A survey is conducted on 700 Californians older than 30 years of age. The study wants to obtain inference on the relationship between years of education and yearly income in dollars. The response variable is income in dollars and the explanatory variable is years of education. A simple linear regression model is fit, and the output from R is below: Im(formula = Income ~ Education, data = CA) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 25200.25 1488.94 16.93 3.08e-10 ** Education 2905.35 112.61 25.80 1.49e-12 *** Residual standard error: 32400 on 698 degrees of freedom Multiple R-squared: 0.7602

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**Analysis of Income Based on Education in Californians Over 30**

A survey was conducted on 700 Californians older than 30 years of age to understand the relationship between years of education and yearly income in dollars. The response variable in this study is income in dollars, while the explanatory variable is the number of years of education.

To analyze this relationship, a simple linear regression model was employed, and the output from R is provided below:

```R
lm(formula = Income ~ Education, data = CA)
```

### Regression Output:
**Coefficients:**

|                 | Estimate | Std. Error | t value | Pr(>|t|)   |
|-----------------|----------|------------|---------|-----------|
| (Intercept)     | 25200.25 | 1488.94    | 16.93   | 3.08e-10 *** |
| Education       | 2905.35  | 112.61     | 25.80   | 1.49e-12 *** |

**Model Summary:**

- Residual standard error: 32400 on 698 degrees of freedom
- Multiple R-squared: 0.7602

### Interpretation of Results:
1. **Intercept:** The intercept value is 25200.25. This suggests that when the years of education is zero, the expected income is $25,200.25. However, since an education level of zero is not realistic in practice, this value primarily serves as a scaling factor in the regression model.

2. **Education Coefficient:** The coefficient for education is 2905.35. This indicates that for each additional year of education, the income is expected to increase by approximately $2905.35, holding other factors constant. 

3. **Significance:** Both the intercept and the education variable are statistically significant at the 0.01 level, as indicated by their extremely low p-values (Pr(>|t|)) of 3.08e-10 and 1.49e-12 respectively.

4. **Residual Standard Error:** The residual standard error is 32400, which measures the average amount that the observed income values deviate from the regression line.

5. **R-squared:** The multiple R-squared value is 0.7602, indicating that approximately 76.02% of the variability in income can be explained by the number of years of education.

This analysis
Transcribed Image Text:**Analysis of Income Based on Education in Californians Over 30** A survey was conducted on 700 Californians older than 30 years of age to understand the relationship between years of education and yearly income in dollars. The response variable in this study is income in dollars, while the explanatory variable is the number of years of education. To analyze this relationship, a simple linear regression model was employed, and the output from R is provided below: ```R lm(formula = Income ~ Education, data = CA) ``` ### Regression Output: **Coefficients:** | | Estimate | Std. Error | t value | Pr(>|t|) | |-----------------|----------|------------|---------|-----------| | (Intercept) | 25200.25 | 1488.94 | 16.93 | 3.08e-10 *** | | Education | 2905.35 | 112.61 | 25.80 | 1.49e-12 *** | **Model Summary:** - Residual standard error: 32400 on 698 degrees of freedom - Multiple R-squared: 0.7602 ### Interpretation of Results: 1. **Intercept:** The intercept value is 25200.25. This suggests that when the years of education is zero, the expected income is $25,200.25. However, since an education level of zero is not realistic in practice, this value primarily serves as a scaling factor in the regression model. 2. **Education Coefficient:** The coefficient for education is 2905.35. This indicates that for each additional year of education, the income is expected to increase by approximately $2905.35, holding other factors constant. 3. **Significance:** Both the intercept and the education variable are statistically significant at the 0.01 level, as indicated by their extremely low p-values (Pr(>|t|)) of 3.08e-10 and 1.49e-12 respectively. 4. **Residual Standard Error:** The residual standard error is 32400, which measures the average amount that the observed income values deviate from the regression line. 5. **R-squared:** The multiple R-squared value is 0.7602, indicating that approximately 76.02% of the variability in income can be explained by the number of years of education. This analysis
**Question:**

What is the estimated standard deviation of income given an education level? Input whole integer number.

**Input Field:**

[Text Box for Response]

(This box allows users to enter a numeric value as their answer to the question.)
Transcribed Image Text:**Question:** What is the estimated standard deviation of income given an education level? Input whole integer number. **Input Field:** [Text Box for Response] (This box allows users to enter a numeric value as their answer to the question.)
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