Independent Variable has same value i'm doing a regression using EViews, but one of eight independent variable has the same values for all sample and shows near singular matrix error, my supervisor said that it still could be done as she said that a lot a research (especially likert scale) has the same value. Here are the data

As you can see the data on the 8th column has the same value. I search the internet but couldn't find a solution, this is my first time doing a regression analysis so maybe i searched the wrong keyword. if really appreciate if someone could help me
Thank you
 A: If you think of the "independent" variables as "predictors," then it does no good to include a "predictor" that doesn't differ among cases. That particular "predictor" can't help predict anything, and you can't get a regression coefficient for it.
The Wikipedia entry on simple linear regression, for a single predictor and single outcome, shows the simplest case. The formula for calculating the regression coefficient $\widehat\beta$ is:
$$\widehat\beta = \frac{ \sum_{i=1}^n (x_i - \bar{x})(y_i - \bar{y}) }{ \sum_{i=1}^n (x_i - \bar{x})^2 } \\[6pt] $$
where $x_i$ are the individual predictor values and $\bar x$ is their mean. If all of the $x_i$ values are the same, then $(x_i - \bar{x})=0$ for all cases. Both the numerator and denominator are thus 0; there is no uniquely defined value $\widehat\beta$.
It doesn't get any better in the multiple-regression (multiple-predictor) context. For multiple regression to work, the columns of the design matrix need to be linearly independent. That is, you can't be able to write any column as a weighted sum or the other columns, or else you have perfect multicollinearity and an undefined solution. For numeric values like yours, the design matrix is the matrix or predictor values along with a column of 1s representing the model intercept. If one predictor is constant, it thus is a constant multiple of that intercept column in the design matrix. That perfect multicollinearity prevents finding a unique solution to the regression if you include an intercept in the model.
Your sense is correct and, based on what you've presented here, your supervisor is incorrect. Remove such constant "predictors" from your model. If your supervisor insists on claiming that it needs to be included, then in return insist that your supervisor show how to do that in a way that's consistent with the expectations in your field of study. Perhaps there are accepted ways in that field to choose one of the infinite possible solutions in a situation like this, but be sure that you and your supervisor know exactly what you are doing if you go down that route.
A: An independent variable with the same values in all observations may serve as a bias for a regression model. Just make sure that the tool you are using is not adding it's own bias feature into the set of independent variables. That should eliminate the singular matrix error which is caused because both your independent variable (having constant values) and the bias feature added by the tool are linearly dependent.
