MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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Question
Use the Stata output below to answer the following question. The data used in this analysis is from a sample of airlines. The variables used are: - **fare**: average price of a one-way fare, in dollars - **dist**: distance of the flight, in miles ``` . reg lfare ldist Source | SS df MS Number of obs = 4,596 ----------------+---------------------------------- F(1, 4594) = 2696.98 Model | 323.702686 1 323.702686 Prob > F = 0.0000 Residual | 551.391705 4,594 .120024135 R-squared = 0.3699 ----------------+---------------------------------- Adj R-squared = 0.3698 Total | 875.094374 4,595 .190444913 Root MSE = .34645 ------------------------------------------------------------------------------ | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------------------------------------------------------------------------ ldist | .4025646 .0077577 51.93 0.000 .3873676 .4177617 _cons | 2.399834 .0521601 46.01 0.000 2.297575 2.502093 ------------------------------------------------------------------------------ ``` In the analysis, the explained sum of squares (SSE) is equal to __________. - 0.346 - 323.70 - 551.39 - 875.09 **Explanation:** The table displays results from a regression analysis where the model attempts to predict the average price of a one-way fare (`lfare`) based on the distance of the flight (`ldist`). The key sections show: 1. **SS (Sum of Squares):** - **Model SS** (SSE): 323.702686, representing the explained sum of squares, which indicates the variation in the dependent variable explained by the model. - **Residual SS**: 551.391705, representing the unexplained variation (error) in the model. - **Total SS**: 875.094374, the total variation in the dependent variable
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Transcribed Image Text:Use the Stata output below to answer the following question. The data used in this analysis is from a sample of airlines. The variables used are: - **fare**: average price of a one-way fare, in dollars - **dist**: distance of the flight, in miles ``` . reg lfare ldist Source | SS df MS Number of obs = 4,596 ----------------+---------------------------------- F(1, 4594) = 2696.98 Model | 323.702686 1 323.702686 Prob > F = 0.0000 Residual | 551.391705 4,594 .120024135 R-squared = 0.3699 ----------------+---------------------------------- Adj R-squared = 0.3698 Total | 875.094374 4,595 .190444913 Root MSE = .34645 ------------------------------------------------------------------------------ | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------------------------------------------------------------------------ ldist | .4025646 .0077577 51.93 0.000 .3873676 .4177617 _cons | 2.399834 .0521601 46.01 0.000 2.297575 2.502093 ------------------------------------------------------------------------------ ``` In the analysis, the explained sum of squares (SSE) is equal to __________. - 0.346 - 323.70 - 551.39 - 875.09 **Explanation:** The table displays results from a regression analysis where the model attempts to predict the average price of a one-way fare (`lfare`) based on the distance of the flight (`ldist`). The key sections show: 1. **SS (Sum of Squares):** - **Model SS** (SSE): 323.702686, representing the explained sum of squares, which indicates the variation in the dependent variable explained by the model. - **Residual SS**: 551.391705, representing the unexplained variation (error) in the model. - **Total SS**: 875.094374, the total variation in the dependent variable
Expert Solution
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Step 1

Given that 

SStotal = 875.094374

dfModel = 1 , dfE = 4594

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