The annual Salary of an electrical engineer is given in terms of the years of experience by the table below. Find the equation of linear regression for the above data and obtain the expected salary for an engineer with 48 years of experience. Round to the nearest $100

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To find the equation of linear regression and the expected salary for an engineer with 48 years of experience, we need the data from the table. However, since you have not provided the table, I am unable to proceed with the specific calculations for this question.

But don't worry! I can explain the general process of finding the equation of linear regression, which you can then apply to your specific dataset.

To find the equation of linear regression, you need at least two variables: the independent variable (years of experience in this case) and the dependent variable (annual salary in this case).

Here are the general steps to find the equation of linear regression:

1. Gather data: Collect the data for years of experience and corresponding annual salaries of electrical engineers.

2. Calculate the means: Calculate the mean of the years of experience (X̄) and the mean of the annual salaries (Ȳ).

3. Calculate the deviations: Calculate the deviation of each year of experience from the mean (X - X̄) and the deviation of each annual salary from the mean (Y - Ȳ).

4. Calculate the products: Multiply the deviations of years of experience and annual salaries for each data point (Σ[(X - X̄) * (Y - Ȳ)]).

5. Calculate the squared deviations: Square each deviation of the years of experience (Σ[(X - X̄)^2]).

6. Calculate the slope (b): Divide the sum of the products (step 4) by the sum of the squared deviations (step 5).

7. Calculate the intercept (a): Calculate the intercept (a) using the equation a = Ȳ - b * X̄.

8. Write the equation of linear regression: Once you have the slope (b) and intercept (a), you can write the equation of the linear regression as Y = a + bX, where Y is the annual salary and X is the years of experience.

Once you have the equation of linear regression, you can substitute the value of X (48 years in this case) into the equation and calculate the expected salary (Y).

I apologize for not being able to provide the specific calculations and expected salary for an engineer with 48 years of experience without the actual data. I recommend using the steps outlined above with your specific data to find the equation of linear regression and calculate the expected salary.