Reference no: EM1315733

True/False:

1. The simple coefficient of determination is the proportion of total variation explained by the regression line.

2. The estimated simple linear regression equation minimizes the sum of the squared deviations between each value of Y and the line.

3. In a simple linear regression model, the coefficient of determination not only indicates the strength of the relationship between independent and dependent variable, but also shows whether the relationship is positive or negative.

4. When using simple regression analysis, if there is a strong correlation between the independent and dependent variable, then we can conclude that an increase in the value of the independent variable causes an increase in the value of the dependent variable.

5. The error term is the difference between an individual value of the dependent variable and the corresponding mean value of the dependent variable.

6. In Regression Analysis if the variance of the error term is constant, we call it the Homoscedasticity property.

7. In simple linear regression analysis, if the error terms exhibit a positive or negative autocorrelation over time or across observations, then the assumption of constant variance is violated.

8. The expected value of the error term changes from observation to observation.

9. A significant positive correlation between X and Y implies that changes in X cause Y to change.