Final answer:
The Gauss Markov Assumptions for Simple Regression include linearity, zero mean error term, uncorrelated error terms, constant error variance, absence of multicollinearity, sufficient sample size, and normally distributed error term for hypothesis testing. The model provided shows sufficient sample size and linearity but requires further diagnostics to assess homoscedasticity and other assumptions.
Step-by-step explanation:
To discuss the Gauss Markov Assumptions for Simple Regression, we should consider that the assumptions are:
- Linearity: The model should be linear in parameters.
- Error term should have mean zero for each level of independent variable.
- All observations of errors are uncorrelated with each other.
- Errors should have constant variance (homoscedasticity).
- No perfect multicollinearity.
- The number of observations n must be larger than the number of parameters estimated.
- Error term is normally distributed (for hypothesis testing).
The provided regression model with n=526 and R-Squared 0.186 indicates linearity and potential for hypothesis testing due to a sufficient sample size. However, detailed diagnostics would be necessary to assess the other assumptions such as homoscedasticity, error terms correlation, and any multicollinearity issues.