Consider the following data regarding students' college GPAs and high school GPAs. The estimated regression equation is Estimated College GPA = 4.59 + (-0.4391) (High School GPA PA). GPAs College GPA High School GPA 2.18 3.25 3.27 4.02 3.20 3.98 3.96 2.26 2.50 3.98 3.32 3.30 Copy Data Step 2 of 3: Compute the mean square error (s) for the model. Round your answer to four decimal places.
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- Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for married respondents who are not female?Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for non-married females?Describe the important characteristics of the variance of a conditional distribution of an error term in a linear regression. What are the implicationsfor OLS estimation?
- What is the model constant when the dummy variable equals 1 in the following equations, where x1 is a continuous variable and x2 is a dummy variable with a value of 0 or 1? a. Ŷ = 4 + 8x1 + 3x2 b. Ŷ = 7 + 6x1 + 5x2 c. Ŷ = 4 + 8x1 + 3x2 + 4x1x2The error term in a regression equation is said to exhibit homoskedasticty if ____________. a) it has the same variance for all values of the explanatory variable(s). b) None of the other possible answers correctly finish the sentence. c) it takes the value of one given any values of the explanatory variable(s). d) it has the same value for all values of the explanatory variable(s). e) it has a zero conditional mean.You have noticed that paying higher wages attracts more productive employees. However, you are concerned that there may be a limit to this relationship. Some experimentation has convinced you that the relationship between wages paid (x) and profits can be modelled by the equation Profit = 25x − 0.1x2 + 200. Use a Nonlinear Solver tool to determine the level of wages that will maximize profits.
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