The following estimated regression equation relating sales to inventory investment and advertising expenditures was given. ý = 24 + 14x, + 6x2 The data used to develop the model came from a survey of 10 stores; for those data, SST = 19,000 and SSR = 15,010. (a) For the estimated regression equation given, compute R2. R2 = (b) Compute R 2. (Round your answer to two decimal places.) R,2 = [ (c) Does the model appear to explain a large amount of variability the data? Explain. (For purposes of this exercise, consider an amount large if it is at least 55%. Round your answer to the nearest integer.) The adjusted coefficient of determination shows that % of the variability has been explained by the two independent variables; thus, we conclude that the model --Select-- v explain a large amount of variability.
The following estimated regression equation relating sales to inventory investment and advertising expenditures was given. ý = 24 + 14x, + 6x2 The data used to develop the model came from a survey of 10 stores; for those data, SST = 19,000 and SSR = 15,010. (a) For the estimated regression equation given, compute R2. R2 = (b) Compute R 2. (Round your answer to two decimal places.) R,2 = [ (c) Does the model appear to explain a large amount of variability the data? Explain. (For purposes of this exercise, consider an amount large if it is at least 55%. Round your answer to the nearest integer.) The adjusted coefficient of determination shows that % of the variability has been explained by the two independent variables; thus, we conclude that the model --Select-- v explain a large amount of variability.
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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![**Regression Analysis Exercise**
The following estimated regression equation relating sales to inventory investment and advertising expenditures was given:
\[ y = 24 + 14x_1 + 6x_2 \]
The data used to develop the model came from a survey of 10 stores; for those data, SST = 19,000 and SSR = 15,010.
### Tasks:
**(a) Compute \( R^2 \):**
- For the estimated regression equation given, compute the coefficient of determination \( R^2 \).
\[ R^2 = \_\_\_\_\_\_ \]
**(b) Compute \( R^2_a \):**
- Compute the adjusted \( R^2 \). (Round your answer to two decimal places.)
\[ R^2_a = \_\_\_\_\_\_ \]
**(c) Evaluate Model Variability:**
- Does the model appear to explain a large amount of variability in the data? Explain. (For purposes of this exercise, consider an amount large if it is at least 55%. Round your answer to the nearest integer.)
The adjusted coefficient of determination shows that \_\_\_\_\_\_ % of the variability has been explained by the two independent variables; thus, we conclude that the model \_\_\_\_\_\_ (use "Select" dropdown to indicate if it explains a large amount of variability or not).](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F358923dd-976b-4983-a367-bafd3b4b55c1%2F1c887e8a-8a9a-4017-9fb1-729275659d49%2Ffo8pnfn_processed.png&w=3840&q=75)
Transcribed Image Text:**Regression Analysis Exercise**
The following estimated regression equation relating sales to inventory investment and advertising expenditures was given:
\[ y = 24 + 14x_1 + 6x_2 \]
The data used to develop the model came from a survey of 10 stores; for those data, SST = 19,000 and SSR = 15,010.
### Tasks:
**(a) Compute \( R^2 \):**
- For the estimated regression equation given, compute the coefficient of determination \( R^2 \).
\[ R^2 = \_\_\_\_\_\_ \]
**(b) Compute \( R^2_a \):**
- Compute the adjusted \( R^2 \). (Round your answer to two decimal places.)
\[ R^2_a = \_\_\_\_\_\_ \]
**(c) Evaluate Model Variability:**
- Does the model appear to explain a large amount of variability in the data? Explain. (For purposes of this exercise, consider an amount large if it is at least 55%. Round your answer to the nearest integer.)
The adjusted coefficient of determination shows that \_\_\_\_\_\_ % of the variability has been explained by the two independent variables; thus, we conclude that the model \_\_\_\_\_\_ (use "Select" dropdown to indicate if it explains a large amount of variability or not).
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