### Analysis of CEO Salary and Company Sales We analyzed data from 209 publicly traded firms around 2010 to explore the relationship between a CEO's salary and the company's annual sales. Our focus was on using the CEO's salary to predict the company's sales. **Variables:** - **sales\(_i\):** Company's annual sales, measured in millions of dollars. - **salary\(_i\):** CEO's salary, measured in thousands of dollars. Using least-squares linear regression, we derived the equation: \[ sales_i = \alpha + \beta \cdot salary_i + e_i \] **Regression Results:** The displayed table shows the output of the regression analysis. - **SS (Sum of Squares)**: - Model: 337920405 - Residual: 2.3180e+10 - Total: 2.3518e+10 - **df (Degrees of Freedom)**: - Model: 1 - Residual: 207 - Total: 208 - **MS (Mean Square)**: - Model: 337920405 - Residual: 111980203 - **F-Statistics**: - F(1, 207) = 3.02 - Prob > F = 0.0838 - **R-squared**: 0.0144 - **Adjusted R-squared**: 0.0096 - **Root MSE**: 10582 **Coefficients:** - **salary**: - Coefficient: 0.9287785 - Standard Error: 0.5346574 - t-value: 1.74 - P>|t|: 0.084 - 95% Confidence Interval: [-0.1252934, 1.98285] - **_cons (Constant)**: - Coefficient: 5733.917 - Standard Error: 1002.477 - t-value: 5.72 - P>|t|: 0.000 - 95% Confidence Interval: [3757.543, 7710.291] **Regression Equation:** The regression line equation is: \[ \hat{sales} = 5,733.917 + 0.928778
### Analysis of CEO Salary and Company Sales We analyzed data from 209 publicly traded firms around 2010 to explore the relationship between a CEO's salary and the company's annual sales. Our focus was on using the CEO's salary to predict the company's sales. **Variables:** - **sales\(_i\):** Company's annual sales, measured in millions of dollars. - **salary\(_i\):** CEO's salary, measured in thousands of dollars. Using least-squares linear regression, we derived the equation: \[ sales_i = \alpha + \beta \cdot salary_i + e_i \] **Regression Results:** The displayed table shows the output of the regression analysis. - **SS (Sum of Squares)**: - Model: 337920405 - Residual: 2.3180e+10 - Total: 2.3518e+10 - **df (Degrees of Freedom)**: - Model: 1 - Residual: 207 - Total: 208 - **MS (Mean Square)**: - Model: 337920405 - Residual: 111980203 - **F-Statistics**: - F(1, 207) = 3.02 - Prob > F = 0.0838 - **R-squared**: 0.0144 - **Adjusted R-squared**: 0.0096 - **Root MSE**: 10582 **Coefficients:** - **salary**: - Coefficient: 0.9287785 - Standard Error: 0.5346574 - t-value: 1.74 - P>|t|: 0.084 - 95% Confidence Interval: [-0.1252934, 1.98285] - **_cons (Constant)**: - Coefficient: 5733.917 - Standard Error: 1002.477 - t-value: 5.72 - P>|t|: 0.000 - 95% Confidence Interval: [3757.543, 7710.291] **Regression Equation:** The regression line equation is: \[ \hat{sales} = 5,733.917 + 0.928778
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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![### Analysis of CEO Salary and Company Sales
We analyzed data from 209 publicly traded firms around 2010 to explore the relationship between a CEO's salary and the company's annual sales. Our focus was on using the CEO's salary to predict the company's sales.
**Variables:**
- **sales\(_i\):** Company's annual sales, measured in millions of dollars.
- **salary\(_i\):** CEO's salary, measured in thousands of dollars.
Using least-squares linear regression, we derived the equation:
\[
sales_i = \alpha + \beta \cdot salary_i + e_i
\]
**Regression Results:**
The displayed table shows the output of the regression analysis.
- **SS (Sum of Squares)**:
- Model: 337920405
- Residual: 2.3180e+10
- Total: 2.3518e+10
- **df (Degrees of Freedom)**:
- Model: 1
- Residual: 207
- Total: 208
- **MS (Mean Square)**:
- Model: 337920405
- Residual: 111980203
- **F-Statistics**:
- F(1, 207) = 3.02
- Prob > F = 0.0838
- **R-squared**: 0.0144
- **Adjusted R-squared**: 0.0096
- **Root MSE**: 10582
**Coefficients:**
- **salary**:
- Coefficient: 0.9287785
- Standard Error: 0.5346574
- t-value: 1.74
- P>|t|: 0.084
- 95% Confidence Interval: [-0.1252934, 1.98285]
- **_cons (Constant)**:
- Coefficient: 5733.917
- Standard Error: 1002.477
- t-value: 5.72
- P>|t|: 0.000
- 95% Confidence Interval: [3757.543, 7710.291]
**Regression Equation:**
The regression line equation is:
\[
\hat{sales} = 5,733.917 + 0.928778](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3f08d3cb-03f2-44ac-afec-90ed33ecc605%2F445fc0ff-30ef-4c20-be0f-2582ae88179f%2Fkuo4xg_processed.png&w=3840&q=75)
Transcribed Image Text:### Analysis of CEO Salary and Company Sales
We analyzed data from 209 publicly traded firms around 2010 to explore the relationship between a CEO's salary and the company's annual sales. Our focus was on using the CEO's salary to predict the company's sales.
**Variables:**
- **sales\(_i\):** Company's annual sales, measured in millions of dollars.
- **salary\(_i\):** CEO's salary, measured in thousands of dollars.
Using least-squares linear regression, we derived the equation:
\[
sales_i = \alpha + \beta \cdot salary_i + e_i
\]
**Regression Results:**
The displayed table shows the output of the regression analysis.
- **SS (Sum of Squares)**:
- Model: 337920405
- Residual: 2.3180e+10
- Total: 2.3518e+10
- **df (Degrees of Freedom)**:
- Model: 1
- Residual: 207
- Total: 208
- **MS (Mean Square)**:
- Model: 337920405
- Residual: 111980203
- **F-Statistics**:
- F(1, 207) = 3.02
- Prob > F = 0.0838
- **R-squared**: 0.0144
- **Adjusted R-squared**: 0.0096
- **Root MSE**: 10582
**Coefficients:**
- **salary**:
- Coefficient: 0.9287785
- Standard Error: 0.5346574
- t-value: 1.74
- P>|t|: 0.084
- 95% Confidence Interval: [-0.1252934, 1.98285]
- **_cons (Constant)**:
- Coefficient: 5733.917
- Standard Error: 1002.477
- t-value: 5.72
- P>|t|: 0.000
- 95% Confidence Interval: [3757.543, 7710.291]
**Regression Equation:**
The regression line equation is:
\[
\hat{sales} = 5,733.917 + 0.928778
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