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Refer to the IEEE International Conference on Web Intelligence and Intelligent Agent Technology (2010) study on using the volume of chatter on Twitter.com to forecast movie box office revenue, Exercise 11.27 (p. 642). Recall that opening weekend box office revenue data (in millions of dollars) were collected for a sample of 24 recent movies. In addition to each movie's tweet rate, i.e., the average number of tweets referring to the movie per hour 1 week prior to the movie's release, the researchers also computed the ratio of positive to negative tweets (called the PN-ratio). a. Give the equation of a first-order model relating revenue (y) to both tweet rate (x₁) and PN-ratio (~2). b. Which ß in the model, part a, represents the change in revenue (y) for every 1-tweet increase in the tweet rate (x₁), holding PN-ratio (x₂) constant? c. Which ß in the model, part a, represents the change in revenue (y) for every 1-unit increase in the PN-ratio (x2), holding tweet rate (x₁) constant?

Refer to the IEEE International Conference on Web Intelligence and Intelligent Agent Technology (2010) study on using the volume of chatter on Twitter.com to forecast movie box office revenue, Exercise 11.27 (p. 642). Recall that opening weekend box office revenue data (in millions of dollars) were collected for a sample of 24 recent movies. In addition to each movie's tweet rate, i.e., the average number of tweets referring to the movie per hour 1 week prior to the movie's release, the researchers also computed the ratio of positive to negative tweets (called the PN-ratio). a. Give the equation of a first-order model relating revenue (y) to both tweet rate (x₁) and PN-ratio (~2). b. Which ß in the model, part a, represents the change in revenue (y) for every 1-tweet increase in the tweet rate (x₁), holding PN-ratio (x₂) constant? c. Which ß in the model, part a, represents the change in revenue (y) for every 1-unit increase in the PN-ratio (x2), holding tweet rate (x₁) constant?

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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12.10 FORECASTING MOVIE REVENUES WITH TWITTER. Refer to the IEEE International Conference on Web Intelligence and Intelligent Agent Technology (2010) study on using the volume of chatter on Twitter.com to forecast movie box office revenue, Exercise 11.27 (p. 642). Recall that opening weekend box office revenue data (in millions of dollars) were collected for a sample of 24 recent movies. In addition to each movie's tweet rate, i.e., the average number of tweets referring to the movie per hour 1 week prior to the movie's release, the researchers also computed the ratio of positive to negative tweets (called the PN-ratio). a. Give the equation of a first-order model relating revenue (y) to both tweet rate (x₁) and PN-ratio (x₂). b. Which 3 in the model, part a, represents the change in revenue (y) for every 1-tweet increase in the tweet rate (x₁), holding PN-ratio (x₂) constant? c. Which 3 in the model, part a, represents the change in revenue (y) for every 1-unit increase in the PN-ratio (x2), holding tweet rate (x₁) constant? d. The following coefficients were reported: R² = .945 and R2 = .940. Give a practical interpretation for both R² and R². e. Conduct a test of the null hypothesis, Ho: B₁ = B₂ = 0. :.05. Use a = f. The researchers reported the p-values for testing Ho: B₁ = 0 and Ho: B₂ = 0 as both less than .0001. Interpret these results (use a = .01).
Transcribed Image Text:12.10 FORECASTING MOVIE REVENUES WITH TWITTER. Refer to the IEEE International Conference on Web Intelligence and Intelligent Agent Technology (2010) study on using the volume of chatter on Twitter.com to forecast movie box office revenue, Exercise 11.27 (p. 642). Recall that opening weekend box office revenue data (in millions of dollars) were collected for a sample of 24 recent movies. In addition to each movie's tweet rate, i.e., the average number of tweets referring to the movie per hour 1 week prior to the movie's release, the researchers also computed the ratio of positive to negative tweets (called the PN-ratio). a. Give the equation of a first-order model relating revenue (y) to both tweet rate (x₁) and PN-ratio (x₂). b. Which 3 in the model, part a, represents the change in revenue (y) for every 1-tweet increase in the tweet rate (x₁), holding PN-ratio (x₂) constant? c. Which 3 in the model, part a, represents the change in revenue (y) for every 1-unit increase in the PN-ratio (x2), holding tweet rate (x₁) constant? d. The following coefficients were reported: R² = .945 and R2 = .940. Give a practical interpretation for both R² and R². e. Conduct a test of the null hypothesis, Ho: B₁ = B₂ = 0. :.05. Use a = f. The researchers reported the p-values for testing Ho: B₁ = 0 and Ho: B₂ = 0 as both less than .0001. Interpret these results (use a = .01).
Regression Equation Revenue (millions) = 1.15 + 0.07877 TweetRate Coefficients Coef SE Coef 1.15 3.68 Term Constant TweetRate 0.07877 Model Summary 95% CI (-6.49, 8.80) 0.00794 (0.06226, 0.09527) S R-sq R-sq(adj) 13.3165 82.42% PRESS R-sq(pred) 81.59% 7096.95 T-Value P-Value 0.31 0.757 9.92 0.000 AICC BIC 66.50% 189.54 191.68 VIF 1.00
Transcribed Image Text:Regression Equation Revenue (millions) = 1.15 + 0.07877 TweetRate Coefficients Coef SE Coef 1.15 3.68 Term Constant TweetRate 0.07877 Model Summary 95% CI (-6.49, 8.80) 0.00794 (0.06226, 0.09527) S R-sq R-sq(adj) 13.3165 82.42% PRESS R-sq(pred) 81.59% 7096.95 T-Value P-Value 0.31 0.757 9.92 0.000 AICC BIC 66.50% 189.54 191.68 VIF 1.00
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