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1. The standard error of the estimate (standard error) is the estimated standard deviation of the distribution of the independent variable (X).
2. In a simple linear regression model, the coefficient of determination only indicates the strength of the relationship between independent and dependent variable, but does not show whether the relationship is positive or negative.
3. 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.
4. The error term is the difference between an individual value of the dependent variable and the corresponding mean value of the dependent variable.
5. In bi-variate regression the Coefficient of Determination is always equal to the square of the correlation coefficient.
6. In Regression Analysis if the variance of the error term is constant, we call it the Heteroscedasticity property.
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X, Y, and Z are independent normally distributed random variables. The following information is known about these three variables:
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