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The U.S. fireworks industry has experienced unprecedented growth during the past few years. Fireworks are a fairly seasonal item with a high proportion of revenue coming from "4th of July" sales. But revenue is also generated with New Year's celebrations, as well as various festivities throughout the year (concerts, sporting events, etc.). Data below from the past 3 years represent the industry revenue (millions of dollars) for the various defined seasons.

The U.S. fireworks industry has experienced unprecedented growth during the past few years. Fireworks are a fairly seasonal item with a high proportion of revenue coming from "4th of July" sales. But revenue is also generated with New Year's celebrations, as well as various festivities throughout the year (concerts, sporting events, etc.). Data below from the past 3 years represent the industry revenue (millions of dollars) for the various defined seasons.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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4:17 The U.S. fireworks industry has experienced unprecedented growth during the past few years. Fireworks are a fairly seasonal item with a high proportion of revenue coming from "4th of July" sales. But revenue is also generated with New Year's celebrations, as well as various festivities throughout the year (concerts, sporting events, etc.). Data below from the past 3 years represent the industry revenue (millions of dollars) for the various defined seasons. #1 Season 1 corresponds to "4th of July" revenue. Season 2 is the revenue from New Years. Season 3 represents the revenue from the remainder of the year. Year Season 1 Season 2 Season 3 2019 $822 $154 $51 2020 $876 $165 $54 2021 S886 S166 $55 a. Create the following dummy variables in columns next to the appropriate data: July-D =1 if Season 1, 0 otherwise Jan-D-1 if Season 2, 0 otherwise b. Create t to represent the time period. Thus, t=1 refers to the observation in Season 1 of 2019; t= 2 refers to the observation in Season 2 of 2019; ... and t= 9 refers to the observation in Season 3 of 2021. c. Use the Excel Data Analysis add-in to develop an estimated multiple regression equation to forecast industry revenue using time period (f) and the dummy variables (July-D and Jan-D) to account for any seasonal effects. d. Write the estimated regression equation. e. Based upon the seasonal effects in the data and the linear trend, compute each seasonal forecast (Season 1, 2 and 3) for 2022 using the multiple regression model.
Transcribed Image Text:4:17 The U.S. fireworks industry has experienced unprecedented growth during the past few years. Fireworks are a fairly seasonal item with a high proportion of revenue coming from "4th of July" sales. But revenue is also generated with New Year's celebrations, as well as various festivities throughout the year (concerts, sporting events, etc.). Data below from the past 3 years represent the industry revenue (millions of dollars) for the various defined seasons. #1 Season 1 corresponds to "4th of July" revenue. Season 2 is the revenue from New Years. Season 3 represents the revenue from the remainder of the year. Year Season 1 Season 2 Season 3 2019 $822 $154 $51 2020 $876 $165 $54 2021 S886 S166 $55 a. Create the following dummy variables in columns next to the appropriate data: July-D =1 if Season 1, 0 otherwise Jan-D-1 if Season 2, 0 otherwise b. Create t to represent the time period. Thus, t=1 refers to the observation in Season 1 of 2019; t= 2 refers to the observation in Season 2 of 2019; ... and t= 9 refers to the observation in Season 3 of 2021. c. Use the Excel Data Analysis add-in to develop an estimated multiple regression equation to forecast industry revenue using time period (f) and the dummy variables (July-D and Jan-D) to account for any seasonal effects. d. Write the estimated regression equation. e. Based upon the seasonal effects in the data and the linear trend, compute each seasonal forecast (Season 1, 2 and 3) for 2022 using the multiple regression model.
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