is the tax revenue forecast for month 58? Enter your answer as a whole number with no commas or dollar signs; example: 87434

Marijuana Tax and Fees in Colorado
Date	Revenue
Feb-14	3,519,756
Mar-14	4,092,575
Apr-14	4,980,992
May-14	5,273,355
Jun-14	5,715,707
Jul-14	6,522,085
Aug-14	7,407,450
Sep-14	7,741,167
Oct-14	7,232,870
Nov-14	7,642,800
Dec-14	7,465,568
Jan-15	8,558,141
Feb-15	8,802,295
Mar-15	9,099,395
Apr-15	9,979,643
May-15	10,617,311
Jun-15	11,326,452
Jul-15	10,856,584
Aug-15	12,811,437
Sep-15	13,181,758
Oct-15	11,656,736
Nov-15	11,290,012
Dec-15	12,231,410
Jan-16	13,247,434
Feb-16	12,466,617
Mar-16	14,195,923
Apr-16	13,097,298
May-16	17,183,162
Jun-16	14,482,648
Jul-16	16,823,849
Aug-16	17,574,149
Sep-16	19,145,992
Oct-16	19,266,922
Nov-16	18,425,864
Dec-16	17,694,953
Jan-17	18,164,787
Feb-17	17,723,858
Mar-17	17,550,692
Apr-17	22,867,587
May-17	18,563,939
Jun-17	19,662,464
Jul-17	20,437,751
Aug-17	23,775,043
Sep-17	23,235,507
Oct-17	23,098,408
Nov-17	21,076,993
Dec-17	21,211,445
Jan-18	21,681,438
Feb-18	21,134,864
Mar-18	20,871,820
Apr-18	22,947,141
May-18	22,367,707
Jun-18	21,928,692
Jul-18	22,963,778
Aug-18	23,044,818
Sep-18	23,770,397
Oct-18	22,589,679