First, select the best linear regression model to predict gross sales and explai
ID: 3357111 • Letter: F
Question
First, select the best linear regression model to predict gross sales and explain why you chose it (you can mention r-value, scatterplot, r-squared value or anything else you feel is appropriate in your explanation):
a) Using attendance to predict gross sales? Did you exclude any outliers or not? If so, explain which (report the outlier’s gross sales, number of shows, artist and tour) and why? If not, why not? If you decide to exclude any outliers then you must show the linear regression analysis before and after the outlier(s) are taken out.
OR
b) Using number of shows in the tour to predict gross sales? Did you exclude any outliers or not? If so, explain which (report the outlier’s gross sales, number of shows, artist and tour) and why? If not, why not? If you decide to exclude any outliers then you must show the linear regression analysis before and after the outlier(s) are taken out.
2. Next, using the linear regression you picked in part 1, answer the following questions:
a. Describe the scatterplot (direction, form, strength, outliers) and list the correlation value.
b. Express your linear regression line.
c. Interpret the slope within the context of this problem.
d. Interpret the y-intercept within the context of this problem.
e. What is the R^2 value? Interpret its meaning in the context of this problem.
f. What is the predicted gross sales of a tour that will have (pick the ONE that corresponds to the linear regression you chose to use: so if you chose to use attendance to predict gross sales then answer part (i). If you chose to use number of shows to predict gross sales then answer part (ii)):
i) an attendance of about 1.5 million people? Do you trust this prediction? Explain.
ii) 100 shows? Do you trust this prediction? Explain.
g) Calculate the residual for Lady Gaga’s Monster Ball Tour (pick the ONE that corresponds to the linear regression you chose to use: so if you chose to use attendance to predict gross sales then answer part (i). If you chose to use number of shows to predict gross sales then answer part (ii)):
i) where attendance was 2,500,000 people. Did the linear regression overestimate or underestimate it? By how much?
ii) where there were 201 shows. Did the linear regression overestimate or underestimate it? By how much?
h) Is the linear regression appropriate? Mention the scatterplot, R^2, and the residual plot.
× P Mail. Eman.ElBouri@ro MindTap Registrationen × statCrunch xC Chegg Study | Guided Sc x . C secure https://www.statcrunch.com/app/index.php? 2 Options (1 of 3) Simple linear regression results: Dependent Variable: attendance Independent Variable: gross attendance 299932.73 0.0090426686 gross Sample size: 40 R (correlation coefficient) = 0.93390583 R-sq = 0.87218009 Estimate of error standard deviation: 444875.85 Parameter estimates: Parameter Estimate Intercept Slope Std. Err. Alternative DF T-Stat p-value 038 1.8610785 0.0705 0.38 16.102586 0.0001 299932.73 161160.71 0.0090426686 0.00056156622 Analysis of variance table for regression model Source DF MS F-stat p-value Model 1 5. 1317907e13 5.131 7907e13 259.29329Explanation / Answer
Linear regression is appropriate to predict the gross sales for all the musical artists because
1) Most of the points in the scatterplot lie close to the regression line, however there are outliers which can be removed to get a better model
2) The residual plot shows randomness which is necessary. Hence, you can relay on this regression model
3) R2 value is 0.8721 which means that 87.21% of variation in dependent variable is explained by the independent variable. Since R2 is 0.8721, it is a ver good model.
You can obtain a better model by removing outliers. Use studentized residual method to remove the outliers. If the absolute value of studentized residual is greater than 3, then its an outlier.
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