**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:```Rlm(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.)

The estimated standard deviation of income given an education level can be calculated using the…

Answered: A survey is conducted on 700…

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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The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret a 90% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $825. The equation of the regression line is y = 1.597x- 169.203. Wages (state), x Wages (federal), y 742 775 783 802 842 890 912 939 949 954 1,002 1,039 1,097 1,145 1,199 1,242 1,271 1,299 1,335 1,398 Construct and interpret a 90% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $825. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) O A. There is a 90% chance that the predicted average weekly wages of federal government employees is between S and S given a state average weekly wage of $825. O B. We can be 90%…
B b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? The scatter diagram indicates a positive linear relationship between a = average number of passing yar and y = the percentage of games won by the team. c. Develop the estimated regression equation that could be used to predict the percentage of games won given the avera passing yards per attempt. Enter negative value as negative number. WinPct =| |)(Yds/Att) (to 4 decimals) d. Provide an interpretation for the slope of the estimated regression equation (to 1 decimal). The slope of the estimated regression line is approximately So, for every increase : of one yar number of passes per attempt, the percentage of games won by the team increases by %. e. For the 2011 season, the average number of passing yards per attempt for the Kansas City Chiefs was was 5.5. Use th regression equation developed in part (c) to predict the percentage of games won by the Kansas City Chiefs.…

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