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All Subjects Mathematics General Mathematics GB513- Business Analytics (Kaplan Univ)

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Question # 00006849
Subject: Mathematics
Topic: General Mathematics
Due on: 01/19/2014
Posted On: 01/19/2014 05:43 AM

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GB513-Unit 5 Business Analytics (Kaplan Univ)

Assignment- This assignment requires you to use Excel. Make sure to use the Assignment 5 template found in your online course when you turn in your answers.

Question 1: Determine the error for each of the following forecasts. Compute MAD and MSE. Period Value Forecast Error

1 202 — —

2 191 202

3 173 192

4 169 181

5 171 174

6 175 172

7 182 174

8 196 179

9 204 189

10 219 198

11 227 211

Question 2: The U.S. Census Bureau publishes data on factory orders for all manufacturing, durable goods, and nondurable goods industries. Shown here are factory orders in the United States over a 13-year period (\$ billion).

a. Use these data to develop forecasts for the years 6 through 13 using a 5-year moving average.

b. Use these data to develop forecasts for the years 6 through 13 using a 5-year weighted moving average. Weight the most recent year by 6, the previous year by 4, the year before that by 2, and the other years by 1.

c. Compute the errors of the forecasts in parts (a) and (b) and then the MAD. Which forecast is

better?

Year Factory Orders (\$ billion)

1 2,512.7

2 2,739.2

3 2,874.9

4 2,934.1

5 2,865.7

6 2,978.5

7 3,092.4

8 3,356.8

9 3,607.6

10 3,749.3

11 3,952.0

12 3,949.0

13 4,137.0

Question 3: The “Economic Report to the President of the United States” included data on the amounts of manufacturers’ new and un filled orders in millions of dollars. Shown here are the figures for new

orders over a 21-year period. Use Excel to develop a regression model to fit the trend effects for

these data. Use a linear model and then try a quadratic model. How well does either model fit the

data?

Year Total Number of New Orders

1 55,022

2 55,921

3 64,1 82

4 76,003

5 87,327

6 85,139

7 99,513

8 115,109

9 131,629

10 147,604

11 156,359

12 168,025

13 162,140

14 175,451

15 192,879

16 195,706

17 195,204

18 209,389

19 227,025

20 240,758

21 243,643

Provide error for each forecast by computing Mean

Absolute Deviation (MAD) for Q1 5

Provide error for each forecast by computing Mean

Square Error (MSE) for Q1 5

Used data in Q2 (a) to develop forecasts for the years 6 through 13 using a 5-year moving average 3

Used data in Q2 (b) to develop forecasts for the years 6 through 13 using a 5-year weighted moving average 3

In the summary tables below, insert only the answers. You will show work after the summary section.

MSE

Question 2 :

Recommended forecast method:

Question 3

R-squared for Linear model

Regression formula for linear model

Work

Show all your work for the questions below.

Question 1 Show the errors you calculated

Question 2 Show the two forecasts and the errors

Question 3 Show the regression output tables

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